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Advances in Improving Efficiency, Decarbonization, Modeling and Intelligent Operations of...
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Advances in Improving Efficiency, Decarbonization, Modeling and Intelligent Operations of Modern Oilfield Development

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3258

Editors

Dr. Xiang Wang

E-Mail Website
Guest Editor
School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China
Interests: big data and artificial intelligence in petroleum engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Xianmin Zhang

E-Mail Website
Guest Editor
School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Interests: reservoir engineering and simulation; theory and technology for unconventional resource development

Special Issue Information

Dear Colleagues,

This Special Issue focuses on advancing the efficiency, sustainability, and intelligence of oilfield development in the era of energy transition, where mature reservoirs and unconventional oil and gas fields require innovative recovery strategies and digital capabilities. It emphasizes several foundational technology domains spanning multiple disciplines, including high-fidelity reservoir modeling and numerical simulation for predictive decision-making, chemical enhanced oil recovery (EOR) methods to mobilize residual oil, and digital transformation initiatives that enable real-time monitoring, automation, and data-driven field operations.

This Special Issue explores cutting-edge developments across these domains, including multiphase flow simulation in heterogeneous media, novel formulations and interfacial engineering for chemical flooding, and the deployment of digital oilfield architectures. Contributions may address one or more of these areas independently, reflecting their distinct yet complementary roles in extending field life, improving recovery factors, reducing operational uncertainty, and supporting decarbonization pathways across conventional and unconventional resources.

We particularly encourage submissions that advance core methodologies and field applications in the following areas: physics-based and data-enhanced reservoir simulation; design, optimization, and economic evaluation of chemical EOR projects; and digital oilfield enablers such as real-time production surveillance, reservoir modeling based on artificial intelligence, automated well control, digital twins for asset management, and cybersecurity in operational technology systems—all aimed at building more responsive, efficient, and resilient oil recovery operations aligned with global decarbonization goals and intelligent energy system transitions.

Dr. Xiang Wang
Dr. Xianmin Zhang
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

  • reservoir modeling and numerical simulation
  • chemical enhanced oil recovery (chemical EOR)
  • digital oilfield and digital transformation
  • multiphase flow in porous media
  • real-time reservoir monitoring and control
  • mature field revitalization
  • cybersecurity in operational technology systems

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Published Papers (4 papers)

