Probabilistic Aggregation of Oil and Gas Field Resource Estimates and Project Portfolio Analysis
- Jan F. van Elk (Woodside Energy) | Ritu Gupta (Curtin University of Technology) | David J. Wann (Woodside Energy)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- February 2010
- Document Type
- Journal Paper
- 72 - 81
- 2010. Society of Petroleum Engineers
- 2.4.3 Sand/Solids Control, 7.1.5 Portfolio Analysis, Management and Optimization, 5.7.6 Reserves Classification, 3.3.6 Integrated Modeling, 4.1.5 Processing Equipment, 5.1.7 Seismic Processing and Interpretation, 5.1.1 Exploration, Development, Structural Geology, 4.1.2 Separation and Treating, 5.6.9 Production Forecasting, 4.3.4 Scale
- skew-normal, reserves, tornado diagram, forecasting, dependency, Monte Carlo, probabilistic aggregation, portfolio theory
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- 1,235 since 2007
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Probabilistic aggregation and dependency estimation are essential in portfolio methods, production forecasting, and resource estimation. The use of arithmetic addition understates the true value of the resource estimates within a portfolio of fields. Potentially, this could result in deferral of a project, or loss of lucrative business and commercial opportunities, such as project investment, facility-sizing decisions, or incremental gas-supply commitments.
A statistically robust method for aggregation of resource estimates that appropriately uses expert opinion is presented in this paper. Using two integrated-project examples, this paper introduces new methods for (1) probabilistic aggregation of the resource estimates for multiple fields and (2) estimating a measure of dependency between the resource estimates of individual fields.
The new analytical method for probabilistic aggregation is based on multivariate skew-normal (MSN) distributions, which can model a wide range of skewness through a shape parameter and are used heavily in financial and actuarial applications.
In studies of the fields in which the multiple-realizations approach is used as a basis for the uncertainty framework, tornado diagrams are generated routinely to describe the dependence of the field resources on reservoir parameters. The improved method for evaluating measures of dependency between the resource estimates within a portfolio of fields uses these tornado diagrams as a basis. Incorporating the expertise and knowledge of geologists and petroleum engineers is a critical element of the method.
These methods for probabilistic aggregation and estimating dependencies were developed within the context of the oil industry, but their use is not limited to the oil industry. They are general and can be used in other probabilistic-aggregation problems. Application of these techniques requires limited time and effort, compared to individual-field studies, and can have a profound impact on the uncertainty range of the total resources for the portfolio of fields.
|File Size||893 KB||Number of Pages||10|
Ayyub, B.M. 2001. Elicitation of Expert Opinions for Uncertainty andRisks. Boca Raton, Florida: CRC Press.
Azzalini, A. and Capitanio, A. 1999. Statistical applications ofthe multivariate skew normal distribution. Journal of the RoyalStatistical Society: Series B: Statistical Methodology 61 (3):579-602. doi:10.1111/1467-9868.00194.
Brashear, J.P., Becker, A.B., and Gabriel, S.A. 1999. Interdependencies among E&PProjects and Portfolio Risk Management. Paper SPE 56574 presented at theSPE Annual Technical Conference and Exhibition, Houston, 3-6 October. doi:10.2118/56574-MS.
Campbell, J.M., Bratvold, R.B., and Begg, S.H. 2003. Portfolio Optimization: Living Up toExpectations? Paper SPE 82005 presented at the SPE Hydrocarbon Economicsand Evaluation Symposium, Dallas, 5-8 April. doi: 10.2118/82005-MS.
Carter, P.J. and Morales, E. 1998. Probabilistic Addition of GasReserves Within a Major Gas Project. Paper SPE 50113 presented at the SPEAsia Pacific Oil and Gas Conference and Exhibition, Perth, Australia, 12-14October. doi: 10.2118/50113-MS.
Clemen, R.T., Fischer, G.W., and Winkler, L.R. 2000. Assessing Dependence:Some Experimental Results. Management Science 46 (8):1100-1115. doi:10.1287/mnsc.46.8.1100.12023.
