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Contribution of Artificial Intelligence/Big Data to Reservoir Engineering and Reservoir...
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Contribution of Artificial Intelligence/Big Data to Reservoir Engineering and Reservoir Modeling

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 3513

Special Issue Editors

Dr. Ming Yue

E-Mail Website
Guest Editor
School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: reservoir simulation; machine learning; seepage mechanics; underground energy storage
Dr. Shuhong Wu

E-Mail Website
Guest Editor
Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Interests: reservoir simulation; machine learning; seepage mechanics
Dr. Jianchun Xu

E-Mail Website
Guest Editor
School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Interests: unconventional oil and gas development; CCUS; numerical simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ever-growing volume of data in the oil and gas industry presents a unique opportunity to explore the contribution of artificial intelligence (AI) and big data to reservoir engineering and reservoir modeling.

This Special Issue delves into how AI algorithms can leverage vast datasets for predictive analytics and real-time decision making, thus leading to enhanced reservoir characterization, the identification of optimal production zones, unprecedented production forecasting, and rapid model improvement through real-time data analysis.

We seek original research that optimizes reservoir production while fostering sustainable practices, focusing on novel AI techniques, big data integration in workflows, real-time optimization strategies, and the role of AI in balancing resource recovery with long-term reservoir health. This collaboration between data scientists, engineers, and geoscientists aims to pave the way for a new era of data-driven reservoir management.

Dr. Ming Yue
Dr. Shuhong Wu
Dr. Jianchun Xu
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

  • artificial intelligence
  • reservoir engineering
  • reservoir modeling

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Published Papers (4 papers)

