Skip to main content
Log in

A Case Based System for Oil and Gas Well Design with Risk Assessment

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

A case base system for a complex problem like oil field design needs to be richer than the usual case based reasoning system. Genesis, the system described in this paper contains large heterogeneous cases with metalevel knowledge. A multi-level indexing scheme with both preallocated and dynamically computed indexing capability has been implemented. A user interface allows dynamic creation of similarity measures based on modelling of the user’s intentions. Both user aiding and problem solution facilities are supported, a novel feature is that risk estimates are also provided. Performance testing indicates that the case base produces on average, better predictions for new well developments than company experts. Early versions of the system have been deployed into oil companies in 6 countries around the world and research is continuing on refining the system in response to industry feedback.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. B. Smythe, M. Keane, and P. Cunningham, “Hierarchical case-based reasoning integrating case-based and decompositional problem-solving techniques for plant-control software design,” in IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 5, pp. 793–811, 2001.

    Article  Google Scholar 

  2. J. Kolodner and D. Leake, “A Tutorial introduction to case-based reasoning,” in Case-Based Reasoning, Experiences, Lessons and Future Directions, edited by D. Leake, MIT Press: Massachusetts USA, 1996, pp. 33–65.

    Google Scholar 

  3. H. Kitano and H. Shimazu, “The experience sharing architecture: A case study in corporate-wide case-based software quality control,” in Case-Based Reasoning, Experiences, Lessons and Future Directions, edited by D. Leake, MIT Press: Massachusetts USA, 1996, Chapt. 13.

    Google Scholar 

  4. B. Teufel, “Informationsspuren Zum numerischen und Graphischen vergleich von reduzierten naturlichsprachlichen texten,” Informatik-Dissertationen ETH Zurich, NR. 13, 1989.

  5. B. Glasgow, A. Mandell, D. Binney, L. Ghemri, and D. Fisher, “MITA, an information-extraction approach to the analysis of free form text in life insurance applications,” AI Magazine spring 1998.

  6. C. Apte, F. Damerau, and S.H. Weis, “Automated learning of decision rules for text categorization,” ACM Transactions on Information Systems, vol. 12, no. 3, pp. 233–251, 1994.

    Article  Google Scholar 

  7. S.M. Harabagiu and D.I. Moldavan, “TextNet—A text based intelligent system,” Natural Language Engineering, vol. 3, nos. 2/3, pp. 171–190, 1997.

    Article  Google Scholar 

  8. R. Irrgang, S. Kravis, E. Maidla, C. Damski, and K. Millheim, “A case based system to cut drilling costs,” SPE Annual Technical Conference and Exhibition, SPE 56504, Oct., 1999, pp. 1–13.

  9. J.F. Brett, M.K. Gregoli, P. Way, and J. Williams, “Successful drilling practices studies,” Gas Research Institute Technical Reports, 95/0132.3, 95/0132.4, 95/0132.5, 95/0132.6, 1995.

  10. D.V. Rama and P. Srinivasan, “An investigation of content representation using text grammars,” ACM Transactions on Information Systems, vol. 11, no. 1, pp. 51–75, 1993.

    Article  Google Scholar 

  11. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees, Wadsworth, 1984.

  12. L. Jaffe, E. Maidla, R. Irrgang, and W. Janisch, “Casing design for extended reach wells” in SPE Annual Technical Conference, San Antonio, Texas, SPE 38617, Oct. 1997, pp. 1–11.

  13. R. Irrgang, H. Irrgang, S. Kravis, S. Irrgang, G. Thonhauser, A. Wrightstone, E. Nakagawa, M. Agawani, P. Lollback, T. Gabler, and E. Maidla, “Assessment of risk and uncertainty for field developments: Integrating reservoir and drilling expertise,” SPE Annual Technical Conference, New Orleans, USA, SPE 71419, Oct. 2001, pp. 1–16.

  14. R. Baker, A Primer of Oil Well Drilling, University of Texas, 1996.

  15. R. Irrgang, S. Kravis, M. Agawani, and E. Maidla, “Automated storage of drilling experience: Capture and re-use of engineering knowledge,” in Petrotech, New Delhi, India, 1999, pp. 1–6.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Simon Kravis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kravis, S., Irrgang, R. A Case Based System for Oil and Gas Well Design with Risk Assessment. Appl Intell 23, 39–53 (2005). https://doi.org/10.1007/s10489-005-2371-7

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-005-2371-7

Navigation