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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
    Dong Shaoqun, Zeng Lianbo, Che Xiaohua, Du Xiangyi, Xu Hui, Ji Chunqiu, Yang Weidong, Li Zhihua, 2023. Application of Artificial Intelligence in Fracture Identification Using Well Logs in Tight Reservoirs. Earth Science, 48(7): 2443-2461. doi: 10.3799/dqkx.2022.088
    Citation: Dong Shaoqun, Zeng Lianbo, Che Xiaohua, Du Xiangyi, Xu Hui, Ji Chunqiu, Yang Weidong, Li Zhihua, 2023. Application of Artificial Intelligence in Fracture Identification Using Well Logs in Tight Reservoirs. Earth Science, 48(7): 2443-2461. doi: 10.3799/dqkx.2022.088

    Application of Artificial Intelligence in Fracture Identification Using Well Logs in Tight Reservoirs

    doi: 10.3799/dqkx.2022.088
    • Received Date: 2022-01-01
    • Publish Date: 2023-07-25
    • Fractures are effective reservoir spaces and important seepage channels of tight reservoirs. Fractures are very important for the exploration and development of tight reservoirs. Fracture identification of single well mainly can use image logs, array acoustic logs and conventional logs. How to accurately identify fractures is a key problem in the field of tight reservoir research. In the new era of intelligent exploration and development of oil and gas, artificial intelligence is a powerful tool to break through the limitations of existing technology and improve the accuracy of fracture identification in single well. Therefore, combined with the fracture identification cases using artificial intelligence in tight reservoirs in recent years and our research in this field, this paper introduces the application in fracture identification using the three types of logging data by unsupervised learning, supervised learning and semi-supervised learning artificial intelligence methods. So far, artificial intelligence is most widely used in fracture identification using conventional logging, followed by imaging logs, and array acoustic relatively less. As for artificial intelligence algorithms, unsupervised methods are less used because of the problem of recognition accuracy. Supervised learning methods are the mainstream at present, but it needs sufficient labeled data to establish an effective fracture prediction model. Semi-supervised learning method is a new trend in recent years, which can integrate the advantages of unsupervised and supervised learning, and make full use of small sample data of labeled logging and large sample data of unlabeled logging. Noted the operation efficiency of this kind of method needs to be improved. At present, the development trends of fracture identification methods by artificial intelligence for single well are from lower to higher nonlinear fitting ability and integrate multiple single prediction methods into an ensemble prediction method. At the same time, this paper also systematically discusses the existing problems and future development trend of artificial intelligence methods for fracture identification in tight reservoirs.

       

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