Lithologic Reservoirs ›› 2021, Vol. 33 ›› Issue (3): 120-128.doi: 10.12108/yxyqc.20210312

• EXPLORATION TECHNOLOGY • Previous Articles     Next Articles

Lithology identification based on LSTM recurrent neural network

WU Zhongyuan1, ZHANG Xin2, ZHANG Chunlei3, WANG Haiying1   

  1. 1. School of Science, China University of Geosciences(Beijing), Beijing 100083, China;
    2. School of Statistics, Beijing Normal University, Beijing 100875, China;
    3. Beijing Zhongdirunde Petroleum Technology Co., Ltd., Beijing 100083, China
  • Received:2020-07-22 Revised:2020-09-06 Published:2021-06-03

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Abstract: A lithology recognition method by long-short-term memory neural network（LSTM）was proposed for complex carbonate reservoirs with complex composition and diverse lithology，to overcome obstacles troubling traditional identification，and effective results were showed with a case from gas field. Due to the inadequate ability of general machine learning methods in extracting the characteristics of sedimentary sequence，the LSTM method was introduced into the improvement for lithology identification. Taking the Lower Paleozoic carbonate reservoir in eastern block of Sulige gas field as an example，six sensitive parameters were selected to construct a lithology identification model based on LSTM，such as GR，Pe，DEN，RLLD， AC and CNL. The results show that lithology identification accuracy based on LSTM increases by 1.40%-12.25% above traditional models（Naive Bayes，KNN，Decision Tree，SVM and HMM），and can provide more reliable support for the characterization and evaluation of complex carbonate reservoirs.

Key words: long-short-term memory neural network, lithology identification, carbonate reservoir, machine learning

CLC Number: 

  • P618.13
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