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A new integrated workflow for improving permeability estimation in a highly heterogeneous reservoir of Sawan Gas Field from well logs data

  • Qamar Yasin
  • Qizhen DuEmail author
  • Atif Ismail
  • Azizullah Shaikh
Original Article
  • 42 Downloads

Abstract

The Sawan Gas Field is one of the most promising gas fields in Pakistan with a cumulative production of 850 BCF. The repetition of coarse sandstone, medium sandstone, and sandstone-shale intercalation in the production zone cause extreme heterogeneity. Consequently permeability varies enormously (from 0.01 mD to more than 1000 mD). Nevertheless, verifiable and accurate estimation  of permeability in the production zone with no previous laboratory-derived data is considered a challenging task. In this study, we explore a methodology for improving permeability estimation based on the combination of neural network (NN), multiple variable regression, and classification of data mining using conventional well logs (GR, LLD, RHOB, DT, and NPHI). The approach works in two-steps. First, we compute permeability using empirical, statistical, and virtual techniques on a fully cored well in order to select the specialized regression model that will be responsible for building data partitioning and classification of data mining task. To improve the efficiency of the classifier model, we combine the NN with multiple variable regression for predicting accurate permeability values. In step-2, the proposed regression model was employed to determine the final permeability values from data partitioning and classification of data mining. The final result of this study revealed that the proposed approach which combines NN, multiple variable regression, and classification of data mining provide more uniform, accurate, and qualitative estimation of permeability compared with stand-alone generic or global regression model. Also electrofacies (EFs) classification was conducted over the model to validate the proposed approach.

Keywords

Permeability Heterogeneous reservoir Multiple variable regression Neural network Hydraulic units Electrofacies 

Notes

Acknowledgements

We greatly appreciate the support of National key research & development plan programs (2017YFB0202900), the National Science Foundation of China (41574125, 41774139) and the Strategic Priority Research of the Chinese Academy of Sciences (XDA14010303). We are truly grateful to the reviewers for comments and suggestion to improve our manuscript. The first author is thankful to the Qingdao Government and China Postdoctoral Science Foundation for providing the research oppertunity in China University of Petroleum (East China) for the completion of this research work.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qamar Yasin
    • 1
  • Qizhen Du
    • 1
    Email author
  • Atif Ismail
    • 2
  • Azizullah Shaikh
    • 3
  1. 1.School of GeosciencesChina University of Petroleum (East China)QingdaoPeople’s Republic of China
  2. 2.Department of Geological EngineeringUniversity of Engineering and TechnologyLahorePakistan
  3. 3.School of Petroleum EngineeringChina University of Petroleum (East China)QingdaoPeople’s Republic of China

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