Probabilistic Modeling for Decision Support in Integrated Operations
- Martin Giese (University of Oslo) | Reidar B. Bratvold (University of Stavanger)
- Document ID
- Society of Petroleum Engineers
- SPE Economics & Management
- Publication Date
- July 2011
- Document Type
- Journal Paper
- 173 - 185
- 2011. Society of Petroleum Engineers
- 7.2.3 Decision-making Processes, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 1.11.2 Drilling Fluid Selection and Formulation (Chemistry, Properties), 1.6 Drilling Operations
- operational decisions, decision support, drilling operations
- 1 in the last 30 days
- 822 since 2007
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We report work carried out within the on collaborative decision support for integrated operations (CODIO) project. As one part of this project, we have designed a system to provide assistance in operational decisions based on real-time sensor readings in a typical scenario: While drilling close to the transition to a high-pressure formation, a gas influx is observed. The drilling team needs to decide whether to circulate, increase the mud weight, plug back, or set a casing.
After a brief description of the technology we used to model decision problems [Bayesian networks, influence diagrams (IDs)], we describe our case study and discuss the particular challenge of applying decision analysis to the kind of operational decision that is central to the case study. We then proceed to apply the method of decision analysis to our case study. We discuss how the resulting ID was tested using a simulation and discuss challenges in applying the technology, as well as lessons learned while under way. We also give a list of desiderata to establish better decision-making practices in the petroleum industry.
|File Size||1 MB||Number of Pages||13|
Abramson, B. and Finizza, A. 1991. Using belief networks to forecast oilprices. International Journal of Forecasting 7 (3):299-315. http://dx.doi.org/10.1016/0169-2070(91)90004-F.
Bhattacharjya, D. and Shachter, R. 2007. Evaluating InfluenceDiagrams with Decision Circuits. Proc., 23rd Conference on Uncertaintyin Artificial Intelligence (UAI 2007), Vancouver, British Columbia, 19-22 July,9-16.
Bratvold, R.B. and Begg, S.H. 2010. Making GoodDecisions. Richardson, Texas: SPE.
Bratvold, R.B., Begg, S.H., and Rasheva, S. 2010. A NewApproach to Uncertainty Quantification for Decision Making. Paper SPE 130157presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Dallas,8-9 March. http://dx.doi.org/10.2118/130157-MS.
Cayeux, E. 2007. eLAD: Well simulator specifications. ReportNo. IRIS-2007/182, Project No. 7081575, IRIS, Stavanger, Norway (20 August2007).
Dake, L.P. 1978. Fundamentals of Reservoir Engineering,No. 8. Amsterdam: Developments in Petroleum Science, Elsevier Science BV.
Giese, M. 2010. Influence Diagrams used in the CODIO pilot 1, http://heim.ifi.uio.no/martingi/codio/models.html.(accessed 2011).
Good, I.J. 1961a. A Causal Calculus (I). Br J Philos Sci XI (44): 305-318. http://dx.doi.org/10.1093/bjps/XI.44.305.
Good, I.J. 1961b. A Causal Calculus (II). Br J Philos Sci XII (45): 43-51. http://dx.doi.org/10.1093/bjps/XII.45.43.
Heckerman, D. 1991. Probabilistic Similarity Networks.Cambridge, Massachusetts: The MIT Press.
Howard, R.A. 1990. From Influence to Relevance to Knowledge. InInfluence Diagrams, Belief Nets and Decision Analysis, ed. R.M. Oliver,and J.Q. Smith, Chap. 1, 3-24. New York: Wiley Series in Probability &Statistics, John Wiley & Sons.
Howard, R.A. and Matheson, J.E. 1984. Influence diagrams. InReadings on the Principles and Foundations of Decision Analysis, ed.R.A. Howard and J.E. Matheson, Vol. 2, 719-762. Menlo Park, California:Strategic Decisions Group.
