IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i23p7890-d1293140.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/23/7890/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/23/7890/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xue, Wenyuan & Lu, Yichen & Wang, Zhi & Cao, Shengxian & Sui, Mengxuan & Yang, Yuan & Li, Jiyuan & Xie, Yubin, 2024. "Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization," Energy, Elsevier, vol. 294(C).
    2. Li, Jiangtao & Zhou, Xiaofeng & Gayubov, Abdumalik & Shamil, Sultanov, 2023. "Study on production performance characteristics of horizontal wells in low permeability and tight oil reservoirs," Energy, Elsevier, vol. 284(C).
    3. Hui, Gang & Chen, Zhangxin & Schultz, Ryan & Chen, Shengnan & Song, Zhaojie & Zhang, Zhaochen & Song, Yilei & Wang, Hai & Wang, Muming & Gu, Fei, 2023. "Intricate unconventional fracture networks provide fluid diffusion pathways to reactivate pre-existing faults in unconventional reservoirs," Energy, Elsevier, vol. 282(C).
    4. Ligen Tang & Guosheng Ding & Shijie Song & Huimin Wang & Wuqiang Xie & Jiulong Wang, 2023. "A Case Study on the CO 2 Sequestration in Shenhua Block Reservoir: The Impacts of Injection Rates and Modes," Energies, MDPI, vol. 17(1), pages 1-19, December.
    5. Zhang, Nanlin & Luo, Zhifeng & Liu, Fei & Chen, Xiang & Li, Jianbin & He, Tianshu, 2024. "Fracture conductivity and rock appearance in volcanic reservoirs treated by various stimulation techniques," Energy, Elsevier, vol. 295(C).
    6. Kasala, Erasto E. & Wang, Jinjie & Lwazi, Hussein M. & Nyakilla, Edwin E. & Kibonye, John S., 2024. "The influence of hydraulic fracture and reservoir parameters on the storage of CO2 and enhancing CH4 recovery in Yanchang formation," Energy, Elsevier, vol. 296(C).
    7. Tang, Jiren & Chen, Long & Liu, Wenchuan & Zhang, Huali & Wang, Junxin & Liu, Qi, 2023. "Investigation on jet diffusion mechanism with applications to enhancing efficiency in forming directional fractures," Energy, Elsevier, vol. 262(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7890-:d:1293140. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.