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)


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.


Graphical Model Petroleum System Failure Scenario Conditional Probability Table Exploration Target 
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  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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