Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study
In the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an...
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Elsevier
2025-03-01
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| Series: | Digital Chemical Engineering |
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| author | Leila Zeinolabedini Forough Ameli Abdolhossein Hemmati-Sarapardeh |
| author_facet | Leila Zeinolabedini Forough Ameli Abdolhossein Hemmati-Sarapardeh |
| author_sort | Leila Zeinolabedini |
| collection | DOAJ |
| description | In the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an oil field operation in Iran. To this end, 154 data points constructed by a central composite design (CCD) experiment were utilized to develop neural network models. Therefore, four robust models, including multilayer perceptron (MLP), radial basis function (RBF), general regression neural network (GRNN), and cascade forward neural network (CFNN), were implemented for modeling. In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. A penalty function was used to incorporate this constraint into the optimization procedure. There has previously been much research in the area of gas lift optimization. However, robust neural networks (GRNN and CFNN) have not been used for integrated production system modeling, nor have GWO algorithms been used to maximize the production or NPV in gas lift operations until now. The results for model errors were found to be %2.09, %2.99, %10.68, and %1.75 for MLP, RBF, GRNN, and CFNN, respectively. These findings imply that the CFNN model is more efficient. Also, comparing the GWO approach to other algorithms, the largest NPV ($788,512,038$) was yielded with less sensitivity of its adjustable parameters. Thereupon, NPV and cumulated oil production indicate a significant increase compared to ordinary NPV and oil production with values of 351,087,876.4 $ and 14,308 STB, respectively. High NPV effectively captures the overall added value of the project and, as a benchmark, helps to make informed decisions about investment and resource allocation, ultimately driving economic growth and increasing competitiveness in using this method. |
| format | Article |
| id | doaj-art-58de1317621a4f248b916c07cc9b7204 |
| institution | Kabale University |
| issn | 2772-5081 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj-art-58de1317621a4f248b916c07cc9b72042025-02-07T04:48:33ZengElsevierDigital Chemical Engineering2772-50812025-03-0114100220Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case studyLeila Zeinolabedini0Forough Ameli1Abdolhossein Hemmati-Sarapardeh2School of Chemical Engineering, Oil and Gas, Iran University of Science and Technology, Tehran, IranSchool of Chemical Engineering, Oil and Gas, Iran University of Science and Technology, Tehran, Iran; Corresponding author.Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran; State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, PR ChinaIn the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an oil field operation in Iran. To this end, 154 data points constructed by a central composite design (CCD) experiment were utilized to develop neural network models. Therefore, four robust models, including multilayer perceptron (MLP), radial basis function (RBF), general regression neural network (GRNN), and cascade forward neural network (CFNN), were implemented for modeling. In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. A penalty function was used to incorporate this constraint into the optimization procedure. There has previously been much research in the area of gas lift optimization. However, robust neural networks (GRNN and CFNN) have not been used for integrated production system modeling, nor have GWO algorithms been used to maximize the production or NPV in gas lift operations until now. The results for model errors were found to be %2.09, %2.99, %10.68, and %1.75 for MLP, RBF, GRNN, and CFNN, respectively. These findings imply that the CFNN model is more efficient. Also, comparing the GWO approach to other algorithms, the largest NPV ($788,512,038$) was yielded with less sensitivity of its adjustable parameters. Thereupon, NPV and cumulated oil production indicate a significant increase compared to ordinary NPV and oil production with values of 351,087,876.4 $ and 14,308 STB, respectively. High NPV effectively captures the overall added value of the project and, as a benchmark, helps to make informed decisions about investment and resource allocation, ultimately driving economic growth and increasing competitiveness in using this method.http://www.sciencedirect.com/science/article/pii/S2772508125000043Gas lift optimizationArtificial neural networksGrey wolf optimizationCFNNNPV |
| spellingShingle | Leila Zeinolabedini Forough Ameli Abdolhossein Hemmati-Sarapardeh Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study Digital Chemical Engineering Gas lift optimization Artificial neural networks Grey wolf optimization CFNN NPV |
| title | Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study |
| title_full | Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study |
| title_fullStr | Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study |
| title_full_unstemmed | Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study |
| title_short | Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study |
| title_sort | integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply a case study |
| topic | Gas lift optimization Artificial neural networks Grey wolf optimization CFNN NPV |
| url | http://www.sciencedirect.com/science/article/pii/S2772508125000043 |
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