Quantification and Prediction of Hydrocarbon Resources (Norwegian Petroleum Society Special Publications)

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Norwegian Petroleum Society (NPF), Special Publication No. 6

Quantification and Prediction of Hydrocarbon Resources Proceedings of the Norwegian Petroleum Society Conference, 6-8 December 1993, Stavanger, Norway

Further titles in the series:

1. R.M. Larsen, H. Brekke, B.T. Larsen and E. Talleraas (Editors) STRUCTURAL AND TECTONIC MODELLING AND ITS APPLICATION TO PETROLEUM GEOLOGY- Proceedings of Norwegian Petroleum Society Workshop, 18-20 October 1989, Stavanger, Norway 2. T.O. Vorren, E. Bergsager, Q.A. DahI-Stamnes, E. Holter, B. Johansen, E. Lie and T.B. Lund (Editors) ARCTIC GEOLOGY AND PETROLEUM POTENTIAL- Proceedings of the Norwegian Petroleum Society Conference, 15-17 August 1990, Tromso, Norway 3. A.G. Dor~ et al. (Editors) BASIN MODELLING" ADVANCES AND APPLICATIONS- Proceedings of the Norwegian Petroleum Society Conference, 13-15 March 1991, Stavanger, Norway 4. S. Hanslien (Editor) PETROLEUM" EXPLORATION AND EXPLOITATION IN NORWAYProceedings of the Norwegian Petroleum Society Conference, 9-11 December 1991, Stavanger, Norway

5. R.J. Steel, V.L. Felt, E.P. Johannesson and C. Mathieu (Editors) SEQUENCE STRATIGRAPHY ON THE NORTHWEST EUROPEAN MARGIN Proceedings of the Norwegian Petroleum Society Conference, 1-3 February, 1993, Stavanger, Norway

Norwegian Petroleum Society (NPF), Special Publication No. 6

Quantification and Prediction of Hydrocarbon Resources Proceedings of the Norwegian Petroleum Society Conference, 6-8 December 1993, Stavanger, Norway

Edited by

A.G. Dore

Statofl UK Ltd, Swan Gardens 10, Piccadilly, London W1V OLJ, UK

and

R. Sinding-Larsen

Department of Geology and Mineral Resources Engineering, The Norwegian Institute of Technology, N-7034 Trondheim, Norway

ELSEVIER Amsterdam - Lausanne - New York-

O x f o r d - S h a n n o n - T o k y o 1996

ELSEVIER SCIENCE B.V. Sara Burgerhartstraat 25 P.O. Box 211, 1000 AE Amsterdam, The Netherlands

ISBN 0-444-82496-0 91996 Elsevier Science B.V. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the publisher, Elsevier Science B.V., Copyright and Permissions Department, P.O. Box 521, 1000 AM Amsterdam, The Netherlands. Special regulations for readers in the USA--This publication has been registered with the Copyright Clearance Center Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, USA. Information can be obtained from the CCC about conditions under which photocopies of parts of this publication may be made in the USA. All other copyright questions, including photocopying outside the USA, should be referred to the copyright owner, Elsevier Science B.V., unless otherwise specified. No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. This book is printed on acid-free paper. Printed in The Netherlands

Preface The international conference "Quantification and Prediction of Hydrocarbon Resources", held in Stavanger in December 1993, was something of an experiment. Unlike other conferences arranged by the Geological and Geophysical wing of NPF, which are mainly aimed at a G&G audience, this conference was advertised to a wider group of disciplines. Why? Because the issue of resource quantification is the confluence at which the upstream petroleum disciplines (geology, geophysics, engineering and economics) meet. This meeting of minds, now a matter of routine in the workplace, can be directly attributed to the economic dictates of the last decade. The oil price shocks of the mid-1980s created major enigmas for those concerned with petroleum resources and their prediction. On the one hand, it was now clear that, contrary to the views of virtually all of the pundits of previous decades, the "big pump" in the Middle East was going to be supplying low-cost oil to the rest of the world for some time to come. On the other hand, petroleum (and particularly oil) was still as finite a resource as it ever had been. How could these two apparently contradictory situations be reconciled? And what were the consequences for resource management and economic prediction? Despite the gloomier forecasts, the upstream oil industry did not go to the wall. Instead, threatened with extinction it evolved to adapt to its new economic environment. In a world apparently awash with oil, new low-cost solutions became a necessity in established operating areas, while new technology evolved rapidly so that exploration could continue in difficult plays or frontier areas. For much of the workforce, however, the gravy train was derailed forever. Sections of the industry were cut to the bone, and those who survived found that little remained of the freewheeling individualism of the early 80s. Increasingly, the emphasis moved towards teamwork, involving the integration of disciplines and with the express purpose of realising short-term value from assets. Hydrocarbon discoveries and exploration prospects were no longer viewed as a series of separate calculated risks, but were instead regarded as an economic portfolio, to be manipulated and optimised in the same way as stocks and shares. Risk analysis and portfolio management, essentially the province of the engineers and economists before the mid-80s, became second nature to the geoscientists too. Given this developing climate, a conference was considered timely to assess the newly emerging approaches to resource quantification and prediction. The scope was kept deliberately broad, ranging from the macro (global) to micro (field and prospect) level. This book essentially follows the same themes of the conference, and is divided into six parts. Worm Hydrocarbon Resources (Part I), contains three papers which, although very different in style and emphasis, are concerned with the same basic issues - the amount of petroleum in the world and its longevity. Part II tackles the thorny question of Definition and Classification of Petroleum Resources. Although at first glance a semantic exercise, closer examination soon shows the importance of this issue. Put quite simply, resources change according to how they are defined, and meaningful debate on resources is therefore impossible without terms of reference. Part III, Assessment of Undiscovered Hydrocarbon Resources examines resource forecasting on a basin or province level from both a national and a company perspective. This is followed by a section on the general topic of Risk Analysis (Part IV), with emphasis on the play and prospect level. A wide-ranging selection of papers covers ground from formal risking techniques, through probabilistic assessment of volumes, to the control of specific input variables via basin modelling. Part V, Management of Discovered Resources, describes efforts to optimise the performance of fields through their life-cycles and the status of enhanced oil recovery techniques. Although economic considerations underpin all of the papers in this volume, Part VI, The Economic Interface contains a series of papers with expressly economic themes. They serve to illustrate the

Vl

Preface

importance of economic planning in allocating financial resources to exploration and production projects at company level, at national level and in the banking sector. As editors, we readily admit that the broad scope of this volume precludes an exhaustive coverage. Each of the sections could, in fact, quite easily be the subject of a separate book. Accordingly, some sections will be of interest to geoscientists, some to economists, and others to engineers and managers. On the other hand, we hope that by casting this net wide we have at least obtained an overview of thought processes currently prevalent in the industry and academia on the subject of Quantification and Prediction. These thought processes range from the integration of geological information and the application of methods to estimate undiscovered resources, to the modelling of the full cycle from exploration to development. Although hydrocarbon exploration has often been examined as an economic activity and described in terms of productivity, there have been few attempts at integration of theory, methods and models for the economic valuation of hydrocarbon resources. We strongly believe that continued close cooperation between geoscientists, engineers and economists is needed to instigate improvements in the methodologies, and to generate a new breed of explorationist that can use these integrated techniques effectively. We would like to warmly thank Elizabeth Holter and Karin Haugnaes (NPF) and Annette Leeuwendal (Elsevier) for their help and patience as this book came together. Special thanks go to the numerous unpaid workers - the authors and referees - who managed to borrow time from their busy schedules to help bring this volume into existence. A.G. Dor6 and R. Sinding-Larsen 1996

VII

List of Contributors A.E. ABBOTT

Department of Geological Sciences, University of South Carolina, Columbia, SC 29208, USA

K.A. ABRAHAMSEN

Statoil, N-4035, Stavanger, Norway

K. ASHTON

Phillips Petroleum Company UK Ltd., 35, Guildford Road, Woking, Surrey GU22 7QT, UK

T.J. BEARDALL

ERC Tigress Ltd., Chapel House, Liston Road, Marlow, Bucks, SL7 1XJ, UK Present address: T.J. Beardall and Associates, Ltd., 2 Silver Lane, West Challow Wantage, Oxon OX12 9TX, UK

H. BREKKE

Exploration Department, Norwegian Petroleum Directorate, P.O. Box 600, N-4001 Stavanger, Norway

C.J. CAMPBELL

c/o Petroconsultants S.A., P.O. Box 152, 1258 Perly, Geneva, Switzerland

S. CAO


Z. CHEN

Department of Geology and Mineral Resources Engineering, The Norwegian Institute of Technology, N- 7034, Trondheim, Norway

B. DAHL

Norsk Hydro Research Centre, Bergen, Norway Present address: Dept. of Geology, University of Bergen, Norway

E. DAMSLETH

Norsk Hydro, P.O. Box 200, 1321 Stabekk, Norway

A.G. DORt~

Statoil UK Ltd, Swan Gardens, 10 Piccadilly, London W1V OHL, UK

D.W. DORN-LOPEZ

Conoco Norway Inc., P.O. Box 488, N-4001 Stavanger, Norway

B.A. DUFF

Fina Exploration Norway, Skogstostraen 37, P.O. Box 4055 Tasta, N-4004 Stavanger, Norway Present address: Fina Exploration & Production, Rue de l'Industrie 52, B-1040 Brussels, Belgium

N. FULLER


S. GRANT

BP Norge UA, P.O. Box 197, Forusbeen 35, 4033 Forus, Norway

H.H. HALDORSEN

Norsk Hydro a.s., P.O. Box 200, N-1321 Stabekk, Norway

D. HALL

Fina Exploration Norway, Skogstostraen 37, P.O. Box 4055 Tasta, N-4004 Stavanger, Norway Present address: Fina Exploration & Production, Rue de l'Industrie 52, B-1040 Brussels, Belgium

C. HERMANRUD

Statoil, Postuttak; 7004 Trondheim, Norway

R.G. HEYWOOD


List of Contributors

VIII R.W. HOLT


C. JOURDAN

Statoil, P.O. Box 300, 4001 Stavanger, Norway

J.-E. KALHEIM

Exploration Department, Norwegian Petroleum Directorate, P.O. Box 600, N-4001 Stavanger, Norway

G.M. KAUFMAN

Sloan School of Management, MIT, Room E53-375, Cambridge, MA 02142, USA

B.L. KING


K.R. KNUDSEN

Norwegian Petroleum Directorate, Postbox 600, 4001 Stavanger, Norway

W. KROKSTAD

IKU Petroleum Research, N-7034 Trondheim, Norway

I. LERCHE


K. LINDBO


E. MAYORGA-ALBA

The World Bank, 1818 H Street NW, Washington, DC 20433, USA

E MCGAUGHRIN


I. MEISINGSET

Norsk Hydro Exploration and Production, Oslo, Norway

R.G. MILLER

BP Exploration Operating Company Limited, 4/5 Long Walk, Stockley Park, Uxbridge, Middlesex UBll 1BP, UK

N. MILTON


S.J. MORBEY

Strategic Exploration, WEBG, Amoco Exploration and Production, Houston, USA

S. NORDAHL

Statoil, P.O. Box 300, 4001 Stavanger, Norway

K.O. SANDVIK


R. SINDING-LARSEN

Department of Geology and Mineral Resources Engineering, The Norwegian Institute of Technology, N-7034, Trondheim, Norway

S. SMITH

The World Bank, 1818 H Street NW, Washington, DC 20433, USA

J.H. SNOW

Conoco Norway Inc., P O. Box 488, N-4001 Stavanger, Norway

J.J.G. STOSUR

U.S. Department of Energy, Fossil Energy-33, Germantown, Washington, D. C. 20586, USA

~. SYLTA


M. THOMPSON


E. TORHEIM

Elf Petroleum Norge a.s., P.O. Box 168, N-4001 Stavanger, Norway

J. VOLLSET


S. WHITE


J. WILKINSON

Esso UK, 94-98 Victoria St., London SWIE 5JW, UK

E.V. ZAKHAROV

VNIIGAS International p. Razvilka, Leninsky raion, Moskovskaya oblast, 142717, Russia

IX

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

V VII

I. World Hydrocarbon Resources World oil: reserves, production, politics and prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.J. Campbell

1

Gas in the 21st century: a world-wide perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Lerche

21

Estimating global oil resources and their duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R.G. Miller

43

II. Definition and Classification of Hydrocarbon Resources The world of reserve definitions - - can there be one set for everyone? . . . . . . . . . . . . . . . . . T.J. Beardall

57

Resource classifications and their usefulness in the resource management of an oil c o m p a n y . . K.A. Abrahamsen, K. Lindbo and J. Vollset

63

Reserve and resource definition: dealing with uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . J. Wilkinson

71

The Norwegian Petroleum Directorate's Resource Classification System . . . . . . . . . . . . . . . . K.R. Knudsen

77

III. Assessment of Undiscovered Hydrocarbon Resources A method for the statistical assessment of total undiscovered resources in an area . . . . . . . . . E. Damsleth The Norwegian Petroleum Directorate's assessment of the undiscovered resources of the Norwegian Continental s h e l f - background and methods . . . . . . . . . . . . . . . . . . . . . . H. Brekke and J.-E. Kalheim Cross-validation of resource estimates from discovery process modelling and volumetric accumulation modelling: example from the Lower and Middle Jurassic play of the Halten Terrace, offshore Norway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Sinding-Larsen and Z. Chen The Russian method for prediction of hydrocarbon resources of continental shelves, with examples from the Barents Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K.O. Sandvik and E.V. Zakharov Offshore Brazil: analysis of a successful strategy for reserve and production growth . . . . . . . . S.J. Morbey

83

91

105

115

123

X

Contents

IV. Risk Analysis Risk analysis: from prospect to exploration portfolio and back . . . . . . . . . . . . . . . . . . . . . . . G.M. Kaufman

135

Risk analysis and full-cycle probabilistic modelling of prospects: a prototype system developed for the Norwegian shelf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J.H. Snow, A.G. Dor6 and D.W. Dorn-Lopez

153

Play fairway analysis and risk mapping: an example using the Middle Jurassic Brent Group in the northern North Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Grant, N. Milton and M. Thompson

167

A model-based approach to evaluation of exploration opportunities . . . . . . . . . . . . . . . . . . . B.A. Duff and D. Hall

183

Risk and probability in resource assessment as functions of parameter uncertainty in basin analysis exploration models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Cao, A.E. Abbott and I. Lerche

199

Risk assessment using volumetrics from secondary migration modelling: assessing uncertainties in source rock yields and trapped hydrocarbons . . . . . . . . . . . . . . . . . . . . . . . . . . W. Krokstad and O. Sylta

219

Prospect resource assessment using an integrated system of basin simulation and geological mapping software: examples from the North Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Dahl and I. Meisingset

237

V, Management of Discovered Resources Enhanced oil recovery ~ the international perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . J.J.G. Stosur

253

Nessie: a process analysis of a genetic North Sea field life cycle . . . . . . . . . . . . . . . . . . . . . . P. McGaughrin, K. Ashton, N. Fuller, R.G. Heywood, R.W. Holt, B.L. King and S. White

261

Changing perceptions of a gas field during its life cycle: a Frigg field case study . . . . . . . . . . E. Torheim

273

Vl. Hydrocarbon Resources: the Economic Interface Choosing between rocks, hard places and a lot more: the economic interface . . . . . . . . . . . . . H.H. Haldorsen The usefulness of resource analysis in national economic planning ~ Norwegian Shelf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J.-E. Kalheim and H. Brekke

291

examples from the 313

Evaluation of undrilled prospects m sensitivity to economic and geological factors . . . . . . . . C. Hermanrud, K. Abrahamsen, J. Vollset, S. Nordahl and C. Jourdan

325

The World Bank's financial support to the petroleum sector in developing countries . . . . . . . . E. Mayorga-Alba and S. Smith

339

References index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

345

Subject index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

349

World oil: reserves, production, politics and prices C.J. Campbell

The assessment of the world's oil endowment is a sensitive subject with political implications and many vested interests. Some published reserve numbers are spurious, and lax definition has led to misconceptions. Study of the world's oil endowment involves the following elements: Cumulative Production (718 Gb; Gb = billion (109) barrels; numbers based on Oil and Gas Journal reserve data, updated for end 1993); Reserves (Reported less "political" u 722 Gb); Total Discovered (1440 Gb); Undiscovered (210 Gb); Remaining (932 Gb); Ultimate (1650 Gb). The rounded numbers in parentheses refer to conventional oil recoverable with today's technology and economics, excluding NGL, heavy oil, small fields etc., as of end 1993. Discovery ( 0

No

Base

Full

No

Base

Full

No

Base

Full

A-class B-class C-class D-class E-class

0.50 3.03 3.38 0.33 0.47

0.50 3.02 3.37 0.34 0.45

0.49 3.00 3.36 0.33 0.47

0.50 1.44 1.69 0.56 0.67

0.50 1.44 1.88 0.58 0.71

0.50 1.61 2.98 0.62 1.00

0 2 2 0 0

0 2 2 0 0

0 2 1 0 0

50 97 97 29 38

50 97 96 29 34

49 96 83 26 26

Total

7.71

7.68

7.65

2.45

2.73

4.35

6

5

4

100

100

99

Table 4b

No A-class 1.2 B-class 68.2 C-class 23.0 D-class 2.7 E-class 19.1 Total

114.2

St. dv. Base 1.2 67.7 22.7 2.8 19.0 113.3

Full

No

P80 Base

Full

1.2 1.3 1.3 1.3 68.0 60.2 60.2 60.3 23.2 18.5 19.9 31.3 2.6 5.6 5.6 5.7 19.5 101.8 102.5 105.4 114.5

119.4 121.3

No

Base

Full

(1) Generate U1, U2 . . . . . U~ as standard multinormally distributed variables with correlation matrix C given by"

Effect of the correlation on the gas volume (106 TOE) Mean

No

Base Full

0.0 9.4 6.6 0.0 0.0

0.0 9.3 5.2 0.0 0.0

0.0 9.5 0.8 0.0 0.0

131.0 39.3 34.2 22.2

Throughout, we use the notation Pr(event) to denote the probability of event. Ideally, the co-variation should be the same between all the prospects in the prospect group. Unfortunately, this is not easy to achieve in practice. In a situation with standard multi-normally distributed variables, correlation and the correlation coefficient are well defined concepts, which do not depend on the means and standard deviations in the marginal distributions. In a binary situation, this is no longer the case. In the Gaussian case the simultaneous distribution for all the n variables will be uniquely given by the means, standard deviations and the correlation matrix. In the binary case, on the other hand, the simultaneous distribution is only uniquely given when Pr(F1 = fl A F2 = f2 A . - . ~ F~ = f~) is specified for all the 2 ~ possible combinations of fi - 0, 1 and i = 1 . . . . . n. In addition, the correlation coefficient between two binary variables can no longer vary in the full [ - 1 , 1] interval. The actual region of variation depends on the marginal probabilities of discovery. The maximum correlation between two prospects is given by:

maxp - min (~/pi(1- pj) ~ (1- pi)pj ) (1 - pi)pj' pi(1- pj)

(4)

Suggested solution Our solution is a variation of a technique proposed by Emrich and Piedmonte (1991)"

c

-

1

)~ ...

.

.

)~

.

.

9

(5)

9

)~ )~ ...

1

where ~, is the required co-variation factor. To ensure that C is non-negative definite, - 1/ (n - 1) _< ~, < 1. (2) Let

Fi-

1

0

i f ~ ( U i ) _ 0

(7)

Fj) is given by (4).

Finding the ~-factors To find the co-variation coefficient ~. within a group one can start by choosing two "typical" prospects within the group, say i and j. Let pi and pj be the marginal probabilities of discovery for the two prospects. Further, denote the conditional probabilities for the same two prospects by Pll~ and P010 respectively, where: Pill -- Pr(discovery in prospect i I discovery in prospect j ) P010 - Pr(no discovery in prospect i I no discovery in prospect j ) Using standard formulae: pl _< Pljl _< min (P~~., 1)

and 1 - pl _< polo -< min

1-

pj'

(8)

89

A method for the statistical assessment of total undiscovered resources in an area

Hopefully, the user can quantify either P l l l or P010. The correlation between the two prospects is then given by:

trix C which can be block-divided as follows:

C -P _

V/

pipj (p,,l _1 ) (1 - pi)(1 - p j ) pi

/(1-pi)(1-pj)(piPj

lP~176

1)

(10)

and the ~.-factor for the two prospects can by found from ~. = P/Pmax, where p is given by (10) and Pmax by (4). The same value for ~. will then be used for all pairs of projects within the group. Example: Consider two prospects, 1 and 2, with probability of discovery pl - 0.1 and p2 - - 0.2. From (9), 0.1 < Pill < 0.5. The user specifies Pill = 0.3, so that a discovery in prospect 2 triples the probability of discovery in prospect 1. Using (10), these figures give p = 0.33, and (4) gives / ) m a x = 0.67. Thus, ~. = 0.33/0.67 = 0.5.

