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.
Authors
- Siwei Meng
- Aoran Jin (ORCID: https://orcid.org/0009-0004-6842-2549)
- Bin Yuan
- Zhang Wei
- Mingze Zhao
- Birol Dindoruk
Institutions
- Sinopec (China) (CN)
- Research Institute of Petroleum Exploration and Development (CN)
- China University of Petroleum, East China (CN)
- Texas A&M University (US)
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