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2nd Edition of Artificial Intelligent Techniques in the Optimal Operation...
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2nd Edition of Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems

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

Deadline for manuscript submissions: 25 August 2025 | Viewed by 1172

Special Issue Editors

Prof. Dr. Guoqing Han

E-Mail Website
Guest Editor
Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Interests: artificial lift; multiphase flow; gas lift; productivity; complex well
Special Issues, Collections and Topics in MDPI journals
Dr. Xingyuan Liang

E-Mail Website
Guest Editor
Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Interests: gas lift; multiphase flow in wellbores; plunger lift; imbibition
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaojun Wu

E-Mail Website
Guest Editor
School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China
Interests: shale oil and gas; carbon dioxide; mass transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first edition of this Special Issue, entitled “Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems”, collected 22 insightful papers that attracted more than 26,254 views. Due to the significant success of the first volume and the considerable interest in this topic, we propose a second edition of this Special Issue, entitled “2nd Edition of Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems”.

In the later stages of gas well production, liquid loading is a crucial problem in terms of reducing gas production. Thus, we focus on methods that can be employed to perform liquid unloading in gas wells and promote the development of liquid unloading technology. Artificial intelligence is also extensively utilized in petroleum engineering, especially in the stages of oil and gas production. We therefore welcome the submission of original research papers whose scope includes, but is not limited to, the following topics:

  • Liquid unloading in gas wells;
  • Multiphase flow in wellbores;
  • New methods or technologies for artificial lifts;
  • Artificial intelligence in the oil and gas production stages;
  • New methods to enhance oil and gas production.

Prof. Dr. Guoqing Han
Dr. Xingyuan Liang
Dr. Xiaojun Wu
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

  • liquid loading
  • artificial lift
  • multiphase flow
  • gas lift
  • plunger lift
  • artificial intelligence
  • oil production

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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