Illustration To illustrate the above procedure, and to demonstrate the effect of the ~.-factor, let us look at a group with five prospects, with probabilities of discovery equal to 0.1, 0.3, 0.5, 0.7 and 0.9, respectively. The maximum correlations between discoveries, using (4), is in this case given in the correlation matrix in Table A1. For )~ = 0, 0.25, 0.5, 0.75 and 1.0, Table A2 shows: (a) Probabilities of discovery for the five prospects. First unconditional, then conditional, given that 0, 1, 2, 3 and 4 of the other prospects resulted in a discovery. (b) The correlation matrix for (F1, F2, F3, F4, Fs). The tables are computed from Monte Carlo simulations with 5000 repetitions.

Simultaneous generation of discoveries for several groups The technique presented above can also be used to generate discoveries/non-discoveries for different prospect groups, when there may be co-variation both within and between the groups. The approach is then: (1) Generate U1, U2 . . . . . UN as standard multinormally distributed variables with correlation maTable A 1

Cll

C12

...

C1M

C21

C22

...

C2M

.

.

.

.

CM1

CM2

...

CMM

(11)

Every CjK, j ~ k represents a nj x nk correlation matrix describing the co-variation between discovery in groups j and k. Cjk is given by:

Cjk --

i~,j k

~jk

9 9 9 )~j k

~.jk

~.jk

...

)~jk

9

i

~176

~

~jk

~,jk

9 9 9 ~jk

j ~ k

(12)

where i~,jk is the required co-variation coefficient between (any) prospects in groups j and k. If prospects in the two groups can be regarded as independent, so that there is no co-variation, Cjk contains only zeros. Every C j j, j = 1 . . . . . M represents the n j • n j correlation matrix which describes the co-variation within group no. j, as described earlier. Cjj is given by:

Cjj

--

1

~.j

...

~,j

~j

1

...

/~,j

.

.

..

9

~j

~,j

(13)

. 9

. ..

.

1

where /~.j is the required co-variation coefficient. If there is no c-variation within the group, then Cjj is the identity matrix with ones on the main diagonal and zeros elsewhere. (2) As before, let Fi-

1 0

if~(Ui)

~ 4o_

10-

i

20_

!

|

|

10

100

1000

a)

i

99.999

10000

VOLUME

b)

9i0

510 liO ~

.1

0 i

Cumulative Probability %

MIXED POPULATION

IOOOO~

8O 1000-

ioo

9 100-

>

"~ 40

10-

2O

1

c)

10

100

VOLUME

1000

10000

'i

99"19 919 90

d)

510

ll0

I 0I 1 .1

Cumulative Probability %

Fig. 4. (a) Cumulative lognormal distribution plot, logarithmic volume scale (x-axis) vs. normal probability scale (y-axis). (b) Same distribution as in (a), plotted on lognormal probability paper. Gaussian probability scale on x-axismakes the distribution plot as a straight line. (c) and (d) show the effect of a mixed population on the shape of a lognormal distribution curve plotted as in (a) and (b).

96

H. Brekke and J.E. Kalheim

Definition and mapping of plays The NPD approach was to try to avoid defining play models with mixed size populations and at the same time keep the total number of plays at a minimum. A total of 44 plays were defined for the whole shelf area, of which 23 have been confirmed by at least one discovery and 21 are still unconfirmed (Table 1). The plays were defined on the basis of stratigraphic columns and parameter maps showing the geographical distribution and quality of potential reservoirs, mature source rock, migration routes and structural and depositional trends. The stratigraphic columns and parameter maps were based on borehole data and seismic interpretation. Fig. 5 shows the stratigraphic positions of the reservoirs of the defined plays. Play Table 1 Number of plays on the Norwegian shelf Confirmed

Unconfirmed

Total

North Sea Norwegian Sea Barents Sea

12 7 4

1 12 8

13 19 12

Total

23

21

44

0

40

80

120

160 Km

summary maps (White, 1988) were constructed from the parameter distribution maps: the play area was defined by the area in which the overlap of all parameter maps shows favourable conditions for the play (Fig. 6). Along with the maps, each play was systematically described in terms of critical geological factors, geography and references (Table 2). Ideally, a play should be defined such that all the prospects and discoveries belonging to the play constitute a geologically homogeneous group (see White, 1980). This is necessary both to ensure a functional and sensible nomenclature of plays and to avoid operating with mixed deposit size populations. However, to decide whether geological differences between prospects are so subtle that they may be ignored or so significant that the prospects must be split into two or more plays, is not a straightforward exercise. Among assessors one finds both splitters and lumpers, as in other branches of geology. Furthermore, there is a general trend that in the early stages of exploration an increase in the data available leads to a need for splitting a play into two or more plays. This reflects the fact that when facing a frontier area with little data, one is constrained to construct simple play models that must be applied to very large areas (for example

0

80

160

:;)40 Km

Fig. 5. (a) Play area of the mature, confirmed play NJI,JM-I in the northern North Sea. This play consists of a reservoir of Lower to Middle Jurassic sandstones in traps made by rotated fault blocks. Well known fields in this play are the Statfjord, Gullfaks and Oseberg Fields. (b) Play area of the unconfirmed, frontier play BCL-1 in the Barents Sea. This play consists of a reservoir of Lower Carboniferous clastics in traps consisting of rotated fault blocks. No discoveries have yet been made.

97

The NPD's assessment of the undiscovered resources of the Norwegian Continental shelf

Fig. 6. Stratigraphic columns showing the stratigraphic positions of the reservoirs of the plays defined on the Norwegian shelf, both confirmed and unconfirmed. Examples of representative discoveries and fields are indicated for the confirmed plays

Table 2 Two examples of play descriptions Name

Reservoir facies

Seal level

BCL-1 Continental/ Lower fluvial/ Carbonif. shallow marine sandstone

NJL, JM-1

Fluvial/ deltaic/ shallow marine sandstones

Lunde Fm. Statfjord Fm. Cook Fm. Brent Grp

Trap

facies

level

Marine shales

Carbonif.

Source

Source

level

area

Rotated Devonian/ fault Carbonif. blocks + stratigraphic elements

Local basins

Play area

Reference

firmed Finnmaek Platf. No Bjarmeland Platf. Loppa High Sentralbanken H. Gardarbanken H. SCrkapp Basin Olga Basin Kong Karls Platf.

Viking Grp Viking Graben North Sea Shallow/ Dunlin Grp Rotated Middle Sogn Graben between open Viking Grp fault Jurassic Tampen Spur 60~ and 62~ marine blocks + shales stratigraphic elements

Fig. 6b). It is obvious that such early frontier play models will prove to be more complicated as information from drilling increases. However, it is impossible to know "up front" in which direction models will be changed through time, and one has to rely upon the early "lump" models to have an idea of the resource potential in frontier areas. This means that in many

Con-

Yes

7128/6-1 shallow drilling 1988

Gullfaks Statfjord Oseberg Snorre Veslefrikk Brage

cases, where little data is available, the NPD estimates will undoubtedly be based on simplified models with mixed deposit size populations. To try to avoid this by splitting a large frontier model into every conceivable model, based on scanty data or mere mind constructions, will only lead to other problems. Basing an estimate on the aggregation of many models


98

leads to a narrower uncertainty range in the estimate (given that the new "split" models are regarded as independent entities) and gives the impression that the risk is reduced. To counteract this, one has to estimate the degree of interdependence between all of the "split" models. Because of the lack of data, this will in most cases be mere guesswork which will probably be even more meaningless than the results from a large "lump" model with a mixed population. So one is left with the compromise between accepting "lump" models and mixed populations or a number of more specific models with highly speculative descriptions and estimates of interdependencies. The problem of a mixed population of a "lump" model is at least a simple concept to keep in mind, whereas all the guesswork in the details of the "split" models is much less transparent when the time comes to take decisions. The NPD therefore decided to base their estimates in frontier areas on "lump" models.

Table 3 Input data for FASPUM computation program m risk parameters and volume parameters Evaluator Date Evaluated

Net rock volume The product of the parameters "area of closure" and "reservoir thickness" (Table 3) gives the net reservoir rock volume of the prospects. The NPD assessors found that applying values for the reservoir thickness or the vertical closure does not give a correct estimate of the reservoir volumes because

Probabilityof Favorable or Prensent

Attribute

Comments

HydrocarbonSource Timing Migration Potential ReservoirFacies

,~ _~" ~ ~:

Marginal Play Probability TrappingMechanism Effective Porosity(>3%)

~ 8.~ A- ~

HydrocarbonAccumulation ConditionalDeposit Probability ReservoirLithology Hydrocad:)on

~

'~,ractiles .Attribute 9 ~ , . .

Sand Carbonate Ga~ Oil

Sand

Probability of equal to or greater than 95 75 50 25

5

0

Area of Closure(Km^2)

Computation As a computation tool the NPD chose the computer program "Fast Appraisal System for Petroleum Universal Metric" (FASPUM) developed by the United States Geological Survey (see Crovelli and Balay, 1988). The reasons for this choice were that the system is based on a lognormal distribution approximation, the computations are quick and the system is user friendly and may be applied to plays of very differing data base levels. FASPUM computes the total oil and gas resources in each play based on certain critical geological volume parameters (see Table 3). Each parameter is entered as seven fractiles of a probability distribution in order to reflect the range of uncertainty and variety of the parameter values. Much effort was put into picking realistic values and range of uncertainty for the different parameters in each play. In general the range of uncertainty was increased for plays with a restricted data base relative to mature plays with a substantial data base. The two most difficult parameters to assess were the size distribution of the traps (net rock volume) and the number of remaining drillable prospects. Details on the methods for selection of the volume parameters are given below.

Play Name

ReservoirThickness /vertical closure(meters)

Ratio of area and net res. ~olullle

EffectivePorosity% Trap Fill(%) ReservoirDepth(m) HC Saturation(%) ! 1

No.of drillable prospects (a playcharacteristic)

the three dimensional shape of the trap is then not taken into consideration. An alternative method was developed in which the reservoir thickness/vertical closure is replaced by the value of the ratio between the net reservoir rock volume and the area of closure. This ratio is characteristic of the separate trap types. The method is based on the assumption that both the area of closure and the net rock volume of the deposits in a play may be fitted to a lognormal distribution curve. Lognormal plots of the area of closure and net rock volume were constructed for all plays by using lognormal probability paper (Fig. 7). The value of the largest known prospect or discovery in the play was plotted at 0.1% cumulative probability and values matching a reasonable economic minimum size were plotted at 95% cumulative probability. A reasonable economic minimum size was set to 1590.106 m 3 net rock volume. This procedure allows for the possibility (though with a low probability) of making a discovery that is larger than the presently known discovery or prospect mapped/assumed in each play. At the same time it allows for a tail of numerous, subeconomic and uneconomic deposits. The main problem in this plotting method is to de-

99


PLAY

- NJL,

PLAY

JM-I

- BCL-

1

b)

a)

'1

STATFJORD

u~ 4

IO,92 -

0,1

1

5 10

25

50

75

90 95

99

99,9

FREQUENCY

0,1 0,1

1

5 10

25

50

75

90 95

99

99,9

FREOUENCY

Fig. 7. Lognormal distribution plots of "area of closure" and "net rock volume" for traps in two plays: (a) NJL,JM-1 in the northern North Sea (see Fig. 6a)" and (b) BCL-1 in the Barents Sea (see Fig. 6b).

cide the minimum size and at which cumulative probability to plot it. The minimum sizes were set according to rough estimates of minimum economic sizes in the different regions of the shelf. However, in many cases this minimum size (plotted at 95% percentile) had to be adjusted so that the curves could be calibrated to give reasonable median values (50% percentile). "Reasonable median values" were based on the available statistics on discoveries taking into consideration the decline in mean deposit sizes with time (plotted at less than 50%). This implied relatively lower mean sizes for the remaining deposits in mature plays with many discoveries than in immature plays with few discoveries and in unconfirmed plays with no discoveries. In immature and unconfirmed plays analogs were used as a guideline where possible. The area and volume plots (e.g. Fig. 7) were used to find the values for the seven percentiles to be entered in the FASPUM table (Table 3) under the parameters "Area of closure" (derived directly) and "Reservoir thickness" (as the rock volume vs. area of closure ratio).

Effective porosity The probability distribution for reservoir porosity was based on well data or analog models. Lateral variations were predicted on the basis of depth maps and facies maps.

Trap fill This parameter was difficult to set. It is a fact that, in many cases traps in areas of late uplift, in areas with a marginally mature source rock, or in areas with long migration distances are not filled to spill point. Experience has shown us that in the Barents Sea most traps are underfilled, and the NPD assessors took this into consideration there and in all areas with a history of late uplift. However, in most other cases there is no systematic or statistically significant empirical basis to predict this parameter, and the portion of underfilled traps was estimated according to a subjective estimate of the effect of assumed degree of source maturity and migration distances.


100

Reservoir depth

analogs. The less problematic of these plays seemed to be those with fault dependent traps, because analogs were easy to find and there was generally a reliable database for predicting lateral variations due to variations in faulting intensity. The prospect density of plays with stratigraphic traps, however, was not easy to predict. Such traps are very difficult to map, and even in proven plays it is not easy to say how many prospects may remain. In addition, it was speculated that different trap types perhaps have their own, characteristic density range so that there may be a systematic difference between structural and stratigraphic traps. This possibility would render the use of prospect densities for structural traps as a model for stratigraphic traps highly speculative. Nevertheless, the NPD assessors assumed, based on experience, that as a rule stratigraphic plays have a lower prospect density than structural plays.

The frequency distribution of the deposits was based on depth maps of the relevant reservoir levels. This information and the given "oil floor depth" is used by FASPUM to compute the portion of oil that may have been cracked to gas.

Hydrocarbon saturation The range of HC saturation was based on well data and general experience with the different types of reservoirs.

Number of drillable prospects The probability distribution of the remaining, drillable prospects was based on an assumed range in prospect density multiplied by the total area of each play. The main effort here was put into deciding the prospect densities. These were based on locally derived empirical data and analogs. In the mature models (like NJL,JM-1, Table 2, Fig. 6a) the prospect density was based on the proven density of mapped prospects, discoveries and dry wildcats within the most thoroughly explored subareas of the play. In plays with no or very few mapped prospects, estimates of prospect densities had to be based on

PVT parameters In addition to the volume parameters FASPUM requires some PVT data as input. These parameters are assumed to be depth dependent. The NPD assessors thought it quite pointless to try to predict how these parameters vary laterally over large areas, and it was decided to use linear depth functions and standard values common to all plays (Table 4).

Table 4 Input data for FASPUM computation program - - reservoir parameters

Geological variables Four types of mathematical functions (1) Zones linear function:

A 9depth + B Maximum of 4 zones with 3 transition depths (m) A 9exp(B 9 depth) A 9depth ** B A 9ln(B 9 depth)

(2) Exponential function: (3) Power function: (4) Logarithmic function:

For each of the five geological variables below, select one type of function and assign values for the parameters A and B Pe: T: Rs: Bo: Z:

Original reservoir pressure (bar) Reservoir temperature (K) Gas-oil ratio (m3/t) Oil formation volume factor (no units) Gas compressibility factor (no units)

Variable Pe T Rs Bo Z

Function

Parameters

Linear Linear Linear Linear Linear

A 0.1 0.035 0.079 0 0.0000992

Oil floor depth (m): Oil recovery factor (%): Gas recovery factor (%):

4800 40 75

B 0 277 0 1.25 0.67

D

A

B

D

A

B

D

A

B

101


Risk analysis The FASPUM approach is based on a conditional probability model where the risk analysis is split into two levels; the play level and the prospect level. In the FASPUM scheme the chance of success at the play level and prospect level are termed "the marginal play probability" and "conditional deposit probability" respectively (Table 3). (NPD prefers to use the terms "play chance" and "prospect chance"). At the play level one assesses the risk of the regional parameters that determine whether the play is favourable (i.e. will lead to a discovery). At the prospect level one assesses the parameters that determine the chance of success in each single prospect if the play chance equals 1 (i.e. if all regional parameters are favourable). The chance of success for each of the parameters on both play level and prospect level is assigned a value between 1 and 0, and the final chance of success, Pf, equals the product of all the risk parameter values. In the FASPUM scheme the critical parameters at play level are identified as the chance for the existence of mature source rock, migration, reservoir rock and favourable timing of trap formation; the critical parameters at prospect level are identified as the chance of effective trapping, effective porosity and hydrocarbon accumulation. The NPD had to modify this scheme to make it fit with the well established NPD internal procedure for prospect risk analysis. The NPD prospect risk analysis is based on four main risk parameters, P1 to P4, some of which are split into two sub-parameters: P(reservoir) = P(reservoir rock) x P(porosity) P1 Pla Plb P(trap) = P(mappability) x P(trap quality) P2 P2a P2b P(source & accumulation) = P3 P(retention) P4

P(quality & volume) x P(migration) P3a P3b

The NPD standard defines a set of status-categories for each parameters, with a corresponding range interval for a likely chance of success of the parameter. The task of the assessor is then to identify the correct status-category for each 15arameter and subjectively pick the most likely chance of success within the stated range. This process is always checked by a group of experienced assessors to ensure a repeatable result. The product of all parameters then equals the chance of success, Pf (i.e. the chance of finding movable hydrocarbons, not necessarily commercial volumes): P f = P1 x P2 x P3 x P4

Pm (COMMON)

1)

Pf = Pla x Plb x P2a x P2b x P3a x P3b x P4

//

Pp ( I N D I V I D U A L )

2)

Pf

= P l a x P2b x P3a x P l b x P 2 a x P 3 b x P 4

Pm

Pp

Pm = 1

CONFIRMED

Pm < 1

UNCONFIRMED

Fig. 8. Structure of the risk assessment in the NPD. The NDP standard for assessing the chance of success of a prospect is given in 1). Adjusting this standard to fit the assessment of the chance of success in play analysis is done by separating the regional parameters that are common to all prospects in a play model, Pm, from the parameters that vary between the individual prospects, Pp. Pm is the chance of success at play model level ("play chance"), Pp is the chance of success at prospect level ("prospect chance"), and Pf is the final chance of success for making discoveries among all the prospects in a play. Plays may be classified into two groups: those that are confirmed by discoveries and hence Pm = 1, and those that are not confirmed and hence 0 < Pm < 1.

In this scheme an assessor who estimates the chance of success for a single prospect, estimates the risk factors common to the play as a whole, together with the factors that vary between the different prospects. To modify this scheme to fit the assessment of risks in a play analysis, the NPD assessors simply identified the common, critical factors and assigned these to the play level and the remaining parameters were kept at prospect level (Fig. 8). This led to the following scheme for the play analysis: At play level: Pm = P la x P2b x P3a (i.e. reservoir x trap quality x source quality and volume) In P la one estimates the chance of finding the stated reservoir rock across the whole of the play area. In P2b one considers the general quality of the defined trapping mechanism and estimates the average chance for such traps to seal hydrocarbons. In P3a one estimates the chance that there will be a sufficient volume of mature source rock within the

102 drainage area to fill all prospects in the play. At prospect level: Pp = Plb x P2a x P3b x P4 (i.e. porosity x mapping quality x migration x retention) In P lb one estimates the chance of finding the minimum porosity in all prospects. In P2a one considers the data quality and coverage and estimates the chance for being able to accurately map the trap type of the play. In P3b one estimates chance of having an effective migration into all prospects in the play area. In P4 one considers the geological factors that may affect the sealing in the traps after accumulation and estimates the chance that they stay sealed until the present. The plays are divided into two categories: those that are confirmed by discoveries and those that are still conceptual (Figs. 8 and 6). The two categories are conceptually quite different when it comes to the risk analysis. In the confirmed plays the discoveries make up a basis for calibration for estimating the chance of success, and the assessor relates his "statistical" thinking to a number of prospects which are local analogs to the discoveries. The assessor knows that the play works, and a large number of remaining prospects gives a sound basis for thinking in terms of frequencies and probabilities. This calibration based on rate of success cannot be used on the play level, simply because the number of plays is not large enough to make frequency counts. Only a few of the unconfirmed plays have known, confirmed direct analogs in time and space on the Norwegian shelf. For most of the plays analogs were required from other parts of the world or from other parts of the stratigraphic column (for example Jurassic sand in rotated, Jurassic fault blocks may be an analog for Cretaceous sand in Cretaceous rotated fault blocks). The restricted number of plays, however, makes it meaningless to base estimates of play chance on a frequency count of success vs. failure, even on a world wide database on playtypes. Instead the NPD assessors based their estimation of play chance very much on their assessment of the critical regional parameters (Pla, P2b and P3a). In many cases only one of these parameters gives the dominant risk, while geological and geophysical data may indicate or even prove that the other parameters are favourable. In general in the NPD scheme, the less that is known about a play, the higher the risk. In frontier areas where the existence of the most uncertain parameter is based on weak indications, regional geology and speculative palaeogeographical models, the play chance was estimated to be generally less than 0.05. This is the case for more than half of the 21 unconfirmed plays in the analysis. In the cases


where good, direct analogues exist locally, and the exploration of the new play only implies one step out from the confirmed play, the play chance was estimated to be between 0.1 and 0.25. Estimating the play chance of an unconfirmed play thus implies subjective assessments to a large degree. However, through the common, regional parameters (Pla, P2b and P3a) there is a link between the well established NPD standard for estimating the chance of success of a prospect and estimating the play chance of an unconfirmed play. By applying the NPD prospect standard on the regional parameters of the unconfirmed plays, we found that we were able to arrive at repeatable values for the play chances. We therefore believe that the NPD is able to make play chance assessments that are consistent over time, similar to the assessments of chance of success for prospects. The play analysis also showed that the assessors were able to make repeatable assessments of the total chance of success for both unconfirmed and confirmed plays. However, one cannot be certain that the relative differences in total risk level between the confirmed and unconfirmed plays are correct in absolute terms. Based on the estimated play chances for the 21 unconfirmed plays in the NPD analysis it is possible to arrive at an estimate for the chance of success for at least one of them to be confirmed. Given five independent plays ml to m5, one may estimate the chance, Pf, for at least one play to be successful in the following way: Pf= 1(1 Pro1)(1 - Pm2)(1 -- Pm3)(1 - Pm4)(1 - Pro5) Estimated in this way, the chance of success for at least one of the 21 unconfirmed plays in the NPD analysis is 0.7. Even considering the uncertainties in the estimation of the play chances, this shows that it is very likely that at least one new, independent play will be confirmed on the Norwegian shelf in the future. -

Summary and conclusions Play analysis combined with a lognormal size distribution model is a flexible assessment method that makes it possible to use all available data in an optimal way. Ideally, the method requires that the individual plays be defined such that the size distribution of the prospects of the play consists of a single statistical population. However, in areas with very little data it is probably better to make use of simple, regional "lump" models that are highly likely to comprise several statistical populations than to try to split such models into more refined models which cannot be substantiated. Such "split" models give a false impression of reduced risk.


