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Water
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Revolutionizing Hydraulic Fracturing with Machine Learning and Data-Driven Approaches
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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 689

Special Issue Editors

Prof. Dr. Qinzhuo Liao

E-Mail Website
Guest Editor
College of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China
Interests: intelligent oil and gas; hydraulic fracturing; machine learning; surrogate modeling; upscaling; uncertainty quantification; data assimilation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huiying Tang

E-Mail Website
Guest Editor
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Interests: hydraulic fracturing simulation; geomechanical modeling; machine-learning-assisted fracturing optimization; integrated simulation of fracturing and production; gas storage stability analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junchao Li

E-Mail Website
Guest Editor
Mechanical Engineering College, Xi’an Shiyou University, Xi’an, China
Interests: reservoir simulation; fracture modeling and upscaling; geomechanical modeling; multi-physics modeling and simulation

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hydraulic fracturing
  • machine learning
  • data-driven
  • geomechanics
  • fluid flow
  • modeling and simulation
  • optimization

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Published Papers (1 paper)

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Research

23 pages, 3914 KB  
Article
Machine Learning-Driven Early Productivity Forecasting for Post-Fracturing Multilayered Wells
by Ruibin Zhu, Ning Li, Guohua Liu, Fengjiao Qu, Changjun Long, Xin Wang, Shuzhi Xiu, Fei Ling, Qinzhuo Liao and Gensheng Li
Water 2025, 17(19), 2804; https://doi.org/10.3390/w17192804 - 24 Sep 2025
Viewed by 125
Abstract
Hydraulic fracturing technology significantly enhances reservoir conductivity by creating artificial fractures, serving as a crucial means for the economically viable development of low-permeability reservoirs. Accurate prediction of post-fracturing productivity is essential for optimizing fracturing parameter design and establishing scientific production strategies. However, current [...] Read more.
Hydraulic fracturing technology significantly enhances reservoir conductivity by creating artificial fractures, serving as a crucial means for the economically viable development of low-permeability reservoirs. Accurate prediction of post-fracturing productivity is essential for optimizing fracturing parameter design and establishing scientific production strategies. However, current limitations in understanding post-fracturing production dynamics and the lack of efficient prediction methods severely constrain the evaluation of fracturing effectiveness and the adjustment of development plans. This study proposes a machine learning-based method for predicting post-fracturing productivity in multi-layer commingled production wells and validates its effectiveness using a key block from the PetroChina North China Huabei Oilfield Company. During the data preprocessing stage, the three-sigma rule, median absolute deviation, and density-based spatial clustering of applications with noise were employed to detect outliers, while missing values were imputed using the K-nearest neighbors method. Feature selection was performed using Pearson correlation coefficient and variance inflation factor, resulting in the identification of twelve key parameters as input features. The coefficient of determination served as the evaluation metric, and model hyperparameters were optimized using grid search combined with cross-validation. To address the multi-layer commingled production challenge, seven distinct datasets incorporating production parameters were constructed based on four geological parameter partitioning methods: thickness ratio, porosity–thickness product ratio, permeability–thickness product ratio, and porosity–permeability–thickness product ratio. Twelve machine learning models were then applied for training. Through comparative analysis, the most suitable productivity prediction model for the block was selected, and the block’s productivity patterns were revealed. The results show that after training with block-partitioned data, the accuracy of all models has improved; further stratigraphic subdivision based on block partitioning has led the models to reach peak performance. However, data volume is a critical limiting factor—for blocks with insufficient data, stratigraphic subdivision instead results in a decline in prediction performance. Full article
(This article belongs to the Special Issue Revolutionizing Hydraulic Fracturing with Machine Learning and Data-Driven Approaches)
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