IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v230y2013i3p688-702.html
   My bibliography  Save this article

Dynamic decision making for graphical models applied to oil exploration

Author

Listed:
  • Martinelli, Gabriele
  • Eidsvik, Jo
  • Hauge, Ragnar

Abstract

We present a framework for sequential decision making in problems described by graphical models. The setting is given by dependent discrete random variables with associated costs or revenues. In our examples, the dependent variables are the potential outcomes (oil, gas or dry) when drilling a petroleum well. The goal is to develop an optimal selection strategy of wells that incorporates a chosen utility function within an approximated dynamic programming scheme. We propose and compare different approximations, from naive and myopic heuristics to more complex look-ahead schemes, and we discuss their computational properties. We apply these strategies to oil exploration over multiple prospects modeled by a directed acyclic graph, and to a reservoir drilling decision problem modeled by a Markov random field. The results show that the suggested strategies clearly improve the naive or myopic constructions used in petroleum industry today. This is useful for decision makers planning petroleum exploration policies.

Suggested Citation

  • Martinelli, Gabriele & Eidsvik, Jo & Hauge, Ragnar, 2013. "Dynamic decision making for graphical models applied to oil exploration," European Journal of Operational Research, Elsevier, vol. 230(3), pages 688-702.
    • Handle: RePEc:eee:ejores:v:230:y:2013:i:3:p:688-702
    DOI: 10.1016/j.ejor.2013.04.057
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713003834
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2013.04.057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. James L. Smith and Rex Thompson, 2008. "Managing a Portfolio of Real options: Sequential Exploration of Dependent Prospects," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 43-62.
    2. Falzon, Lucia, 2006. "Using Bayesian network analysis to support centre of gravity analysis in military planning," European Journal of Operational Research, Elsevier, vol. 170(2), pages 629-643, April.
    3. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    4. Jeffrey M. Alden & Robert L. Smith, 1992. "Rolling Horizon Procedures in Nonhomogeneous Markov Decision Processes," Operations Research, INFORMS, vol. 40(3-supplem), pages 183-194, June.
    5. J. Eric Bickel & James E. Smith, 2006. "Optimal Sequential Exploration: A Binary Learning Model," Decision Analysis, INFORMS, vol. 3(1), pages 16-32, March.
    6. Srinivas Bollapragada & Thomas E. Morton, 1999. "Myopic Heuristics for the Random Yield Problem," Operations Research, INFORMS, vol. 47(5), pages 713-722, October.
    7. R. Reeves, 2004. "Efficient recursions for general factorisable models," Biometrika, Biometrika Trust, vol. 91(3), pages 751-757, September.
    8. Ilya O. Ryzhov & Warren B. Powell & Peter I. Frazier, 2012. "The Knowledge Gradient Algorithm for a General Class of Online Learning Problems," Operations Research, INFORMS, vol. 60(1), pages 180-195, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maarten J. Punt & Brooks A. Kaiser, 2021. "Seismic Shifts from Regulations: Spatial Trade-offs in Marine Mammals and the Value of Information from Hydrocarbon Seismic Surveying," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 80(3), pages 553-585, November.
    2. Yu, Shiwei & Gao, Siwei & sun, Han, 2016. "A dynamic programming model for environmental investment decision-making in coal mining," Applied Energy, Elsevier, vol. 166(C), pages 273-281.
    3. Finn Olesen, 1999. "Monetær integration i EU," Working Papers 2/99, University of Southern Denmark, Department of Sociology, Environmental and Business Economics.
    4. Bonneau, Mathieu & Gaba, Sabrina & Peyrard, Nathalie & Sabbadin, Régis, 2014. "Reinforcement learning-based design of sampling policies under cost constraints in Markov random fields: Application to weed map reconstruction," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 30-44.
    5. Hobæk Haff, Ingrid & Aas, Kjersti & Frigessi, Arnoldo & Lacal, Virginia, 2016. "Structure learning in Bayesian Networks using regular vines," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 186-208.
    6. Debarun Bhattacharjya & Jo Eidsvik & Tapan Mukerji, 2013. "The Value of Information in Portfolio Problems with Dependent Projects," Decision Analysis, INFORMS, vol. 10(4), pages 341-351, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David B. Brown & James E. Smith, 2013. "Optimal Sequential Exploration: Bandits, Clairvoyants, and Wildcats," Operations Research, INFORMS, vol. 61(3), pages 644-665, June.
