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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: 20 May 2026 | Viewed by 524

Special Issue 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-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

  • 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 (1 paper)

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Research

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 396
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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