Fluid flow behavior prediction in naturally fractured reservoirs using machine learning models
The naturally fractured reservoirs are one of the most challenging due to the tectonic movements that are caused to increase the permeability and conductivity of the fractures. The instability of the permeability and conductivity effects on the fluid flow path causes problems during the transfer of...
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2023
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my.utm.1063852024-06-29T07:12:59Z http://eprints.utm.my/106385/ Fluid flow behavior prediction in naturally fractured reservoirs using machine learning models Shawkat, Mustafa Mudhafar Risal, Abdul Rahim Mahdi, Noor J. Safari, Ziauddin Naser, Maryam H. Al Zand, Ahmed W. TP Chemical technology The naturally fractured reservoirs are one of the most challenging due to the tectonic movements that are caused to increase the permeability and conductivity of the fractures. The instability of the permeability and conductivity effects on the fluid flow path causes problems during the transfer of the fluids from the matrix to the fractures and fluid losses during production. In addition, these complications made it difficult for engineers to estimate fluid flow during production. The fracture properties' study is important to model the fluid flow paths such as the fracture porosity, permeability, and the shape factor, which are considered essential in the stability of fluid flow. To examine this, this research introduced new models including decision tree (DT), random forest (RF), K-nearest regression (KNR), ridge regression (RR), and LASSO regression model,. The research studied the fracture properties in naturally fractured reservoirs like the fracture porosity (FP) and the shape factor (SF). The datasets used in this study were collected from previous studies "i.e., Texas oil and gas fields"to build an intelligence-based predictive model for fluid flow characteristics. The prediction process was conducted based on interporosity flow coefficient, storativity ratio, wellbore radius, matrix permeability, and fracture permeability as input data. This study revealed a positive finding for the adopted machine learning (ML) models and was superior in using statistical accuracy metrics. Overall, the research emphasized the implementation of computer-aided models for naturally fractured reservoir analysis, giving more details on the extensive execution techniques, such as injection or the creation of artificial cracks, to minimize hydrocarbon losses or leakage. Hindawi Limited 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/106385/1/AbdulRahimRisal2023_FluidFlowBehaviorPredictioninNaturally.pdf Shawkat, Mustafa Mudhafar and Risal, Abdul Rahim and Mahdi, Noor J. and Safari, Ziauddin and Naser, Maryam H. and Al Zand, Ahmed W. (2023) Fluid flow behavior prediction in naturally fractured reservoirs using machine learning models. Complexity, 2023 (NA). pp. 1-19. ISSN 1076-2787 http://dx.doi.org/10.1155/2023/7953967 DOI : 10.1155/2023/7953967 |
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The naturally fractured reservoirs are one of the most challenging due to the tectonic movements that are caused to increase the permeability and conductivity of the fractures. The instability of the permeability and conductivity effects on the fluid flow path causes problems during the transfer of the fluids from the matrix to the fractures and fluid losses during production. In addition, these complications made it difficult for engineers to estimate fluid flow during production. The fracture properties' study is important to model the fluid flow paths such as the fracture porosity, permeability, and the shape factor, which are considered essential in the stability of fluid flow. To examine this, this research introduced new models including decision tree (DT), random forest (RF), K-nearest regression (KNR), ridge regression (RR), and LASSO regression model,. The research studied the fracture properties in naturally fractured reservoirs like the fracture porosity (FP) and the shape factor (SF). The datasets used in this study were collected from previous studies "i.e., Texas oil and gas fields"to build an intelligence-based predictive model for fluid flow characteristics. The prediction process was conducted based on interporosity flow coefficient, storativity ratio, wellbore radius, matrix permeability, and fracture permeability as input data. This study revealed a positive finding for the adopted machine learning (ML) models and was superior in using statistical accuracy metrics. Overall, the research emphasized the implementation of computer-aided models for naturally fractured reservoir analysis, giving more details on the extensive execution techniques, such as injection or the creation of artificial cracks, to minimize hydrocarbon losses or leakage. |
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Article |
author |
Shawkat, Mustafa Mudhafar Risal, Abdul Rahim Mahdi, Noor J. Safari, Ziauddin Naser, Maryam H. Al Zand, Ahmed W. |
author_facet |
Shawkat, Mustafa Mudhafar Risal, Abdul Rahim Mahdi, Noor J. Safari, Ziauddin Naser, Maryam H. Al Zand, Ahmed W. |
author_sort |
Shawkat, Mustafa Mudhafar |
title |
Fluid flow behavior prediction in naturally fractured reservoirs using machine learning models |
title_short |
Fluid flow behavior prediction in naturally fractured reservoirs using machine learning models |
title_full |
Fluid flow behavior prediction in naturally fractured reservoirs using machine learning models |
title_fullStr |
Fluid flow behavior prediction in naturally fractured reservoirs using machine learning models |
title_full_unstemmed |
Fluid flow behavior prediction in naturally fractured reservoirs using machine learning models |
title_sort |
fluid flow behavior prediction in naturally fractured reservoirs using machine learning models |
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Hindawi Limited |
publishDate |
2023 |
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http://eprints.utm.my/106385/1/AbdulRahimRisal2023_FluidFlowBehaviorPredictioninNaturally.pdf http://eprints.utm.my/106385/ http://dx.doi.org/10.1155/2023/7953967 |
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