Hulsund, J.E., Nilsen, S., Nystad, E., Rø, M., Strand, S., andBisio, R. 2009. Requirements for experiments and laboratory set-up in eLAD.Technical Report, Institutt for Energiteknikk (IFE), Halden, Norway.
Ivanovska, M. and Giese, M. 2010. Probabilistic Logic withConditional Independence Formulae. Proc., 19th European Conference onArtificial Intelligence (ECAI 2010), Lisbon, Portugal, 16-20 August, Vol. 215,983-984. http://dx.doi.org/10.3233/978-1-60750-606-5-983.
Kim, J.H. and Pearl, J. 1983. A Computational Model forCombined Causal and Diagnostic Reasoning in Inference Systems. Proc.,Eighth International Joint Conference on Artificial Intelligence (IJCAI-83),Karlsruhe, Germany, 8-12 August, Vol. 1, 190.
Koller, D. and Pfeffer, A. 1997. Object-Oriented BayesianNetworks. Proc., 13th Annual Conference on Uncertainty in AI(UAI ‘97), Providence, Rhode Island, USA, 1-3 August, 302-313.
Lauritzen, S.L. and Spiegelhalter, D.J. 1988. Localcomputations with probabilities on graphical structures and their applicationto expert systems. Journal of the Royal Statistical Society, SeriesB 50 (2): 157-224.
Madsen, A.L., Jensen, F., Kjærulff, U.B., and Lang, M. 2005. The Hugin Toolfor Probabilistic Graphical Models. Intl. J. of Artificial IntelligenceTools (IJAIT) 14 (3): 507-543. http://dx.doi.org/10.1142/S0218213005002235.
Mansure, A.J., Whitlow, G.L., Corser, G.P., Harmse, J., and Wallace., R.D.1999. A Probabilistic Reasoning Tool for Circulation Monitoring Based on FlowMeasurements. Paper SPE 56634 presented at the SPE Annual Technical Conferenceand Exhibition. http://dx.doi.org/10.2118/56634-MS.
Marcot, B.G., Holthausen, R.S., Raphael, M.G., Rowland, M., and Wisdom, M.2001 Using Bayesian belief networks to evaluate fish and wildlife populationviability. Forest Ecology and Management 153 (1-3): 29-42.http://dx.doi.org/10.1016/S0378-1127(01)00452-2.
Ottonello, C., Peri, M., Regazzoni, C., and Tesei, A. 1992.Integration of multisensor data for overcrowding estimation. Proc., IEEEInternational Conference on Systems, Man and Cybernetics, Chicago, 18-21October, Vol. 1, 791-796.
Pearl, J. 1988. Probabilistic Reasoning in IntelligentSystems: Networks of Plausible Inference, revised second printing. SanFrancisco, California: Morgan Kaufmann Publishers.
Rajaieyamchee, M.A. and Bratvold, R.B. 2009. Real Time DecisionSupport in Drilling Operations Using Bayesian Decision Networks. Paper SPE124247 presented at the SPE Annual Technical Conference and Exhibition, NewOrleans, 4-7 October. http://dx.doi.org/10.2118/124247-MS.
Shachter, R.D. 1990. An ordered examination of influence diagrams.Networks 20 (5): 535-563. http://dx.doi.org/10.1002/net.3230200505.
Smith, C.S. and Bosch, O.J.H. 2004. Integrating disparateknowledge to improve natural resource management. Paper No. 1028 presented atthe 13th International Soil Conservation Organisation Conference (ISCO 2004),Brisbane, Queensland, Australia, 4-8 July.
Virtanen, K., Raivio, T., and Hämäläinen, R.P. 1998. A DecisionAnalytic Approach to Flight Simulation. Proc., EUROSIM'98 SimulationCongress, Espoo, Finland, 14-15 April, Vol. 2, 292-293.
Wright, S. 1921. Correlation and Causation. Journal ofAgricultural Research 20 (2): 557-585.
Wright, S. 1934. The Method of Path Coefficients. Ann. Math. Statist. 5 (3): 161-215. http://dx.doi.org/10.1214/aoms/1177732676.