A Fuzzy Inference Algorithm for Lithology Analysis in Formation Evaluation

  • Hujun Li
  • Fansheng Li
  • Andrew H. Sung
  • William W. Weiss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1821)


This paper presents a novel application of fuzzy logic in the interpretation of well logs, specifically, in determining the formation rock types in Petroleum Engineering. To solve this practical problem, a new inference algorithm is proposed. The interpretation of well logs is a decision-making problem where the issue is to utilize (and compromise) knowledge from human experts, evidence from well logs, and information from other sources. This research was motivated by the fact that fuzzy logic has proven to be highly effective in many applications involving uncertainties. Comparing with neural networks, this fuzzylogic-based method avoids the problems of training data collection, network training, and unavailability of rules or knowledge used in the interpretation. This results in an algorithm that is effective and inexpensive.


Fuzzy Logic Rock Type Membership Grade Formation Evaluation Training Data Collection 
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.


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  1. 1.
    Z.G. Lian, et al, Integration of Fuzzy Methods into Geostatistics for Petrophysical Property Distribution, SPE 49964, SPE Asia Pacific Oil and Gas Conference and Exhibition, 1998.Google Scholar
  2. 2.
    C.H. Wu, et al, Statistical and Fuzzy Infill Drilling Recovery Models for Carbonate Reservoirs, SPE 37728, Middle East Oil Conference & Exhibition, Manama, 1997.Google Scholar
  3. 3.
    T.H. Chung, et al, Application of Fuzzy Expert Systems for EOR Project Risk Analysis, SPE 30741, SPE Annual Technical Conference & Exhibition, 1995.Google Scholar
  4. 4.
    V.P. Rivera, Fuzzy Logic Controls Pressure In Fracturing Fluid Characterization Facility, SPE 28239, SPE Petroleum Computer Conference, 1994.Google Scholar
  5. 5.
    H.J. Xiong, et al, An Investigation Into the Application of Fuzzy Logic to Well Stimulation Treatment Design, SPE 27672, SPE Computer Applications, 1995.Google Scholar
  6. 6.
    C.D. Zhou, et al, Determining Reservoir Properties in Reservoir Studies Using a Fuzzy Neural Network, SPE 26430, the 68th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers, 1993.Google Scholar
  7. 7.
    H.C. Chen, et al, Novel Approaches to the Determination of Archie Parameters II: Fuzzy Regression Analysis, SPE 26288, SPE Advanced Technology Series, 1996.Google Scholar
  8. 8.
    David E. Johnson and Kathryne E. Pile, Well Log For the Nontechnical Person (Tulsa, Oklahoma: Penn Well Publishing Company, 1988).Google Scholar
  9. 9.
    Ed L. Bigelow, Introduction to Wireline Log Analysis (Houston: WESTERN Atlas International, Inc., 1995).Google Scholar
  10. 10.
    G.J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications (Prentice Hall, 1995).Google Scholar
  11. 11.
    H.J. Li, et al, Fuzzy Interpretation of Log Curves, Technical Report, Computer Science Department, New Mexico Tech, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Hujun Li
    • 1
  • Fansheng Li
    • 2
  • Andrew H. Sung
    • 2
  • William W. Weiss
    • 1
  1. 1.New Mexico Petroleum Recovery Research CenterMexico
  2. 2.Department of Computer ScienceNew Mexico Institute of Mining and TechnologySocorroUSA

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