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Handling Seismic Anomalies in Multiple Segment Prospects with Graphical Models

  • Gabriele Martinelli
  • Charles Stabell
  • Espen Langlie
Conference paper
Part of the Lecture Notes in Earth System Sciences book series (LNESS)

Abstract

Bayesian Risk Modification (BRM) [4] is a standard statistical framework for de-risking exploration targets using seismic anomalies. When an anomaly is observed it translates exploration team assumptions into an increase or decrease in the Chance of Success (COS). A unique advantage of the Bayesian approach is that it provides a statistical framework for assessing COS along with resources generated by prospects with multiple targets with seismic anomalies (see [3]).This paper presents a new prospect-level BRM approach by introducing a parameter that captures the degree of dependence among seismic anomalies on targets in a prospect. We compare this new BRM approach with the classical risking framework. BRM fits well into any graphical model risking framework such as those presented in [1] and [2]. This allows straightforward Monte Carlo simulation of our risking procedure.

Keywords

Graphical Model Petroleum System Failure Scenario Conditional Probability Table Exploration Target 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Martinelli, G., Eidsvik, J., Hauge, R., & Drange-Forland, M. (2011). Bayesian networks for prospect analysis in the north sea. AAPG Bulletin, 95(8), 1423–1442.CrossRefGoogle Scholar
  2. 2.
    Martinelli, G., Eidsvik, J., Hauge, R., & Hokstad K. (2012). Strategies for petroleum exploration based on bayesian networks: A case study. SPE Paper 159722, SPE ATCE 2012.Google Scholar
  3. 3.
    Stabell, C.B., & Langlie, E. (2008). Handling seismic anomalies on multiple targets. In Back to Exploration, 2008 CSPG CSEG CWLS Convention.Google Scholar
  4. 4.
    Stabell, C.B., Lunn, S., & Breirem K. (2003). Making effective use of a dfi: A practical bayesian approach for risking prospects for seismic anomaly information. In SPE HC Economics and Evaluation Symposium, SPE 82020.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gabriele Martinelli
    • 1
  • Charles Stabell
    • 1
  • Espen Langlie
    • 1
  1. 1.GeoKnowledge AS, A Schlumberger CompanyOsloNorway

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