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Research

13 pages, 3383 KiB  
Article
AI-Driven Optimization of Drilling Performance Through Torque Management Using Machine Learning and Differential Evolution
by Farouk Said Boukredera, Ahmed Hadjadj, Mohamed Riad Youcefi and Habib Ouadi
Processes 2025, 13(5), 1472; https://doi.org/10.3390/pr13051472 - 12 May 2025
Viewed by 268
Abstract
The rate of penetration (ROP) is the key parameter to enhance drilling processes as it is inversely proportional to the overall cost of drilling operations. Maximizing the ROP without any limitation can induce drilling dysfunctions such as downhole vibrations. These vibrations are the [...] Read more.
The rate of penetration (ROP) is the key parameter to enhance drilling processes as it is inversely proportional to the overall cost of drilling operations. Maximizing the ROP without any limitation can induce drilling dysfunctions such as downhole vibrations. These vibrations are the main reason for bottom hole assembly (BHA) tool failure or excessive wear. This paper aims to maximize the ROP while managing the torque to keep the depth of cut within an acceptable range during the cutting process. To achieve this, machine learning algorithms are applied to build ROP and drilling torque models. Then, a metaheuristic algorithm is used to determine the optimal technical control parameters, the weight on bit (WOB) and revolutions per minute (RPM), that simultaneously enhance the ROP and mitigate excessive vibrations. This paper introduces a new methodology for mitigating drill string vibrations, improving the rate of penetration (ROP), minimizing BHA failures, and reducing drilling costs. Full article
(This article belongs to the Special Issue Contribution of Artificial Intelligence/Big Data to Reservoir Engineering and Reservoir Modeling)
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19 pages, 5881 KiB  
Article
Automated Particle Size and Shape Determination Methods: Application to Proppant Optimization
by Dongjin Xu, Junting Wang, Zhiwen Li, Changheng Li, Yukai Guo, Xuyi Qiao and Yong Wang
Processes 2025, 13(1), 21; https://doi.org/10.3390/pr13010021 - 25 Dec 2024
Cited by 1 | Viewed by 771
Abstract
The performance of proppants is critical to the effectiveness of reservoir hydraulic fracturing. Traditional methods such as sieving and visual inspection are commonly used in proppant production lines, at fracturing sites, and in research institutions to assess particle size and shape. However, these [...] Read more.
The performance of proppants is critical to the effectiveness of reservoir hydraulic fracturing. Traditional methods such as sieving and visual inspection are commonly used in proppant production lines, at fracturing sites, and in research institutions to assess particle size and shape. However, these methods are highly subjective, inefficient, and prone to significant random errors. To address these issues, an automated particle size and shape detection method based on image processing algorithms was developed, leading to the design of a proppant parameter detection system. The system’s detection results on the Krumbein–Sloss chart closely align with standard templates, with a maximum error of only 3%. This method enables precise particle extraction and analysis from images, accurately determining particle size and shape parameters. Comparative experiments conducted on commonly used quartz sand samples in 20/40 mesh, 30/50 mesh, and 40/70 mesh specifications demonstrated that the new method can evaluate the particle size without damaging the particles; the detection process does not create proppant waste, has environmental benefits, and can reduce the cost of professional inspection personnel, with the detection efficiency improved by over 200 times compared to traditional sieving and visual inspection methods, with repeatability errors within 1.9%. This study introduces a novel approach to particle size and shape detection, providing technical references for optimizing proppant selection, enhancing material quality control for hydraulic fracturing, and reducing costs while improving efficiency. Full article
(This article belongs to the Special Issue Contribution of Artificial Intelligence/Big Data to Reservoir Engineering and Reservoir Modeling)
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18 pages, 1421 KiB  
Article
Research on Well Selection Method for Intermittent Pumping in Oil Wells Based on the Analytic Network Process and Fuzzy Logic
by Yanfeng He, Shilin Xu, Xiang Wang, Rongrong Wang and Xianxiang Chu
Processes 2024, 12(11), 2556; https://doi.org/10.3390/pr12112556 - 15 Nov 2024
Viewed by 636
Abstract
In the later stages of oilfield development, the decline in oil well production and the increase in development costs, attributed to issues such as insufficient liquid supply, necessitate the implementation of intermittent pumping measures. However, current methods for selecting these wells lack comprehensiveness [...] Read more.
In the later stages of oilfield development, the decline in oil well production and the increase in development costs, attributed to issues such as insufficient liquid supply, necessitate the implementation of intermittent pumping measures. However, current methods for selecting these wells lack comprehensiveness in the decision-making process. This article proposes a novel method for selecting intermittent pumping wells utilizing the analytic network process (ANP) and fuzzy logic. Initial surveys identified the main factors influencing intermittent pumping effectiveness. The ANP was employed to screen and integrate six core factors, including submergence depth and water cut. Subsequently, a fuzzy-logic-based model was developed, incorporating trapezoidal and rectangular membership functions to establish detailed correlations among the factors. The model’s efficacy was validated and tested using real-world data from the oilfield. Results indicate that the model’s assessments of intermittent pumping wells align closely with professional engineering judgments. This approach not only provides clear guidance for well selection but also demonstrates high scalability and adaptability across different oilfields by adjusting membership functions, thereby showcasing significant practical value. Full article
(This article belongs to the Special Issue Contribution of Artificial Intelligence/Big Data to Reservoir Engineering and Reservoir Modeling)
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21 pages, 4806 KiB  
Article
Sedimentary Facies Identification Technique Based on Multimodal Data Fusion
by Yuchuan Yi, Yuanfu Zhang, Xiaoqin Hou, Junyang Li, Kai Ma, Xiaohan Zhang and Yuxiu Li
Processes 2024, 12(9), 1840; https://doi.org/10.3390/pr12091840 - 29 Aug 2024
Viewed by 1218
Abstract
Identifying sedimentary facies represents a fundamental aspect of oil and gas exploration. In recent years, geologists have employed deep learning methods to develop comprehensive predictions of sedimentary facies. However, their methods are often constrained to some kind of unimodal data, and the practicality [...] Read more.
Identifying sedimentary facies represents a fundamental aspect of oil and gas exploration. In recent years, geologists have employed deep learning methods to develop comprehensive predictions of sedimentary facies. However, their methods are often constrained to some kind of unimodal data, and the practicality and generalizability of the resulting models are relatively limited. Therefore, based on the characteristics of oilfield data with multiple heterogeneous sources and the difficulty of complementary fusion between data, this paper proposes a sedimentary facies identification technique with multimodal data fusion, which uses multimodal data from core wells, including logging, physical properties, textual descriptions, and core images, to comprehensively predict the sedimentary facies by adopting decision-level feature fusion after predicting different unimodal data separately. The method was applied to a total of 12 core wells in the northwestern margin of the Junggar Basin, China; good results were obtained, achieving an accuracy of over 90% on both the validation and test sets. Using this method, the sedimentary microfacies of a newly drilled core well can be predicted and the interpretation of the sedimentary framework in the well area can be updated in real-time based on data from newly drilled core wells, significantly improving the efficiency and accuracy of oil and gas exploration and development. Full article
(This article belongs to the Special Issue Contribution of Artificial Intelligence/Big Data to Reservoir Engineering and Reservoir Modeling)
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