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

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

Deadline for manuscript submissions: closed (10 April 2026) | Viewed by 12737

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Prof. Dr. Guoqing Han

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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

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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

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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

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Keywords

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

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Published Papers (11 papers)

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Research

17 pages, 22749 KB

Open AccessArticle

Identification and Application of Carbonate Reservoir Based on Bayesian Model

by Bei Wang, Xixiang Liu, Yong Hu, Lianjin Zhang, Ruiduo Zhang, Liang Wang, Xin Dai and Jie Tian

Processes 2026, 14(6), 955; https://doi.org/10.3390/pr14060955 - 17 Mar 2026

Viewed by 416

Abstract

Aiming at the challenges in accurately identifying complex pore-space types, significant scale variations, and overlapping log responses in carbonate reservoirs, this study takes the Jurassic Da’anzhai Member in the central Sichuan Basin as the research object. By integrating core observations, cast thin sections, scanning electron microscopy, and well log data, the genetic types and log response characteristics of pore spaces at different scales are systematically analyzed. Building on this, a multivariate distribution identification model for pore-space scales is established based on Bayesian discriminant theory. To enhance the model’s identification accuracy, Z-score normalization is introduced to eliminate dimensional differences. Nonlinear combined features, such as the ratio of the compensated acoustic log (AC) to the gamma ray log (GR) and the logarithmic difference between deep and shallow resistivity logs (RT and RI), are constructed to achieve a multidimensional coupling representation of reservoir physical properties; a class-balancing augmentation method based on Gaussian perturbation is adopted to mitigate decision bias caused by sample imbalance. The results show that the improved Bayesian model achieves F1 scores exceeding 0.80 for large-, small-, and micro-scale pore spaces, with an overall identification accuracy of 84.38%, significantly outperforming the conventional crossplot method’s accuracy of 59.38%. Validation through experiments and well log data demonstrates that the model’s identification results are consistent with core and thin-section observations, indicating that this method can effectively identify large-, small-, and micro-scale pore spaces in strongly heterogeneous carbonate reservoirs. This study provides a valuable approach for reservoir log interpretation and favorable reservoir prediction. 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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21 pages, 4453 KB

Open AccessArticle

Early Warning of Lost Circulation Based on Physical Models and a Hybrid Neural Network

by Fangfei Huang, Yanwei Sun, Jin Yang, Zhibin Sha, Jingsong Lu and Rongrong Qi

Processes 2026, 14(3), 559; https://doi.org/10.3390/pr14030559 - 5 Feb 2026

Cited by 1 | Viewed by 492

Abstract

Lost Circulation (LC) is one of the most common and high-risk complex situations encountered during drilling operations, posing a serious threat to the safe extraction and economic viability of oil and gas resources. Traditional wellbore leakage detection methods based on human experience often suffer from delays and uncertainties, making it difficult to meet real-time warning requirements under complex geological conditions. This paper proposes an LC warning method that combines a physical model with a combination of neural networks (Crested Porcupine Optimizer (CPO)–Long Short-Term Memory (LSTM)–Random Forest (RF)). The physical model utilises changes in mud pit volume, inlet–outlet flow rate differences, and riser pressure to construct interpretable event labels, thereby enhancing the physical plausibility of prediction results. The deep learning component employs LSTM networks to extract temporal features and RF for non-linear discrimination and introduces the CPO algorithm for feature selection and hyperparameter optimisation, thereby enhancing the model’s stability and generalisation capability. Validation using actual field data from the western Bohai Bay oilfield demonstrates that the proposed method outperforms traditional models in accuracy, precision, recall, and F1-score. It also offers a significant improvement in early warning time, detecting potential leakage about 17 min before traditional methods. These results highlight the effectiveness of the approach in managing risks during drilling operations. 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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18 pages, 2109 KB

Open AccessArticle

Considering the Effects of Temperature on FRP–Steel Hybrid Sucker-Rod String Design

by Xin Lu, Zhisheng Xing, Xingyuan Liang, Zhuangzhuang Zhang, Guoqing Han, Peidong Mai and Shuping Chang

