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New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs

[Image: see text] Water saturation assessment is recognized as one of the most critical aspects of formation evaluation, reserve estimation, and prediction of the production performance of any hydrocarbon reservoir. Water saturation measurement in a core laboratory is a time-consuming and expensive...

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Autores principales: Gomaa, Sayed, Soliman, Ahmed Ashraf, Mohamed, Abdulrahman, Emara, Ramadan, Attia, Attia Mahmoud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811764/
https://www.ncbi.nlm.nih.gov/pubmed/35128262
http://dx.doi.org/10.1021/acsomega.1c06044
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author Gomaa, Sayed
Soliman, Ahmed Ashraf
Mohamed, Abdulrahman
Emara, Ramadan
Attia, Attia Mahmoud
author_facet Gomaa, Sayed
Soliman, Ahmed Ashraf
Mohamed, Abdulrahman
Emara, Ramadan
Attia, Attia Mahmoud
author_sort Gomaa, Sayed
collection PubMed
description [Image: see text] Water saturation assessment is recognized as one of the most critical aspects of formation evaluation, reserve estimation, and prediction of the production performance of any hydrocarbon reservoir. Water saturation measurement in a core laboratory is a time-consuming and expensive task. Many scientists have attempted to estimate water saturation accurately using well-logging data, which provides a continuous record without information loss. As a result, numerous models have been developed to relate reservoir characteristics with water saturation. By expanding the use and advancement of soft computing approaches in engineering challenges, petroleum engineers applied them to estimate the petrophysical parameters of the reservoir. In this paper, two techniques are developed to estimate the water saturation in terms of porosity, permeability, and formation resistivity index through the use of 383 data sets obtained from carbonate core samples. These techniques are the nonlinear multiple regression (NLMR) technique and the artificial neural network (ANN) technique. The proposed ANN model achieved outstanding performance and better accuracy for calculating the water saturation than the empirical correlation using NLMR and Archie equation with a high coefficient of determination (R(2)) of 0.99, a low average relative error of 1.92, a low average absolute relative error of 13.62, and a low root mean square error of 0.066. To the best of our knowledge, the current research establishes a novel foundation using the ANN model in the estimation of water saturation.
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spelling pubmed-88117642022-02-04 New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs Gomaa, Sayed Soliman, Ahmed Ashraf Mohamed, Abdulrahman Emara, Ramadan Attia, Attia Mahmoud ACS Omega [Image: see text] Water saturation assessment is recognized as one of the most critical aspects of formation evaluation, reserve estimation, and prediction of the production performance of any hydrocarbon reservoir. Water saturation measurement in a core laboratory is a time-consuming and expensive task. Many scientists have attempted to estimate water saturation accurately using well-logging data, which provides a continuous record without information loss. As a result, numerous models have been developed to relate reservoir characteristics with water saturation. By expanding the use and advancement of soft computing approaches in engineering challenges, petroleum engineers applied them to estimate the petrophysical parameters of the reservoir. In this paper, two techniques are developed to estimate the water saturation in terms of porosity, permeability, and formation resistivity index through the use of 383 data sets obtained from carbonate core samples. These techniques are the nonlinear multiple regression (NLMR) technique and the artificial neural network (ANN) technique. The proposed ANN model achieved outstanding performance and better accuracy for calculating the water saturation than the empirical correlation using NLMR and Archie equation with a high coefficient of determination (R(2)) of 0.99, a low average relative error of 1.92, a low average absolute relative error of 13.62, and a low root mean square error of 0.066. To the best of our knowledge, the current research establishes a novel foundation using the ANN model in the estimation of water saturation. American Chemical Society 2022-01-20 /pmc/articles/PMC8811764/ /pubmed/35128262 http://dx.doi.org/10.1021/acsomega.1c06044 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Gomaa, Sayed
Soliman, Ahmed Ashraf
Mohamed, Abdulrahman
Emara, Ramadan
Attia, Attia Mahmoud
New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs
title New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs
title_full New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs
title_fullStr New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs
title_full_unstemmed New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs
title_short New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs
title_sort new correlation for calculating water saturation based on permeability, porosity, and resistivity index in carbonate reservoirs
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811764/
https://www.ncbi.nlm.nih.gov/pubmed/35128262
http://dx.doi.org/10.1021/acsomega.1c06044
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