Lithologic Reservoirs ›› 2020, Vol. 32 ›› Issue (4): 98-106.doi: 10.12108/yxyqc.20200410

• EXPLORATION TECHNOLOGY • Previous Articles     Next Articles

Identification of complex carbonate lithology by logging based on XGBoost algorithm

SUN Yushu1,2, HUANG Yun3, LIANG Ting1,2, JI Hancheng1,2, XIANG Pengfei1,2, XU Xinrong1,2   

  1. 1. College of Geoscience, China University of Petroleum(Beijing), Beijing 102249, China;
    2. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum(Beijing), Beijing 102249, China;
    3. Research Institute of Exploration & Development, PetroChina Huabei Oilfield Company, Renqiu 062550, Hebei, China
  • Received:2019-09-12 Revised:2019-11-29 Online:2020-08-01 Published:2020-06-16

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Abstract: Carbonate reservoirs are affected by a variety of factors during the formation process,and the reservoir lithology is complex and diverse. Logging data are of great significance for carbonate lithology identification. In order to solve the problem that the traditional logging lithology identification method and traditional machine learning have low recognition accuracy for complex carbonate lithology,taking the Ordovician carbonate rocks in the northern Langgu Depression as an example, based on log data, XGBoost algorithm was appied to lithology identification of complex carbonate rocks,and the performance of the model was comparied with the decision tree C4.5 algorithm and the support vector machine algorithm. The results show that the lithology identification model based on XGBoost algorithm has an accuracy rate of 88.18% for the identification of carbonate lithology in the study area. Compared with decision tree C4.5 and support vector machine,the accuracy rate is increased by about 10%. And the XGBoost algorithm uses multi-threaded and distributed computing methods,the training time is greatly shortened. It shows that the lithology identification model established by XGBoost algorithm can effectively identify complex carbonate lithology and provide a new idea for logging identification of complex carbonate lithology.

Key words: XGBoost algorithm, machine learning, carbonate, lithology identification, log interpretation

CLC Number: 

  • P618.13
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