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Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

[Image: see text] The accurate determination of water saturation in shaly sandstone reservoirs has a significant impact on hydrocarbons in place estimation and selection of possible hydrocarbon zones. The available numerical equations for water saturation estimation are unreliable and depend on labo...

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Autores principales: Abdel Azim, Reda, Hamada, Ghareb
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9435039/
https://www.ncbi.nlm.nih.gov/pubmed/36061681
http://dx.doi.org/10.1021/acsomega.2c01945
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author Abdel Azim, Reda
Hamada, Ghareb
author_facet Abdel Azim, Reda
Hamada, Ghareb
author_sort Abdel Azim, Reda
collection PubMed
description [Image: see text] The accurate determination of water saturation in shaly sandstone reservoirs has a significant impact on hydrocarbons in place estimation and selection of possible hydrocarbon zones. The available numerical equations for water saturation estimation are unreliable and depend on laboratory core analysis. Therefore, this paper attempts to use artificial intelligence methods in developing an artificial neural network model (ANN) for water saturation (Sw) prediction. The ANN model is developed and validated by using 2700 core measured points from the fields located in the Gulf of Suez, Nile Delta, and Western Desert of Egypt, with inputs including the formation depth, the caliper size, the sonic time, gamma rays (GRs), shallow resistivity (Rxo), neutron porosity (NPHI), the photoelectric effect (PEF), bulk density, and deep resistivity (Rt). The study results show that the optimization process for the ANN model is achieved by distributing the collected data as follows: 80% for training and 20% for testing processes, with an R(2) of 0.973 and a mean square error (MSE) of 0.048. In addition, a mathematical equation is extracted out of the ANN model that is used to estimate the formation water saturation in a simple and direct approach. The developed equation can be used incorporating with the existing well logs commercial software to increase the accuracy of water saturation prediction. A comparison study is executed using published correlations (Waxman and Smits, dual water, and effective models) to show the robustness of the presented ANN model and the extracted equation. The results show that the proposed correlation and the ANN model achieved outstanding performance and better accuracy than the existing empirical models for calculating the formation water saturation with a high correlation coefficient (R(2)) of 0.973, lowest mean-square error (MSE) of 0.048, lowest average absolute percent relative error (AAPRE) of 0.042, and standard deviation (SD) of 0.24. To the best of our knowledge, the current study and the proposed ANN model establish a novel base in the estimation of formation water saturation.
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spelling pubmed-94350392022-09-02 Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields Abdel Azim, Reda Hamada, Ghareb ACS Omega [Image: see text] The accurate determination of water saturation in shaly sandstone reservoirs has a significant impact on hydrocarbons in place estimation and selection of possible hydrocarbon zones. The available numerical equations for water saturation estimation are unreliable and depend on laboratory core analysis. Therefore, this paper attempts to use artificial intelligence methods in developing an artificial neural network model (ANN) for water saturation (Sw) prediction. The ANN model is developed and validated by using 2700 core measured points from the fields located in the Gulf of Suez, Nile Delta, and Western Desert of Egypt, with inputs including the formation depth, the caliper size, the sonic time, gamma rays (GRs), shallow resistivity (Rxo), neutron porosity (NPHI), the photoelectric effect (PEF), bulk density, and deep resistivity (Rt). The study results show that the optimization process for the ANN model is achieved by distributing the collected data as follows: 80% for training and 20% for testing processes, with an R(2) of 0.973 and a mean square error (MSE) of 0.048. In addition, a mathematical equation is extracted out of the ANN model that is used to estimate the formation water saturation in a simple and direct approach. The developed equation can be used incorporating with the existing well logs commercial software to increase the accuracy of water saturation prediction. A comparison study is executed using published correlations (Waxman and Smits, dual water, and effective models) to show the robustness of the presented ANN model and the extracted equation. The results show that the proposed correlation and the ANN model achieved outstanding performance and better accuracy than the existing empirical models for calculating the formation water saturation with a high correlation coefficient (R(2)) of 0.973, lowest mean-square error (MSE) of 0.048, lowest average absolute percent relative error (AAPRE) of 0.042, and standard deviation (SD) of 0.24. To the best of our knowledge, the current study and the proposed ANN model establish a novel base in the estimation of formation water saturation. American Chemical Society 2022-08-16 /pmc/articles/PMC9435039/ /pubmed/36061681 http://dx.doi.org/10.1021/acsomega.2c01945 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 Abdel Azim, Reda
Hamada, Ghareb
Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields
title Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields
title_full Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields
title_fullStr Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields
title_full_unstemmed Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields
title_short Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields
title_sort novel correlation for calculating water saturation in shaly sandstone reservoirs using artificial intelligence: case study from egyptian oil fields
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9435039/
https://www.ncbi.nlm.nih.gov/pubmed/36061681
http://dx.doi.org/10.1021/acsomega.2c01945
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