Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation
Relative permeability is a substantial parameter for estimating multi-phase fluid flow in porous rocks. It is a complex physical property that is influenced by the behavior and interactions between the fluid and rock phases. Relative permeability measurement of rock samples in laboratory can be carr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/30/e3sconf_interconnects2024_03019.pdf |
| _version_ | 1827311645556211712 |
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| author | Fathaddin Muhammad Taufiq Sari Alvita Kumala Sutansyah Daddy Ulfah Baiq Maulinda Bae Wisup Rakhmanto Pri Agung Irawan Sonny |
| author_facet | Fathaddin Muhammad Taufiq Sari Alvita Kumala Sutansyah Daddy Ulfah Baiq Maulinda Bae Wisup Rakhmanto Pri Agung Irawan Sonny |
| author_sort | Fathaddin Muhammad Taufiq |
| collection | DOAJ |
| description | Relative permeability is a substantial parameter for estimating multi-phase fluid flow in porous rocks. It is a complex physical property that is influenced by the behavior and interactions between the fluid and rock phases. Relative permeability measurement of rock samples in laboratory can be carried out using steady-state or non-steady-state techniques. Permeability measurement is relatively difficult and time consuming. Because of the difficulty in measurement, empirical models are often used to estimate relative permeability or extrapolate to limited laboratory data. Artificial neural network (ANN) is a method that can be applied to obtain complex correlations of parameters that influence each other. In this study, ANN is used to predict the relative permeability of oil and water. The proposed model evaluates the relative permeability of a phase as a function of rock absolute permeability, porosity, depth, permeability of other phases and water saturation. A total of 159 relative permeability data from Talang Akar Formation were used for the training and testing processes. Based on the comparison between measured and calculated data, the correlation coefficients for relative permeability to water and oil using ANN method are 0.77 and 0.94 respectively. While those using regression analysis are 0.88 and 0.73 respectively. |
| first_indexed | 2024-04-24T20:22:16Z |
| format | Article |
| id | doaj.art-000a8db19eb2421c88e85bc5cc4ee383 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-24T20:22:16Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-000a8db19eb2421c88e85bc5cc4ee3832024-03-22T07:54:25ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015000301910.1051/e3sconf/202450003019e3sconf_interconnects2024_03019Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar FormationFathaddin Muhammad Taufiq0Sari Alvita Kumala1Sutansyah Daddy2Ulfah Baiq Maulinda3Bae Wisup4Rakhmanto Pri Agung5Irawan Sonny6Department of Petroleum Engineering, Universitas TrisaktiDepartment of Petroleum Engineering, Universitas TrisaktiDepartment of Petroleum Engineering, Universitas TrisaktiDepartment of Petroleum Engineering, Universitas TrisaktiEnergy and Mineral Resource Engineering Department, Sejong University, SeoulDepartment of Petroleum Engineering, Universitas TrisaktiDepartment of Petroleum Engineering, Nazarbayev UniversityRelative permeability is a substantial parameter for estimating multi-phase fluid flow in porous rocks. It is a complex physical property that is influenced by the behavior and interactions between the fluid and rock phases. Relative permeability measurement of rock samples in laboratory can be carried out using steady-state or non-steady-state techniques. Permeability measurement is relatively difficult and time consuming. Because of the difficulty in measurement, empirical models are often used to estimate relative permeability or extrapolate to limited laboratory data. Artificial neural network (ANN) is a method that can be applied to obtain complex correlations of parameters that influence each other. In this study, ANN is used to predict the relative permeability of oil and water. The proposed model evaluates the relative permeability of a phase as a function of rock absolute permeability, porosity, depth, permeability of other phases and water saturation. A total of 159 relative permeability data from Talang Akar Formation were used for the training and testing processes. Based on the comparison between measured and calculated data, the correlation coefficients for relative permeability to water and oil using ANN method are 0.77 and 0.94 respectively. While those using regression analysis are 0.88 and 0.73 respectively.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/30/e3sconf_interconnects2024_03019.pdf |
| spellingShingle | Fathaddin Muhammad Taufiq Sari Alvita Kumala Sutansyah Daddy Ulfah Baiq Maulinda Bae Wisup Rakhmanto Pri Agung Irawan Sonny Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation E3S Web of Conferences |
| title | Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation |
| title_full | Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation |
| title_fullStr | Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation |
| title_full_unstemmed | Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation |
| title_short | Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation |
| title_sort | application of artificial neural networks for predicting relative permeability in talang akar formation |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/30/e3sconf_interconnects2024_03019.pdf |
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