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Artificial Intelligent Techniques in the Optimal Operation of Oil and...
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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: 30 June 2024 | Viewed by 4211

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 Issue Information

Dear Colleagues,

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 of liquid unloading in gas wells that can promote the development of liquid unloading technology. Artificial intelligence is also widely used in petroleum engineering, especially in the oil and gas production stages. Researchers can also share new findings in this Special Issue.

The topics include, but are not limited to, the following:

  • 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

Published Papers (6 papers)

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Research

15 pages, 16102 KiB  
Article
Performance and Formula Optimization of Graphene-Modified Tungsten Carbide Coating to Improve Adaptability to High-Speed Fluid Flow in Wellbore
by Minsheng Wang, Lingchao Xuan, Lei Wang and Jiangshuai Wang
Processes 2024, 12(4), 714; https://doi.org/10.3390/pr12040714 - 31 Mar 2024
Viewed by 471
Abstract
In order to improve the erosion resistance of steel PDC (Polycrystalline Diamond Compact) bit under high-speed fluid flow conditions underground, it is necessary to develop a high-performance erosion-resistant coating. In this paper, laser cladding was used to prepare the new coating by modifying [...] Read more.
In order to improve the erosion resistance of steel PDC (Polycrystalline Diamond Compact) bit under high-speed fluid flow conditions underground, it is necessary to develop a high-performance erosion-resistant coating. In this paper, laser cladding was used to prepare the new coating by modifying tungsten carbide with graphene. And the effects of tungsten carbide content and graphene content on the coating performance have been thoroughly studied and analyzed to obtain the optimal covering layer. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding tungsten carbide, the hardness has significantly improved. However, when the tungsten carbide content further increases more than 30%, the increase in hardness is limited. In addition, when the content of graphene is more than 0.3%, the branched structure becomes thicker. In detail, this is a phenomenon where the segregation of Cr, Si, and W becomes very obvious again, and the segregation of Fe occurs at the Ni enrichment site. The research results contribute to the development and optimization of high-quality erosion-resistant coatings under the high-speed flow conditions in wellbore. These are of great significance for improving the efficiency of oil and gas exploration and development. Full article
(This article belongs to the Special Issue Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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10 pages, 776 KiB  
Article
A Production Prediction Model of Tight Gas Well Optimized with a Back Propagation (BP) Neural Network Based on the Sparrow Search Algorithm
by Zhengyan Zhao, Zongxiao Ren, Shun’an He, Shanjie Tang, Wei Tian, Xianwen Wang, Hui Zhao, Weichao Fan and Yang Yang
Processes 2024, 12(4), 632; https://doi.org/10.3390/pr12040632 - 22 Mar 2024
Viewed by 522
Abstract
The production of tight gas wells decreases rapidly, and the traditional method is difficult to accurately predict the production of tight gas wells. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there [...] Read more.
The production of tight gas wells decreases rapidly, and the traditional method is difficult to accurately predict the production of tight gas wells. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some technical problems. For example, the traditional error back propagation neural network (BP) still has the problem of finding the local optimal value, resulting in low prediction accuracy. In order to solve this problem, this paper establishes the output prediction method of BP neural network optimized with the sparrow search algorithm (SSA), and optimizes the hyperparameters of BP network such as activation function, training function, hidden layer, and node number based on examples, and constructs a high-precision SSA-BP neural network model. Data from 20 tight gas wells, the SSA-BP neural network model, Hongyuan model, and Arps model are predicted and compared. The results indicate that when the proportion of the predicted data is 20%, the SSA-BP model predicts an average absolute mean percentage error of 20.16%. When the proportion of forecast data is 10% of the total data, the SSA-BP algorithm has high accuracy and high stability. When the proportion of predicted data is 10%, the mean absolute average percentage error is 3.97%, which provides a new method for tight gas well productivity prediction. Full article
(This article belongs to the Special Issue Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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27 pages, 10005 KiB  