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Research

30 pages, 6907 KB  
Article
A Refined Numerical Simulation Method for Amine-Ether Gemini Surfactant Emulsion Flooding
by Gaowen Liu, Qianli Shang, Zhenqiang Mao, Yuhai Sun, Cong Wang, Huimin Qu and Qihong Feng
Processes 2026, 14(10), 1594; https://doi.org/10.3390/pr14101594 - 14 May 2026
Viewed by 325
Abstract
The physicochemical mechanisms and numerical characterization of amine-ether gemini surfactant emulsion flooding remain insufficient, limiting its field application in low-permeability reservoirs. This study developed a refined numerical simulation method that integrates full-process emulsion kinetics, including generation, coalescence, dispersion-assisted oil displacement, and demulsification, with [...] Read more.
The physicochemical mechanisms and numerical characterization of amine-ether gemini surfactant emulsion flooding remain insufficient, limiting its field application in low-permeability reservoirs. This study developed a refined numerical simulation method that integrates full-process emulsion kinetics, including generation, coalescence, dispersion-assisted oil displacement, and demulsification, with graded emulsion characterization using the differentiated inaccessible pore volume (IPV) and residual resistance factor (RRF). Core-flooding validation demonstrated that the model accurately reproduced the key dynamic responses of water cut reduction and oil production increase, with a relative error of about 3.0%. Mechanistic analysis showed that the enhanced oil recovery performance arose from the combined effects of ultralow interfacial tension and emulsion-induced profile control. Relative to conventional surfactant flooding, emulsion flooding increased oil recovery by an additional 4.8–5.0% and lowered water cut by about 12 percentage points. For the Shengli Oilfield pilot block, the optimized injection design involved a surfactant concentration of 1.2 wt.%, an injection rate of 60 m3/d, a slug size of 0.01 PV, an injection–production ratio of 0.95, and a stepwise concentration-decline strategy. The field pilot further confirmed the applicability of the method: daily oil production of the well group increased by 46.5%, while comprehensive water cut decreased by 8.6 percentage points. These results demonstrate the value of the proposed method for both mechanistic characterization and field design of amine-ether gemini surfactant emulsion flooding in heterogeneous low-permeability reservoirs. Full article
(This article belongs to the Special Issue Advances in Improving Efficiency, Decarbonization, Modeling and Intelligent Operations of Modern Oilfield Development)
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16 pages, 6135 KB  
Article
Interlayer Identification Method Based on SMOTE and Ensemble Learning
by Shengqiang Luo, Bing Yu, Tianrui Zhang, Junqing Rong, Qing Zeng, Tingting Feng and Jianpeng Zhao
Processes 2026, 14(2), 351; https://doi.org/10.3390/pr14020351 - 19 Jan 2026
Viewed by 387
Abstract
The interlayer is a key geological factor that regulates reservoir heterogeneity and remaining oil distribution, and its accurate identification directly affects the reservoir development effect. To address the strong subjectivity of traditional identification methods and the insufficient recognition accuracy of single machine learning [...] Read more.
The interlayer is a key geological factor that regulates reservoir heterogeneity and remaining oil distribution, and its accurate identification directly affects the reservoir development effect. To address the strong subjectivity of traditional identification methods and the insufficient recognition accuracy of single machine learning models under imbalanced sample distributions, this study focuses on three types of interlayers (argillaceous, calcareous, and petrophysical interlayers) in the W Oilfield, and proposes an accurate identification method integrating the Synthetic Minority Over-Sampling Technique (SMOTE) and heterogeneous ensemble learning. Firstly, the corresponding data set of interlayer type and logging response is established. After eliminating the influence of dimension using normalization, the sensitive logging curves are optimized using the crossplot method, mutual information, and effect analysis. SMOTE technology is used to balance the sample distribution and solve the problem of the identification deviation of minority interlayers. Then, a heterogeneous ensemble model composed of the k-nearest neighbor algorithm (KNN), decision tree (DT), and support vector machine (SVM) is constructed, and the final recognition result is output using a voting strategy. The experiments show that SMOTE technology improves the average accuracy of a single model by 3.9% and effectively improves the model bias caused by sample imbalance. The heterogeneous integration model improves the overall recognition accuracy to 92.6%, significantly enhances the ability to distinguish argillaceous and petrophysical interlayers, and optimizes the F1-Score simultaneously. This method features a high accuracy and reliable performance, providing robust support for interlayer identification in reservoir geological modeling and remaining oil potential tapping, and demonstrating prominent practical application value. Full article
(This article belongs to the Special Issue Advances in Improving Efficiency, Decarbonization, Modeling and Intelligent Operations of Modern Oilfield Development)
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24 pages, 3622 KB  
Article
Deep Learning-Based Intelligent Monitoring of Petroleum Infrastructure Using High-Resolution Remote Sensing Imagery
by Nannan Zhang, Hang Zhao, Pengxu Jing, Yan Gao, Song Liu, Jinli Shen, Shanhong Huang, Qihong Zeng, Yang Liu and Miaofen Huang
Processes 2026, 14(1), 28; https://doi.org/10.3390/pr14010028 - 20 Dec 2025
Viewed by 955
Abstract
The rapid advancement of high-resolution remote sensing technology has significantly expanded observational capabilities in the oil and gas sector, enabling more precise identification of petroleum infrastructure. Remote sensing now plays a critical role in providing real-time, continuous monitoring. Manual interpretation remains the predominant [...] Read more.