The two most difficult parameters to assess were the size distribution of the traps (net rock volume) and the number of drillable prospects. The NPD based its size distribution on the assumption that the area of closure and the net rock volume of the prospects in a play may be fitted to a lognormal distribution curve. In that way the problem was reduced to finding curves that would include both the maximum possible prospect of the play and a reasonable median size which could be calibrated by maps and discovery statistics. The number of drillable prospects in a play was estimated from prospect densities based on statistics and analogs. In the risk analysis the geological risk factors were split into two levels: the play level and the prospect level. The play level includes the regional factors that are the critical factors common to all prospects in a play, while the prospect level includes the factors that vary from prospect to prospect. Prospect chance (prospect level), and therefore chance of success of confirmed plays, was calibrated by analogs and frequency counts. Play chance (play level) of unconfirmed plays cannot be calibrated by frequency counts, and was based on the risk assessment of the individual regional factors which indirectly may be calibrated by confirmed analog plays. In many cases only one of the regional factors gives the dominant risk of the play. The NPD internal procedure for risk analysis is

H. BREKKE J.E. KALHEIM

103

seen to give repeatable results for both play chance and total chance of success. References Arps, J.K. and Roberts, T.G., 1958. Economics of drilling for Cretaceous oil and gas on the East Flank of the Denver-Julesberg Basin. AAPG Bull., 42(11): 2549-2566. Crovelli, R.A., 1984. Procedures for petroleum resource assessment used by the U.S. Geological Survey - - statistical and probabilistic methodology. In: C.D. Masters (Editor), Petroleum Resource Assessment. IUGS Publ., 17: 24-38. Crovelli, R.A., 1986. A comparison of analytical and simulation methods for petroleum play analysis and aggregation. U.S. Geol. Surv. Open-File Rep. 86-79, 21 pp. Crovelli, R.A., 1987. Probability theory versus simulation of petroleum potential in play analysis. In: S.L. Albin and C.M. Harris (Editors), Statistical and Computational Issues in Probability Modelling, 1. Ann. Oper. Res., 8:363-381. Crovelli, R.A. and Balay, R.H., 1988. FASPUM metric version: analytic petroleum resource appraisal microcomputer programs for play analysis using a reservoir-engineering model. U.S. Geol. Surv. Open-File Rep. 87-414, 14 pp. Dolton, G.L., 1984. Basin assessment methods and approaches in the U.S. Geological Survey. In: C.D. Masters (Editor), Petroleum Resource Assessment. IUGS Publ., 17: 4-23. Kaufman, G.M., 1993. Statistical issues in the assessment of undiscovered oil and gas resources. Energ. J., 14(1): 183-215. NPD, 1993. Petroleum resources E Norwegian Continental Shelf. External report, Norwegian Petroleum Directorate, 40 pp. White, D.A., 1980. Assessing oil and gas plays in facies-cycle wedges. AAPG Bull., 64(8): 1158-1178. White, D.A., 1988. Oil and gas play maps in exploration and assessment. AAPG Bull., 72(8): 944-949.

ExplorationDepartment, Norwegian Petroleum Directorate, P.O. Box 600, N-4001 Stavanger, Norway Exploration Department, Norwegian Petroleum Directorate, P.O. Box 600, N-4001 Stavanger, Norway

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105

Cross-validation of resource estimates from discovery

process modelling and volumetric accumulation modelling: example from the Lower and Middle Jurassic play of the Halten Terrace, offshore Norway Richard Sinding-Larsen and Zhuoheng Chen

Volumetric accumulation modelling attempts to capture the essentials of the hydrocarbon generation, entrapment and retention processes leading to a family of accumulations in a petroleum play. Discovery process modelling uses the size distribution of discovered hydrocarbon accumulations and the efficiency of the exploration effort to estimate the number and sizes of undiscovered accumulations in a play. Direct incorporation of exploration data and geological judgement is a prerequisite for the volumetric modelling, whereas accurately representing exploration efficiency through the discovery sequence is a prerequisite for discovery process modelling. How to achieve a valid play definition is a problem common to both methods. Cross-validation of the two methods applied to the same play can sharpen the focus of each method if used individually, as well as capitalize on the strong points of both methods if used in an integrated approach. These issues are exemplified through a case study focussing on the assessment of the undiscovered potential of the pre-rift Lower and Middle Jurassic play of the Halten Terrace, Mid-Norway continental shelf.

Introduction Many methods and models for petroleum resource assessment have been developed using different geological perspectives. Most hydrocarbon assessment approaches in current use can be classified by a simple dichotomy consisting of those that rely on modelling the natural processes of deposit formation and those that rely on modelling the process of exploration and subsequent discovery of hydrocarbon accumulations. Each method requires a specific level of geologic knowledge or degree of exploration information, and provides in many cases compatible types of results (e.g. aggregate estimates and field size distributions). The natural hydrocarbon accumulation process is modelled in this study by a volumetric approach leading to estimates of the underlying field size distribution and to a distribution of the total number of fields in a petroleum play. Because each step in this methodology is associated with genetic parameters, each step is also interpretable in terms of specific geological processes. However, many factors affect the reliability of the estimation results. Volumetric variables may, for example, be partially correlated. Ignoring these correlations may cause biases in the

e s t i m a t e s o f the field size distribution and the resulting play potential. A discovery process model estimates the play potential by modelling the interaction between the natural processes leading to hydrocarbon accumulation and the results of exploration drilling. Bias due to non-random sampling from the underlying field size distribution is modelled by specifying that the discovery probability is proportional to size. Problems related to the application of this method often come from an inadequate play definition or from the fact that insufficient information is provided by the discovery sequence to estimate the underlying parent field size distribution. The application of both methods to the same play permits us to sharpen our focus on the limitation of each method if used individually. The credibility of the assessment will however be greatly improved if results from individual approaches are in agreement. Any difference in the assessment results, if significant, will challenge the assumptions of the individual methods or cast insight into the geological concepts used. In this paper, the aforementioned cross-validation approach is illustrated with assessment of the play potential of the pre-rift Halten Terrace play on the Mid-Norway continental shelf.

Quantification and Prediction of Petroleum Resources edited by A.G. Dor6 and R. Sinding-Larsen. NPF Special Publication 6, pp. 105-114, Elsevier, Amsterdam. 9 Norwegian Petroleum Society (NPF), 1996.

106

R. Sinding-Larsen and Z. Chen

Geological setting The Halten Terrace play comprises discoveries and drillable prospects in the pre-rift sequence of Early to Middle Jurassic age and has been the focus of exploration since 1980. The major reservoir units are the Lower-Middle Jurassic shallow marine sands. The petroleum accumulations are sourced by two major units, a gas/condensate-prone Upper TriassicLower Jurassic coal unit, and a Upper Jurassic oilprone black shale. A top-seal is provided by the Upper Jurassic and/or Lower Cretaceous impermeable shales. The major trap styles are similar to the traps in the North Sea, including simple extensional traps consisting of rotated fault blocks, horsts, and the combination of these two geometries formed during the Late Jurassic-Early Cretaceous Cimmerian rifting phase. Salt movement is also believed to have contributed to the formation of structural traps (Jackson and Hastings, 1987; Koch and Heum, 1995; Blystad et al., 1995). Fig. 1 is a play map of the pre-rift play of the Halten Terrace, offshore Mid-Norway. The database used in this study is mainly built upon the information provided by Kalheim (1989) and has been updated from recent publications (Norwegian Petroleum Directorate, 1992; Ehrenberg, 1990; Fagerland, 1990; Koch and Heum, 1995). Table 1 lists the field and prospect data and the estimated recoverable reserves. Reserves for each field were

estimated by different organizations and differ one from the other. Three different estimates are shown in Table 1. The assessment from a discovery process model may vary if different estimates of reserves are used. A hypothetical order of discovery is included in Table 1 and used to test the sensitivity of the Norwegian licensing procedure on the discovery process modelling results.

Volumetric approach The volumetric accumulation modelling technique used is based on a statistical play analysis method developed by United States Geological Survey (USGS) for their 1989 assessment of undiscovered petroleum resources in the United States. This method has recently been used by the Norwegian Petroleum Directorate (NPD) for their assessment of petroleum resources on the Norwegian Continental Shelf (NPD, 1993) and a description of the methodology is provided by Brekke and Kalheim (1996). The size of any given field in the population is determined by an appropriate variant of the reservoir engineer's field size equation. The solution of the equation gives the volume of recoverable hydrocarbons for the field. In order to get the distribution of all possible field sizes in the play, it is necessary to input each variable as a frequency distribution describing both the range of possibilities and probabilities for all possible accu-

Fig. 1. Pre-rift play of Halten Terrace, Mid-Norway continental shelf (based on Kalheim, 1989).

107

Cross-validation of resource estimates from discovery process modelling and volumetric accumulation modelling Table 1 Field and prospect data (from NPD, 1992 and Scenario 1.3, table 3 of Kalheim, 1989)

Midgard Tryihans S. Tyrihans N. SmCrbukk

Heidrun SmCrbukk S. Mikkel Njord

Trestakk 6507/08-4 P-04 P- 13 P-37 P-48 P-42

N.R.V. (km 3)

C1.A. (km 2)

N.Th (m)

N/G

Res. I a ( 106 )

Res. II a ( 106 )

Res. III a ( 106 )

2.25 0.77 1.04 8.38 3.59 2.18 0.51 2.01 9 9 1.20 0.77 4.60 1.96 0.44

47 21 13 120 32.5 25 10 25 9 9 24 8 80 25 5

48 36 80 70 110 87 51 80 9 9 50 96 58 78 88

0.9 0.85 0.9 0.55 0.80 0.45 0.85 0.6 9 9 0.95 0.75 0.45 0.55 0.55

101 15.5 18.9 125.3 109.4 49.7 21.1 36.0 3.9 20.4 dry dry dry dry dry

124.1 15.5 18.9 108.7 116.0 42.0 14.7 30.6 3.9 11.2 dry dry dry dry dry

112.0 17.0 20.0 102.0 164.0 46.0 20.6 43.0 8.2 20.0 dry dry dry dry dry

Hypoth. order 3 4 9 2

1 6 5 8 7 10

a Reserves are measured in t.o.e. (recoverable). N.R.V = net rock volume; C1.A = closure area; N.Th and N/G -- net thickness and net/gross ratio; Res. I -- estimated reserves by NPD (NPD, 1992); Res. II = estimated reserves by industry (unpublished); Res. III = estimated reserves by Statoil (Kock and Heum, 1992); Hypoth. order = hypothetical order of discovery only influenced by exploration information (J.E. Kalheim, 1993, pers. commun., 1993).

Table 2 Input parameters and estimating results from the volumetric approach Geologic variables

F 100

F95

F75

F50

F25

F05

F0

Closure (km 2) Net thickn. (m) Porosity (%) Trap fill (%) HC sat. (%)

4.00 10.0 10.0 50.0 50.0 20

6.00 12.0 12.0 55.0 52.0 21

12.0 25.0 13.0 60.0 60.0 22

19.0 40.0 16.0 75.0 70.0 25

29.0 60.0 19.0 90.0 78.0 30

55.0 110.0 23.0 95.0 85.0 35

110.0 150.0 26.0 100.0 90.0 40

Nr. of prosp.

Success ratio = 0.273; recovery factor for oil = 0.35; recovery for gas = 0.65 Probability for oil = 0.25; probability for gas = 0.75 Expected play potential for oil: 33.4.106 ton 110.6-109 m 3 Expected play potential for gas: Total play hydrocarbon potential: 144.0.106 t.o.e.

mulations in the play. Data reflecting the geologist's opinion on the range of values and their probabilities are specified for each variable (Table 2). The distribution of field sizes is computed by the multiplication of a recovery factor with the frequency distributions of closure, thickness, porosity, trap fill, and hydrocarbon saturation, and divided by a formation volume factor. Field size -

C.RF.

A . T. ~ . TF.

FVF

Snc

(1)

where C is a constant equal to 0.84 for 10 6 metric tons of oil and 1.0 for 10 9 m 3 of non-associated gas, R F is recovery factor (%), A is area of closure (km), T is net reservoir thickness (m), ~ is porosity (%), SHc is hydrocarbon saturation (%), T F is trap fill (%), and F V F is the formation volume factor (no unit). The distribution of the number of fields is computed by multiplying the estimated distribution of

the number of prospects with the anticipated success ratio. The multiplication of the volumetric input expressed as distributions to obtain the field size distribution is an approach that can be readily understood, but can be flawed, sometimes seriously (Lee et al., 1990) because of the interdependency between variables. If the co-variances are believed to be nonnegligible and are unknown, they will have to be estimated either by analogy or prior knowledge.

Volumetric modelling results In a preliminary assessment, all geological variables (Table 2) were assumed to be independent and correlations were ignored. The recovery factors are assumed to be equal for all the fields in the play and all resource figures are expressed as recoverable

R. Sinding-Larsen and Z Chen

108

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Field size (10^6 TOE, recoverable) Fig. 2. Field size distributions estimated from the volumetric approach: remaining (REM) (/z = 2.5, cr2 a 2 = 1.07), and observed (DISC).

resources. The expected remaining oil potential was calculated equal to 30.10 6 tons with 95% chance of having more than 4.106 tons and a 5% chance of having more than 94-10 6 tons. The expected remaining gas potential was calculated to be 100.109 m 3 with a 95% chance of having more than 32-109 m 3 and a 5% chance of having more than 220.109 m 3. In terms of hydrocarbons, the expected undiscovered recoverable play potential was calculated to be 130.106 t.o.e. (tons of oil equivalents) which represents the total resources coming from seven undiscovered accumulations greater than a minimum economic cut-off of size of 4-106 t.o.e. The assumption of independence of the geological variables is challenged by the analysis of the input data which suggests the presence of correlations among several geological variables. Table 3 shows a matrix of covariances estimated from well data. The covariance (0.0824) between the logarithmic net reservoir thickness and HC saturation in this play Table 3 Covariance matrix

Porosity Net thickn. HC sat. Closure

Porosity

Net thickn.

HC sat.

Closure

0.1014 0.0213 0.0549 0.0

0.0213 0.7203 0.0824 -0.065

0.0549 0.0824 0.0754 0.0

0.0 -0.065 0.0 0.7838

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0.94), parent (PAR) (# = 3.1,

represents a significant contribution to the overall covariance, but its impact on the volumetric assessment is small because the positive covariances are being compensated by negative covariance. Because of this compensating effect, the expected remaining play potential now calculated equal to 144.106 t.o.e., is only about 10% larger than the previous estimate obtained when dependencies were ignored. The estimated mean size of the remaining fields is now calculated equal to 21-106 t.o.e. Fig. 2 shows the final estimated remaining field size distribution (REM) and the underlying parent size distribution (PAR) computed as the aggregate of REM with the ten discoveries (DISC).

Discovery modelling The attributes of a deposit and its surrounding environment which influence the deposit's probability of being discovered can, in most cases, be interpreted as arguments of a function that may usefully be defined as a deposit's "magnitude". To date, most models have taken deposit size (area, rock volume, forms of oil equivalent) as a measure of magnitude, but the relationship between such simple definitions of magnitude imply that complexity must be represented in the next generation of models. Identification of attributes that determine magnitude is a first step toward estimating the functional relation between magnitude and probability of discovery. A second step is to determine how definition of magnitude

Cross-validation of resource estimates from discovery process modelling and volumetric accumulation modelling

should change with changes in geologic environment, technology, economics and resources remaining to be discovered (Kaufman et al., 1988). Discovery modelling is based on an understanding of the discovery process and requires a set of assumptions. The following two features characterize a discovery process model: (1) a discovery process involves sampling from a frequency distribution of field sizes without replacement; and (2) that the order of discovery is governed by sampling proportional to magnitude and without replacement. Magnitude is defined here as field size measured as tons of oil equivalent, raised to a power ft. The successive sampling scheme conforms to empirically based industry experience that the discovery sequence is biased towards finding the larger fields early in the exploration process. Assuming N fields in a play with associated sizes Y(yl, y2,... , YN), and j - 1 fields that have been discovered in the order Yl first, y2 second and so forth, the probability that the field labelled j will be the next discovery conditioned on the j - 1 discoveries is" t~ Yj P(yj IY) - ~ (2) Yj + Yj+I -+-""-k Y~N where fl is called the bias parameter or discoverability parameter. The lognormal discovery process model (LDP) used in this study is developed by the Geological Survey of Canada (GSC) and is based on the successive sampling model of Kaufman et al. (1975). Although the size distribution can be in any form, lognormal is recognized as a frequently used model and its use facilitates the statistical estimation. Details of the model are given in Lee and Wang (1985, 1986). Using a discovery sequence, the LDP model provides /z and 0 "2, the parameters of the estimated lognormal parent population, N, the total number of deposits in the play, and fl, the discoverability parameter. If the fl exponent is zero, the probability of discovering any field is proportional only to the number of fields. If the fl factor is one, the probability of discovery is directly proportional to field size. The fl parameter is influenced by many factors, such as licensing sequence, water depth in the offshore, availability of infrastructure and increase in the ability with time to seismically discriminate certain types of prospects. Discovery modelling is applicable to semi-mature and mature plays where the discovery process can be characterized by a time sequence of discovered oil and gas magnitudes. The data required are the magnitudes of field sizes and their order of discovery. The geological analysis that allows the organization of this information into an adequate definition of a petroleum play is, however, critical for obtaining reasonable answers.

109

Discovery modelling results The discovery sequence of ten discoveries is shown in Table 1. Using the NPD discovery sequence, global maximum likelihood estimates of fl, /z and 0-2 (discoverability, mean and variance) and the log maximum likelihood for different numbers of deposits, N, can be obtained as shown in Table 4. The most plausible value for N is 12 which coincides with the minimum value used as the range of possible input values, but N = 12, is not consistent with the number of prospects and the success ratio. The failure to obtain a reasonable maximum likelihood estimate of N may indicate that the discovery sequence does not contain enough information for determining N. In this case other methods or additional data such as the geological control provided by the prospect list are needed for a more reliable determination of N. However, the use of maximum likelihood analysis may still be a useful tool in judging what is the most plausible value of N (Lee and Wang, 1985). The application of a non-parametric discovery process model, the geo-anchored method (Chen, 1993), indicates that the discoverability parameter fl may lie in the interval of 1.1-1.2. Accepting the choice of the discoverability parameter fl = 1.2, marginal maximum likelihood estimates of/z, a 2, and the marginal likelihood values for different values of N can be calculated by the LDP model as shown in Table 5. The marginal estimates have now a maximum log likelihood value at N = 18 which is consistent with the number of prospects and the success ratio of the play. The corresponding marginal estimates of the parameters for the underlying field size distribution Table 4 Maximum likelihood estimates of/z, 0.2 and /3 and log-likelihood values (ML) for different N's (LDP) N

/~

/2

6 .2

ML

12.0000 13.0000 14.0000 15.0000 16.0000 17.0000 18.0000 19.0000 20.0000 22.0000 24.0000 26.0000 28.0000 30.0000 32.0000 34.0000

0.9223 0.9954 1.0492 1.0919 1.1273 1.1571 1.1840 1.2071 1.2280 1.2641 1.2946 1.3213 1.3437 1.3641 1.3826 1.3990

3.2310 3.1240 3.0290 2.9430 2.8640 2.7910 2.7240 2.6610 2.6010 2.4930 2.3960 2.3070 2.2270 2.1520 2.0830 2.0190

1.2290 1.3290 1.4110 1.4800 1.5400 1.5930 1.6420 1.6850 1.7250 1.7970 1.8590 1.9140 1.9630 2.0070 2.0470 2.0840

-58.4552 -58.4872 -58.5250 -58.5611 -58.5941 -58.6240 -58.6509 -58.6752 -58.6974 -58.7361 -58.7690 -58.7972 -58.8219 -58.8437 -58.8631 -58.8805

N = the number of fields"/3 = the discoverability index;/z and 0 .2 are the estimated parameters for a lognormal size distribution.

