Application of multi-threshold BIRCH clustering to facies-controlled porosity estimation

SUN Qifeng1 DUAN Youxiang1 LIU Fan1 LI Hongqiang2

(1.College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China 266580)
(2.Drilling Technology Research Institute of Shengli Petroleum Engineering Corporation Limited, SINOPEC, Dongying, Shandong, China 257000)

【Abstract】In view of the importance of lithofacies and porosity study in hydrocarbon exploration, we present an approach of multi-threshold BIRCH clustering (M-BIRCH) for lithofacies classification, based on which we estimate porosity using ridge regression. The heuristic initial threshold is set in terms of wave impedance distribution, and the number of thresholds is increased dynamically according to inter-clustering volume inconsistency. Global Agglomerative clustering is then employed for lithofacies classification. For each lithofacies, a modified ridge regression algorithm is used to predict porosity based on well porosity. Model tests show that M-BIRCH exhibits good robustness and computational efficiency for accurate lithofacies classification. A field data test shows that porosity could be estimated accurately using this method.

【Keywords】 lithofacies; multi-threshold; BIRCH clustering; ridge regression; porosity;


【Funds】 National Science and Technology Major Project (2017ZX05009-001) National Major Science and Technology Major Project (2016ZX05011-002) Fundamental Research Funds for the Central Universities (18CX02020A)

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This Article


CN: 11-1043/C

Vol , No. 05, Pages 62-73+127

October 2017


Article Outline


  • 0 Introduction
  • 1 M-BIRCH analysis
  • 2 Principle of ridge regression
  • 3 Facies-controlled porosity prediction
  • 4 Application
  • 5 Conclusion
  • References