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Processes
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Application of Artificial Intelligence in Oil and Gas Engineering
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Application of Artificial Intelligence in Oil and Gas Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Petroleum and Low-Carbon Energy Process Engineering".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2207

Editors

Prof. Dr. Chaodong Tan

E-Mail Website
Guest Editor
Department of Automation, China University of Petroleum (Beijing), Beijing 102249, China
Interests: data-driven fault diagnosis and its application in oil industry; intelligent oil lifting technology; sensoring device development for oil industry
Special Issues, Collections and Topics in MDPI journals
Dr. Kang Li

E-Mail Website
Guest Editor
College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249, China
Interests: fault diagnosis; process control; artificial intelligence; machine learning
Prof. Dr. Xiaoyong Gao

E-Mail Website
Guest Editor
Department of Automation, China University of Petroleum (Beijing), Beijing 102249, China
Interests: mechanism analysis and data-driven fault diagnosis; modeling; control and optimization of complex industrial processes
Special Issues, Collections and Topics in MDPI journals
Dr. Ziming Feng

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325000, China
Interests: oilfield production optimization and diagnosis technology based on big data and artificial intelligence; innovative design for multiphase flow; special function impeller pumps in fluid machinery; sealing mechanism and sealing technology of special mechanical equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Liu

E-Mail Website
Guest Editor
College of Information Science and Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Interests: stability analysis of high-penetration new energy power systems; intelligent regulation of flexible adjustable resources; optimized integration of oil and gas exploration with new energy

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has become a core enabling technology for the intelligent transformation of petroleum engineering. By combining machine learning, big data analytics, digital twins and intelligent optimization, AI supports the efficient, safe and low-carbon development of oil and gas resources. The main research contents include:

  1. Intelligent Exploration & Geophysical Analysis AI-based automated fault and reservoir prediction, lithology and physical property inversion, and high-precision identification of hydrocarbon reservoirs.
  2. Intelligent Drilling EngineeringReal-time monitoring and early warning of downhole risks (kick, lost circulation, stuck pipe), automatic well trajectory control, rate of penetration (ROP) optimization, and intelligent drilling fluid design.
  3. AI-driven Reservoir EngineeringFast reservoir simulation, dynamic production prediction, intelligent optimization of development plans, and data-driven enhanced oil recovery (EOR) strategy design.
  4. Intelligent Production OptimizationReal-time production data analysis, productivity prediction, intelligent control of artificial lift systems, and parametric optimization of hydraulic fracturing and stimulation.
  5. Intelligent Gathering & TransportationPipeline leak detection and location, flow assurance prediction, energy consumption optimization of surface facilities, and intelligent operation of gathering systems.
  6. Equipment Health & Safety Risk ControlAI-based equipment fault diagnosis and remaining useful life prediction, real-time safety risk early warning, and intelligent HSE management.
  7. Digital Twin & Smart OilfieldConstruction of full-cycle digital twin models for wells and reservoirs, unmanned production platforms, and integrated intelligent decision-making for the entire upstream industrial chain.
  8. Low-carbon & Sustainable DevelopmentAI-assisted optimization of carbon capture, utilization and storage (CCUS), energy efficiency improvement, and intelligent decision-making for green and low-carbon oilfield development.

Prof. Dr. Chaodong Tan
Dr. Kang Li
Prof. Dr. Xiaoyong Gao
Dr. Ziming Feng
Dr. Bin Liu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes 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 2400 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

  • artificial intelligence algorithm
  • unconventional reservoir
  • engineering integration
  • shale reservoirs
  • petroleum

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Published Papers (4 papers)

