Revolutionizing Hydraulic Fracturing with Machine Learning and Data-Driven Approaches
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 15 November 2025 | Viewed by 205
Special Issue Editors
Interests: intelligent oil and gas; hydraulic fracturing; machine learning; surrogate modeling; upscaling; uncertainty quantification; data assimilation
Special Issues, Collections and Topics in MDPI journals
Interests: hydraulic fracturing simulation; geomechanical modeling; machine learning assisted fracturing optimization; shale gas production prediction; integrated simulation of fracturing and production; gas storage stability analysis
Special Issue Information
Dear Colleagues,
Hydraulic fracturing has become a cornerstone of modern energy extraction, enabling the efficient recovery of oil and gas from unconventional reservoirs. However, the complexity of subsurface environments, coupled with the challenges of optimizing fracture design and operational efficiency, demands innovative approaches. This Special Issue seeks to explore the transformative potential of machine learning (ML) and data-driven methodologies in revolutionizing hydraulic fracturing. By leveraging vast datasets from field operations, seismic monitoring, and laboratory experiments, ML techniques offer unprecedented opportunities to enhance fracture prediction, real-time decision-making, and environmental sustainability.
This Special Issue aims to bring together cutting-edge research that bridges the gap between data science and hydraulic fracturing engineering. Topics of interest include, but are not limited to:
(1) Predictive modeling of fracture propagation and reservoir responses;
(2) Real-time monitoring and optimization of fracturing operations;
(3) Data-driven approaches for reducing environmental impacts;
(4) Integration of ML with geomechanical and fluid flow simulations;
(5) Uncertainty quantification and risk assessment in fracturing design;
(6) Case studies showcasing successful ML applications in field operations.
We invite researchers and industry experts to contribute original research, reviews, and case studies that highlight the transformative role of ML and data-driven approaches in advancing hydraulic fracturing technologies. Join us in shaping the future of energy extraction through innovation and collaboration.
Prof. Dr. Qinzhuo Liao
Dr. Huiying Tang
Prof. Dr. Junchao Li
Guest Editors
Manuscript Submission Information
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Keywords
- hydraulic fracturing
- machine learning
- data-driven
- geomechanics
- fluid flow
- modeling and simulation
- optimization
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