Processes 2026, 14(2), 305; https://doi.org/10.3390/pr14020305 - 15 Jan 2026

Viewed by 515

Abstract

With the continuous increase in well depth and the gradual depletion of formation energy, the pump-setting depths in rod-pumped wells have increased significantly, leading to higher suspension loads at the pumping unit. The application of glass fiber-reinforced plastic (FRP) sucker rods can effectively reduce suspension loads due to their low density and high tensile strength. However, the mechanical performance of FRP rods is highly sensitive to temperature, which poses challenges for their application in deep and high-temperature wells. In FRP–steel hybrid sucker-rod string design, the influence of temperature—particularly on FRP rods—must therefore be carefully considered to prevent failures such as rod parting or coupling separation. This study systematically investigates the effects of temperature on the mechanical properties of FRP sucker rods, including elastic modulus, flexural shear strength, and tensile strength. Based on the operating characteristics of sucker-rod pumping systems and established design criteria, a temperature-aware design methodology for FRP–steel hybrid rod strings is developed and implemented in dedicated design software. The proposed approach enables rational determination of the FRP–steel partition depth under thermal constraints while satisfying mechanical safety requirements. A field case study is conducted to validate the design results, demonstrating that the software provides reliable and practical guidance for hybrid rod-string design in deep wells. 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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22 pages, 5193 KB

Open AccessArticle

An Intelligent Directional Drill Steering Method Based on Real-Time Adaptive Closed-Loop Control

by Yan Sun, Kun Shao, Zhaojun Wang, Yongtao Fan and Dong Chen

Processes 2025, 13(12), 3798; https://doi.org/10.3390/pr13123798 - 25 Nov 2025

Cited by 1 | Viewed by 968

Abstract

Drilling trajectory closed-loop control in directional drilling is a key technology for achieving high-precision drilling. However, due to the complex geological conditions, and engineering limitations of drilling tools, traditional control methods of drilling often face challenges, such as error accumulation, response delays, and control instability. To address these issues, this paper proposes an intelligent closed-loop steering method based on online adaptive optimization. The core of this method lies in the construction of an integrated “perception–optimization–execution” intelligent steering framework. First, real-time attitude feedback is used to accurately perceive trajectory deviations. Then, an optimization model is triggered, aiming to minimize deviations under the dogleg severity constraint, and genetic algorithms are employed to dynamically calibrate the PID controller online, effectively eliminating error accumulation. Finally, based on the optimization results, real-time calculations of tool face angle and steering tool force are performed to ensure precise execution of steering commands. Simulation results show that, compared to the traditional PID and PID-APF methods, the proposed method demonstrates significant advantages in trajectory control accuracy and wellbore quality. Under noise-free conditions, the normal distance accuracy improves by 88.89% and 34.02%, respectively, and dogleg severity is reduced by 6.30% and 5.81%. Under noise interference, the normal distance accuracy improves by 56.73% and 54.97%, respectively, and dogleg severity is reduced by 23.38% and 4.85%. In conclusion, the proposed intelligent closed-loop control method not only significantly enhances the system’s real-time response capability and control precision but also exhibits stronger robustness, with broad potential for engineering applications. 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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21 pages, 1443 KB

Open AccessArticle

From Forecasting to Prevention: Operationalizing Spatiotemporal Risk Decoupling in Gas Pipelines via Integrated Time-Series and Pattern Mining

by Shengli Liu

Processes 2025, 13(11), 3589; https://doi.org/10.3390/pr13113589 - 6 Nov 2025

Viewed by 997

Abstract

Accurate prediction of gas pipeline incidents through risk factor interdependencies is critical for proactive safety management. This study develops a hybrid SARIMA–association rule mining (ARM) framework integrating time-series forecasting with causal pattern decoding, using 60-month U.S. pipeline incident records (2010–2024) from the Pipeline and Hazardous Materials Safety Administration (PHMSA) database, covering leaks, mechanical punctures, and ruptures. Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling with six-month rolling-window validation achieves precise leak forecasts (MAPE = 14.13%, MASE = 0.27) and reasonable mechanical damage predictions (MAPE = 31.21%, MASE = 1.15), while ruptures exhibit pronounced stochasticity. Crucially, SARIMA incident probabilities feed Apriori-based ARM, revealing three failure-specific mechanisms: (1) ruptures predominantly originate from natural force damage, with underground cases causing economic losses (lift = 3.70) and aboveground class 3 incidents exhibiting winter daytime ignition risks (lift = 2.37); (2) leaks correlate with equipment degradation, where outdoor meter assemblies account for 69.7% of fire-triggering cases (108/155 incidents) and corrosion dominates >50-year-old pipelines; (3) mechanical punctures cluster in pipelines <20 years during spring excavation, predominantly occurring in class 2 zones due to heightened construction activity. These findings necessitate cause-specific maintenance protocols that integrate material degradation laws and dynamic failure patterns, providing a decision framework for pipe replacement prioritization and seasonal monitoring in high-risk zones. 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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21 pages, 7455 KB