Collinson, R., Gupta, R., Smith, G.C., and Van Elk, J.F. 2008. Scenario Analysis Tool forProbabilistic Analysis of Reserves. Paper SPE 116378 presented at the SPEAsia Pacific Oil and Gas Conference and Exhibition, Perth, Australia, 20-22October. doi: 10.2118/116378-MS.
Delfiner, P. and Barrier, R. 2008. Partial Probabilistic Addition: APractical Approach for Aggregating Gas Resources. SPE Res Eval &Eng 11 (2): 379-385. SPE-90129-PA. doi: 10.2118/90129-PA.
Etherington, J.R., Hunt, E.J., and Adewusi, A. 2001. Aggregating Reserves and Resourcesfor Portfolio Management. Paper SPE 71424 presented at the SPE AnnualTechnical Conference and Exhibition, New Orleans, 30 September-3 October. doi:10.2118/71424-MS.
Iman, R.L. and Conover, W.J. 1982. A Distribution-Free Approach to InducingRank Correlation Among Input Variables. Communications in Statistics 11 (3): 311-334.
Jensen, T.B. 1998. Estimationof Production Forecast Uncertainty for a Mature Production License. PaperSPE 49091 presented at the SPE Annual Technical Conference and Exhibition, NewOrleans, 27-30 September. doi: 10.2118/49091-MS.
Ministerie van Economische Zaken; The Hague, The Netherlands. Aardgas enaardolie in Nederand en op de Noordzee 1983. 1983.
Mudford, B. and Kuch, S. 2003. Stochastic Sensitivity Analysis: Orwhat happened to my Tornado Plot? Paper SPE 84235 presented at the SPEAnnual Technical Conference and Exhibition, Denver, 5-8 October. doi:10.2118/84235-MS.
O'Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H.,Jenkinson, D.J., Oakley, J.E., and Rakow, T. 2006. Uncertain Judgements:Eliciting Experts' Probabilities . West Sussex, UK: Statistics in Practice,John Wiley and Sons.
Simpson G.S. 2002. ThePotential for State-of-the-Art Decision and Risk Analysis to Contribute toStrategies for Portfolio Management. Paper SPE 77663 presented at the SPEAnnual Technical Conference and Exhibition, San Antonio, Texas, USA, 29September-2 October. doi: 10.2118/77663-MS.
Simpson G.S., Lamb F.E., Finch J.H., and Dinnie N.C. 2000. The Application of Probabilistic andQualitative Methods to Asset Management Decision Making. Paper SPE 59455presented at the SPE Asia Pacific Conference on Integrated Modelling for AssetManagement, Yokohama, Japan, 25-26 April. doi: 10.2118/59455-MS.
Society of Petroleum Engineers (SPE). 2007. Petroleum Resources ManagementSystem, http://www.spe.org/spe-site/spe/spe/industry/reserves/Petroleum_Resources_Management_System_2007.pdf.
Swinkels, W.J.A.M. 2001. Aggregation of Reserves. In Guidelines for theEvaluation of Petroleum Reserves and Resources: A Supplement to the SPE/WPCPetroleum Reserves Definitions and the SPE/WPC/AAPG Petroleum ResourcesDefinitions, Chap. 6, 53-71. Richardson, Texas: SPE.
The R Development Core Team. 2008. R: A Language and Environment forStatistical Computing (a.k.a. The R Reference Index). R Foundation forStatistical Computing, http://www.r-project.org/.
van Elk, J.F., Vijayan, K., and Gupta, R. 2000. Probabilistic Addition of Reserves--ANew Approach. Paper SPE 64454 presented at the SPE Asia Pacific Oil and GasConference and Exhibition, Brisbane, Australia, 16-18 October. doi:10.2118/64454-MS.
Woodside Petroleum. 2008. 2007 Annual Report, http://www.woodside.com.au/Investors+and+Media/Annual+Reports/Archive.htm.