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Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China

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  • Yi, Jun
  • Qi, ZhongLi
  • Li, XiangChengZhen
  • Liu, Hong
  • Zhou, Wei

Abstract

Assessment of production potential and prediction of sweet spots in unexploited shale gas wells are crucial technologies for achieving a high success rate in drilling. Most existing methods are only applicable for yield forecasting of shale gas wells with known geological and drilling/completion factors after production. In addition, the shale gas reservoir in the Longmaxi Formation in the southern Sichuan Basin of China is characterized by low porosity, low permeability, and diverse gas reservoir states. Thus, forecasting production from unexploited wells and predicting the exact location of the sweet spot under complex geological conditions have become challenging topic for both academia and industry. According to the first law of geography, shale gas wells that are closer in space have more similar geologic features. Based on this foundation, a machine-learning prediction model based on spatial correlation (SC-MLPM) is proposed for evaluating potential production of shale gas wells in the Changning play located in Longmaxi Formation in the southern Sichuan basin, China. Specifically, an improved K-nearest neighbor algorithm (SC-KNN) based on the spatial correlation characteristics of the exploited wells is designed to estimate the relevant geological factors of the unexploited wells in the same area. In addition, an extreme gradient lifting model based on Huber loss function (Hu-XGB) is developed to predict the potential production of unexploited shale gas wells, and then the shale gas sweet spot area in the research area is predicted quickly and accurately. The experimental results on 88 shale gas wells from Changning play show that the mean relative error of the prediction results of the proposed method is 9.8%. Furthermore, the results also determine that the sweet spots of shale gas wells in the research area are mainly developed in the northwest and northeast directions, and gradually become worse in the southeast direction. Eventually, experimental results for the Fox Creek play in Canada show that the proposed model obtains better predictive performance as the number of gas wells increases, and also verifies the generalizability. This work provides a basis for the efficient development of the research area.

Suggested Citation

  • Yi, Jun & Qi, ZhongLi & Li, XiangChengZhen & Liu, Hong & Zhou, Wei, 2024. "Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China," Applied Energy, Elsevier, vol. 357(C).
    • Handle: RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018470
    DOI: 10.1016/j.apenergy.2023.122483
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    1. Hui, Gang & Chen, Zhangxin & Wang, Youjing & Zhang, Dongmei & Gu, Fei, 2023. "An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity," Energy, Elsevier, vol. 266(C).
    2. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    3. Hou, Bing & Zhang, Qixing & Liu, Xing & Pang, Huiwen & Zeng, Yue, 2022. "Integration analysis of 3D fractures network reconstruction and frac hits response in shale wells," Energy, Elsevier, vol. 260(C).
    4. Qun Zhao & Leifu Zhang & Zhongguo Liu & Hongyan Wang & Jie Yao & Xiaowei Zhang & Rongze Yu & Tianqi Zhou & Lixia Kang, 2022. "A Big Data Method Based on Random BP Neural Network and Its Application for Analyzing Influencing Factors on Productivity of Shale Gas Wells," Energies, MDPI, vol. 15(7), pages 1-13, March.
    5. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    6. Amini Toosi, Hashem & Del Pero, Claudio & Leonforte, Fabrizio & Lavagna, Monica & Aste, Niccolò, 2023. "Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization," Applied Energy, Elsevier, vol. 334(C).
    7. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    8. Vo Thanh, Hung & Yasin, Qamar & Al-Mudhafar, Watheq J. & Lee, Kang-Kun, 2022. "Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers," Applied Energy, Elsevier, vol. 314(C).
    9. Ma, Kuiyou & Pang, Xiongqi & Pang, Hong & Lv, Chuanbing & Gao, Ting & Chen, Junqing & Huo, Xungang & Cong, Qi & Jiang, Mengya, 2022. "A novel method for favorable zone prediction of conventional hydrocarbon accumulations based on RUSBoosted tree machine learning algorithm," Applied Energy, Elsevier, vol. 326(C).
    10. Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
    11. Liu, Yazhou & Zeng, Jianhui & Qiao, Juncheng & Yang, Guangqing & Liu, Shu'ning & Cao, Weifu, 2023. "An advanced prediction model of shale oil production profile based on source-reservoir assemblages and artificial neural networks," Applied Energy, Elsevier, vol. 333(C).
    12. Xie, Weidong & Wang, Meng & Chen, Si & Vandeginste, Veerle & Yu, Zhenghong & Wang, Hua, 2022. "Effects of gas components, reservoir property and pore structure of shale gas reservoir on the competitive adsorption behavior of CO2 and CH4," Energy, Elsevier, vol. 254(PB).
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