Article
A Novel Ensemble Machine Learning Model for Oil Production Prediction with Two-Stage Data Preprocessing
by Zhe Fan, Xiusen Liu, Zuoqian Wang, Pengcheng Liu and Yanwei Wang
Processes 2024, 12(3), 587; https://doi.org/10.3390/pr12030587 - 14 Mar 2024
Viewed by 710
Abstract
Petroleum production forecasting involves the anticipation of fluid production from wells based on historical data. Compared to traditional empirical, statistical, or reservoir simulation-based models, machine learning techniques leverage inherent relationships among historical dynamic data to predict future production. These methods are characterized by [...] Read more.
Petroleum production forecasting involves the anticipation of fluid production from wells based on historical data. Compared to traditional empirical, statistical, or reservoir simulation-based models, machine learning techniques leverage inherent relationships among historical dynamic data to predict future production. These methods are characterized by readily available parameters, fast computational speeds, high precision, and time–cost advantages, making them widely applicable in oilfield production. In this study, time series forecast models utilizing robust and efficient machine learning techniques are formulated for the prediction of production. We have fused the two-stage data preprocessing methods and the attention mechanism into the temporal convolutional network-gated recurrent unit (TCN-GRU) model. Firstly, the random forest (RF) algorithm is employed to extract key dynamic production features that influence output, serving to reduce data dimensionality and mitigate overfitting. Next, the mode decomposition algorithm, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is introduced. It employs a decomposition–reconstruction approach to segment production data into high-frequency noise components, low-frequency regular components and trend components. These segments are then individually subjected to prediction tasks, facilitating the model’s ability to capture more accurate intrinsic relationships among the data. Finally, the TCN-GRU-MA model, which integrates a multi-head attention (MA) mechanism, is utilized for production forecasting. In this model, the TCN module is employed to capture temporal data features, while the attention mechanism assigns varying weights to highlight the most critical influencing factors. The experimental results indicate that the proposed model achieves outstanding predictive performance. Compared to the best-performing comparative model, it exhibits a reduction in RMSE by 3%, MAE by 1.6%, MAPE by 12.7%, and an increase in R2 by 2.6% in Case 1. Similarly, in Case 2, there is a 7.7% decrease in RMSE, 7.7% in MAE, 11.6% in MAPE, and a 4.7% improvement in R2. Full article
(This article belongs to the Special Issue Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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14 pages, 4423 KiB  
Article
Study on Micro-Pressure Drive in the KKM Low-Permeability Reservoir
by Heng Zhang, Mibang Wang, Wenqi Ke, Xiaolong Li, Shengjun Yang and Weihua Zhu
Processes 2024, 12(3), 571; https://doi.org/10.3390/pr12030571 - 14 Mar 2024
Viewed by 483
Abstract
Kazakhstan has abundant resources of low-permeability oil reservoirs, among which the KKM low-permeability oil reservoir has geological reserves of 3844 × 104 t and a determined recoverable reserve of 1670 × 104 t. However, the water flooding efficiency is only 68%, [...] Read more.
Kazakhstan has abundant resources of low-permeability oil reservoirs, among which the KKM low-permeability oil reservoir has geological reserves of 3844 × 104 t and a determined recoverable reserve of 1670 × 104 t. However, the water flooding efficiency is only 68%, and the recovery efficiency is as low as 32%. The development of the reservoir faces challenges such as water injection difficulties and low oil production from wells. In order to further improve the oil recovery rate of this reservoir, our team developed micro-pressure-driven development technology based on pressure-driven techniques by integrating theories of fluid mechanics and artificial intelligence. We also combined this with subsequent artificial lift schemes, resulting in a complete set of micro-pressure-driven process technology. The predicted results indicate that after implementing micro-pressure-driven techniques, a single well group in the KKM oilfield can achieve a daily oil production increase of 32.08 t, demonstrating a good development effect. Full article
(This article belongs to the Special Issue Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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19 pages, 3459 KiB  
Article
A Novel Prediction Model for Steam Temperature Field of Downhole Multi-Thermal Fluid Generator
by Yanfeng He, Zhiqiang Huang, Xiangji Dou, Yisong Zhang, Le Hua and Jing Guo
