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A Powerful Prediction Framework of Fracture Parameters for Hydraulic Fracturing Incorporating eXtreme Gradient Boosting and Bayesian Optimization

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  • Zhe Liu

    (CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
    Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Qun Lei

    (CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
    Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Dingwei Weng

    (CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
    Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Lifeng Yang

    (CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
    Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Xin Wang

    (CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
    Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Zhen Wang

    (CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
    Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Meng Fan

    (CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
    Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Jiulong Wang

    (Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China)

Abstract

In the last decade, low-quality unconventional oil and gas resources have become the primary source for domestic oil and gas storage and production, and hydraulic fracturing has become a crucial method for modifying unconventional reservoirs. This paper puts forward a framework for predicting hydraulic fracture parameters. It combines eXtreme Gradient Boosting and Bayesian optimization to explore data-driven machine learning techniques in fracture simulation models. Analyzing fracture propagation through mathematical models can be both time-consuming and costly under conventional conditions. In this study, we predicted the physical parameters and three-dimensional morphology of fractures across multiple time series. The physical parameters encompass fracture width, pressure, proppant concentration, and inflow capacity. Our results demonstrate that the fusion model applied can significantly improve fracture morphology prediction accuracy, exceeding 0.95, while simultaneously reducing computation time. This method enhances standard numerical calculation techniques used for predicting hydraulic fracturing while encouraging research on the extraction of unconventional oil and gas resources.

Suggested Citation

  • Zhe Liu & Qun Lei & Dingwei Weng & Lifeng Yang & Xin Wang & Zhen Wang & Meng Fan & Jiulong Wang, 2023. "A Powerful Prediction Framework of Fracture Parameters for Hydraulic Fracturing Incorporating eXtreme Gradient Boosting and Bayesian Optimization," Energies, MDPI, vol. 16(23), pages 1-24, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7890-:d:1293140
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    References listed on IDEAS

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    1. Du, Shuyi & Wang, Meizhu & Yang, Jiaosheng & Zhao, Yang & Wang, Jiulong & Yue, Ming & Xie, Chiyu & Song, Hongqing, 2023. "An enhanced prediction framework for coalbed methane production incorporating deep learning and transfer learning," Energy, Elsevier, vol. 282(C).
    2. Yang, Lei & Wu, Shan & Gao, Ke & Shen, Luming, 2022. "Simultaneous propagation of hydraulic fractures from multiple perforation clusters in layered tight reservoirs: Non-planar three-dimensional modelling," Energy, Elsevier, vol. 254(PC).
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