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

Zhe Liu, Qun Lei, Dingwei Weng, Lifeng Yang (), Xin Wang, Zhen Wang, Meng Fan and Jiulong Wang
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Zhe Liu: CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
Qun Lei: CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
Dingwei Weng: CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
Lifeng Yang: CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
Xin Wang: CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
Zhen Wang: CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
Meng Fan: CNPC Key Laboratory of Oil and Gas Reservoir Stimulation, Langfang 065007, China
Jiulong Wang: Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China

Energies, 2023, vol. 16, issue 23, 1-24

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.

Keywords: hydraulic fracture; fracture parameters; machine learning; eXtreme Gradient Boosting model; unconventional reservoir (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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