Processes 2024, 12(3), 485; https://doi.org/10.3390/pr12030485 - 27 Feb 2024
Viewed by 786
Abstract
Aiming at the low efficiency of heavy-oil thermal recovery, a downhole multi-thermal fluid generator (DMTFG) can improve the viscosity reduction effect by reducing the heat loss of multi-thermal fluid in the process of wellbore transportation. The steam generated by the MDTFG causes damage [...] Read more.
Aiming at the low efficiency of heavy-oil thermal recovery, a downhole multi-thermal fluid generator (DMTFG) can improve the viscosity reduction effect by reducing the heat loss of multi-thermal fluid in the process of wellbore transportation. The steam generated by the MDTFG causes damage to the packer and casing, owing to the return upwards along the annular space passage of the oil casing. To mitigate this damage, a heat transfer model for multi-channel coiled tubing wells and a prediction model for the upward return of the steam temperature field in the annulus were established with the basic laws of thermodynamics. Models were further verified by ANSYS. The results indicate the following four conclusions. First of all, when the surface pressure is constant, the deeper the located DMTFG, the shorter the distance for the steam to return would be. It is easier to liquefy the steam. Second, the higher the temperature of the steam produced by the downhole polythermal fluid generator, the larger the upward distance of the steam would be. Third, the higher the steam pressure at the outlet of the downhole polythermal fluid generator, the smaller the distance of steam upward return would be. Finally, the larger the diameter of the multi-channel conversion piping, the greater the distance of the steam return would be. It is meaningful to provide valuable theoretical guidance for packer position designing in the field. Meanwhile, the study also provides a modeling basis for the subsequent study of artificial intelligence in the downhole temperature field. Full article
(This article belongs to the Special Issue Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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18 pages, 4612 KiB  
Article
Cooling Damage Characterization and Chemical-Enhanced Oil Recovery in Low-Permeable and High-Waxy Oil Reservoirs
by Xuanran Li, Lun Zhao, Ruijie Fei, Jincai Wang, Shanglin Liu, Minghui Li, Shujun Han, Fujian Zhou and Shuai Yuan
Processes 2024, 12(2), 421; https://doi.org/10.3390/pr12020421 - 19 Feb 2024
Viewed by 563
Abstract
The well productivity of high-waxy reservoirs is highly influenced by temperature changes. A decrease in temperature can cause the precipitation of wax from the crude oil, leading to a decrease in the formation’s drainage capacity and a drop in oil production. In this [...] Read more.
The well productivity of high-waxy reservoirs is highly influenced by temperature changes. A decrease in temperature can cause the precipitation of wax from the crude oil, leading to a decrease in the formation’s drainage capacity and a drop in oil production. In this study, the wax precipitation of crude oil is characterized by rheological properties tests and differential scanning calorimetry (DSC) thermal analysis. The wax damage characteristics of cores and the relative permeability curves at different temperatures were investigated through coreflood experiments. Furthermore, nanoemulsion is selected as a chemical agent for injection fluid. The nuclear magnetic resonance (NMR) scanning technique is used to investigate the effects of oil recovery enhancement at different pores by increasing temperature and adding nanoemulsion. By comparing the changes in T2 spectra and the distribution pattern of residual oil before and after liquid injection, the results have shown that both increasing temperature and adding nanoemulsion have a significant effect on oil recovery. The improvement of micropores is less pronounced compared to macropores. The produced oil mainly comes from the large pores. When the temperature is lower than the crude oil dewaxing point temperature, there is a serious dewaxing plugging phenomenon in the pores. Additionally, by observing the pattern of residual oil distribution at the end of the NMR online drive, it is hereby classified into wax deposition retention type, weak water washing retention type, and immobilized type, each with its own distinct characteristics. Wettability alteration and interfacial tension reduction can help to improve the drainage capacity of high-wax oil reservoirs, which is the main mechanism of nanoemulsion for enhanced oil recovery. These findings are highly valuable for enhancing the comprehension of the impact of highly waxed crude oils on drainage capacity and the ultimate oil recovery rate, particularly in relation to wax precipitation deposition. Full article
(This article belongs to the Special Issue Artificial Intelligent Techniques in the Optimal Operation of Oil and Gas Production Systems)
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