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    中国百强科技报刊

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    Volume 48 Issue 7
    Jul.  2023
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    Article Contents
    Lu Guoqing, Dong Shaoqun, Huang Liliang, Zeng Lianbo, Liu Guoping, He Wenjun, Du Xiaoyu, Yang Sen, Gao Wenying, 2023. Fracture Intelligent Identification Using Well Logs of Continental Shale Oil Reservoir of Fengcheng Formation in Mahu Sag, Junggar Basin. Earth Science, 48(7): 2690-2702. doi: 10.3799/dqkx.2022.409
    Citation: Lu Guoqing, Dong Shaoqun, Huang Liliang, Zeng Lianbo, Liu Guoping, He Wenjun, Du Xiaoyu, Yang Sen, Gao Wenying, 2023. Fracture Intelligent Identification Using Well Logs of Continental Shale Oil Reservoir of Fengcheng Formation in Mahu Sag, Junggar Basin. Earth Science, 48(7): 2690-2702. doi: 10.3799/dqkx.2022.409

    Fracture Intelligent Identification Using Well Logs of Continental Shale Oil Reservoir of Fengcheng Formation in Mahu Sag, Junggar Basin

    doi: 10.3799/dqkx.2022.409
    • Received Date: 2022-05-12
    • Publish Date: 2023-07-25
    • The continental shale oil reservoir of Fengcheng Formation in the northern slope area of Mahu Sag, Junggar Basin is a mixed deposition of multiple provenances, with frequent interbedding of various lithologies and small thickness of rock mechanics layer, resulting in small fracture scale, weak conventional logging response of fractures and great difficulty in identification. Aiming at the logging identification of shale fractures, in this paper it applies the extreme gradient boosting (XGBoost) method in ensemble learning to deeply mine the nonlinear relationship between fracture information and logging data, integrates multiple weak classifiers into strong classifiers, reduces the uncertainty of fracture identification, and improves the accuracy of fracture identification. In this method, core fracture description and fracture interpretation results of image logging are used as labels, and conventional logging information is used as input data for model training. On the basis of outlier screening, SMOTE oversampling and feature optimization, the optimal hyperparameters of fracture intelligent identification model are obtained through grid search method. Compared with the commonly used machine learning methods such as support vector machine and logical regression, the XGBoost has better fracture identification ability than the other two nonlinear machine learning methods, and the accuracy of test set identification reaches 90%. The identification results of the P1f3 of Well A1 reflect that the fractures in this section are relatively developed, and the model has a good identification ability for both fractured and non-fractured sections, with a high coincidence rate with the core observation results. It can provide effective means for intelligent identification of fractures in continental shale oil reservoirs in Mahu Sag.

       

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