Open AccessArticle

A Method for Predicting Gas Well Productivity in Non-Dominant Multi-Layer Tight Sandstone Reservoirs of the Sulige Gas Field Based on Multi-Task Learning

by Dawei Liu, Shiqing Cheng, Han Wang and Yang Wang

Processes 2025, 13(8), 2666; https://doi.org/10.3390/pr13082666 - 21 Aug 2025

Cited by 1 | Viewed by 1120

Abstract

This study proposes a multi-task learning-based production capacity prediction model aimed at improving the prediction accuracy for gas wells in multi-layer tight sandstone reservoirs of the Sulige gas field under small-sample conditions. The model integrates mutation theory and progressive hierarchical feature extraction to achieve adaptive nonlinear feature extraction and autonomous feature selection tailored to different prediction tasks. Using the daily average production of each gas-bearing layer during the first month after well commencement and the cumulative production of each gas-bearing layer over the first year as targets, the model was applied to predict the production capacity of 66 gas wells. Compared with single-task models and classical machine learning methods, the proposed multi-task model significantly improves prediction accuracy, reducing the root mean squared error (RMSE) by over 40% and increasing the coefficient of determination (R²) to 0.82. Experimental results demonstrate the model’s effectiveness in environments with limited training data, offering a reliable approach for productivity prediction in complex multi-layer tight sandstone 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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27 pages, 6026 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Application of an Automated Machine Learning-Driven Grid Block Classification Framework to a Realistic Deep Saline Aquifer Model for Accelerating Numerical Simulations of CO₂ Geological Storage

by Eirini Maria Kanakaki, Sofianos Panagiotis Fotias and Vassilis Gaganis

Processes 2025, 13(8), 2658; https://doi.org/10.3390/pr13082658 - 21 Aug 2025

Cited by 2 | Viewed by 1213

Abstract

Numerical simulations are essential for optimizing CO₂ geological storage in deep saline aquifers; however, their substantial computational demands pose a significant challenge. This study introduces an automated machine learning (ML)-driven grid block classification framework applied to a realistic deep saline aquifer model to accelerate numerical simulations while maintaining accuracy. The methodology employs an ML and interquartile range-based classifier to distinguish grid blocks as either fast- or slow-varying. ML-based proxy models are applied exclusively to slow-varying regions, while traditional iterative methods handle dynamic, fast-varying regions. Results confirm a considerable reduction in computational costs without compromising predictive accuracy. Validated under realistic reservoir conditions, the approach demonstrates scalability and robustness, supporting efficient, accurate large-scale CO₂ storage simulations and advancing sustainable subsurface sequestration strategies. 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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15 pages, 8278 KB

Open AccessArticle

Impact of Gravity Segregation on Gas Injection Development in Condensate Gas Reservoirs: A Numerical Simulation Study

by Fangfang Chen, Mengqin Li, Yang Yang, Qizhu Zhang, Ning Lin and Keliu Wu

Processes 2025, 13(6), 1659; https://doi.org/10.3390/pr13061659 - 26 May 2025

Cited by 2 | Viewed by 1523

Abstract

Gravity segregation is a critical phenomenon in thick condensate gas reservoirs, significantly influencing fluid composition and phase behavior. Reservoir-scale numerical simulation, serving as an indispensable technical approach in modern petroleum engineering, provides both quantitative data support and theoretical frameworks for development strategy optimization. However, the impact of gravity segregation on the distribution of initial fluid compositions is often overlooked in conventional numerical simulations due to data limitations or underestimated importance. This oversight leads to systematic deviations between simulated reservoir performance and actual field observations, ultimately compromising the efficient development of reservoirs. This study analyzed PVT data from reservoir fluid samples at different depths to determine the initial fluid composition distribution. Two models were developed: one incorporating gravity segregation and another neglecting it, to evaluate their performance during gas injection. Key findings include: (i) Gravity segregation alters the initial fluid composition, creating lighter components near the reservoir top and heavier ones at the bottom, resulting in distinct phase behaviors and production dynamics. (ii) The model accounting for gravity segregation aligns better with historical production data, while the model neglecting it underestimates oil production rates by about 9% and overestimates oil recovery by 2–5% during gas injection, due to inaccurate fluid composition assumptions. (iii) The model without gravity segregation also underestimates differences in oil recovery between injection–production strategies, such as top versus bottom injection. This study highlights the critical role of gravity segregation in reservoir simulation and provides valuable insights for optimizing the development of condensate gas reservoirs with complex fluid distributions. The findings reveal that accounting for gravity segregation in reservoir simulation models through proper initialization of fluid distribution leads to improved simulation accuracy, thereby enabling more precise development strategy design. 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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21 pages, 4816 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1294

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 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 KB

Open AccessArticle

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

Cited by 4 | Viewed by 1033

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 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

R^{2}

of 0.971, a smaller RMSE and MAE and a larger

R^{2}

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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1766

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, 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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