21 pages, 4816 KiB  
Article
Design and Adaptability Analysis of Integrated Pressurization–Gas Lifting Multifunctional Compressor for Enhanced Shale Gas Production Flexibility
by Kunyi Wu, Lin Qu, Jun Zhou, Yan He, Yu Wu, Zonghang Zhou, Can Qin, Longyu Chen and Chenqian Zhang
Processes 2025, 13(4), 1233; https://doi.org/10.3390/pr13041233 - 18 Apr 2025
Viewed by 149
Abstract
Shale gas development has made significant contributions to the increase in natural gas production capacity in recent years, particularly in promoting the transformation of the energy structure and enhancing energy autonomy. However, with the deepening of shale gas field exploitation, particularly in the [...] Read more.
Shale gas development has made significant contributions to the increase in natural gas production capacity in recent years, particularly in promoting the transformation of the energy structure and enhancing energy autonomy. However, with the deepening of shale gas field exploitation, particularly in the later stages of development, low-pressure gas wells and liquid accumulation issues have become increasingly apparent, posing significant challenges to the normal production of gas wells. Traditional single gas lifting and pressurization techniques have disadvantages such as high equipment investment, high operating costs, and inflexibility in switching, which make it difficult to meet the long-term and stable production needs of shale gas fields. Therefore, to overcome these challenges, this study proposes an innovative integrated pressurization–gas lifting multifunctional compressor process, which achieves the “pressurization ↔ gas lifting ↔ pressurization–gas lifting synergy” multi-mode intelligent switching function through modular integration design, resulting in higher production flexibility and efficiency. Adaptability assessments were completed on two typical shale gas platforms, and field test results show that the equipment can achieve stable production increases across all three functional modes. The pressurization mode demonstrates good adaptability in gas processing, efficiently pressurizing and transporting natural gas produced from the platform’s wells, meeting the increasing demand for gas export. The gas lifting function of the equipment can effectively address gas wells affected by wellbore or bottom-hole liquid accumulation, improving production conditions. In the synergy mode, the equipment design enables the effective collaboration of pressurization and gas lifting functions. Driven by the same power source, the two functional modules work efficiently together, adapting to complex production conditions where both gas lifting and pressurization for gas export occur simultaneously. The innovative process paradigm developed by this study provides an engineering solution for the entire lifecycle of shale gas field development, characterized by equipment integration and intelligent operation, offering significant economic benefits and promotional value. Full article
(This article belongs to the Special Issue 2nd Edition of Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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20 pages, 5045 KiB  
Article
Sand Screenout Early Warning Models Based on Combinatorial Neural Network and Physical Models
by Yanwei Sun, Qingyou Liu, Feng Zhu and Lefan Zhang
Processes 2025, 13(4), 1018; https://doi.org/10.3390/pr13041018 - 28 Mar 2025
Viewed by 204
Abstract
Sand screenout is a critical challenge in hydraulic fracturing, affecting both the construction process and operational safety. This paper proposes a sand screenout warning model that integrates a combinatorial neural network and physical approaches to enhance both the speed and accuracy of sand [...] Read more.
Sand screenout is a critical challenge in hydraulic fracturing, affecting both the construction process and operational safety. This paper proposes a sand screenout warning model that integrates a combinatorial neural network and physical approaches to enhance both the speed and accuracy of sand screenout warnings. Firstly, the combined neural network uses a Transformer to capture key features during fracturing construction from historical data, and the extracted features are input to the Gated Recurrent Unit (GRU) for temporal prediction and the Crested Porcupine Optimizer (CPO) to further optimise the GRU-Transformer hyperparameters of the model. Additionally, the physical model improves the conventional inverse slope method by incorporating a threshold and sliding module, which enhances slope calculation and warning accuracy. The results showed that for fracturing pressure prediction, the proposed CPO-GRU-Transformer model obtained an RMSE value of 0.842 MPa, MAE of 0.613 Mpa, and R2 of 0.971, a smaller RMSE and MAE and a larger R2 than the three pressure prediction models, namely LSTM, GRU, and CPO-GRU. The proposed sand screenout warning model has been applied in the field construction of the U shale gas area in the Sichuan Basin. The warning points of the model proposed in this study were advanced by 73.5 s on average compared with the manual warning points in the three validated fracturing segments, with a successful warning rate of 85.71%, which greatly avoids the possibility of sand screenout and provides a method of fast calculation speed and high prediction accuracy, providing an early warning of sand screenout. Full article
(This article belongs to the Special Issue 2nd Edition of Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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14 pages, 3590 KiB  
Article
Dynamic Load-Optimized Selection Charts for Flexible Ultra-Long Stroke Pumping Units in Low-Yield Oil Wells
by Jinsong Yao, Guoqing Han, Jiaqi Gao, Yao Yang and Mengyu Wang
Processes 2025, 13(2), 482; https://doi.org/10.3390/pr13020482 - 10 Feb 2025
Viewed by 626
Abstract
Flexible ultra-long stroke pumping units (FULSPUs) are widely adopted in low-yield oil wells due to their structural simplicity and high operational efficiency. However, current equipment selection methods lack precision, leading to mismatched configurations, low utilization rates, and unnecessary costs. To address this challenge, [...] Read more.
Flexible ultra-long stroke pumping units (FULSPUs) are widely adopted in low-yield oil wells due to their structural simplicity and high operational efficiency. However, current equipment selection methods lack precision, leading to mismatched configurations, low utilization rates, and unnecessary costs. To address this challenge, this study develops a systematic optimization framework integrating motion dynamics analysis and empirical data. First, a simplified formula for peak polished rod load (PPRL) is concluded by analyzing the unit’s stable motion characteristics. Second, a multi-parameter selection method incorporating stroke length, frequency, pump efficiency, and dynamic liquid level constraints is developed. This method generates interactive selection charts that map maximum liquid production across varying pumping depths, providing a rapid decision-making tool for optimal equipment pairing. A double-layer circle visualization that quantifies equipment utilization by linking pumping unit load and pump load, offering actionable insights for cost-effective upgrades. The model is validated through a field case, where overdesign risks are reduced. Significantly, this work replaces traditional beam-pump selection models with a tailored solution for flexible FULSPUs, delivering two major contributions: (1) a standardized workflow balancing technical feasibility and economic efficiency and (2) a visual tool that when adopted in the oilfield, the efficiency and applicability of equipment selection are improved. These advancements establish a transformative framework for sustainable resource management in mature low-permeability reservoirs. Full article
(This article belongs to the Special Issue 2nd Edition of Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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