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Journals
Processes
Special Issues
Applications of Intelligent Models in the Petroleum Industry
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Applications of Intelligent Models in the Petroleum Industry

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 704

Special Issue Editors

Dr. Hai Wang

E-Mail Website
Guest Editor
Key Laboratory of Enhanced Oil & Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163318, China
Interests: physics-informed machine learning; unconventional reservoir; gas storage
Dr. Gang Hui

E-Mail Website
Guest Editor
Unconventional Petroleum Research Institute, China University of Petroleum (Beijing), Beijing 102249, China
Interests: machine learning in oil and gas development; unconventional fracture characterization; geological modeling and numerical simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The petroleum industry plays a critical role in the global energy landscape, supplying fuel and raw materials essential for modern economies. With the advent of advanced computing and data-driven technologies, intelligent models are revolutionizing petroleum exploration and production by offering more accurate, efficient, and adaptive solutions to complex problems.

This Special Issue on "Applications of Intelligent Models in the Petroleum Industry" seeks high-quality research contributions that explore the integration of intelligent modeling techniques into various facets of petroleum engineering. The focus is on cutting-edge developments in intelligent models and data analytics that enhance decision-making processes, optimize resource extraction, and improve operational efficiency. Topics of interest include, but are not limited to, the following:

  • Machine learning applications in reservoir characterization, such as permeability and porosity prediction, facies classification, and digital rock reconstruction;
  • AI-driven production forecasting models for conventional and unconventional reservoirs, incorporating time-series analysis, deep learning architectures, and hybrid modeling approaches;
  • Multimodal techniques that integrate tabular data, time-series, and images to enhance subsurface understanding and improve reservoir simulation models;
  • Physics-informed machine learning approaches that couple domain knowledge with data-driven methodologies to enhance model interpretability and generalization;
  • AI-powered real-time monitoring and predictive maintenance systems for drilling, wellbore stability, enhanced oil recovery (EOR), and equipment failure prediction;
  • Addressing challenges such as data scarcity, quality assurance, and uncertainty quantification in intelligent model applications within the petroleum sector.

Dr. Hai Wang
Dr. Gang Hui
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. Processes is an international peer-reviewed open access monthly 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

  • oil and gas
  • digital oilfield
  • big data
  • machine learning
  • petroleum engineering
  • reservoir characterization
  • production forecasting
  • well test analysis
  • multimodal model
  • physics-informed model

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

16 pages, 8383 KiB  
Article
Stratigraphic Correlation of Well Logs Using Geology-Informed Deep Learning Networks
by Zhaohui Xu, Boyu Zheng, Bo Liu and Wendan Song
Processes 2025, 13(5), 1288; https://doi.org/10.3390/pr13051288 - 23 Apr 2025
Viewed by 141
Abstract
Stratigraphic correlation plays a crucial role in reservoir characterization. However, it is often time-consuming and heavily dependent on geological expertise. To address this issue, we propose a novel method called CMT-enhanced Hiformer, which integrates convolutional neural networks meet vision transformers (CMT) and hierarchical [...] Read more.
Stratigraphic correlation plays a crucial role in reservoir characterization. However, it is often time-consuming and heavily dependent on geological expertise. To address this issue, we propose a novel method called CMT-enhanced Hiformer, which integrates convolutional neural networks meet vision transformers (CMT) and hierarchical multi-scale representations using transformers (Hiformer). First, the architecture of CMT-enhanced Hiformer fuses the advantages of convolutional neural networks and transformers, effectively extracting complex features from well logs and capturing both local and global dependencies via a well-designed attention mechanism. Next, a geological constraint with regularization parameters is incorporated into the loss function. The new loss function promotes the accuracy of stratigraphic boundaries. The proposed method was validated using data from the Shuanghe oil field in central China. Specifically, the model achieved a maximum F1 score of 0.8857 and a precision of 0.8865 on the blind test dataset, demonstrating its robustness and high classification accuracy. Moreover, we conducted ablation studies and performed a detailed comparison with state-of-the-art deep learning models. The results demonstrate that the proposed method significantly improves the accuracy and efficiency of stratigraphic correlation. Full article
(This article belongs to the Special Issue Applications of Intelligent Models in the Petroleum Industry)
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23 pages, 11726 KiB  
Article
Integrated Data-Driven Framework for Forecasting Tight Gas Production Based on Machine Learning Algorithms, Feature Selection and Fracturing Optimization
by Fuyu Yao, Gang Hui, Dewei Meng, Chenqi Ge, Ke Zhang, Yili Ren, Ye Li, Yujie Zhang, Xing Yang, Yujie Zhang, Penghu Bao, Zhiyang Pi, Dan Wu and Fei Gu
Processes 2025, 13(4), 1162; https://doi.org/10.3390/pr13041162 - 11 Apr 2025
Viewed by 231
Abstract
A precise assessment of tight gas operational efficiency is critical for investment decisions in unconventional reservoir development. However, quantifying production efficiency remains challenging due to the complex relationships between geological and operational factors. This study proposes a novel data-driven framework for predicting tight [...] Read more.
A precise assessment of tight gas operational efficiency is critical for investment decisions in unconventional reservoir development. However, quantifying production efficiency remains challenging due to the complex relationships between geological and operational factors. This study proposes a novel data-driven framework for predicting tight gas productivity, effectively integrating computing algorithms, machine learning algorithms, feature selection, production prediction and fracturing parameter optimization. A dataset of 3146 horizontal wells from the Montney tight gas field was used to train six machine learning models, aiming to identify the most significant factors. Results indicate that fluid-injection volumes, burial depth, number of stages, Young’s modulus, formation pressure, saturation, sandstone thickness and total organic carbon are the key variables for tight gas production. The Random Forest-based model achieved the highest accuracy of 88.6%. Case studies for the test demonstrate well that gas production could be nearly doubled by increasing fracturing fluid injection by 97.5%. This work provides evidence-based recommendations to refine development strategies and maximize reservoir performance. Full article
(This article belongs to the Special Issue Applications of Intelligent Models in the Petroleum Industry)
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