The rapid advancement of high-resolution remote sensing technology has significantly expanded observational capabilities in the oil and gas sector, enabling more precise identification of petroleum infrastructure. Remote sensing now plays a critical role in providing real-time, continuous monitoring. Manual interpretation remains the predominant approach, yet is plagued by multiple limitations. To overcome the limitations of manual interpretation in large-scale monitoring of upstream petroleum assets, this study develops an end-to-end, deep learning-driven framework for intelligent extraction of key oilfield targets from high-resolution remote sensing imagery. Specific aims are as follows: (1) To leverage temporal diversity in imagery to construct a representative training dataset. (2) To automate multi-class detection of well sites, production discharge pools, and storage facilities with high precision. This study proposes an intelligent monitoring framework based on deep learning for the automatic extraction of petroleum-related features from high-resolution remote sensing imagery. Leveraging the temporal richness of multi-temporal satellite data, a geolocation-based sampling strategy was adopted to construct a dedicated petroleum remote sensing dataset. The dataset comprises over 8000 images and more than 30,000 annotated targets across three key classes: well pads, production ponds, and storage facilities. Four state-of-the-art object detection models were evaluated—two-stage frameworks (Faster R-CNN, Mask R-CNN) and single-stage algorithms (YOLOv3, YOLOv4)—with the integration of transfer learning to improve accuracy, generalization, and robustness. Experimental results demonstrate that two-stage detectors significantly outperform their single-stage counterparts in terms of mean Average Precision (mAP). Specifically, the Mask R-CNN model, enhanced through transfer learning, achieved an mAP of 89.2% across all classes, exceeding the best-performing single-stage model (YOLOv4) by 11 percentage points. This performance gap highlights the trade-off between speed and accuracy inherent in single-shot detection models, which prioritize real-time inference at the expense of precision. Additionally, comparative analysis among similar architectures confirmed that newer versions (e.g., YOLOv4 over YOLOv3) and the incorporation of transfer learning consistently yield accuracy improvements of 2–4%, underscoring its effectiveness in remote sensing applications. Three oilfield areas were selected for practical application. The results indicate that the constructed model can automatically extract multiple target categories simultaneously, with average detection accuracies of 84% for well sites and 77% for production ponds. For multi-class targets over 100 square kilometers, manual detection previously required one day but now takes only one hour. Full article
(This article belongs to the Special Issue Advances in Improving Efficiency, Decarbonization, Modeling and Intelligent Operations of Modern Oilfield Development)
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33 pages, 7356 KB  
Article
Data-Driven Sidetrack Well Placement Optimization
by Xiang Wang, Ming Li, Cheng Rui, Qi Guo, Yuhao Zhuang, Wenjie Yu and Tingting Zhang
Processes 2025, 13(11), 3756; https://doi.org/10.3390/pr13113756 - 20 Nov 2025
Viewed by 1109
Abstract
Sidetracking technology has become a relatively mature approach for redeveloping mature fields and restoring the productivity of old wells. However, the design of conventional sidetracking projects has largely relied on expert experience or numerical simulation, methods that are often time-consuming, labor-intensive, and subjective. [...] Read more.
Sidetracking technology has become a relatively mature approach for redeveloping mature fields and restoring the productivity of old wells. However, the design of conventional sidetracking projects has largely relied on expert experience or numerical simulation, methods that are often time-consuming, labor-intensive, and subjective. To overcome these limitations, this study proposes a data-driven optimization framework for sidetrack well placement. It utilizes machine learning techniques trained on a large-scale synthetic dataset generated from field-informed numerical simulations, to establish a robust machine-learning proxy model. Four predictive models—Linear Regression, Polynomial Regression, Random Forest, and a Backpropagation (BP) Neural Network—were systematically compared, among which the Random Forest model achieved the best predictive accuracy. After hyperparameter optimization, a robust prediction model for sidetracking performance was established, achieving a Mean Squared Error (MSE) of 0.0008 (Root Mean Squared Error, RMSE, of 0.0283) and an R2 of 0.8059 on the test set. To further optimize well placement, a mathematical model was formulated with the objective of maximizing the production enhancement rate. Three optimization algorithms—the Multi-Level Coordinate Search (MCS), Differential Evolution (DE), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES)—were evaluated, with the DE algorithm demonstrating superior performance. By integrating the optimized Random Forest predictor with the DE optimizer, a systematic methodology for sidetrack well placement optimization was developed. A field case study validated the approach, showing significant improvements, including a reduced water cut and an incremental cumulative oil production of 82.7 tons. This research demonstrates the simulation-based feasibility of intelligent sidetrack well placement optimization and provides practical guidance for future sidetracking development strategies. Full article
(This article belongs to the Special Issue Advances in Improving Efficiency, Decarbonization, Modeling and Intelligent Operations of Modern Oilfield Development)
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