110


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Fig. 3. Lognormal field size distributions. "RD" estimated under assumption of random sampling (/z = 3.47, 0.2 = 1.17)" "LDP" estimated by the lognormal discovery process model of CGS with N = 18 and 13 = 1.2 (/z = 2.81, 0.2 = 1.552).

Table 5 Marginal log-likelihood and estimates of/z and 0.2 for/3 = 1.2 and different N's N

/2

~.2

log-likelihood

12 14 16 18 20 22 24 26 28 30

3.238 3.062 2.923 2.810 2.716 2.636 2.568 2.508 2.455 2.409

1.258 1.384 1.467 1.525 1.567 1.598 1.621 1.639 1.652 1.662

-54.2303 -54.0913 -54.0447 -54.0305 -54.0307 -54.0382 -54.0496 -54.0633 -54.0783 -54.0939

N - - the number of fields;/3 = the discoverability index; # and are the estimated parameters for a lognormal size distribution.

0 .2

are measured in units of 10 6 tons of oil equivalents, /2 - 2.8 and 6 2 = 1.5 (Table 5). A/3 value of 1.2 suggests that field size has exerted a strong influence on the order of discovery, as can be inferred from Fig. 3 where the parent lognormal distribution estimated by assuming a random sampling (RD) is compared to the lognormal distribution estimated by the LDP model discussed previously. The separation between these two distributions reflects the magnitude of/~ and indicates that there is a strong tendency for large fields to be discovered at an early stage. Using the parameters of the LDP curve in Fig. 3, /2 - 2.8, ~.2 __ 1.5 and N -- 18 (Table 5), the expected total resources in the play are calculated to be 630.10 6 t.o.e. (tons of oil equivalent). Subtracting the discoveries

gives an expected remaining play potential of 130.10 6 t.o.e., corresponding to a mean remaining field size of 18.10 6 t.o.e. The lognormal discovery process model used on simulated data shows that the LDP model may slightly underestimate the field size distribution when /~ is large (Chen and Sinding-Larsen, 1994). The estimates of the field size distribution from the LDP model in this study may therefore be regarded as somewhat pessimistic. It may be desirable to test the sensitivity of/3 to change in the order of observations, because the number of discoveries are small. The reserves of fields estimated by different organizations are significantly different in some cases (Table 1). On the other hand, the licensing process may also cause problems in the application of discovery process modelling. However, a sensitivity study of the two factors (licensing process and differing field estimates) has been done by using the discovery sequence with different estimates of the reserves of fields and re-ordering the discovery sequence according to the hypothetical order in Table 1. The test shows that in the case of this play no significant impact on the assessment of the resource potential can be observed.

Integrated approach The discovery process model employed here uses only the magnitudes in a discovery sequence. It is therefore desirable to incorporate as much as possible of the geological information available to constrain the estimates. On the other hand, from our experience,


111

of some individual discoveries are known or inferred. Searching the two parameters /z and 0-2 from the range, 2.8 < /z < 3.1, 1.2 < 0-2 < 1.5, defined by PAR in Fig. 2 and LDP in Fig. 3, one can obtain many combinations between the outer ranges of these two parameters that describe candidates for the "true" parent log-normal field size distribution. Such combinations can be ranked according to how well they reflect the degree to which means of predicted fields fit discovered fields. Fig. 5 shows one match that accommodates both the size of discoveries made in the play and the estimate of N -- 17 from the volumetric approach. The figure indicates that the first four largest fields have been discovered with the seven undiscovered fields in the 5th, 7th and 12th-16th rank. This match was examined along with others and is consistent with our geological interpretation that not more than one undiscovered field is expected to be greater than Njord (36.106 t.o.e.). The same matching technique was applied to the estimates provided by the LDP model with N - 18 and conditioned on the same set of geological arguments (the first four largest discoveries are found and no more than one remaining field should have a size larger than the Njord). Fig. 6 shows the matched field sizes according to rank representing the estimated sizes of individual remaining fields. This matching procedure can be regarded as a feedback process which allows estimates of field-size distribution parameters and the total number of fields in place to be successfully refined. It may provide an

the field size distribution derived by the volumetric approach often leads to an overestimation because of a bias towards the more favorable situations in the geological observations. One way of comparing these assessments is to construct the field sizes by rank from the estimated parameters/z and 0 -2 derived by the two methods. This relation is used in a matching procedure that allows for cross-calibration through the introduction of judgmental information about the geology.

Field size by rank distribution A major advantage of using the play analysis approach for hydrocarbon assessment is its ability to produce estimates of expected field sizes that should exist within the play. Given a field size distribution shape and specific numerical parameters for the number of fields, order statistics can be used to generate the expected field sizes of the play (Taylor et al., 1991). A rank plot (Fig. 4) is made of the expected field sizes arranged in their order of discovery conditioned on N, the total number of fields. Individual boxes encompass a 90% range in the predicted size of each field. The lower limit represents a 95% probability that the field is at least that large and the upper limit indicates that there is only a 5% probability of the field being larger than that value.

Matching process The ability to produce field rank plots using order statistics has another important application if the size

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112

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Size rank Fig. 6. Individual field size by rank constrained to the discovery record and N = 18, the pre-rift play of Halten Terrace. The lognormal parameters are estimated by the LDP model. The empty boxes represent fields undiscovered and the uncertainty is expressed as 25th and 75th upper perecentiles. The dots are the discoveries.

alternative to the estimation of N (number of fields) for cases in which one is totally dependent on expert judgment. A convenient way of portraying this procedure is to represent different candidates for the parent distribution in a / z and r 2 plot. In Fig. 7 the final estimates for/z and cr2, marked F, representing our

"best" estimate of the parent distribution, lies between the estimates by each method individually. The total resources in the play can now be represented as the sum of all the discovered fields plus all the mean values of the "empty boxes" shown in Fig. 4. Once the assessor is confident that a successful "match" has been made, he has effectively


11 3

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contributed additional information. That is, the ranges (uncertainty) of the largest four fields, as well as other "matched" fields, can now be replaced with a single value (no uncertainty), and the rest of the field rank sizes are adjusted to reflect this added information. Conditioned on the match in Fig. 5, the estimated conditional play potential from the volumetric approach is adjusted to lie in the range from 84 to 145.106 t.o.e, with probability 0.9 and with an expected value of 116.106 t.o.e. Conditioned on the match in Fig. 6, the remaining play potential predicted by the LDP model is adjusted to be in the range from 110 to 1 8 0 . 1 0 6 t.o.e, with probability 0.9 and with an expected value of 144.106 t.o.e. The uncertainties in the size of fields remaining to be discovered are reduced by attaching probability 1.0 to the discoveries in Fig. 4. The largest projected remaining field is to be about 40.106 t.o.e, and the mean size of the remaining fields is 18.106 t.o.e. Table 6 shows a comparison of the play potentials estimated from the two methods. Although the final result of the play potentials estimated from these two methods are a function of the difference in numbers of remaining fields, the mean values of the estimated parent distributions are less than 1.106 t.o.e, apart.

Discussion Play potential, field size/frequency distribution and the number of economic accumulations have been estimated and cross-checked by simultaneously apply-

Table 6 Summary of the estimates (expected values)

Rem. fields Mean rem. size Mean par. size Rem. resources Total resources

Vol. [I]

Vol. [II]

L D P [I]

L D P [II]

7 20.6 37.9 144.0 645.1

7 16.6 36.3 116.2 617.3

8 17.5 35.6 139.9 641.0

8 18.0 35.8 143.9 645.0

Total discovered reserves: 501.1 9106 t.o.e Total number of discoveries: 10 Vol. [I] and Vol. [II]: volumetric estimates before and after the integrated approach. L D P [I] and L D P [II]: L D P model estimates before and after the integrated approach.

ing both the volumetric approach and the lognormal discovery process model. The field size distribution and play potential of this pre-rift play could have been overestimated if the volumetric method was applied alone because of uncalibrated bias in the geological variables. In contrast, without additional geological constraints and the use of other type of information, the LDP model, in this case, could not have generated a reasonable estimate of the expected number of economic petroleum accumulations and could therefore have produced a severely underestimated play potential. The sensitivity of the discovery modelling approach to the licensing process and differences in the reserve figures of discoveries given by different sources were studied by using the three reserve figures and the hypothetical order of dis-

11 4


covery given in Table 1. No significant departure in the estimation results was found. This application shows that cross-validation is a valuable tool for controlling the quality of resource assessment. By cross-validation, differences in the initial assessment results will force the assessor to check the applicability of the methodology in use and cast doubts on the geological model. Finally if the differences are reduced to an acceptable level, the credibility of the assessment result will be improved by the degree of the agreement obtained between the assessment results from the two methods used individually. The quality control of resource assessment can be established by setting standards for the comparison of assessment results, for example, by the methodology followed in this paper and ensuring that these standard are met in routine assessment. Subsequent to the presentation of this paper the Saga Petroleum Company made a discovery in well 6406/2-1. Total recoverable resources are estimated to lie in the range from 50 to 100.106 tons of oil equivalents and would fit perfectly the largest undiscovered field predicted in Fig. 5 or Fig. 6.

References Blystad, P., F~erseth, R.B., Larsen, B.T., Skosgeid, J. and TCrudbakken, B., 1995. Structural elements of the Norwegian continental shelf, Part II. The Norwegian sea region, NPD Bull., 8, 45 pp. Brekke, H. and Kalheim, J.E., 1996. The Norwegian Petroleum Directorate's assessment of the undiscovered resources of the Norwegian Continental S h e l f - background and methods. In: A.G. Dor6 and R. Sinding-Larsen (Editor), Quantification and Prediction of Hydrocarbon Resources. Norwegian Petroleum Society (NPF), Special Publication 6, Elsevier, Amsterdam, pp. 91103 (this volume). Chen, Z., 1993. Quantification of petroleum resources through sampiing from a parent population and as a function of basin yield. Doctorate dissertation, Norwegian Institute of Technology, 1993, 223 pp. Chen, Z. and Sinding-Larsen, R., 1994. Discovery process mod-

R. SINDING-LARSEN Z. CHEN

elling, a sensitivity study. Nonrenewable Resources, 3(4): 295303. Ehrenberg, S.N., 1990. Relationship between diagenesis and reservoir quality in sandstones of the Garn Formation, Haltenbanken, Mid-Norwegian continental shelf. AAPG Bull., 74(10): 15381558. Fagerland, N., 1990. Mid-Norway shelf-hydrocarbon habitat in relation to tectonic elements. Nor. Geol. Tidsskr., 70: 65-79. Jackson, J.S. and Hastings, D.S., 1987. The role of salt movement in the tectonic history of Haltenbanken and Tr~enabanken and its relationship to structural style. In: Spencer et al. (Editors), Habitat of Hydrocarbons on the Norwegian Continental Shelf. Norwegian Petroleum Society (NPF), Graham and Trotman, London, pp. 241-258. Koch, J.O. and Heum, O.R., 1995. Exploration trends of the Halten Terrace. In: S. Hanslien (Editor), Petroleum Exploration and Exploitation in Norway. Norwegian Petroleum Society (NPF) Special Publication 4, Elsevier, Amsterdam Kalheim, J.E., 1989. A play description scenario offshore Norway (Scenario 1.3). Workshop material prepared for CCOP, The Coordinating Committee for Offshore Prospecting in South East Asia, Bangkok. Unpublished manuscript, 32 pp. Kaufman, G.M., Balcer, Y. and Kruyt, D., 1975. A probabilistic model of oil and gas discovery. In: J.D. Haun (Editor), Methods of Estimating Undiscovered Volume of Oil and Gas Resources. AAPG Stud. Geol., 1: 113-142. Kaufman, G.M., Crovelli, R.A., Chow, S. Grace, J.D., SindingLarsen, R., Sollie, B.H. and Wang, P.C.C., 1988. Oil and gas resource modeling and forecasting. In: C.F. Chung et al. (Editors), Quantitative analysis of Mineral and Energy Resources. Reidel, Dordrecht, pp. 695-700. Lee, P.J. and Wang, P.C.C., 1985. Prediction of oil or gas pool sizes when discovery record is available. Math. Geol., 17(2): 95-113. Lee, P.J. and Wang, P.C.C., 1986. Evaluation of petroleum resources from pool size distributions. In D.D. Rice (Editor), Oil and Gas Assessment - - Methods and Applications. AAPG Stud. Geol., 21: 33-42. Lee, P.J., Snowdon, L.R. and Wang, P.C.C., 1990. Petroleum resource evaluation, short course notes. Canadian Society of Petroleum Geology, 1990 Convention, Calgary, 108 pp. NPD (Norwegian Petroleum Directorate), 1992. NPD Annual Report, 195 pp. NPD (Norwegian Petroleum Directorate) 1993. Petroleum resources Norwegian Continental Shelf. External report, Norwegian Petroleum Directorate, 40 pp. Taylor, G.C., Procter, R.M. and Menely, R.A., 1991. Petroleum Resource Appraisal System. Geol. Surv. Can., Open-File Rep., 2374, 65 pp.

Department of Geology and Mineral Resources Engineering, The Norwegian Institute of Technology, N-7034, Trondheim, Norway Department of Geology and Mineral Resources Engineering, The Norwegian Institute of Technology, N-7034, Trondheim, Norway

11b

The Russian method for prediction of hydrocarbon resources of continental shelves, with examples from the Barents Sea K.O. Sandvik and E.V. Zakharov

The Russian method of carrying out offshore hydrocarbon resource assessments is briefly described. This method is applied in three examples based on analogies between productive units in northwest Russia and prospective units in the Norwegian Barents Sea. Although there has been little public data available to the authors in the latter area, it is believed that use of Russian methods and data might bring some new ideas about what prospect types might be productive in the Norwegian sector of the Barents Sea.

Introduction Very little co-operative work has been carried out between people doing hydrocarbon resource assessment in Russia and in the West. This largely reflects the fact that Russian/Soviet authorities have been reluctant to provide background data. Furthermore, the situation has been compounded by the lack of a common assessment language. This paper addresses both these questions. The examples presented could have been more accurate and detailed if all existing data, both Norwegian and Russian had been available. Hopefully the future will show a more open and fruitful collaboration between geoscientists of all nationalities with interests in this matter. In Russia, several organizations are involved in the development and use of hydrocarbon assessment methods. This has resulted in a voluminous domestic literature on the subject. It is not the intention of this paper to compare the Russian methods to each other, since this is a subject in itself and deserves more attention than can be given here. The first petroleum resource assessment in the USSR was made by Gubkin in 1937. Later, petroleum resource assessment was established as an integral part of the 5-year planning system starting with the assessment made in 1958. The methods used were published by Bujalov et al. (1962). Several modifications have been made and an updated version was published in 1990 by Bujalov et al. The methods described in the present paper were first published by Zakharov (1971). They have since been applied in various regions of CIS (the Barents, Kara, Caspian,

and Black Seas), other CIS countries, the former GDR, and Cuba. The basis of the Russian assessment method is the use of the specific concentration of ISR (initial oil in place) of hydrocarbons in 1000 tonnes/km 2. ISR includes both the produced hydrocarbon reserves, remaining reserves, and the potential hydrocarbon resources. Table 1 shows a comparison between the Russian classification system and other world-wide systems. The Russian nomenclature for resource classification is as follows: A: reserves produced or under production B: reserves that yielded commercial flows from wells at different depth levels. C l: reserves characterised by flow of commercial quantities of oil and gas from some wells, combined with positive geological and geophysical results in untested wells. C2: reserves in untested zones adjacent to reserves of higher categories, or in untested beds within the producing sector of a field. C3: prospective resources in mapped traps prepared for exploration drilling, or in developed fields in beds untested by drilling but proved to be productive in other fields. D l: prognostic resources in sedimentary deposits on major trends with proved commercial potential. D2: prognostic resources in sedimentary deposits within major regional structures without proved commercial potential. The three groups C3, D~, and D2 constitute the undiscovered resources and differ from each other in


116

K. O. Sandvik and E. V.. Zakharov

Table 1 Hydrocarbon reserve and resource classifications (slightly modified from Oil and Gas, 7 Oct. 1992)

Russia

France, North Germany, African Netherland,, nations

U.S., Canada, Saudi Arabia Drilled developed

Measured

proved

Prove n

Explored

Demonstrated Undeveloped

B

reserves

Proved Proved

Identified

Indicated

C1

Probable Preliminarily C2

evaluated

Prospective C3

Inferred

Probable

Possible

Non-proven reso u rces

D1

Hypothetical

D2

Speculative

Prognostic

the reliability of the information on the oil and gas bearing potential of the geological structures in the area under consideration. Four assessment levels

The assessments are made in accordance with a division into four levels: regional, sub regional, zonal, or local. At the regional level, the whole part or a major part of the sedimentary section of a basin is addressed. At a zonal level, a particular part of the sedimentary section is dealt with, i.e. Jurassic or Upper Jurassic. The four levels are described in Table 2. The validity of the prediction is not only dependent on the degree of investigation, but also on the dimensions and complexity of the geological conditions. The greater the volume of sedimentary rocks or the more complex the structure, the less the possibility of using advanced evaluation methods. For each of the four levels, there is a corresponding level of geological and geophysical input, along with information on the oil and gas bearing ability of the evaluated area. The basis for the latter input is information from analogous oil and gas bearing reference areas. Whether this evaluation gives valid values is of course dependent upon how close the similarities are between the evaluated area and the reference area. If both the evaluated and the reference units are of the same level and in the same oil and gas bearing province ("inner" analogy), the quality of the assessment will be much better than

with a reference area in another oil and gas bearing province, even if the latter is located in a similar tectonic setting or has the same age ("outer" analogy). Zakharov and Kondakov (1978) presented a new approach for systematising resource evaluations in which the prediction of oil and gas potential was linked to the actual exploration stage. This systernatisation required the following conditions to be fulfilled: (1) All available data from the area should be included in the evaluation. (2) All actual trap-types should be placed in a hierarchy dependent on their structural setting and size. (3) The prognosis assessments should be in accordance with subdivisions D1 and D2. When comparing areas from different structural settings, it is important to be aware of the transition zone between oceanic and continental crust in order to determine how far offshore one can apply onshore analogies. In principle, the geological criteria for present marine areas and adjoining onshore areas should be the same, but in practice many factors (e.g. thickness and reservoir and seal properties) may change. For instance, in the North Sea, accumulations of oil were discovered in Jurassic, Cretaceous, and Palaeogene reservoir-rocks which have no analogy in the adjoining onshore areas. When making "inner" and "outer" analogies, for offshore oil and gas resources, we should compare reference and evaluation units of similar geometry and size. The boundaries of these units should be

The Russian method for prediction of hydrocarbon resources of continental shelves

delineated similarly, i.e. based on the same principles and type of geological-geophysical data. Absolute geological analogy between two areas does not exist in nature. To take this into account, correction factors are included which consider the main calculation parameters in the evaluated areas in comparison with the reference ones. The most important parameters for calculating the prognosed resources are the extent of the prospective areas, net reservoir thicknesses, and porosity of the reservoirs. The practical value for explorationists increases dramatically when going from regional to zonal level, i.e. from dealing with a whole basin to dealing with separate reservoir successions.

Calculation of D~ and D2 resources using specific densities of ISR of hydrocarbons In Russia, the use of specific densities of ISR of hydrocarbons is the standard method for the prediction of offshore resources. Prediction for Russian offshore oil and gas provinces and basins has so far mainly been carried out at a regional level, as defined in Table 2. This places the resource assessments, with a few exceptions, within category D2. The ISR method is characterised by its simplicity and acceptable results. It is based on a transfer of the average specific ISR from the reference areas to the area under evaluation. Correction coefficients are used to compensate for estimated differences between the two areas. Of the two major correction parameters,

117

the first is linked to the thickness of the stratigraphic/ lithologic units or the net reservoir thickness. The second is connected to the porosity of the reservoir units. These evaluations are normally based on information from well logs. More information about correction coefficients has been published by Zakharov (1985). Different specific concentrations of recoverable ISR of hydrocarbons have been proposed by Weeks (1979). These are as follows: - Transition between onshore and offshore part of a basin: 35,000 tonnes oil equivalent per square kilometre (t/km2). Shelf, intercontinental and inter-shelf basins" 17,000 t / k n l 2 . - Deep-water continental slope basins: 13,000 t/ km 2. - Deep-water continental rise basins: 6000 ffkm 2. In 1986, Zakharov showed that Weeks' numbers were too high for the combined offshore/onshore basins and too low for intercontinental basins. Zakharov's numbers, resulting from studies of more than 100 basins around the world, are presented in Figs. 1 and 2 together with a division between oil and gas. In this scheme, the greatest ISRs occur in Mesozoic and Palaeozoic deposits in platform basins, and in Cenozoic sediments in intermontane depressions. The smallest values occur in intermontane basins filled by Palaeozoic deposits. When this new concept was used, the results showed a systematic tendency to somewhat downgrade the most prospective areas while upgrading the least prospective areas. -

118

K. O. Sandvik and E. V. Zakharov

Fig. 3. Sketch of how different trap types are handled in the resource prediction method. 1 = Areas with proven reservoirs; 2 = anticline; 3 = regional fault sealing the productive unit; 4 = borders of eroded trap facies; 5 -- boundary of eroded trap facies; 6 = syncline axes; I -- dome/anticlinal traps; H -- fault-sealed traps; III = stratigraphic traps - - lithology related; IV -- stratigraphic traps - - unconformity related.

