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Processes
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Advances in Reservoir Development and Enhanced Oil Recovery Techniques
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Advances in Reservoir Development and Enhanced Oil Recovery Techniques

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

Deadline for manuscript submissions: 25 June 2025 | Viewed by 1226

Special Issue Editors

Prof. Dr. Zhijie Wen

E-Mail Website
Guest Editor
Mining Colledge, Guizhou University, Guiyang 550025, China
Interests: underground reservoir development; deep resource extraction; geological disaster prevention; intelligent warning system
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Tao

E-Mail Website
Guest Editor
Mining Colledge, Guizhou University, Guiyang 550025, China
Interests: thermo-hydro-mechanical-chemical (THMC) modeling; reservoir simulation; underground seepage analysis; sustainable energy development

Special Issue Information

Dear Colleagues,

The development of oil and gas reservoirs and the enhancement of oil recovery techniques are critical for maximizing the extraction of hydrocarbons, especially as conventional reservoirs decline and energy demands continue to rise. Reservoir development, combined with Enhanced Oil Recovery (EOR) methods such as chemical, thermal, and gas injection, plays a vital role in improving the efficiency of hydrocarbon extraction from mature and unconventional reservoirs. However, these processes present complex challenges related to reservoir characterization, fluid dynamics, and recovery optimization, which require continuous innovation and research. The integration of advanced methods such, as numerical simulations, in-situ monitoring, machine learning, and big data analytics, has significantly enhanced the ability to predict reservoir behavior, optimize recovery techniques, and improve the accuracy of environmental impact assessments. These technological advancements offer new opportunities to improve the accuracy and efficiency of reservoir management and EOR methods, enhancing recovery rates while maintaining operational and environmental sustainability.

This Special Issue on “Advances in Reservoir Development and Enhanced Oil Recovery Techniques” seeks high-quality works focusing on gathering cutting-edge research and development in the fields of reservoir development and EOR techniques, highlighting innovations, field applications, and data-driven approaches that are transforming the oil and gas industry.

Topics include, but are not limited to:

  • Advanced reservoir characterization techniques and modeling;
  • Innovations in chemical, thermal, gas, and microbial EOR methods;
  • Hybrid EOR techniques integrating multiple recovery methods;
  • Field case studies demonstrating the application of EOR techniques;
  • Machine learning and AI-driven approaches in reservoir management and EOR optimization;
  • Environmental sustainability in reservoir development and EOR operations;
  • Real-time data analysis and digital twin applications in reservoir development;
  • New materials and chemicals used in EOR processes;
  • Reservoir monitoring and management technologies.

Prof. Dr. Zhijie Wen
Dr. Jian Tao
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

  • Enhanced Oil Recovery (EOR)
  • reservoir characterization
  • geomechanics in reservoir development
  • hydrocarbon extraction
  • unconventional reservoirs
  • sustainability in oil recovery
  • reservoir modeling and simulation
  • data-driven reservoir management