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Research

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19 pages, 4320 KB  
Article
A Method for Predicting Three Formation Pressures in Ultra-Deep Wells in the Southern Margin Based on Well–Seismic Data Fusion and Machine Learning
by Jiangang Shi, Wenhui Dang, Wei Zhang, Hong Huang, Yuyuan Hu and Yuqiang Xu
Processes 2026, 14(11), 1687; https://doi.org/10.3390/pr14111687 - 22 May 2026
Viewed by 339
Abstract
Aiming at the challenges faced by ultra-deep wells in the southern margin of the Junggar Basin, such as strong uncertainty in formation information, difficulties in accurate prediction of three formation pressures, narrow safe density windows, and prominent downhole risks, this paper establishes a [...] Read more.
Aiming at the challenges faced by ultra-deep wells in the southern margin of the Junggar Basin, such as strong uncertainty in formation information, difficulties in accurate prediction of three formation pressures, narrow safe density windows, and prominent downhole risks, this paper establishes a method for predicting three formation pressures with confidence levels and quantitatively evaluating drilling risks based on well–seismic fused data. Firstly, rock mechanics and in situ stress calculation models are optimized to characterize the strong uncertainty of deep formations. Then, high-precision well–seismic fused interval velocity is generated through multi-source data preprocessing, well–seismic data fusion, and construction of the MLP-TFT-MCMC hybrid model. On this basis, three formation pressures are calculated, and a pressure profile with confidence levels is constructed using an improved t-distribution. Verified by a field well case, the predicted results are consistent with actual data, providing technical support for the safe and efficient drilling of subsequent ultra-deep wells. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Oil and Gas Engineering)
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24 pages, 1912 KB  
Article
A Data-Efficient Surrogate Model via Simplified Feature Extraction and Pre-Training for Automatic History Matching
by Yisen Qin, Huayu Li, Xiangling Meng, Xiao He, Jinding Zhang and Haijun Zhang
Processes 2026, 14(10), 1635; https://doi.org/10.3390/pr14101635 - 19 May 2026
Viewed by 407
Abstract
Automatic history matching traditionally depends on a large number of time-consuming numerical simulations, which makes the overall workflow computationally expensive. Deep learning-based surrogate models provide an efficient alternative, but their predictive performance often relies on large labeled datasets, whose generation through reservoir simulation [...] Read more.
Automatic history matching traditionally depends on a large number of time-consuming numerical simulations, which makes the overall workflow computationally expensive. Deep learning-based surrogate models provide an efficient alternative, but their predictive performance often relies on large labeled datasets, whose generation through reservoir simulation remains costly. To alleviate this issue, we propose a data-efficient surrogate modeling framework for automatic history matching. The framework consists of two components. First, the reservoir parameter field is reformulated as a flattened representation and processed using one-dimensional convolutions. This representation provides a direct connection between parameter encoding and production-sequence prediction while maintaining competitive forecasting accuracy. Second, an autoencoder is pre-trained on unlabeled parameter realizations, and the learned encoder is then used to initialize the surrogate model for supervised regression, thereby improving the utilization of inexpensive, unlabeled data. The proposed framework is evaluated on the three-dimensional Brugge benchmark reservoir model. Results show that the one-dimensional representation achieves competitive predictive accuracy with shorter training time. In addition, the pre-training strategy is particularly beneficial when labeled simulation data are limited. Overall, the proposed framework improves the data efficiency of surrogate-assisted automatic history matching and reduces the dependence on extensive labeled simulations in the Brugge benchmark. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Oil and Gas Engineering)
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13 pages, 1311 KB  
Article
Method for Formation Pore Pressure Prediction Based on Heterogeneous Transfer Learning
by Wenhui Dang, Yingjie Wang, Zhen Zhong, Xin Wang, Hao Chen, Yuqiang Xu, Lei Yang and Hailong He
Processes 2026, 14(8), 1280; https://doi.org/10.3390/pr14081280 - 17 Apr 2026
Viewed by 424
Abstract
Accurate prediction of formation pore pressure is of great significance for drilling safety, the efficient development of oil and gas resources, and engineering risk control. Traditional methods based on empirical parameters or mechanical models are difficult to fully adapt to complex geological conditions. [...] Read more.
Accurate prediction of formation pore pressure is of great significance for drilling safety, the efficient development of oil and gas resources, and engineering risk control. Traditional methods based on empirical parameters or mechanical models are difficult to fully adapt to complex geological conditions. Although intelligent models have strong nonlinear modeling capabilities, they are highly dependent on large-scale and high-quality training data, and tend to suffer from poor generalization ability and insufficient adaptability in blocks with limited samples or significant differences in geological characteristics. To improve the adaptability of the model between different blocks, this study introduces a heterogeneous transfer learning method to construct a formation pore pressure prediction model suitable for scenarios with inconsistent feature spaces. This method can effectively transfer knowledge from the source domain to the target domain, alleviating the prediction difficulties caused by differences in data distribution. Experimental results show that the proposed method still maintains excellent prediction accuracy and stability under the conditions of limited training samples and complex geological conditions, and has better generalization ability and cross-block applicability compared with traditional models. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Oil and Gas Engineering)
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Review

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24 pages, 10878 KB  
Review
Artificial Intelligence for Hydraulic-Fracturing Decision Support: A Workflow-Oriented Critical Review
by Xiaobing Bian, Jiaxing Zhou, Liang Fu, Aoran Jin and Wei Zhang
Processes 2026, 14(16), 2537; https://doi.org/10.3390/pr14162537 - 7 Aug 2026
Abstract
Hydraulic fracturing is a critical technology for unconventional oil and gas development, but its performance is strongly affected by geological heterogeneity, complex fracture propagation, operational uncertainty, and nonlinear interactions among engineering parameters. Artificial intelligence (AI) provides tools for extracting relationships from geological, geophysical, [...] Read more.
Hydraulic fracturing is a critical technology for unconventional oil and gas development, but its performance is strongly affected by geological heterogeneity, complex fracture propagation, operational uncertainty, and nonlinear interactions among engineering parameters. Artificial intelligence (AI) provides tools for extracting relationships from geological, geophysical, operational, and production data. This structured narrative review synthesizes AI applications across four sequential stages of the hydraulic-fracturing workflow: sweet-spot identification, fracturing-parameter optimization, operational diagnosis and risk warning, and post-fracturing flowback prediction and control. Representative studies reported sweet-spot classification accuracy of 97.5% and R2 = 0.97 for production-performance prediction; a simulator-coupled optimization study reported a 13% economic improvement, and field-data models used cohorts of up to 295 wells. Operational studies reported point-event recognition above 97%, pressure forecasting 30 s ahead, and risk forecasts over three consecutive 60 s intervals. A post-fracturing model trained on 286 wells predicted responses over 30-, 90-, 180-, and 360-day horizons. These values are study-specific and are not directly comparable because the datasets, targets, partitions, and metrics differ. Collectively, the evidence indicates measurable but uneven progress; field readiness remains limited by data quality, multimodal alignment, physical consistency, uncertainty quantification, external validation, and weak coupling between model outputs and operational decisions. The review contributes a reproducible workflow-oriented coding framework and defines validation and deployment priorities for reliable, interpretable, and executable AI-assisted fracturing decision support. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Oil and Gas Engineering)
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