Artificial Intelligence Driven Subsurface Hydraulic Fracturing Engineering: Connotation and Practices

Motivated by the global energy transition and subsurface energy resource (oil, gas, coal-bed-methane, geothermal, etc.) development, subsurface hydraulic fracturing technology is undergoing a paradigm shift from traditional experience-driven approaches to data- or intelligence-driven techniques. This work systematically elaborates on the connotation, recent practices, and future trends of artificial intelligence (AI) driven subsurface hydraulic fracturing technology. This work proposes a three-step technical evolution framework centered on data-driven→smart optimization→autonomous decision-making. Recent key practices in the framework are also introduced, including smart characterization and optimization of hydraulic fracturing, smart forecast of production operation after fracturing, and real-time regulation of entire fracturing-to-production lifecycle. The smart characterization of three-dimensional fracture propagation is achieved by constructing the Dy-Fracture-Net model. A dual-model collaborative architecture is developed to enable real-time warning and smart optimization during the fracturing process. Furthermore, the innovative Dy-Production-Net network is designed to predict the dynamics of post-fracturing reservoir parameters and production. Through integrating with intelligent optimization algorithms, a real-time regulation system encompassing the entire fracturing-to-production workflow is formed. To address the bottlenecks such as the lack of downhole monitoring data and insufficient model interpretability, future efforts are recommended as follows: miniaturization of multimodal perception agents, self-interpretability of mechanism-data fusion modeling, and autonomous closed-loop control. The outcomes of this work provide theoretical support and practical pathways for realizing the future AI driven subsurface AI-driven fracturing technology, holding significant strategic importance for advancing the digital transformation of the oil and gas industry.

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Publication Details

Journal
Engineering
Published
2025-12-01
DOI
https://doi.org/10.1016/j.eng.2025.12.024
Citations
1
Primary Topic
Hydraulic Fracturing and Reservoir Analysis
Type
article
Field-Weighted Citation Impact
0.71
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article

Artificial Intelligence Driven Subsurface Hydraulic Fracturing Engineering: Connotation and Practices

Siwei Meng, Aoran Jin, Bin Yuan, Zhang Wei et al.
1 citations
Engineering
Hydraulic Fracturing and Reservoir Analysis
0.71
article

Artificial Intelligence Driven Subsurface Hydraulic Fracturing Engineering: Connotation and Practices

Siwei Meng, Aoran Jin, Bin Yuan, Zhang Wei, Mingze Zhao, Birol Dindoruk
article en
1 citations

Abstract

Motivated by the global energy transition and subsurface energy resource (oil, gas, coal-bed-methane, geothermal, etc.) development, subsurface hydraulic fracturing technology is undergoing a paradigm shift from traditional experience-driven approaches to data- or intelligence-driven techniques. This work systematically elaborates on the connotation, recent practices, and future trends of artificial intelligence (AI) driven subsurface hydraulic fracturing technology. This work proposes a three-step technical evolution framework centered on data-driven→smart optimization→autonomous decision-making. Recent key practices in the framework are also introduced, including smart characterization and optimization of hydraulic fracturing, smart forecast of production operation after fracturing, and real-time regulation of entire fracturing-to-production lifecycle. The smart characterization of three-dimensional fracture propagation is achieved by constructing the Dy-Fracture-Net model. A dual-model collaborative architecture is developed to enable real-time warning and smart optimization during the fracturing process. Furthermore, the innovative Dy-Production-Net network is designed to predict the dynamics of post-fracturing reservoir parameters and production. Through integrating with intelligent optimization algorithms, a real-time regulation system encompassing the entire fracturing-to-production workflow is formed. To address the bottlenecks such as the lack of downhole monitoring data and insufficient model interpretability, future efforts are recommended as follows: miniaturization of multimodal perception agents, self-interpretability of mechanism-data fusion modeling, and autonomous closed-loop control. The outcomes of this work provide theoretical support and practical pathways for realizing the future AI driven subsurface AI-driven fracturing technology, holding significant strategic importance for advancing the digital transformation of the oil and gas industry.

Engineering
Sinopec (China) (CN), Research Institute of Petroleum Exploration and Development (CN), China University of Petroleum, East China (CN), Texas A&M University (US)
Openalex Percentile: Top 23%
Hydraulic Fracturing and Reservoir Analysis
0.71
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