Fig. 2. The ratio of ISR of oil to ISR of gas in the uppermost 7 km of basins in different settings around the world. Compiled by E.V. Zakharov, 1987.

Volumetric method to calculate D~ resources on zonal level For predicting D1 resources on the zonal level, Zakharov (1971) developed a modification of the volumetric method. When a part of a basin is investigated it is possible to estimate how much gas will be found as op-

posed to oil. When transferring this knowledge to a new basin, one should look for similarities such as presence of source rocks, reservoirs, and seals. In addition, the structural settings of the reservoirs should be compared; a Russian system is available for this comparison. In some cases, reservoirs should be compared in terms of stratigraphic conditions along secondary migration pathways. A sketch of how different trap types are handled is shown in Fig. 3. Traps may be simplified into three categories: anticlinal, fault, and stratigraphic. Stratigraphic traps are controlled by either impermeable rocks of the same age (lithologically related) or by erosion and later sedimentation of impermeable rocks (unconformity related). Anticlinal traps form both clastic and carbonate reservoirs. In fault-sealed traps, the vertical displacement has to be less than the thickness of the cap rock or greater than/equal to the thickness of the reservoir. Resource assessment made on evaluation units at the zonal level for category D1, is based on a mapping


of all prospects, both confirmed and unconfirmed reservoirs. For anticlinal reservoirs the predicted total resources of a new area, Q2, are calculated using the following formula:

Ql Q2 = g2" E ~ vl

(1)

where V1 is the total maximum reservoir volume taken from the previously explored reference area, V2 is the volume of the reservoirs in the new area with the same geological age as in the reference area, and Q1 is the ISR of the oil and gas detected in the reference area. The most difficult parameter to determine in faultsealed traps is the size of the area under consideration. In practice, however, statistical analyses in several areas (Volga-Ural, Pre-Caucasus, Azerbaijan, and Middle Asia), shows that the average width of this type of accumulation is relatively small (in the range of 1.5-2.5 km). For the fault-sealed types, the following modifications to Eq. (1) are recommended:

Q2- ~Q1

L2.K1-K2

(2)

L1 where L1 is the fault displacement in the reference area, L2 is the corresponding length of the evaluated area, K1 is the ratio of the oil/gas column in the new area compared to the same thickness in the reference area (heval/href), K2 is the ratio of the porosity of the reservoirs in the new area and the porosity in the reference area (4~eval/4~ref). The resources in stratigraphic traps may be estimated using the following formula:

Q2 -~ A 9d . h .q -K2

(3)

where A is total area of the supposed prospective zone in km 2, d is ratio of the sum of productive trap areas found in the reference area to the total area of the reference area, h is expected net reservoir thickness in the evaluated area (in metres), q is average specific initial oil and gas reserves per km 2 and metre of the saturated reservoir thickness as measured in the reference area. The main sources of error in predicting hydrocarbon resources are: (1) insufficient level of study of both the reference and evaluation units; (2) errors in the determination of input parameters It is necessary to emphasize that the coefficient of productive area [d in Eq. (3)] can only be used in the case of proven or supposed non-uniform areal distribution of reservoir rocks. Correction factors for changes in reservoir rock thickness (K2) should be taken into consideration only in the case of a close

119

lithological relationship between deposits in reference and evaluated areas. Within each particular region, or separate stratigraphic level of the region, the character of the correction coefficients may differ. Most informative are those parameters which have the closest correlation to already proven reserves. Correlations carried out at VNIIGAS (Zakharov and Yudin, 1988) have shown the following: - T h e variation of the most informative parameters is related to the tectonic style and geological evolution of the area. - We cannot exclude that in some regions, some important parameters are not taken into account (because of insufficient study at the time of estimation).

Evaluation of C3 and D~ resources on local level In Russia, evaluation of resources of categories C3 and predicted resources D1 in fields prepared for drilling or local structures are made using the volumetric method of Zhdanov et al. (1966).

Generation and accumulation potential When making prognoses at zonal and local levels, the accumulation potential is the main parameter used as input to the assessment of the area under evaluation. When dealing with regional units, such as basins, we must take into account both generation and accumulation potentials in the form of generalised indices. The generation potential of sedimentary deposits is found to be directly related to the sedimentation rate of these deposits (in meters/million years). The accumulation potential of the same deposit is characterised by the ratio of hydrocarbon ISR to the average summary thickness of the individual reservoir beds. This can be determined in areas with proven oil and gas accumulations. It is given in million tonnes HC/metre. These generalised parameters, multiplied by each other, provide an estimate of what proportion of the prognosed resources is confined to 1 million years of deposition for a particular stratigraphic subdivision (in million tonnes HC/million years). For less investigated subdivisions of sedimentary sections, the generation and accumulation potential can be determined using the ratio between the average ISR in the reference units and the total reservoir thickness in evaluation unit. This thickness is multiplied by a factor which describes the most likely proportion of reservoirs in the basin under examination. The latter factor changes according to the dominant facies of the deposits. Gomelkova et al.

120

(1978) recommend these factors to be as follows: - marine clastics 25-30%, - marine carbonates 5-15% (for reefal varieties 3540%), - mixed marine clastics and carbonates 20%, evaporitic deposits 10%, continental-marine deposits 1-3%. In cases where various lithofacies are interbedded, it is recommended that weighted values representing the different facies be used. After this procedure, one can estimate specific values of each of the complexes (in%) and with this result distribute the predicted resources in the whole sedimentary section. -

-

Examples from the Norwegian Barents Sea The reported methods have been applied to resource prognoses for both Russian and Norwegian areas of the Barents Shelf. In the selection of the analogies, the following conditions were taken into account: tectonic setting, geological evolution, types of traps, lithology/ sedimentary facies, and the phase condition of the HC accumulation. In the described examples, the analogies have been applied to separate lithological units and not to the complete sedimentary section. With more detailed geologic input from the assessed Norwegian areas a less fragmentary approach could have been applied. Examples are described for three units. The areas are defined on Fig. 4, which is taken from Zakharov et al. (1993).

Example 1: Loppa High (A) Fault-sealed, dolomitized limestones of Lower Carboniferous age in the northern part of the Loppa High evaluated with reference to Serpukhovian limestones of the Sorokin Bar in the Timan-Pechora Basin. In the reference area, the Serpukhovian limestone of the Sorokin Bar, the ISR of HC is 3.5 tonnes/km 2. The input to Eq. (2) is L1 -- 55 km, L2 = 53 kin, K1 = 4.0/5.1, and K2 -- 8%/15%. D1 resources of this part of the Loppa High are thereby calculated to be 1.4 million tonnes (10.4 million barrels) of oil. (B) Anticlinal traps with Middle CarboniferousLower Permian carbonates and sandstones of the Loppa High, evaluated with reference to the Kolvin Bar in the Timan-Pechora Basin. The published data from the Loppa High contain little information on the quality and distribution of the reservoir rocks. Based on maps and profiles presented by Gabrielsen et al. (1990), the volume of the possible

K.O. Sandvik and E.V. Zakharov

oil-filled pores in the Middle Carboniferous-Lower Permian carbonates and sandstones on the Loppa High may be in the order of 6 km 3. (V2 = A . d . h . q 5 = 3892 k n l 2 90 . 5 90.022 k m . 0.14 = 5.994 km 3. Factor d refers to the proportion of the total area that is prospective. In this case, d is the same as in the reference area.) For the reference area, Kolvin Bar, the prognosed resources are calculated to be 31.2 million tonnes of oil and the volume of the possible oil filled pores, V1, is estimated to be 6.320 km 3. The anticlinal D2 resources on the Loppa High are then estimated to be: 5.994-31.2/6.320 = 29.6 million tonnes of oil (219 million barrels). (C) Total resources at Loppa High. Summing the above results the total estimated resources on the Loppa High are 31 million tonnes. With a recovery factor of 0.33, this equates to ca. 10 million tonnes of recoverable oil on the Loppa High.

Example 2: Mercurius High Triassic stratigraphic traps on the Mercurius High, evaluated with reference to the northern Kildin Nose. The reference area, i.e. Northern Kildin Nose, has an area of 1875 km 2. Only 25% of the traps are filled. The average effective HC saturated thickness is 8.4 m, the porosity is 19%, and the average concentration of ISR is 65,000 tonnesNm 2. The total area of the Mercurius High is 6900 km 2. The extension of the Mercurius High is taken from Zakharov et al. (1993). If only 25% of this area is prospective, as in the reference area, the effective area will be 1750 km 2. The effective average thickness of the reservoir is estimated to be 4.3 m and the porosity to be 25%. The prognostic resources of Mercurius High are then estimated to be: 1750-4, 3/8, 4-65,000.25/19 = 77 million tonnes oil equivalent (t.o.e.). In the reference area, the ratio between oil and gas + condensate is estimated to be 0.22. Using this figure on the Mercurius High gives total oil resources of 17 million tonnes. With a recovery factor of 0.33 the recoverable resources will be in the order of 5.5 million tonnes. The total resources of gas + condensate are 60 billion m 3 of gas and 2 million tonnes condensate. With a recovery factor of 0.85 for gas and 0.65 for condensate, the recoverable gas and condensate will be in the order of 51 billion m 3 and 1 million tonnes respectively.

Example 3: West Malingiskaya Saddle (A) West Malinginskaya Saddle evaluated with reference to the Ludlov Saddle.


121

Fig. 4. Example areas. 3 -- West Maliginskaya Saddle; 8 = Loppa High; 9 = Mercurius High. From Zakharov et al. (1993).

One discovery, Ludlovskaya, has been made on the Ludlov Saddle. In this discovery, the thickness of the Jurassic is 800 m, out of which 90 m is saturated with hydrocarbons. From Johansen et al. (1993), the thickness of the Jurassic on the West Malinginskaya Saddle is estimated to be 180 m. Applying the same saturation ratio as at the Ludlov Saddle, the saturated thickness will be 20.25 m. The prospective part of the Ludlov Saddle has an area of 38,965 km 2, and at the West Malinginskaya Saddle, 9062 km 2. D1 resources of the Ludlov Saddle are calculated to be 750 million t.o.e. The resources of West Malinginskaya Saddle will then be: D 2 = 7 5 0 . 2 0 . 2 5 / 9 0 . 9062/38,956 = 39.68 million t.o.e. (B) West Malinginskaya Saddle evaluated with reference to the Eastern Fedinsky Nose. Two discoveries have been made on the Eastern Fedinsky Nose, Shtokmanovskaya and Ledovoye.

Here the average concentration of ISR is 90,000 t.o.e./km 2. The prospective part of the structure represents 25% of the total area and the saturated thickness is 90 m. The prospective part of West Malinginskaya Saddle is: 9062 km 2. 0.25 = 2265 klTl2 Alternatively, using the same saturated thickness as above, i.e. 20.25 m, the resources on the West Malinginsakya saddle can be calculated as: D 2 = 2 2 6 5 . 2 0 . 2 5 / 9 0 . 9 0 , 0 0 0 = 45.9 million t.o.e. (C) Resources of West Malinginskaya Saddle. From (A) and (B) the resources of the West Malinginskaya Saddle are estimated to be greater than 39.7 million t.o.e., or 39.7 billion m 3 gas equivalent since the Russian discoveries have shown that the fluids are gas and condensate. The condensate content is 50 g/m 3 gas. By using a recovery factor of 0.85 for gas and 0.65 for condensate, the resources of the West

K.O. Sandvik and E.V. Zakharov

1 22

Malinginskaya Saddle are estimated to be in the order of 37 billion m 3 of free gas and 1.5 million tonnes of condensate.

Conclusions Exploration success in the Norwegian Barents Sea to date has been disappointing compared to the discoveries made in the northwestern part of Russia. This concerns both Late Palaeozoic and Mesozoic hydrocarbon accumulations. Only rudimentary geological information was available for the three Norwegian examples presented in this paper: Loppa High, Mercurius High, and West Malinginskaya Saddle. In spite of this, the resource estimates correlate well with Norwegian exploration results. The total recoverable resources of investigated sections of the Loppa High are estimated to be 10 million t.o.e., on the Mercurius High 51 billion m 3 gas, and 1 million tonnes condensate, and on the West Malinginskaya Saddle 37 billion m 3 gas and 1.5 tonnes of condensate. More detailed geological information from these areas would probably improve the accuracy of these estimates. It is an obvious task for geoscientists experienced in both Western and Russian assessment methods and with access to detailed exploration results from northwest Russia, to co-operate in improving the reliability of estimates of the hydrocarbon potential of the Norwegian Barents Sea.

References Bujalov, N.I., Vasilyev, V.G., Erofeeva, N.S. et al., 1962. Methodology of Evaluation of Oil and Gas Predicted Reserves. Gostoptechizdat, Moscow, 82 pp. (in Russian). Bujalov, N.I., Vinnikovsky, S.A., Zakharov, E.V., Kontorovich, A.E.

K.O. SANDVIK E.V. ZAKHAROV

et al., 1990. Methodological Basis for the Prediction of Oil and Gas Potential. Nedra, Moscow, 248 pp. (in Russian). Gabrielsen, R.H., Fa~rseth, R.B., Jensen, L.N. Kalheim, J.E. and Riis, F., 1990. Structural Elements of the Norwegian Continental Shelf, Part 1. The Barents Sea Region. Norwegian Petroleum Directorate, Bull., 6, 33 pp. Gomelkova, N.R, Modelevsky, M.S. and Polster, L.A., 1978. Some characteristics of sedimentary thickness of oil and gas bearing basins. In: Modern Problems of Oil and Gas Geology. MGU, Moscow, pp. 165-172 (in Russian). Johansen, S.E., Ostisty, B.K., Birkeland, 0., Federovsky, Y.E, Martirosjan, V.N., Bruun Christensen, O., Cheredeev, S.I., Ignatenko, E.A. and Margulis, L.S., 1993. Hydrocarbon potential in the Barents Sea: play distribution and potential. In: T.O. Vorren, E. Bergsager, O.A. Dahl-Stamnes, E. Holter, B. Johansen, E. Lie and T.B. Lund (Editors), Arctic Geology and Petroleum Potential. Norwegian Petroleum Society (NPF), Special Publication No. 2. Elsvier, Amsterdam, pp. 273-320. Weeks, L.G., 1979. Geology of Continental Margins, Vol. 3. Mir, Moscow, pp. 313-327 (in Russian). Zakharov, E.V., 1971. Methods for prediction of reserves of oil and gas of subgroup DI . Geol. Oil Gas, 4: 48-51. Zakharov, E.V., 1985. The account of analogy when predicting oil and gas resources. J. Azerbaijan's Oil Ind., 9: 12-14. Zakharov, E.V., 1986. The Specifics of Continental Shelfs Oil an Gas Bearing Ability Prediction. Survey Information Series. Geology and Survey of Marine Oil and Gas Fields. M., VNIIEGAZPROM, 196, 1, 44 pp. (in Russian). Zakharov, E.V. and Kondakov, A.V., 1978. Systematization of prediction for oil and gas bearing units. Oil Gas Geol. Geophys., 9: 17-22 (in Russian). Zakharov, E.V. and Yudin, S.G., 1988. Geological appearance of predicted hydrocarbon resources in sedimentary deposits. J. Azerbaijan's Oil Ind., 4: 5-9. Zakharov, E.V., Kulibakina, I.B. and Bogoslovskaya, G.N. 1993. The Jurassic complex is an object of oil and gas prospecting in the Barents Sea. In: T.O. Vorren, E. Bergsager, O.A. Dahl-Stamnes, E. Holter, B. Johansen, E. Lie and T.B. Lund (Editors), Arctic Geology and Petroleum Potential. Norwegian Petroleum Society (NPF), Special Publication No. 2. Elsevier, Amsterdam, pp. 257260. Zhdanov, M.A., Grishin, F.A. and Gordinsky, E.V., 1966. Geological Reservoir Evaluation of Oil and Gas Fields. Nedra, Moscow, pp. 158-159.

IKU Petroleum Research, N-7034 Trondheim, Norway VNlIGAS International, p. Razvilka, Leninsky raion, Moskovskaya oblast, 142717, Russia

123

Offshore Brazil: analysis of a successful strategy for reserve and production growth S. Jack Morbey

Major oil companies cannot continue to tolerate the high cost of exploration failure in a world of low future oil prices. There is a trend in the industry to a rationalization of worldwide exploration and production portfolios. Companies are looking to reduce their cost of finding by lessening the risk of failure at all levels of the exploration process, and stimulating resource replacement and growth through the efficient exploration of basins and plays. The coastal basins of Brazil offer an opportunity to assess the effectiveness of turbidite exploration along a passive continental margin, where non-marine, shelf, and deepwater exploration and production have taken place. The predominant role and strategy of Petrobras, the Brazilian national oil company, in the development of exploration offshore Brazil is worth looking at closely. When oil companies are planning new country-new basin ventures in passive margin deeper water environments worldwide, a lot can be learned from comparisons with similar but mature exploration environments with successful proven petroleum systems. Petrobras are showing the way forward in efficient, lower-risk, passive margin exploration and exploitation at more acceptable costs of E&E An exploration strategy based on play focus, play environment, and deepwater technology development seems to be working in the Campos Basin, offshore Brazil. Driven by a desire for Brazilian energy self-sufficiency before the year 2000, Petrobras' mission is to achieve significant reserve and production growth through an exploration focus on the deep water Campos Basin. Exploration and production success in the Campos Basin is enabling Petrobras to manage their exploration portfolio better, and to minimise the cost of failure in other passive margin basins. A thematic statistical approach to the analysis of future play potential has given rise to a clearer assessment of offshore exploration risk, particularly exploration on the shelf versus in deeper water. This has helped Petrobras to develop a more focused exploration strategy, and has enabled them to allocate their E&P annual budgets more cost efficiently in their drive for oil self-sufficiency. New windows of opportunity are now opening for exploration success in Brazilian passive margin coastal basins hitherto considered past their best. The recent ability of Petrobras to explore outside the Campos Basin in new deeper water frontier passive margin basins, and achieve early discovery success, shows the value of experience and strategic focus. Worldwide, the exploration for new, often subtle, clastic turbidite plays is a potentially high cost enterprise. Passive margin exploration is moving swiftly into deeper waters of the South Atlantic off West Africa, as well as the North Atlantic West of the Shetlands. These areas offer a high-risk, high-reward challenge, and drilling environments in need of innovative new E&P technology and creative initial exploration strategies. Brazil, through its national oil company Petrobras, leads the way in this effort.

Country focus The vulnerability of the Brazilian economy to fluctuations in world oil prices, in addition to government-subsidised "below market" oil prices and recent hyperinflation, have spurred a drive for selfsufficiency and the expansion of energy resources. Today, Brazil is the top non-OPEC replacer of its production worldwide. This follows significant annual oil and gas reserve additions and production increases since 1980 (Fig. 1), which are associated with considerable reductions in exploratory well drilling, a marked increase in discovery rates, and the ongoing control of exploration and production costs. Petrobras currently pursue a program of exploration and production ventures focused mainly on

the offshore and the challenging deeper water areas of the Campos Basin. This has been achieved through a highly focused exploration and production strategy. High rates of exploratory drilling success in 1992 of 35% offshore and 25% onshore reflect the impact of this strategy. The accelerated growth in domestic proven oil and gas reserves in the last 15 years is the result of a significant change in offshore exploration strategy towards exploration in turbidite plays. Resource growth has been fueled by ongoing deeper water exploration and the development of new deep water production technology in the Campos Basin. At the present time, offshore reserves amount to 87.5% of the total Brazil reserves, and 77% of these offshore reserves are to be found in water


S.J. Morbey

124

Fig. 2. Brazil - - coastal basins.

Offshore Brazil: analysis of a successful strategy for reserve and production growth

125

Exploration efficiency

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deeper than 400 m in the Campos Basin (Fig. 2). The Campos Basin currently accounts for 65% or 470,000 b/d of Brazil's total oil production, and 38% of Brazil's total gas production, largely from clastic turbidite reservoirs of Tertiary age. Rift, pre-rift, and transitional post-rift reservoirs of the Brazilian coastal basins (Fig. 3) were the mainstay of exploration and production from 1938 to 1980 onshore and in the shallow water offshore areas of the continental shelf. However, internal Brazilian oil supply over this period proved inadequate given a rise in downstream demand. A 10 year decline in oil and gas resource growth from 1970 to 1980 (Fig. 1) saw reserve replacement fail to keep up with the growing production needs of Brazil. This failure was a function of the overall high costs of finding and development, the nature of the plays and their reservoir quality, and the discovery of mainly small fields of insufficient size (

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The other two parameters in the production profile are the plateau build-up and decline rate. Plateau build-up is defined from the plateau rate and the number of years estimated to reach plateau. The decline rate is a normal distribution of percentage which defines the plateau rate reduction per year. If both the oil and gas reserves are less than a predetermined amount, no development will occur for that iteration. Only the exploratory costs, both seismic and drilling, will then be included. This cut-off is usually set as a reasonable minimum reserve for the area, based on reserve sizes that have been developed. This is a reserve size criterion, not economic value, and differentiates between the "de-

velop" or "walk away" branches of the decision tree (Fig. 2).

