Strategies for Petroleum Exploration on the Basis of Bayesian Networks: A Case Study
- Gabriele Martinelli (Norwegian University of Science and Technology) | Jo Eidsvik (Norwegian University of Science and Technology) | Ketil Hokstad (Norwegian University of Science and Technology) | Ragnar Hauge (Norwegian Computing Center)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2014
- Document Type
- Journal Paper
- 564 - 575
- 2013. Society of Petroleum Engineers
- 7.2.3 Decision-making Processes, 7.3.1 Exploration and Appraisal Strategies
- 4 in the last 30 days
- 414 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
The paper presents a new approach for modeling important geologicalelements, such as reservoir, trap, and source, in a unified statistical model.This joint modeling of these geological variables is useful for reliableprospect evaluation, and provides a framework for consistent decision makingunder uncertainty. A Bayesian network (BN), involving different kinds ofdependency structures, is used to model the correlation within the variousgeological elements and to couple the elements. On the basis of the constructednetwork, an optimal sequential exploration strategy is established with dynamicprogramming (DP). This strategy is useful for selecting the first prospect toexplore and for making the decisions that should follow, depending on theoutcome of the first well. A risk-neutral decision maker will continueexploring new wells as long as the expected profit is positive. The model andchoice of exploration strategy are tailored to a case study represented by fiveprospects in a salt basin, but they will also be useful for other contexts. Forthe particular case study, we show how the strategy clearly depends on theexploration and development cost and the expected volumes and recovery factors.The most lucrative prospect tends to be selected first, but the sequentialdecisions depend on the outcome of the exploration well in this firstprospect.
|File Size||763 KB||Number of Pages||12|
Bickel, J. and Smith, J. 2006. Optimal Sequential Exploration: A BinaryLearning Model. Decision Analysis 3 (1): 16-32.
Bickel, J., Smith, J., and Meyer, J. 2008. Modeling Dependence AmongGeologic Risks in Sequential Exploration Decisions. SPE Res Eval & Eng 11(2): 352-361. http://dx.doi.org/10.2118/102369-PA.
Cowell, R., Dawid, P., Lauritzen, S. et al. 2007. ProbabilisticNetworks and Expert Systems. Springer series in Information Science andStatistics.
Cunningham, P. and Begg, S.H. 2008. Using the Value of Information toDetermine Optimal Well Order in a Sequential Drilling Program. AAPG Bull. 92(10): 1393-1402. http://dx.doi.org/10.1306/06040808071.
Lauritzen, S.L. and Spiegelhalter, D.J. 1988. Local Computations WithProbabilities on Graphical Structures and Their Application to Expert Systems.J. Royal Statist. Soc., Series B 50 (2): 157-224.
Martinelli, G., Eidsvik, J., and Hauge, R. 2011a. Dynamic decision makingfor graphical models applied to oil exploration. Mathematics Department, NTNU,Technical report in Statistics 12.
Martinelli, G., Eidsvik, J., Hauge, R. et al. 2011b. Bayesian Networks forProspect Analysis in the North Sea. AAPG Bull. 95(8):1423-1442. http://dx.doi.org/10.1306/01031110110.
Murphy, K. 2001. The Bayes Net Toolbox for Matlab. Computing Science andStatistics 33.
Rose, P. 2001. Risk Analysis and Management of Petroleum ExplorationVentures. AAPG Methods in Exploration Series 12.
Smith, J. and Thompson, R. 2008. Managing a Portfolio of Real Options:Sequential Exploration of Dependent Prospects. The Energy J., InternationalAssociation for Energy Economics 29: 43-62.
Stabell, C., Lunn, S., and Breirem, K. 2003. Making Effective Use of a Dfi:A Practical Bayesian Approach for Risking Prospects for Seismic AnomalyInformation. Paper SPE 82020 presented at the SPE HC Economics and EvaluationSymposium, Dallas, Texas, 5-8 April. http://dx.doi.org/10.2118/82020-MS.
Suslick, S. and Schiozern, D. 2004. Risk Analysis Applied to PetroleumExploration and Production: An Overview. J. Petrol. Sci. and Eng. 11 (1): 1-9.
VanWees, J., Mijnlieff, H., Lutgert, J. et al. 2008. A Bayesian BeliefNetwork Approach for Assessing the Impact of Exploration ProspectInterdependency: An Application to Predict Gas Discoveries in The Netherlands.AAPG Bull. 92 (10): 1315-1336.