    2. Smith, James L. & Thompson, Rex, 2009. "Rational plunging and the option value of sequential investment: The case of petroleum exploration," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 1009-1033, August.
    3. Voelkel, Michael A. & Sachs, Anna-Lena & Thonemann, Ulrich W., 2020. "An aggregation-based approximate dynamic programming approach for the periodic review model with random yield," European Journal of Operational Research, Elsevier, vol. 281(2), pages 286-298.
    4. Mark Gilchrist & Deana Lehmann Mooers & Glenn Skrubbeltrang & Francine Vachon, 2012. "Knowledge Discovery in Databases for Competitive Advantage," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 3(2), pages 2-15, April.
    5. Eugenio Vecchia & Silvia Marco & Alain Jean-Marie, 2012. "Illustrated review of convergence conditions of the value iteration algorithm and the rolling horizon procedure for average-cost MDPs," Annals of Operations Research, Springer, vol. 199(1), pages 193-214, October.
    6. Kutzner, Sarah C. & Kiesmüller, Gudrun P., 2013. "Optimal control of an inventory-production system with state-dependent random yield," European Journal of Operational Research, Elsevier, vol. 227(3), pages 444-452.
    7. Saurabh Bansal & Mahesh Nagarajan, 2017. "Product Portfolio Management with Production Flexibility in Agribusiness," Operations Research, INFORMS, vol. 65(4), pages 914-930, August.
    8. Woonghee Tim Huh & Mahesh Nagarajan, 2010. "Technical note ---Linear Inflation Rules for the Random Yield Problem: Analysis and Computations," Operations Research, INFORMS, vol. 58(1), pages 244-251, February.
    9. E A Silver, 2004. "An overview of heuristic solution methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 936-956, September.
    10. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    11. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    12. Gel, Esma S. & Salman, F. Sibel, 2022. "Dynamic ordering decisions with approximate learning of supply yield uncertainty," International Journal of Production Economics, Elsevier, vol. 243(C).
    13. L. Robin Keller & Ali Abbas & Manel Baucells & Vicki M. Bier & David Budescu & John C. Butler & Philippe Delquié & Jason R. W. Merrick & Ahti Salo & George Wu, 2010. "From the Editors..," Decision Analysis, INFORMS, vol. 7(4), pages 327-330, December.
      • L. Robin Keller & Manel Baucells & Kevin F. McCardle & Gregory S. Parnell & Ahti Salo, 2007. "From the Editors..," Decision Analysis, INFORMS, vol. 4(4), pages 173-175, December.
      • L. Robin Keller & Manel Baucells & John C. Butler & Philippe Delquié & Jason R. W. Merrick & Gregory S. Parnell & Ahti Salo, 2008. "From the Editors..," Decision Analysis, INFORMS, vol. 5(4), pages 173-176, December.
      • L. Robin Keller & Manel Baucells & John C. Butler & Philippe Delquié & Jason R. W. Merrick & Gregory S. Parnell & Ahti Salo, 2009. "From the Editors ..," Decision Analysis, INFORMS, vol. 6(4), pages 199-201, December.
    14. Paul Zipkin, 2008. "Old and New Methods for Lost-Sales Inventory Systems," Operations Research, INFORMS, vol. 56(5), pages 1256-1263, October.
    15. Danja Sonntag & Gudrun P. Kiesmüller, 2016. "The shape of the yield and its impact on inventory decisions," 4OR, Springer, vol. 14(4), pages 405-415, December.
    16. Soumita Kundu & Tripti Chakrabarti & Dipak Kumar Jana, 2014. "Optimal Manufacturing-Remanufacturing Production Policy for a Closed-Loop Supply Chain under Fill Rate and Budget Constraint in Bifuzzy Environments," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2014, pages 1-14, June.
    17. Sinitskaya, Ekaterina & Tesfatsion, Leigh, 2015. "Macroeconomies as constructively rational games," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 152-182.
    18. Qi Feng & J. George Shanthikumar, 2018. "Supply and Demand Functions in Inventory Models," Operations Research, INFORMS, vol. 66(1), pages 77-91, 1-2.
    19. van Valkenhoef, Gert & Tervonen, Tommi, 2016. "Entropy-optimal weight constraint elicitation with additive multi-attribute utility models," Omega, Elsevier, vol. 64(C), pages 1-12.
    20. Daniel Gartner & Yiye Zhang & Rema Padman, 2018. "Cognitive workload reduction in hospital information systems," Health Care Management Science, Springer, vol. 21(2), pages 224-243, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:230:y:2013:i:3:p:688-702. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.