Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal Wells
The production of shale oil wells is typically low, and logging instruments such as the flow scanning imager (FSI) may exhibit poor turbine response in low-production wells, leading to measurement deviations and reduced interpretation accuracy. To investigate the oil-water two-phase flow behavior in...
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Editorial Office of Well Logging Technology
2025-04-01
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| Series: | Cejing jishu |
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| Online Access: | https://www.cnpcwlt.com/en/#/digest?ArticleID=5734 |
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| author | QIN Honglong LIU Junfeng SHI Shoubo DAI Yuexiang |
| author_facet | QIN Honglong LIU Junfeng SHI Shoubo DAI Yuexiang |
| author_sort | QIN Honglong |
| collection | DOAJ |
| description | The production of shale oil wells is typically low, and logging instruments such as the flow scanning imager (FSI) may exhibit poor turbine response in low-production wells, leading to measurement deviations and reduced interpretation accuracy. To investigate the oil-water two-phase flow behavior in low-production near-horizontal wells, 11 downhole pipeline models with varying well deviations (inclination angles), an inner diameter of 0.124 m, and a length of 6 m were established using ANSYS Fluent. The built-in volume of fluid (VOF) model is employed to simulate oil-water two-phase flow in wells with different inclinations, yielding flow patterns, water holdup data, and holdup imaging under varying well deviations, flow rates, and water-cut conditions. A water holdup database is constructed based on simulation results, and water holdup variation charts were generated. Numerical simulations are analyzed to derive the relationship between water holdup and well deviation under different parameter conditions, resulting in a functional expression for water holdup versus inclination. A multilayer perceptron (MLP) neural network is trained using the simulation data to predict the nonlinear relationships among well deviation, flow rate, water-cut, and water holdup. This approach achieved accurate water holdup predictions for near-horizontal well oil-water two-phase flow under diverse conditions. Comparative analysis with experimental data demonstrated high prediction accuracy, with a relative error of 3.222%, meeting the validation requirements for downhole instrument water holdup measurements. |
| format | Article |
| id | doaj-art-473a3a73ed08494c8a5a849851f44dde |
| institution | Kabale University |
| issn | 1004-1338 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Editorial Office of Well Logging Technology |
| record_format | Article |
| series | Cejing jishu |
| spelling | doaj-art-473a3a73ed08494c8a5a849851f44dde2025-08-20T03:47:33ZzhoEditorial Office of Well Logging TechnologyCejing jishu1004-13382025-04-0149231031710.16489/j.issn.1004-1338.2025.02.0181004-1338(2025)02-0310-08Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal WellsQIN Honglong0LIU Junfeng1SHI Shoubo2DAI Yuexiang3College of Geophysics and Petroleum Resources, Yangtze University, Wuhan, Hubei 430100, ChinaCollege of Geophysics and Petroleum Resources, Yangtze University, Wuhan, Hubei 430100, ChinaCollege of Geophysics and Petroleum Resources, Yangtze University, Wuhan, Hubei 430100, ChinaXinjiang Branch, China National Logging Corporation, Karamay, Xinjiang 834000, ChinaThe production of shale oil wells is typically low, and logging instruments such as the flow scanning imager (FSI) may exhibit poor turbine response in low-production wells, leading to measurement deviations and reduced interpretation accuracy. To investigate the oil-water two-phase flow behavior in low-production near-horizontal wells, 11 downhole pipeline models with varying well deviations (inclination angles), an inner diameter of 0.124 m, and a length of 6 m were established using ANSYS Fluent. The built-in volume of fluid (VOF) model is employed to simulate oil-water two-phase flow in wells with different inclinations, yielding flow patterns, water holdup data, and holdup imaging under varying well deviations, flow rates, and water-cut conditions. A water holdup database is constructed based on simulation results, and water holdup variation charts were generated. Numerical simulations are analyzed to derive the relationship between water holdup and well deviation under different parameter conditions, resulting in a functional expression for water holdup versus inclination. A multilayer perceptron (MLP) neural network is trained using the simulation data to predict the nonlinear relationships among well deviation, flow rate, water-cut, and water holdup. This approach achieved accurate water holdup predictions for near-horizontal well oil-water two-phase flow under diverse conditions. Comparative analysis with experimental data demonstrated high prediction accuracy, with a relative error of 3.222%, meeting the validation requirements for downhole instrument water holdup measurements.https://www.cnpcwlt.com/en/#/digest?ArticleID=5734shale oilwater holdupnear horizontal wellnumerical simulationoil-water two-phasemlp neural network |
| spellingShingle | QIN Honglong LIU Junfeng SHI Shoubo DAI Yuexiang Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal Wells Cejing jishu shale oil water holdup near horizontal well numerical simulation oil-water two-phase mlp neural network |
| title | Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal Wells |
| title_full | Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal Wells |
| title_fullStr | Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal Wells |
| title_full_unstemmed | Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal Wells |
| title_short | Numerical Simulation and Holdup Prediction of Oil-Water Two-Phase in Low Yield Shale Oil Near Horizontal Wells |
| title_sort | numerical simulation and holdup prediction of oil water two phase in low yield shale oil near horizontal wells |
| topic | shale oil water holdup near horizontal well numerical simulation oil-water two-phase mlp neural network |
| url | https://www.cnpcwlt.com/en/#/digest?ArticleID=5734 |
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