Construction and development expenditure Reserve-dependent look-up tables are also used for the duration of construction and the duration of development drilling. Together, these define the total duration for the development phase of the project. The total costs associated with the construction and development phase are derived from a correlation of total capital expenditure with estimated oil equivalent production rate per year. This relationship is derived from data for 36 fields

162

J.H. Snow, A. G. Dord and D. W. Dorn-Lopez

that have been developed or will be developed on the Norwegian shelf (e.g., County Natwest Woodmac, 1992). In practice, this general correlation is not used for all exploratory projects, recognizing that a prospect's location, particularly with respect to infrastructure and water depth, can significantly impact the costs. Instead, a customized relationship must be derived for each prospect. This provides cost estimates, specific to the prospect, for the full range of costs associated with the prospect reserves distribution. Of course, comparison of the customized curve to the general curve is done to confirm that cost estimates are realistic. For each iteration, a value for the total capital expenditure is calculated. This calculated value is used as the mean of a normal distribution, and a percentage of that expenditure is defined as a standard deviation. This normal distribution is then sampled for that iteration. Once a total capital expenditure value is determined, operating costs and abandonment costs are then estimated as percentages of that value. This relationship is based on statistical analysis of historical data. Once the total capital expenditure and the duration of development have been obtained for an iteration, the cost expenditure profile is generated. This then defines how expenditures are scheduled over time. Many of the engineering parameters are based on look-up tables which correlate that parameter to reserve size. As stated earlier, all of these parameters have been defined by a set of default values, based on historical data from the Norwegian shelf or common engineering rules-of-thumb. These defaults are

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The fourth module in IPE, and last part of the Monte Carlo simulation, is the economic and tax analysis (Fig. 5). From the engineering analysis, the exploration costs, development expenditures, operating expenditures and production, have all been determined and scheduled over time. Key parameters for the economic analysis are working interest, inflation rate, discount rate, tariffs and product prices. For oil and gas prices, a broad triangular distribution is used to represent the price forecast. A distribution is specified for each year, for as many years as are needed in the model: typically 30 to 60 years. The distribution would typically be based on the current corporate forecast for oil and gas prices. Four tariffs are included: oil transportation and processing and gas transportation and processing.

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suitable "as is" for some prospects. However, each of these look-up tables should be examined, and where appropriate, customized to the prospect being evaluated. Such factors as trap geometry and complexity, expected drive mechanism, reservoir homogeneity, etc., should be taken into account. For some prospects, there may be parameters for which the relationship with reserve size is not clear or even in correct. In these cases, this type of model may not be the most appropriate method for estimating their worth. However, experience in evaluation of prospects on the Norwegian shelf, is that the reservedependent parameters give average results that are very similar to what would be derived from deterministic analysis.

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Risk analysis and full-cycle probabilistic modelling of prospects Any distribution type can be used to represent these different tariffs depending on the data available for estimating these parameters. From the engineering results and economic assumptions, the before-tax cash flows, tax payments and the key result, after-tax cash flow, are calculated. From the cash flows, net present value and internal rate of return are determined. This then completes the full-cycle Monte Carlo analysis.

Given discovery and before discovery results The entire process, reserves through to net present value, is repeated for each iteration. The distributions shown in Fig. 18 summarize these iteration data. These are referred to as the "given discovery" reserves and NPV distributions for the prospect. They are the "unrisked" probability distributions, unburdened by the risk of finding hydrocarbons. The frequency of drilling dry holes, which was estimated from the probability of testable hydrocarbons module, is used to scale these distributions to derive the "before discovery" final results, or the total probability versus reserves and NPV curves (Fig. 1). From these final distributions, it is possible to determine the complete range of outcomes and associated probabilities from the model. These include the probabilities of discovering any particular reserve sizes, of making certain amounts of money, the probability of losing money, and how much money can be lost if everything goes wrong. A minimum economic or break-even reserve size can be graphically determined.

Break-even reserves

163

ities. Fixing some parameters and varying others shows very clearly which parameters have the greatest impact on the results. In the example shown in Fig. 21, we have progressively fixed parameters, starting with the reserves, then engineering and finally economics. The resulting standard deviations are numbered 1 through 5. Reserve uncertainty has the greatest impact for this prospect. However, uncertainty in engineering cost and product prices also cause a substantial variation in the net present value of the project. In case 2 (Fig. 21), the fixed reserve case, the

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Break-even reserves can be determined in a more elegant or rigorous way during the simulation by capturing reserve values that generate NPVs around the zero value. This is shown schematically by the two graphs in Fig. 19. This produces a reserve distribution, the mean of which represents the average economic break-even reserve size. As shown in the actual reserve distribution example in Fig. 20, there is a large range of reserves that can give a small or zero NPV. This is typical for most models. This also shows that, while the reserves may be larger than what is considered the economic breakeven, the project can still make no money or lose money in some cases.

Sensitivity analysis Another aspect of the full-cycle Monte Carlo model is the ability to analyze parameter sensitiv-

Fig. 19. Statistical method for deriving break-even reserves in the IPE system. The upper plot is an inverse cumulative distribution for Net Present Value. The lower plot is a frequency distribution of all reserves cases (sampled by the system) that resulted in close to zero NPV. The expected value (mean) of the lower distribution is a measure of the break-even size.

J.H. Snow, A.G. Dor~ and D.W. Dorn-Lopez

164

In a traditional deterministic evaluation of a prospect, all of the input assumptions may have validity and result from meticulous ground-work. The result of this detailed work, however, is a single set of figures which statistically has a very small chance of being realized. The basic value of the IPE approach is that it includes a broader range of outcomes, both good and bad, than can be evaluated using deterministic methods. Input is statistically founded on real, historical data and uncertainty can be quantified. Consequently, the approach provides a more complete, realistic and consistent evaluation of the possible outcomes for a prospect.

Fig. 20. Example break-even reserve histogram.

value of the project can still vary by plus or minus 500 million Norwegian kroner. In other words, the value ranges between 250 and 1250 million kroner, a substantial range.

Conclusions The IPE methodology integrates traditional decision tree analysis with Monte Carlo simulation and applies these techniques to the entire life cycle of a prospect. The integrated nature of the model invites criticism that the system is a "black box" or "magic bullet" for solving problems or making decisions. Care has been taken, therefore, to make the system interactive and to make all implicit assumptions visible to the user.

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Acknowledgements The authors wish to thank Conoco for permission to publish this paper. Also gratefully acknowledged are the contributions made by Thorbj0n Pedersen, Randy Theilig, Kurt Thomas, Bob Dixon, Anders Gjesdal and Kjetil Ausland, to the various IPE modules. We thank Stuart Anderson (ICTS) for his major part in programming the system. Andrew Conway, Michael Frost, Tina Langtry, Sayers Kyle, Jim McColgin and Matt Strickland (Conoco), and B.A. Duff and D. Hall (Fina) are acknowledged for critical reading of the manuscript. Most of all, however, we are grateful to the geoscientists, engineers and economists of Conoco Norway Inc. for their extensive beta testing of the IPE system during the Norwegian 14th Round and other exercises.

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Risk analysis and full-cycle probabilistic modelling of prospects

References Corrigan, A.F., 1993. Estimation of recoverable reserves: the geologist's job. In: J.R. Parker (Editor), Petroleum Geology of Northwest Europe, Proceedings of the 4th Conference. Geological Society, London, pp. 1473-1481. County Natwest Woodmac, 1992. Denmark, Ireland, Norway. Northwest Europe Service, Reference section Volume 2.

J.H. SNOW A.G. DORI~ D.W. DORN-LOPEZ

Cronquist, C., 1991. Reserves and probabilities m synergism or anachronism? J. Pet. Technol., 43: 1258-1264. Newendorp, ED., 1975. Decision Analysis for Petroleum Exploration. Petroleum Publishing Company, Tulsa, 668 pp. White, D.A. and Gehman, H.M., 1979. Methods of estimating oil and gas resources. Am. Assoc. Pet. Geol. Bull., 63(12): 21832192.

Conoco Norway Inc., P.O. Box 488, N-4001 Stavanger, Norway Statoil UK Ltd, Swan Gardens, 10 Piccadilly, London WI V OHL, UK Conoco Norway Inc., P.O. Box 488, N-4001 Stavanger, Norway


167

Play fairway analysis and risk mapping: an example using the Middle Jurassic Brent Group in the northern North Sea Shona Grant, Nick Milton and Mark Thompson

A methodology for play fairway analysis and risk assessment is illustrated using the Middle Jurassic Brent Group play in the northern North Sea as an example of the technique. This is the most successful play in the northern North Sea with over 50% of the discovered reserves. Prospect risk can be subdivided into play risk and prospect-specific risk. The play risk comprises the regional risk elements, i.e. those elements of risk which can be estimated and mapped regionally without detailed mapping of the prospect structure. Prospect specific risk reflects local risk elements within the fairway. In order to assess relative risk across the play fairway, each play can be subdivided into several risk elements or factors. These include: - the presence and effectiveness of a reservoir; - the presence of a source rock and the effectiveness of the carrier system; - the presence of an effective vertical seal. Each risk element is then subdivided into areas of common risk. This is done by subdividing each element into areas of low, moderate or high risk and assigning a corresponding green, yellow or red colour to each to produce a common risk segment (CRS) map. In the case of reservoir, source or seal presence, the delineation of common risk areas is based on our regional understanding of the basin stratigraphy. Further data are then integrated to assess the risk that the predicted stratigraphy provides an effective reservoir, carrier system and/or seal. This data include core porosity and permeability, pressure and leak-off data, well test results, geochemical data, well log analysis and thermal/basin modelling work. Other regional risk elements (such as timing of trap formation and biodegradation) are often mapped but are not important regional risk elements for the play discussed here. Individual common risk segment maps can be combined to provide play fairway summary maps and composite common risk segment (CCRS) maps for each play. These provide a powerful pictorial representation of relative risk within the play fairway. Uncertainty maps are also produced for each play. These illustrate our confidence in the geologic model. They are controlled by the density, quality and reliability of well and seismic data. They are used in conjunction with the risk maps, taking care not to confuse risk with uncertainty.

Introduction B P has developed a system of regional play analysis, which was used extensively in the evaluation of acreage during the 14th Licensing round on the Norwegian continental shelf. All potential plays in the northern North Sea were analysed in terms of relative risk. In this way it was possible to high grade plays within the blocks on offer, and to use drilling success ratios as a constraint for risking individual prospects. The methodology is described below using the Middle Jurassic Brent Group as an example. The North Viking Graben of the Northern North Sea (60~176 is a major petroleum province (Fig. 1) with several working play systems at many different stratigraphic levels. The reserves discovered in the major plays for the UK and Norwegian sec-

tors (UKCS and NOCS) combined are illustrated in Fig. 2a. Also shown in Fig. 2b is the number of target tests by play. The Brent Group play is the most successful to date with over 50% of the discovered hydrocarbon reserves. This corresponds to some 15.6 billion barrels of oil in 82 separate pools. The Brent Group comprises Aalenian to Early Bathonian age deltaic sediments (Mitchener et al., 1992). Oil is mostly sourced from Middle to Late Jurassic marine mudstones and trapped in tilted fault blocks (Bowen, 1992). A large numbers of prospects and leads remain undrilled in the Norwegian sector (NOCS). The play is analysed in more detail below as an example of the use of common risk segment maps (defined below), play maps, and drilling success ratios for risk estimation and play fairway analysis. The maps demonstrate the underlying geolog-


168

S. Grant, N. Milton and M. Thompson

Fig. 1. Location map showing existing discoveries in the North Viking Graben.

ical understanding of the play system under review. In the text, the North Viking Graben stratigraphy is described in terms of the established lithostratigraphy (Deegan and Scull, 1977; Vollset and Dor6, 1984) for the Northem North Sea and B P's genetic sequence stratigraphy (presented in Mitchener et al., 1992, and Partington et al., 1993). The relationship between the two is shown in Fig. 3. For example, the

Middle Jurassic Brent Group reservoir interval corresponds to the J20-J32 genetic sequence stratigraphic units.

Terminology A play is defined here as a grouping of prospects with one or more common factors. Throughout this

169

Play fairway analysis and risk mapping

Fig. 2. Discovered reserves (a) and number of wells drilled (b) subdivided by sequence for the North Viking Graben.

study, plays have been defined by their reservoir stratigraphy, thus all prospects with a Brent Group reservoir form the Brent play. A play fairway, in this case, is therefore the area defined by the maximum possible extent of reservoir rocks within the stratigraphic interval of the play. The limits of the Brent play are defined by the maximum possible limits'of the Brent Group reservoir rocks. Prospect-specific risk consists of those elements of risk which can only be determined locally (e.g. trap risk, specific fault seal risk, local reservoir erosion). This is equivalent to the prospect success factor of White (1992). Play risk consists of regional risk elements, which can be determined by regional mapping, and are not just specific to a single prospect. Our play risk is equivalent to the play chance of White (1992). Thus the probability of discovering petroleum in a prospect often includes elements of play risk as well as those risks solely associated with the prospect, i.e."

Overall Prospect Chance = Play risk x Prospect specific risk It is possible to draw a map for each of the regional risk elements showing areas of common relative risk, for example, areas where there is a relatively high risk (low chance) of finding an effective reservoir due to depth of burial. These areas are termed risk segments, and the map of a regional relative risk element is termed a common risk segment (CRS) map. All of the regional risk elements for a specific play can be combined into one map which illustrates the overall variation in play risk across the fairway. Such maps are termed composite common risk segment (CCRS) maps or play fairway summary maps.

Play fairway definition The extent of a play fairway is here defined as the maximum possible extent of reservoir rocks. This is based on sequence stratigraphic analysis of well

170


Fig. 3. Pre-Cretaceouslithostratigraphyand geneticsequencestratigraphyin the NorthVikingGraben. and seismic data to predict the distribution of systems tracts. The maximum basinward extent of reservoir is based on the predicted distribution of the low stand

deposits. An example of the resulting depositional environment map for the Late Bajocian part of the Brent Group (which includes the Upper Ness Fm.) is

171

Play fairway analysis and risk mapping shown in Fig. 4. Similar maps have been constructed for all sequences within the Brent Group, in order to define reservoir limits (Milton and Ewen, in prep.).

Risk segment mapping Play risk can be subdivided into various regional risk elements. The regional risk elements which have been mapped and evaluated for the North Viking Graben are: - reservoir presence; - reservoir effectiveness; - top seal; - hydrocarbon charge. Other potential regional risk elements, such as timing of trap formation, or biodegradation of trapped oil, are uniformly favourable (low risk) for the Brent play. A map is produced for each element showing areas of common relative risk. The assessment of risk is qualitative in terms of low (green), moderate (yellow) and high (red) risk. "Low risk", for a particular regional risk element, implies that this element will be favourable, e.g. there is evidence from regional work that reservoir will be present. "High risk" implies evidence that the element is unfavourable, e.g. that there is evidence that reservoir will be absent. "Moderate risk" may reflect conflicting or absent evidence. Maps are presented below for the Brent Group play to illustrate the technique.

Reservoir presence risk Reservoir presence risk maps have been derived from semi-regional geological and geophysical analyses, and show the risk of reservoir facies being present. Generally these maps show low risk (green) segments where reservoir facies is proven by drilling, or can reasonably be predicted by all likely geological models. High risk (red) segments contain some evidence for the absence of the reservoir interval, while moderate risk (yellow) segments are often zones of little knowledge. The geological model for the deposition of the Brent Group is based on sequence stratigraphic analysis of biostratigraphic, wireline and core data (Milton and Ewen, in prep.). An example of the depositional environment map for the Late Bajocian is shown in Fig. 4. The resulting reservoir presence common risk segments map is presented as Fig. 5. The low risk area (green) represents the area of the proven conventional Brent play. The yellow area to the north represents the unproven extension of the conventional play. It includes a postulated low stand system north of conventional limit of the Brent Delta (Milton and Ewen, in prep).

Reservoir effectiveness risk Reservoir effectiveness maps illustrate the risk that the reservoir sandstone has effective porosity and permeability. The maps have been derived from a regional study of reservoir quality, involving an extensive well database from the UK and Norwegian sectors of the North Sea. The reservoir quality database was analysed statistically to investigate and identify the key controls on reservoir quality. From this the following factors were found to be important for prediction: - depth (maximum depth of burial if area is uplifted); - overpressure; - facies; - grain size; - pore fluid type (petroleum versus water); - thermal/diagenetic history. Porosity and permeability versus depth cross-plots were generated for each of the major reservoir intervals from the Triassic to Tertiary. Examples of cross-plots for the Brent Group are shown in Figs. 6 and 7. In Fig. 6 the data are subdivided into three overpressure categories: < 1500 psi, 1500 to 3500 psi and >3500 psi. For any given depth of burial the highest porosities and permeabilities are found in the highly overpressured wells (corresponding approximately to an increase of two porosity units for every 1000 psi of overpressure at any given depth of burial). Multivariate analysis using both depth and overpressure as variables produces the tighter regression in Fig. 7. The equations that result from this analysis use depth and overpressure as variables to predict poroperm. The reservoir effectiveness map is then based on the Top Brent Group depth map (taking into account Tertiary uplift where necessary) and the predicted overpressure distribution in the Brent Group. The actual depth cut-offs assumed for the Brent Group are given in Table 1. These were calibrated against well test results. Because gas can be produced from lower permeability sandstones than oil, a deeper depth cut-off applies for the moderate to high risk segment. The common risk segment map for effective reservoir is given in Fig. 8. It illustrates a complex distribution of common risk segments. Throughout the Brent Province the maximum depth limit for low-risk effective reservoir (green) is around 3700 m (Fig. 6). Even allowing for the effects of overpressure the axis of the North Viking Graben is mostly high risk (red). In deeper prospects diagenesis is responsible for seriously reducing porosity and permeability. If petroleum enters the trap before or during cementation it may retard porosity and permeability reduction. This results in steep porosity and permeability-depth

Fig. 4.Depositional environment map for the Late Bajocian (includes Upper Ness Fm.).

Fig. 5. Common risk segment map for Brent Gp. reservoir presence.

173

Play fairway analysis and risk mapping Table 1 Estimated depth cut-offs for effective reservoir in the Brent Group Gas

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trends within many North Sea fields (e.g. Emery et al., 1993; Gluyas et al., 1993). This observation has been used to lower the reservoir effectiveness risk (from yellow to green or from red to yellow) on large structural closures in Fig. 8 where thermal modelling indicates an early petroleum charge to the trap and the potential for poroperm preservation (e.g. the Brent Group prospect in Block 34/11).

The Brent Group charge map in Fig. 9 is a combination of source presence and charge effectiveness risks. The main source rocks are the Statfjord Fm. coals, the Brent Group coals, the Heather Fm. oilprone source rocks, and the Draupne Fm. oil-prone source rocks. Maturation studies have been carried out by 1D and 2D basin modelling. Potential carrier systems are identified from regional geological work and from pressure data. The Brent Group is commonly charged via downward migration from overlying oil-prone source rocks, and internally by gas generated from coals within the Ness Formation of the Brent Group. The Brent Group is locally stratigraphically separated from the Late Jurassic Draupne Formation source by thick Heather Formation. Here downward migration from the Draupne Formation into the Brent Group is restricted to high relief half grabens. As a result one of the main sources for oil in Brent Group traps is the Lower Heather Formation mudstone which lies stratigraphically closer to the reservoir. Gas sourced from the intra-formational coals

Charge risk Charge risk maps have been derived from semiregional geochemical studies and fault seal studies. These illustrate the regional variation of risk for an effective charge mechanism. The hydrocarbon charge model is based on an extensive database, including source rock analyses, 1D thermal modelling, 2D modelling of two regional lines using TEMISPACK (Duppenbecker and Dodd, 1993), geochemical analysis of hydrocarbon samples, RFT and well test data, and geochemical data from a regional sea bed coring programme.

174


Play fairway analysis and risk mapping can easily migrate within the Brent Group carrier system. Long distance lateral migration along spill chains linking footwall traps is possible in the west of the North Viking Graben due to the linear nature of the structures and the absence of major crossfaults. It is likely to be more difficult and hence higher risk on the east flank of the North Viking Graben due to compartmentalisation by sealing faults. Charge effectiveness is likely to be high risk in the shallow peripheral areas, where source rocks are locally absent or immature. There are examples of migration shadows in the north, where charge was not able to move down dip to fill traps; examples include areas west of the Don and Thistle Fields where dry wells are located on valid targets. Collectively these comprise the high (red) and moderate (yellow) risk areas on the CRS map for the Brent Group (Fig. 9).

Seal risk Seal risk maps have been derived from the semiregional geological and geophysical analyses in the same way as reservoir presence maps, and show the risk of an effective top seal facies being present. Top seal to Brent Group prospects is provided in the first instance by Callovian to Oxfordian Heather Formation mudstones within genetic sequences J30, J40 and J50 (Fig. 3). Where these are eroded over fault block crests, top seal may be provided by Kimmeridgian to Ryazanian Draupne Formation mudstones, or marine claystones of the Lower Cretaceous Cromer Knoll Group. Depositional environment maps for the Late Jurassic were combined with pressure data to assess the risk of effective top seal. An example of the depositional environment map for the Early Callovian interval is shown in Fig. 10. The map is based on the predicted distribution of low stand deposits. Most of the area is mud-prone resulting in low risk for effective top seal but there are two areas where sandy facies are predicted to be present above the Brent Group reservoir. The larger of the two sand-prone areas is the prograding shelf system on the east flank of the North Viking Graben (Krossfjord Fm.), the other is a smaller shelf system centred around the Emerald Field in UK Block 2/10. The east flank progradational system continued from the Early Callovian (J33) through to Early Kimmeridgian (Fig. 3) resulting in a relatively thick sequence of sandstone and mudstones. As a result Brent Group prospects may not have an effective top seal in this area. This is illustrated in the common risk segment map in Fig. 11. The moderate to high risk area around the Snorre

175 fault block in Fig. 11 reflects the local development of the J70 (Munin Formation) sandstones which do not provide an effective seal to the underlying Brent Group sandstones.

