Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case study
This study investigates the application of a Mamdani type-2 fuzzy inference system (FIS) for optimizing well selection in gas lift design within the HYSOL field of the Niger Delta, Nigeria. Gas lift is a widely used artificial lift method for enhancing oil production, particularly in wells with insu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025000155 |
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| author | Nnaemeka Princewill Ohia Chadi Paul Emmanuel Asolo Taiwo Adetomiwa Adewa Chidimma Favour Chukwu Paschal Ateb Ubi Daniel Hogan Itam Daniel Ugochukwu Nnaji |
| author_facet | Nnaemeka Princewill Ohia Chadi Paul Emmanuel Asolo Taiwo Adetomiwa Adewa Chidimma Favour Chukwu Paschal Ateb Ubi Daniel Hogan Itam Daniel Ugochukwu Nnaji |
| author_sort | Nnaemeka Princewill Ohia |
| collection | DOAJ |
| description | This study investigates the application of a Mamdani type-2 fuzzy inference system (FIS) for optimizing well selection in gas lift design within the HYSOL field of the Niger Delta, Nigeria. Gas lift is a widely used artificial lift method for enhancing oil production, particularly in wells with insufficient natural reservoir pressure. However, selecting suitable wells for gas lift is complex due to the inherent uncertainty and variability in petroleum production data. Traditional decision-making approaches often struggle with this uncertainty, leading to suboptimal choices that can reduce efficiency and increase operational costs. Given the need for a more flexible and accurate well selection process, the Mamdani type-2 fuzzy logic model was employed in this study to address the high levels of imprecision associated with key production parameters, including Gas-Oil Ratio (GOR), Barrel of Oil Produced per Day (BOPD), Basic Sediment and Water (BS&W), Barrel of Fluid Produced per Day (BFPD), Gas Produced (MCFPD), Total Gas-Liquid Ratio (TGLR), Tubing Pressure (TBG PR), and Manifold Pressure (MNFLD PR). By utilizing both upper and lower membership functions to define the Footprint of Uncertainty (FOU), the model effectively categorized wells into ''Excellent,'' ''Good,'' and ''Poor'' for gas lift suitability. Out of the initial fifty-nine wells, the system identified nineteen as excellent candidates, six as good, and twenty as poor, with thirteen wells excluded due to insufficient data. The Mamdani type-2 fuzzy inference system demonstrated robustness in managing complex decision-making processes under uncertainty, offering a flexible and scalable approach to optimize gas lift operations. This method provides a nuanced evaluation of well suitability, adapting to fluctuating reservoir conditions and operational challenges, and underscores the utility of fuzzy logic in petroleum engineering for handling large datasets and uncertain environments. |
| format | Article |
| id | doaj-art-b559b358fcd1445abbd2d1752d9e6b46 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-b559b358fcd1445abbd2d1752d9e6b462025-01-16T04:29:12ZengElsevierResults in Engineering2590-12302025-03-0125103927Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case studyNnaemeka Princewill Ohia0Chadi Paul1Emmanuel Asolo2Taiwo Adetomiwa Adewa3Chidimma Favour Chukwu4Paschal Ateb Ubi5Daniel Hogan Itam6Daniel Ugochukwu Nnaji7Department of Future Energies, African Centre of Excellence in Future Energies and Electrochemical Systems (ACE-FUELS), Federal University of Technology Owerri, NigeriaDepartment of Petroleum Engineering, Federal University of Technology Owerri, Nigeria; Corresponding author.Department of Information and Communication Technology, Osun State University, Osogbo, NigeriaDepartment of Computer Science and Networks, Institut Mines-Télécom Atlantique, Nantes, FranceDepartment of Mathematics Education, University of Nigeria, Nsukka, NigeriaDepartment of Mechanical Engineering, University of Calabar, Calabar, NigeriaDepartment of Civil and Environmental Engineering, University of Calabar, Calabar, NigeriaDepartment of Mathematics and Statistics, Technology and Innovation, Pan African University Institute for Basic Sciences, Nairobi, KenyaThis study investigates the application of a Mamdani type-2 fuzzy inference system (FIS) for optimizing well selection in gas lift design within the HYSOL field of the Niger Delta, Nigeria. Gas lift is a widely used artificial lift method for enhancing oil production, particularly in wells with insufficient natural reservoir pressure. However, selecting suitable wells for gas lift is complex due to the inherent uncertainty and variability in petroleum production data. Traditional decision-making approaches often struggle with this uncertainty, leading to suboptimal choices that can reduce efficiency and increase operational costs. Given the need for a more flexible and accurate well selection process, the Mamdani type-2 fuzzy logic model was employed in this study to address the high levels of imprecision associated with key production parameters, including Gas-Oil Ratio (GOR), Barrel of Oil Produced per Day (BOPD), Basic Sediment and Water (BS&W), Barrel of Fluid Produced per Day (BFPD), Gas Produced (MCFPD), Total Gas-Liquid Ratio (TGLR), Tubing Pressure (TBG PR), and Manifold Pressure (MNFLD PR). By utilizing both upper and lower membership functions to define the Footprint of Uncertainty (FOU), the model effectively categorized wells into ''Excellent,'' ''Good,'' and ''Poor'' for gas lift suitability. Out of the initial fifty-nine wells, the system identified nineteen as excellent candidates, six as good, and twenty as poor, with thirteen wells excluded due to insufficient data. The Mamdani type-2 fuzzy inference system demonstrated robustness in managing complex decision-making processes under uncertainty, offering a flexible and scalable approach to optimize gas lift operations. This method provides a nuanced evaluation of well suitability, adapting to fluctuating reservoir conditions and operational challenges, and underscores the utility of fuzzy logic in petroleum engineering for handling large datasets and uncertain environments.http://www.sciencedirect.com/science/article/pii/S2590123025000155Fuzzy inference systemMamdani Type-2FuzzificationDefuzzificationFuzzy setsMembership function |
| spellingShingle | Nnaemeka Princewill Ohia Chadi Paul Emmanuel Asolo Taiwo Adetomiwa Adewa Chidimma Favour Chukwu Paschal Ateb Ubi Daniel Hogan Itam Daniel Ugochukwu Nnaji Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case study Results in Engineering Fuzzy inference system Mamdani Type-2 Fuzzification Defuzzification Fuzzy sets Membership function |
| title | Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case study |
| title_full | Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case study |
| title_fullStr | Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case study |
| title_full_unstemmed | Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case study |
| title_short | Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case study |
| title_sort | artificial intelligence driven fuzzy logic approach for optimal well selection in gas lift design a brown field case study |
| topic | Fuzzy inference system Mamdani Type-2 Fuzzification Defuzzification Fuzzy sets Membership function |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025000155 |
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