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

25 pages, 1724 KiB  
Article
Intelligent Classification Method for Tight Sandstone Reservoir Evaluation Based on Optimized Genetic Algorithm and Extreme Gradient Boosting
by Zihao Mu, Chunsheng Li, Zongbao Liu, Tao Liu, Kejia Zhang, Haiwei Mu, Yuchen Yang, Liyuan Liu, Jiacheng Huang and Shiqi Zhang
Processes 2025, 13(5), 1379; https://doi.org/10.3390/pr13051379 (registering DOI) - 30 Apr 2025
Abstract
Reservoir evaluation is essential in oil and gas exploration, influencing development decisions. Traditional classification methods are often limited by small sample sizes and low accuracy, restricting their effectiveness. To address this, we propose an intelligent classification method, GA-XGBoost, which integrates Genetic Algorithm (GA) [...] Read more.
Reservoir evaluation is essential in oil and gas exploration, influencing development decisions. Traditional classification methods are often limited by small sample sizes and low accuracy, restricting their effectiveness. To address this, we propose an intelligent classification method, GA-XGBoost, which integrates Genetic Algorithm (GA) optimization with Extreme Gradient Boosting (XGBoost) to enhance the classification accuracy in small-sample scenarios. The lithological, physical, and lithofacies characteristics of tight sandstone reservoirs are analyzed, and key evaluation parameters—including the mineral composition, porosity, permeability, oil saturation, and logging data (GR, SP, CAL, DEN, AC, LLS)—are selected. After data normalization, the GA-XGBoost model is developed and compared with SVM, XGBoost, and AdaBoost models. The experimental results demonstrate that GA-XGBoost achieves an 88.8% classification precision, outperforming traditional algorithms in both efficiency and accuracy. This study advances experiments on and the standardization of intelligent reservoir evaluations, providing a more reliable classification approach for tight sandstone reservoirs. Additionally, it contributes to the integration of geological exploration and computational intelligence, offering new insights into the application of machine learning in geosciences. Full article
(This article belongs to the Special Issue Advances in Reservoir Development and Enhanced Oil Recovery Techniques)
15 pages, 11658 KiB  
Article
Polymer Flooding Injectivity Maintaining and Enhancement Strategies: A Field Case Study of Chinese Offshore EOR Project
by Chenxi Wang, Jian Zhang, Bo Huang, Hong Du, Xianghai Meng, Xianjie Li, Xinsheng Xue, Yi Su, Chao Li and Haiping Guo
Processes 2025, 13(3), 903; https://doi.org/10.3390/pr13030903 - 19 Mar 2025
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Abstract
Polymer flooding has been gradually applied in Chinese offshore oilfields to enhance oil recovery (EOR). Injectivity loss during polymer flooding is a common issue that could cause lower displacement speed and efficiency, and eventually compromise the polymer flooding result. This paper presents a [...] Read more.
Polymer flooding has been gradually applied in Chinese offshore oilfields to enhance oil recovery (EOR). Injectivity loss during polymer flooding is a common issue that could cause lower displacement speed and efficiency, and eventually compromise the polymer flooding result. This paper presents a case study of a Chinese offshore field where injectivity loss issues were encountered in the polymer flooding project. A series of measures are applied to enhance the injectivity. The injectivity enhancement strategies are proposed and conducted from three main aspects, namely, (1) surface polymer fluid preparation; (2) downhole wellbore stimulation; and (3) reservoir–polymer compatibility, respectively. For the surface polymer fluid preparation, a series of sieve flow tests are conducted to obtain the optimal mesh size to improve the polymer fluid preparation quality and reduce the amount of “fish eyes”. The downhole wellbore stimulations involve oxidization-associated acidizing treatment and re-perforation. Polymer–reservoir compatibility tests are conducted to optimize the molecular weight (MW). Regarding the surface measures, the optimal filtration sieve mesh number is 200, which could reduce fish eyes to a desirable level without causing mesh plugging. After mesh refinement, the average injection pressure of the twelve injection wells decreases by 0.5 MPa. For the downhole stimulations, acidizing treatment are applied to six injection wells, which decreases the injection pressures by 6 to 7 MPa. For Well A, where acidizing does not work, the re-perforation measure is used and enhances the injectivity by 300%. Moreover, the laboratory and field polymer–reservoir compatibility tests show that the optimal polymer molecular weight (MW) is sixteen million. Proposed strategies applied from the surface, downhole, and reservoir aspects could be used to resolve different levels of injectivity loss, which could provide guidance for future offshore polymer projects. Full article
(This article belongs to the Special Issue Advances in Reservoir Development and Enhanced Oil Recovery Techniques)
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13 pages, 3879 KiB  
Article
Preparation and Mechanism of EP-HMTA-SiO2 Nanocomposite Polymer Gel for Enhancing Oil Recovery
by Weiyou Zhang, Yongpeng Sun, Xianghua Meng and Rutong Dou
Processes 2025, 13(3), 596; https://doi.org/10.3390/pr13030596 - 20 Feb 2025
Viewed by 594
Abstract
During oilfield production, organic substances such as asphaltenes and resins present in crude oil are prone to adsorb onto the surfaces of reservoir rocks. This adsorption process can lead to the conversion of hydrophilic rock surfaces into more oleophilic interfaces, which in turn [...] Read more.
During oilfield production, organic substances such as asphaltenes and resins present in crude oil are prone to adsorb onto the surfaces of reservoir rocks. This adsorption process can lead to the conversion of hydrophilic rock surfaces into more oleophilic interfaces, which in turn reduces the permeability and porosity of the reservoir, ultimately affecting the overall recovery efficiency. Consequently, targeted modification of reservoir wettability presents a promising strategy for enhancing recovery rates. In this study, a polymer layer comprising large molecular side chains and hydroxyl groups was synthesized on the surface of nano-SiO2 to enhance the thermal stability of the polymer. Additionally, the hydroxyl groups were employed to improve the wettability of the core material and reduce interfacial tension. The polymers were characterized using techniques such as FTIR and TG, and the results revealed that the modified SiO2 exhibited superior performance in reducing both interfacial tension and contact angle when compared to the SiO2. Injection of a 0.4 wt% EP-HMTA-SiO2 solution resulted in an increase in recovery rates by 16.4% and 13.2% in medium- and low-permeability cores, respectively. Full article
(This article belongs to the Special Issue Advances in Reservoir Development and Enhanced Oil Recovery Techniques)
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