Play fairway summary Common risk segment maps can be combined to produce a composite common risk segment (CCRS) map. This illustrates the regional risk of an effective reservoir being present, below an effective top seal, and with access to an effective source. It provides a regional summary and relative measure of play risk, which allows ranking of acreage before analysing a local dataset. The map is constructed by assuming that the composite risk at any given point reflects the highest risk component of an individual CRS map. Thus the presence of only one high risk (red) component is sufficient to cause the corresponding area in the CCRS map to be red. For an area to be assigned a low risk (green) all the corresponding component risk maps must also be green in that area. All remaining areas are assigned a moderate risk (yellow). A play fairway map is complementary to a CCRS map and provides additional geological data for the play, including the reason for the high risk (red) assignation in the CCRS map, the location of discovered pools, and dry target tests. A play fairway map illustrates high graded and prospective areas of the play (in green and yellow) and provides a geological sense check for the CCRS map (which only illustrates relative risk). Play fairway maps are particularly useful in relatively mature exploration areas where considerable well data are available for incorporation into the overall play fairway analysis. In frontier exploration where there are very few data points, play fairway maps may be less useful and may not provide any more data than can be illustrated on a CCRS map. The play fairway map and the composite common risk segment map for the Brent Group play are shown in Figs. 12 and 13. The large area of the conventional low risk Brent play (green) explains the highly successful nature of this play. There is no play risk in such areas, i.e. play risk can be assigned a value of one (100%). The yellow areas on the play fairway summary map, north of the proven conventional play, illustrates an area where there is play risk, ranging from 0.5 to 0.95. Here the presence of reservoir sandstones is the key risk. The red areas are relatively high risk with a play risk of less than 0.5. This does not necessarily mean that wells should not be drilled in the high risk areas, rather that these areas are downgraded relative to the lower risk parts of the fairway.

Fig. 10. Depositional environment map for the Callovian (Heather Frn.).

Fig. 1 1. Common risk segment map for effective top seal to the Brent Gp

Fig. 12. Composite common risk segment map illustrating relative play risk for the Brent Gp. play.

Fig. 13. Play fairway summary map for the Brent Gp. showing the location of discovered pools, dry target tests and an explanation of the critical risk elements within the fairway.

178


It is often the case that the green (low risk) areas of a play fairway have seen considerable exploration, and that all traps with low prospect-specific risks have been drilled. Further exploration may involve pursuing low risk traps in areas of moderate or high play risk (yellow or red areas), or pursuing high risk trapping geometries (stratigraphic or hanging wall traps) where play risk is low (green

areas).

Drilling statistics The play fairways in the North Viking Graben have been sampled by a large number of wells in both the UKCS and NOCS. Within each play fairway the drilling success ratios, well failures, and pool size distributions have been analysed statistically. These data are used to calibrate prospect specific risks and prospect sizes within the play fairway. The drilling success ratio (DSR) is defined as: DSR =

No. of Technical Successes Total No. of Target Tests

where a target test is a well drilled to test a specific play and a technical success produced hydrocarbons on test. The results of the well failure analysis for the Brent Group are shown on Fig. 14. About half the failures were due to invalid trapping (no fault seal, or drilled outside closure). This is especially true of hanging wall traps. A number of target tests are interpreted to be located in migration shadows, mostly in synclines. Some of the larger migration shadows are shown on

the regional play fairway summary (Fig. 13). Other failed wells are located on the crests of tilted fault blocks where the Brent Group reservoir interval has been eroded. Fig. 15 illustrates the pool size distribution for the Brent Group play. The vast majority of reserves discovered to date have been found in footwall traps (over 15 billion barrels) with a drilling success ratio of 1 in 2 (140 tests). Hanging wall tests total 34 with a drilling success ratio of 1 in 3, and contain less than 2% of the reserves discovered to date.

Uncertainty mapping The play fairway is defined from the geological model for the maximum possible extent of reservoir. Many areas of the North Viking Graben, however, have a sparse database, with no or few wells and poor seismic quality to constrain the geological model. In these areas interpretations must be extrapolated from areas with greater data density, and there is a variable degree of confidence in the final interpretation. To delineate these areas interpretation confidence maps have been produced. For geological studies, these maps directly reflect data density and the proximity of control points. For seismic maps they reflect the density and quality of the data, the existence of well ties, and uncertainties in the horizon pick. These maps are divided into areas of high, moderate and low interpretation confidence. An example for the Brent play is shown in Fig. 16. Interpretation confidence maps and CRS maps are complementary. A true estimate of risk across the

Fig. 14. Well results pie chart where the Brent Group was the primarytarget. This illustrates the breakdown of critical play elements that brought about well failure.

179


Fig. 15. Discovered pool size distribution for the Brent Gp. play.

play fairway requires perfect knowledge. The confidence in the play risk is a function of interpretation confidence. Low confidence should not however be equated with high risk. The absence of direct evidence may produce low confidence, however, this is a function of ignorance and not true geological or exploration risk. In general, low confidence areas should be equated with moderate, not high, risk.

Summary The methodology for play fairway analysis has been described above, and is summarised in Fig. 17. It involves several key steps: (1) The play fairway is defined by a geological model for the maximum possible extent of reservoir. Confidence in this geologic model varies across the fairway, depending on the amount of seismic and well data. This is illustrated by interpretation confidence maps. (2) The perceived risk across the fairway is subdivided into three main risk elements: (a) the presence and effectiveness of a reservoir; (b) the presence of a source rock and the effectiveness of the carrier system;

(c) the presence of an effective top seal. These are illustrated as separate common risk segments (CRS) maps where each segment is assigned a low, moderate or high relative risk. (3) Individual CRS maps are combined to produce composite common risk segment and play fairway summary maps. These illustrate the overall variation in relative play risk for the fairway. They are used to high grade parts of the fairway and provide a technical foundation for prospect analysis. (4) Well failure analysis is used to understand the critical play elements. For example trap definition and trap integrity are a key concern for the Brent Group play in the North Viking Graben. This is hardly surprising for a mature exploration province where the regional geological model for the play is well constrained. In a frontier area, with little well data, reservoir or source presence, for example, may be the key risk. (5) Prospect specific risks can be calibrated using drilling success ratios for the play.

Acknowledgements B P Norge are acknowledged for granting permission to publish. The large number of colleagues in


180

Fig. 16. Interpretation confidence map for the geological model of the Brent Gp. play.

B P are thanked for their contributions to the regional work during the 14th Round Application. Grateful

thanks go to Pia Walmsn~ess, Ellen Lindland and Kjell Falnes for drafting the figures.

181


Fig. 17. A summary of the methodology for play fairway analysis.

References Bowen, J.M., 1992. Exploration of the Brent Province. In: A.C. Morton, R.S. Haszeldine, M.R. Giles and S. Brown (Editors), Geology of the Brent Group. Geol. Soc. Spec. Publ., 61: 314. Deegan, C.E. and Scull, B.J., 1977. A proposed standard lithostratigraphic nomenclature for the central and northern North Sea. Institute of Geological Sciences, Rep. 77/25. Norwegian Petroleum Directorate Bull., 1. Duppenbecker, S. and Dodd, T., 1993. Petroleum charge model for Brent accumulations - - application of integrated basin modelling. E.A.EG. 5th Conference. Extended Abstr. F028. Emery, D., Smalley, EC. and Oxtoby, N.H., 1993. Synchronous oil migration and cementation in sandstone reservoirs demonstrated by quantitative description of diagenesis. Philos. Trans. R. Soc., A 344:115-125. Gluyas, J., Robinson, A.G., Emery, D., Grant, S.M. and Oxtoby, N.H., 1993. The link between petroleum emplacement and sandstone cementation. In: J.R. Parker (Editor), Petroleum Geology of

S. GRANT N. MILTON M. THOMPSON

Northwest Europe, Proceedings of the 4th Conference. Geological Society London, pp. 1395-1402. Milton, N. and Ewen, D. (in prep). A regional sequence stratigraphy for the Brent Group. Mitchener, B.C., Lawrence, D.A., Partington, M.A., Bowman, M.B.J. and Gluyas, J., 1992. Brent Group: sequence stratigraphy and regional implications. In: A.C. Morton, R.S. Haszeldine, M.R. Giles and S. Brown (Editors), Geology of the Brent Group. Geol. Soc. Spec. Publ., 61: 45-80. Partington, M.A., Mitchener, B.C., Milton, N.J. and Fraser, A.J., 1993. Genetic sequence stratigraphy for the North Sea Late Jurassic and Early Cretaceous: distribution and prediction of Kimmeridgian-late Ryazanian reservoirs in the North Sea and adjacent areas. In: J.R. Parker (Editor), Petroleum Geology of Northwest Europe, Proceedings of the 4th Conference. Geological Society London, pp. 347-370. Vollset, J. and Dor6, A.G., 1984. A revised Triassic and Jurassic lithostratigraphic nomenclature for the Norwegian North Sea. Norwegian Petroleum Directorate Bull., 3.

BP Norge UA, P.O. Box 197, Forusbeen 35, 4033 Forus, Norway BP Norge UA, P.O. Box 197, Forusbeen 35, 4033 Forus, Norway BP Norge UA, P.O. Box 197, Forusbeen 35, 4033 Forus, Norway


183

A model-based approach to evaluation of exploration opportunities B.A. Duff and D. Hall

Effective exploration comprises a cycle determined by the synthesis of all available technical data into a unifying basin model, the testing of model predictions through the acquisition of further exploration information, and a "post-mortem" evaluation phase during which the model and its associated process-response systems are refined to accord with the new information. Evaluation of exploration opportunities, and resource assessment, is approached through a process-based understanding of exploration plays. Risked reserves estimation for plays and prospects is facilitated by the recognition of process and chance domains within the play, which are based on the specific predictions of the basin model. This model-based exploration cycle provides the most efficient and cost-effective framework for reducing risk to prospectivity prior to drilling wells or taking up acreage. Accordingly, it guarantees that these business decisions are optimized.

Introduction More than ever before, explorationists are using sophisticated analytical and interpretation techniques to minimize risk to prospectivity and maximize reward of their exploration portfolios in a cost-effective way. However, whilst this aim of exploration and the associated tools are well known, the strategy under which these tools are cost-effectively selected, applied and interpreted to add value to exploration assets has been far less apparent (Fig. 1a). The logical strategy for achieving this is the application of conceptual models of the processes impacting on hydrocarbon prospectivity. Such models of hydrocarbon trap-forming processes within individual basins have often been used to facilitate and enhance the interpretation phase of the exploration cycle. At the other extreme of scale, "global" causal models of basin processes, calibrated by world-wide data-sets (Nederlof, 1982; Nijhuis and Baak, 1990), have been used to estimate prospect reserves within particular basins. A model-based understanding of relevant basin processes and their responses however should be pivotal to all phases of cost-effective exploration. Furthermore, through meaningful play analysis this should be directed at the intra-basin scale, and the resulting model predictions should underpin prospect evaluation and resource assessment (Fig. l b). In this paper we propose a practical, model-based method for improving the consistency with which

exploration risks and the uncertainties of volumetric variables are estimated for input to probabilistic reserves evaluation. This is founded on an awareness of the value of exploration information and how this is unified predictively within the conceptual basin model. It overcomes the subjectivity of individual intuition and of the "Delphic" approach.

Common shortcomings in exploration The following failures to fully utilize exploration knowledge of a basin are very familiar: (1) Separate, sequential application of selected analytical techniques at the expense of other, possibly more appropriate, methods. In extreme cases, single analytical and interpretive tools are sometimes applied as if they alone are an exploration panacea rather than useful adjuncts to a broader data-set. (2) The lack of a conceptual framework to guide what data to acquire, process or interpret, when to use particular techniques, and what coverage, sample density and parameters to use. (3) Inadequate post-mortem analysis of the results of successful or unsuccessful drilling. In our view, the most obvious solution to these problems has frequently been overlooked. For this we need to consider the properties of the conceptual model and how these may be of practical value in reserves estimation and exploration risk reduction. First, however, we consider the nature of exploration risk and uncertainty, and the value of information.


184

B.A. Duff and D. Hall

Exploration in a Conceptual Vacuum: Costly, directionless

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Fig. 1. (a) Directionless exploration without a strategy for using results of data acquisition to achieve the exploration objective is costly and inefficient. (b) The basin model is pivotal at all stages of efficient, cost-effective exploration: it "drives" the "exploration cycle".

Risk and uncertainty in exploration It is now generally accepted that the volumetric variables and their product, recoverable reserves, must be described as statistical distributions (e.g. White and Gehman, 1979; Nederlof, 1982; Nijhuis and Baak, 1990): net Recoverable reserves = Bulk rock volume x gross x porosity x hydrocarbon saturation x

1

x recovery factor (1) volume factor Conversely because uncertainty is always present to a greater or lesser degree it is meaningless to d e scribe a prospect s reserves by a single deterministic value Monte Carlo simulation is the multivariate statis tical method typically used to honour this impreci sion surrounding the volumetric variables and their product This builds up an accurate estimate of the probability distribution for recoverable reserves by

repeated random sampling of the individual distributions considered by the explorationist to best describe the uncertainty associated with each of the volumetric variables. In practice, the exploration risk of failing to prove reserves is often distinguished from the uncertainty expressed by the variance or spread of values associated with the reserves distribution for a given prospect (Fig. 2). The most useful way to represent exploration risk and uncertainty is the expectation curve (e.g. Nederlof, 1982; Nijhuis and Baak, 1990). This portrays the cumulative probability (P1 in Fig. 2) that a certain reserves value (X1, Fig. 2), or a value greater than this, will be realized. The intercept of the curve indicates the chance of success, and the slope is a measure of the uncertainty, or conversely the precision, associated with reserves estimation. Probabilistic modelling of reserves has certainly helped improve resource estimates. However, too often it seems to be considered as an end in itself rather than a useful statistical tool for analyzing the uncertainty of knowledge of the process-response systems impacting on prospectivity.

Effective exploitation of exploration knowledge The value of information We define effective exploration as the evaluation of a portfolio of exploration opportunities in a manner which minimizes risk to prospectivity, and maximizes potential reward in the most cost-effective and efficient way. Exploration opportunities may comprise both new ventures acreage acquisition and options to drill a wildcat exploration well on a prospect, or appraisal drilling of an existing discovery. The evaluation strategy must have sufficient generality to address each of these categories as they are all likely to be competing for the same, finite budget. In other words, effective exploration is a process of risk reduction (Fig. 3). Exploration typically commences during a new ventures stage with only sparse data available, and the associated uncertainty is expressed probabilistically as a rather broad, weakly-sloping expectation curve. The effect of ongoing exploration is to progressively decrease the uncertainty associated with each of the volumetric variables, which together determine the expectation curve for reserves (Fig. 3). In this way, the steepness and therefore precision of the expectation curve estimated for reserves is successively increased by the incremental addition of exploration knowledge. The acquisition of exploration information does not proceed indefinitely however. Eventually a stage of rapidly diminishing returns in terms of further

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risk reduction per unit of exploration information is reached. The reserves expectation curve during this stage tends toward an intercept on the cumulative probability axis which we here term the irreducible exploration risk (IER; Fig. 3). The IER represents notionally the lowest possible estimated risk to prospectivity achievable without drilling a well. The value of exploration information is therefore clear. There is value in the acquisition, processing and interpretation of geological, geophysical and geochemical data prior to drilling only as long as this contributes significantly to a reduction in the estimated risk to prospectivity toward the IER. Here, data should be distinguished from knowledge. Additional

data only imply improved knowledge if they can be used to refine the basin model. From the inception of exploration in a basin there are many possible pathways toward the IER, depending on the specific exploration programme of information acquisition pursued. When exploration is conducted arbitrarily (Fig. l a), these will be grossly inefficient in terms of identifying the best sequence of activities comprising the programme, and judging when the optimum amount of data has been acquired. As a result, they will be unnecessarily costly, and invariably characterized by all or some of the shortcomings indicated previously. Our main thesis is that a model-based foundation for probabilistic reserves estimation maximizes the chance that the most efficient and cost-effective exploration programme is identified and followed. It may not be possible to quantify the IER as a unique criterion. However, by application of the basin model and judicious incorporation of its predictions, it should still be possible to judge the point of diminishing incremental value added in the description of risk attached to an exploration opportunity. Gauging this point can help indicate when exploration activities other than drilling should be discontinued to contain costs. We recommend that the term prospect be restricted to features that have, in this way, been matured for drilling, and that all other less mature features be referred to as leads. This view of exploration therefore has the merits of highlighting the logical relationships between risk, uncertainty and reward, and the acquisition costs of exploration information. We now turn to the conceptual model and its vital and very practical role in

186

controlling the acquisition of this information, and its organization for optimum reserves estimation.

The conceptual model The two fundamental properties of a conceptual model are also its great strengths: its predictive power and its susceptibility to continual testing, refining or even rejecting. Indeed the criterion of falsifiability is a fundamental pre-requisite for any hypothesis which aspires to being truly scientific (Popper, 1959). As more information is acquired, the number of working models is reduced as those which are denied by this new information are discarded. Eventually, one model remains which can be refined by further data acquisition to a highly-detailed description of particular natural processes, with very specific predictions. This model is sometimes referred to as the processresponse model. Basin analysis can usefully be regarded as the application of this methodology in the geological context of a sedimentary basin. But in this context we must recognize that typically many processes have, during the life of the basin, contributed to an even greater number of responses (e.g. Krumbein and Sloss, 1963). The term basin model is used in this paper to refer to the totality of individual processresponse systems that have been active in the basin. We are concerned in particular with those which together govern hydrocarbon prospectivity (Fig. 10). These comprise models of depositional, diagenetic, structural, halokinetic, and hydrocarbon expulsion and migration processes, and their specific, measurable responses, the knowledge of which should constitute a prime aim of an exploration programme. In the exploration context, the following properties of the basin model follow from the nature of conceptual models in general. (1) It is predictive: concrete responses are predicted for all of the various process-response systems of which it is comprised. (2) As a result, it is always testable and potentially falsifiable. The basin model is therefore the only logical context for planning acquisition, processing and interpretation of geological, geophysical and geochemical data. (3) The unified basin model is more powerful predictively than the sum of its individual constituent data elements. To take a simple example, porosity preservation may be linked to hydrocarbon charge retention and because of this the reservoir model cannot be fully understood without also understanding the predictions of the charge model. (4) Application of the basin model optimally explains spatial and geological temporal variance


throughout a multivariate data-set, dimensions often ignored to the cost of effective prospect evaluation. (5) Application of the basin model approach is an iterative process, comprising phases of data acquisition, processing and interpretation, followed by model modification and refinement. From these properties, it follows that the basin model provides the soundest basis for risking overall prospectivity and estimating parameter distributions for prospects within a play.

The evaluation of exploration opportunities

The exploration cycle The post-mortem review and the basin analysis phase are inextricably linked through "feed-back" to the basin model, and this makes exploration, when it is pursued effectively, cyclic (Figs. l b and 4). If this exploration cycle is the factory processing and adding value to an exploration portfolio, then the basin model is its engine, ensuring that all available exploration data are optimally exploited in order to maximize the value added. In short, the basin model is pivotal in cost-effective exploration (Fig. lb). We now suggest specific ways in which this general approach to exploration can be applied to the challenges of effective play analysis, and the associated evaluation of prospects and assessment of resource potential.

Model-based play analysis The definition of play There appears to be no clear consensus as to what constitutes an exploration play, and this has unfortunately made it a blunt tool in articulating what explorationists believe concerning the prospective potential of a basin. Consistent with our view of the pivotal role of the basin model, we define the exploration play as follows: one or more closures of similar structural, depositional or hydrodynamic style, which result from a specific set of tectonic, depositional/ diagenetic or halokinetic processes within a sedimentary basin, and which, with suitable reservoir and sealing lithologies, and hydrocarbon charge, may form prospective hydrocarbon traps. This definition identifies the major geological processes and their unique responses within the basin as determining in turn the uniqueness of plays. In particular, it emphasises closure as the key distinguishing process-related element of a play. The term closure (e.g. Fig. 6) refers only to the morphology of the sealing surfaces enveloping the single gross rock volume. Closures of structural or

A model-based approach to evaluation of exploration opportunities

187

Fig. 5. Some examples of play families, and their constituent plays (P1, P2 . . . . . Pn), illustrating the definition of play as resulting from a unique closure-forming process. Play families are determined by first-order processes such as halokinesis (a), extensional tectonism (b), depositional and diagenetic processes (c and d), compressional tectonism (e) and basin compaction (f). These generate one or more particular closure styles which represent the individual plays.


188 .r

3D SEISMIC

p... ~

2D SEISMIC

PLAY 1 (P1)

PLAY 2 (P2)

SALT ANTICLINES

EXTENSIONAL FAULT BLOCKS

PLAY 3 (P3)

INVERSION

i

P2 0.8 - 0.9

Fig. 6. A schematic example of plays (P1, P2 and P3) distinguished by the closure-forming process and associated style. In the case of closure, process domains (a) correspond to the entire extent of the play. The corresponding closure chance domains (b) result from the application of the risk tranches method.

depositional/diagenetic origin within a given play only become traps when the essential ingredients for prospectivity, reservoir, seal and hydrocarbon charge, are added to the closure. The responses of specific first-order tectonic, depositional, diagenetic and halokinetic basin processes are "play families" These comprise one or more plays which, whilst possessing quite different styles of closure, have all originated from the same, first-order processes (e.g. Fig. 5). Play-based procedures for estimating the volumetric parameter distributions and the risks of absence of reservoir, seal and hydrocarbon charge are now considered.

within the spatial extent of any play. However, this is typically not a single, uniform variation. Rather, there are specific domains within which a process generates rather similar responses, and between which these responses show much greater variance. This arises because natural processes within the upper crust are themselves discretized into domains. Accordingly we define reservoir, seal and charge process domains as follows: Reservoir process domains are specific areas within a play within which the same or similar processes of deposition and diagenesis operated, and between which these processes varied significantly. Seal process domains are specific areas within a play within which the same or similar processes of deposition, burial, diagenesis, deformation, and hydrocarbon hydraulics operated, and between which these processes varied significantly. Charge process domains are specific areas within a play within which the same or similar processes of hydrocarbon expulsion, migration and entrapment occurred, and between which these processes varied significantly. Examples of these are given in Figs. 7, 8 and 9. Processes such as deformation and diagenesis typically overprint and may modify the primary reservoir and seal domains inherited from the depositional environment. For example, diagenesis may enhance or

a)

9

/

o~4e2x.~j e-/t/~TIc "T~'~=" ~

( "t')

oW /

I

/

SANDS

b)

Play process domains We wish to estimate the volumetric parameter distributions for porosity, net-to-gross (n/g), gross rock volume (grv) and hydrocarbon saturation (Sh), and to assign estimates to the chances of reservoir, seal, charge and closure. We begin by recognizing that natural processes such as deposition, diagenesis, and deformation interact in the depositional environment or with the older basin fill sequence to produce observable responses. Furthermore, these responses can vary significantly

0.6 - 0.7

0.6

ill

0.8

Fig. 7. A schematic example of reservoir process domains (a) and corresponding chance domains (b).

A model-based approach to evaluation of exploration opportunities

Fig. 8. A schematic example of seal process domains (a) and corresponding chance domains (b).

degrade reservoir, and deformation may compromise sealing integrity. However, basin analysis experience suggests that certain depositional processes and their response lithofacies "pre-condition" subsequent processes in the basin's history. It is therefore not unusual within a play to observe a high degree of spatial correlation (or anti-correlation) between the responses of a number of processes. A common example is the restriction of specific types of diagenesis in both carbonate and siliclastic settings to specific lithofacies depositional belts; the textural and mineralogical maturities of these belts effectively control the type of cementation and framework alteration that occurs. This impacts on both sealing and reservoir quality. Accordingly, it is common for reservoir and seal domains to follow the primary depositional lithofacies belts within any play, and this tendency should be fully exploited in defining a play's reservoir and seal domains, and simplifying play analysis. The reservoir, seal and charge process domains of a play are derived from the spatially-discretized variation in lithologies, structural style and hydrocarbon type predicted by the depositional, diagenetic, struc-

189

tural, and expulsion and migration process-response systems comprising the basin model (Fig. 10). The reservoir and charge domains provide a process-based way of best-estimating the volumetric parameter distributions (e.g. P05, P50, P95 percentiles) for porosity, n/g, and Sh for input to probabilistic reserves modelling. Furthermore, these domains also facilitate estimation of the hydrocarbon recovery factor, oil shrinkage and gas expansion factor, because these also generally relate to both reservoir and hydrocarbon phase properties. The definition of the spatial extent of process domains is only as good as the associated processresponse model, and therefore the amount of information on which this is based. For example, a geologist's reservoir lithofacies domains may, in detail, be in error in areas of the play for which data are few. In the next section, a procedure which consistently addresses the irregularity in data distribution within and between process domains is described.

Model-based estimation of chance of reservoir, seal, charge and closure: chance domains The key risks to hydrocarbon prospectivity within a play are absence of reservoir, absence of one or more seals to fully seal this reservoir throughout the

190

B.A. Duff and D. Hall PROCESS (EXAMPLES)

PROCESS RESPONSE

CHANCE DOMAINS

----1

DISTINCT RESERVOIR FAIRWAYS

DEPOSITIONAL I~ DIAGENETIC

RESERVOIR

LITHOFACIES BELTS POROSITY DOMAINS

[, SECONDARY DISSOLUTION I j r '

A LU -r 0

,

..J

U.I

o=i

Z u) ,
= xp'

l)i- 1-

[

__ a

xP

b

~x

I

0.0

(b'xi)2 (b-a) (b-Xl))

~x a

Xp x i

!)

Fig. 5. Four common probability distributions and their corresponding cumulative probability distributions: (a) uniform; (b) triangular.

in a compacting system. Sediment overburden is allowed for as is the ability of fluid to escape from formations with time, depending on both the permeability and fluid-pressure drive. Empirically (Dutta, 1989) it would appear that each lithology has a permeability/void ratio equation of state of the form: B k -

k,

--

e,

where k , ( e , ) is the depositional permeability (void ratio) of the lithology; k ( e ) are the values after burial, and B is a parameter in the rough range of about 3 + 3 for most lithologies. (Porosity, 4), is related to void ratio, e, by 4) = e/(1 + e)). Equally, the empirical relation between framepressure (effective stress), pf, and void ratio, e, for each lithology is of the general form: pf-

pf,

-e,

where pf, is a scaling value, and A is a constant, also in the rough range 3 4- 3. Each lithology (sand, shale, sandy-shale, carbonate, salt, etc.) has different values and ranges for each of the parameters k,, e,, pf,, A, B. The evolution of excess fluid pressure, porosity, and other dynamical quantities of interest is then influenced by choices made for the parameter values. The difficulty, as always, is that we do not know the values of these parameters with precision; so any dynamical predictions of, say, the build-up of excess fluid pressure with time will entail an uncertainty. The

task of the cumulative probability technique is to address that uncertainty in some objective, reproducible, quantitative manner. To illustrate the method, consider the two lithologies shale and sandy-shale which constitute the lithologic zones of the Navarin Basin COST No. 1 well from mudline to near base Oligocene (at 4000 m). Start with the empirical ranges thought to represent approximately the values of parameters considered geologically acceptable based on whatever criteria are considered controlling factors on the ranges. For instance, observations and extrapolations of presentday porosity with depth trends from many wells suggest that it is very unlikely that the surface void ratio for a sandy-shale lithology lies outside the broad range 0.16 _< e, _< 2.03 with a prevalent value of 1.07. In Table 2 we present the minimum, maximum and most likely values of 9 parameters which influence both the dynamical (8 parameters) and thermal (8 dynamical plus the rate of change of heat flux with time) evolutions. The thermal parameter, fl, is determined from the heat flow as: Q(t) -

Qo

exp(flt)

where Qo is present-day inferred heat flux, t is geological time (with t - 0 being the present day) and fl (units of Ma -1) measures whether palaeoheat flux was higher (/3 > 0), constant (fl - 0) or lower (/3 < 0) in the past. The range of fl can be assessed either from a model behaviour (e.g. a rifting model) or from uncertainty in fl when attempting to perform a thermal inversion with control against present-day observed thermal indicators with depth.

205

Risk and probability in resource assessment as functions of parameter uncertainty

p(x)

f(x)

~

ttistogranl

0'9991t

Normal

0.84

o.soo+

(c)

0.159i___

-

X

I

/ ~x

0.001_1

i

~.t- I2(n x) / 2n

[ ! i

"

~..x

2

2

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fx) ~t:(x

0.999 t

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(ci)

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)/3 I11 ;ix

02=1.t2/2 -(x . x

0.841F . ~ 0.500-{0.159[------ ,._o.I I ~1"-~

Xmin

+x+x iiiin

xp(~t) 2

[_..x,,,cxi)(.~t )

~x 0.001_1 Xmax

Inlll

+x (X . +x......))/6 II:aX

I)

111III

x = ~1(I+o2/~t2)"in 1/2

xl/zexi)(-~t2) - ~t(1+O2/~L2)"3/2 xs/2exp(~t) - xs/zexp[(ln(l+oZ/lt2)) I/2]

~log (x) XI/2

Fig. 5 (continued). (c) Normal; (d) log-normal.

Table 2 Uncertainties in the nine parameters used in the model, presented as minimum, maximum, and most likely values Parameter value

Minimum value

Maximum value

Most likely value

Heat flow/3 (Ma -l) Shale A Shale B Shale k* (md) Sandy shale A Sandy shale B Sandy shale k* (mD) Sandy shale p* (atm) Sandy shale e*

-0.015 0.60 1.10 70.0 1.20 0.80 10.0 0.21 0.16

-0.450 5.00 5.10 220.0 10.00 4.70 530.0 3.00 2.03

0.015 1.77 2.48 160.00 4.97 2.50 300.0 1.70 1.07

With the range and most likely value of each parameter provided, the burial and thermal maturity histories of the COST No. 1 well can be run in the manner outlined in the previous section. Two simple pictures can be drawn: first one can plot present-day predicted behaviour with depth for a quantity of interest and superpose on the plot both any observed or inferred values of the quantity as well as the cumulative probability likelihood values. We do so here for porosity, fluid pressure, and vitrinite reflectance. Second, one can plot the burial history with time (which varies for each computer run depending on the parameter values chosen for each run) and superpose the development with time of each quantity of interest (porosity, fluid pressure, and vitrinite reflectance) together with the cumulative probability. In addition to these two simple pictures, one can also plot the present-day cumulative probability of a particular iso-

value being reached at a given depth and, with time, the corresponding cumulative probability. From the perspective of when fluids were most likely in motion in the sub-surface and when thermal maturity was sufficient to generate hydrocarbons, the cumulative probability plots with time are extremely useful.

Porosity and probability Following the prescription given above, and using the ranges of parameter values of Table 2, Fig. 7 plots the predicted probability of porosity with depth at the present day. In addition the nominal measured porosity values are represented by filled circles. No attempt has been made to allow for uncertainty on the measured porosity values, although the scatter in the data raises a cautionary flag that some error of measurement (or inference) is likely present, depending on how the porosity was measured. Nevertheless, in order to focus on the essence of the cumulative probability procedure, the error in measurement is not discussed here (see, however, Lerche, 1993). Four curves are drawn on Fig. 7, representing cumulative probability iso-values of 30, 50, 70 and 90%, respectively. For instance, at a present-day depth of 3000 m there is a 70% chance that the porosity will be less than about 17% but only a 30% chance the porosity will be less than 8%. A differential probability can be obtained by subtraction: thus the probability that the porosity lies between 28% and 8% at 3000 m depth is 60% (90% cumulative probability of less

S. Cao, A.E. Abbott and L Lerche

206

Fig. 6. (a) Location of COST No. 1 well in Navarin basin, Bering Sea, Alaska.

than 28%, 30% cumulative probability less than 8%). Clearly, as the ranges allowed for different parameters are chosen to vary in different ways so, too, will the cumulative probability values. An alternate way to view the uncertainty in outputs is to plot cumulative probability with depth for fixed values of the porosity. Thus, in Fig. 8 are given the likelihoods of obtaining a fixed porosity. For example, at 3000 m the cumulative probability that the porosity is greater than 10% is 45%, while the cumulative probability that the porosity is less than 20% is 80%. Again, a differential statement can be constructed by subtraction: the probability for the porosity to lie between 10 and 20% at 3000 m depth is about 35% (80% cumulative probability of less than 20% porosity; 45%

cumulative probability of less than 10% porosity). In one extreme (Fig. 7) one plots the variation of porosity with depth for constant cumulative probability values, while in the other extreme (Fig. 8) one plots the variation of cumulative probability with depth for constant porosity values; both plots allow information to be assessed quickly as to the degree of uncertainty of information at the present day in different formats. Of equal or greater importance than assessing the values of present-day porosity outputs is the development of porosity with time, which influences migration of fluids, development of excess pressure, and thermal gradients. Again two different types of plots show different aspects of the uncertainty. For example Fig. 9 shows the burial history versus poros-


,

FEET ,

i

DEPTH BELOW SEA LEVEL

LITHOLOGIC ZONE

SEISMIC PERIOD EPOCH SEQUENCE

207

Sea Bottom

PlioPleistocene

,,

METERS

! -

1,000

First Sample --

Pliocene

I

2,000

A-1 -

3,000

-

4,000

A-2

-

1,000

Miocene

II < [.-,

C- 1

- 5,000

C-2

- 6,000 2,000

7,000

i D-1

III

Oiigocene D-2

r

8,0O0

1

9,000

- 10,000

- 3,000

- 11,000

IV

i- 12,000 Eocene 1 - 13,000

Low Cretaceous

G&H

9 < [-

-

4,000

- 14,000 15,000

LOW

Cretaceous

- 16,000 - 5,000

Total Depth --"

i 17,000

Fig. 6 (continued). (b) Corresponding geological column for the well (modified from Turner et al., 1984).

ity plots at different constant cumulative probability values. Thus on Fig. 9a, for example, there is only a 30% chance that the porosity values are less than or equal to the values shown on the figure; while if the cumulative probability is set to 70%, as in Fig. 9b, then there is a 70% chance that the porosity values are less than the values given; while at the extreme case of a 90% value for the cumulative probability the porosity is 90% likely to be less than the values recorded on the burial history curve of Fig. 9c. For instance at 20 m.y. B P it is 90% certain that the porosity at 2000 m depth is less than about 4050%, it is 70% certain that the porosity is less than 20-30%, and it is only 30% certain that the porosity

is less than 10-20%. Thus an evaluation of likely porosity evolution with time for each formation, or with burial history depth, can be obtained. By flipping the argument around we can ask: for a fixed porosity value what is the likely cumulative probability evolution with burial history? Such a situation is sketched in Fig. 10 for porosities of 10%, 20% and 30%, respectively. Note that at 20 m.y. BP at a depth of 2000 m, the probability is 30% that the porosity is less than 10%, the probability is 40% that the porosity is less than 20%, while the probability is 90% that the porosity is less than 30%. Thus it is unlikely (3 chances out of 10) that a porosity less than 10% occurred in the

208


Fig. 9. Burial history versus porosity at different cumulative probability values. (a) 30% cumulative probability; (b) 70% cumulative probability; (c) 90% cumulative probability.

Fluid pressure and probability Fig. 8. Porosity versus depth with different cumulative probability curves for Navarin COST No. 1 well.

formation which was at 2000 m depth at 20 m.y. BE but it is also unlikely (1 chance in 10) that the porosity was greater than 30% at that time and depth. In short an estimate can be made rather quickly of the expected evolution of porosity and the likely range of variation of porosity based on the range of variation of the eight input parameters of Table 2.

Because the fluid-flow/compaction code allows fluids to escape according to Darcy's law, and because the difference between overburden weight and frame pressure is supported by a fluid pressure in excess of hydrostatic, it becomes a relatively simple matter to calculate the influence of varying ranges of parameters on the likely total fluid pressure (excess fluid pressure plus hydrostatic pressure) with depth at the present day. For the parameter ranges given in Table 2, Fig. 11 presents observed fluid-pressure measurements with depth (again without ascribing


209

Fig. 10. Burial history versus cumulative probability for porosity. (a) 10% porosity; (b) 20% porosity; (c) 30% porosity.

any error or uncertainty to the measurements), together with cumulative probability curves. One can observe, for instance, that there is only a 30% predicted chance of a fluid pressure less than 150 kg c m - 2 at 3000 m, a 70% chance of less than 300 kg c m - 2 and a 90% chance that the fluid pressure is less than 600 kg cm -2. Curves of present-day predicted behaviour can be projected in a different manner, as in Fig. 12, where cumulative probability of fluid pressure with depth is plotted for selected values. For instance at 2000 m depth, Fig. 12 indicates less than 10% chance of obtaining a fluid pressure less than 50 kg cm -2, about

Fig. 12. Predicted cumulative probability with depth at fixed fluidpressure values at present day for Navarin COST No. 1 well.

40% chance of a fluid pressure less than 100 kg cm -2, a 65% chance of fluid pressure less than 150 kg c m - 2 and nearly 80% chance of a fluid pressure less than 200 kg cm -2. Viewed from the perspective of Fig. 12, this way of presenting results is important for drilling operations because a guide can be given as to the likely pressures expected to be encountered and the chance of encounter. The build-up of overpressure with time is also of significance in that such build-up indicates the

210

development of sealing capability of the system for likely hydrocarbon retention, the preservation of porosity, and thermal gradient increase due to lower thermal conductivity than would occur under nonoverpressured sediment compaction ~ of interest in developing earlier genesis of hydrocarbons than might otherwise have been thought to occur. Fig. 13 shows the burial history with cumulative probability curves superposed. Thus, in Fig. 13a, for a fixed value of 50 kg cm -2 fluid pressure, the cumulative probability curves indicate that the chance of a regime deeper than 1000 m having less than


50 kg c m -2 fluid pressure is less than 40% throughout the whole burial history. Fig. 13b, drawn for a fluid pressure of 100 kg c m -2, indicates that the probability is less than 40% throughout the whole burial history that the fluid pressure is less than 100 kg c m - 2 at depths in excess of 2000 m (60% chance the pressure is greater than 100 kg c m - 2 ) . Fig. 13c (drawn for 200 kg c m - 2 fluid pressure) indicates that it is 80% likely that the fluid pressure is less than 200 kg c m - 2 during the whole burial history at all depths shallower than about 3000 m. The other projection of information is given in Fig. 14 which shows fluid-pressure development with burial history for different, fixed, cumulative probability values. Thus, Fig. 14a shows that there is only a 30% chance that fluid-pressure development will attain values less than those, at any time and depth, on Fig. 14a; Fig. 14b shows that there is a 70% chance of obtaining values less than those drawn; while Fig. 14c provides a 90% chance that fluid-pressure values will be less than those given. Thus one can assess the likelihood of the amount and timing of fluid-pressure development in terms of probabilistic ranges determined by the intrinsic assigned uncertainties in the input parameters of Table 2.

Thermal maturity and probability

Fig. 13. Burial history with cumulative probability curves for different fluid-pressure values. (a) 50 kg cm-2; (b) 100 kg cm-2; (c) 200 kg cm -2.

Vitrinite reflectance provides an indirect measure of thermal maturity of hydrocarbon proneness in a basin. The reflectance is dependent not only on parameters influencing the dynamical behaviour of the system, but also on both the present-day heat flux and the palaeoheat flux. Indeed measurements of vitrinite reflectance with depth have often been used in an inverse sense to determine, or at least bracket, the palaeoheat flux (Lerche, 1989). In Table 2 we provide a range of palaeoheat flux values considered likely to bracket the extremes of the true palaeoheat flux. The predicted evolution of vitrinite reflectance depends on both the palaeoheat flux and on parameters controlling palaeotemperature due to dynamical conditions (thermal conductivity is tied to porosity; formation thicknesses and depths are tied to permeability, etc.). In Fig. 15 we provide present-day measured values of vitrinite reflectance with depth (once again, the data are taken "as is, where is" and no attempt has been made to include error or uncertainty measures of the data per se); superposed on Fig. 15 are the corresponding cumulative probability curves based on the ranges of the 9 parameters of Table 2. For instance, if one takes a value of reflectance of 0.6% as marking the onset of the oil window, then

211

Risk and probability in resource assessment as functions of parameter uncertainty Vitrinite 0 0

0

0.5 !

(%)

Reflectance

1.0 I

1.5 I

2.0 I

2.5 I

3.0 I

/z,\,.

1,000-

2,000-

'iX,..

{'h 3 000-

4,000-

Cumulative

Probability

(< =)

30 50 70 90

5,000-9

Input

Data

Fig. 15. Vitrinite reflectance versus depth with cumulative probability values for Navarin COST No. 1 well.

0

0

~ / ,'//'

Cumulative

Probability

~0 2o 3o 40

so

I

I

I

!

I

6o I

7o I

(

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Geoarchaeology: Exploration, Environments, Resources (Geological Society Special Publication, No. 165) (Geological Society Special Publication, No. 165)

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Particle Size Analysis (Special Publications)

Geometry and Petrogenesis of Dolomite Hydrocarbon Reservoirs (Geological Society Special Publication No. 235)

Marine Palaeoenvironmental Analysis from Fossils (Geological Society Special Publications)

Lipids in Health and Nutrition (Special Publications)

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Particle Size Analysis (Special Publications)

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Geometry and Petrogenesis of Dolomite Hydrocarbon Reservoirs (Geological Society Special Publication No. 235)

Fracture and In-Situ Stress Characterization of Hydrocarbon Reservoirs (Geological Society Special Publication)

Combining and Reporting Analytical Results (Special Publications)

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