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Data-driven models to predict shale wettability for CO(2) sequestration applications

The significance of CO(2) wetting behavior in shale formations has been emphasized in various CO(2) sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of...

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Autores principales: Ibrahim, Ahmed Farid, Elkatatny, Salaheldin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287739/
https://www.ncbi.nlm.nih.gov/pubmed/37349517
http://dx.doi.org/10.1038/s41598-023-37327-2
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author Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
author_facet Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
author_sort Ibrahim, Ahmed Farid
collection PubMed
description The significance of CO(2) wetting behavior in shale formations has been emphasized in various CO(2) sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of machine learning (ML); artificial neural networks (ANN), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) tools to estimate the contact angle, a key indicator of shale wettability, providing a more efficient alternative to conventional laboratory methods. A dataset comprising various shale samples under different conditions was collected to predict shale-water-CO(2) wettability by considering shale properties, operating pressure and temperature, and brine salinity. Pearson’s correlation coefficient (R) was utilized to assess the linearity between the contact angle (CA) value and other input parameters. Initial data analysis showed that the elements affecting the shale wettability are primarily reliant on the pressure and temperature at which it operates, the total organic content (TOC), and the mineral composition of the rock. Between the different ML models, the artificial neural network (ANN) model performed the best, achieving a training R(2) of 0.99, testing R(2) of 0.98 and a validation R(2) of 0.96, with an RMSE below 5. The adaptive neuro-fuzzy inference system (ANFIS) model also accurately predicted the contact angle, obtaining a training R(2) of 0.99, testing R(2) of 0.97 and a validation R(2) of 0.95. Conversely, the support vector machine (SVM) model displayed signs of overfitting, as it achieved R(2) values of 0.99 in the training dataset, which decreased to 0.94 in the testing dataset, and 0.88 in the validation dataset. To avoid rerunning the ML models, an empirical correlation was developed based on the optimized weights and biases obtained from the ANN model to predict contact angle values using input parameters and the validation data set revealed R(2) of 0.96. The parametric study showed that, among the factors influencing shale wettability at a constant TOC, pressure had the most significant impact, and the dependency of the contact angle on pressure increased when TOC values were high.
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spelling pubmed-102877392023-06-24 Data-driven models to predict shale wettability for CO(2) sequestration applications Ibrahim, Ahmed Farid Elkatatny, Salaheldin Sci Rep Article The significance of CO(2) wetting behavior in shale formations has been emphasized in various CO(2) sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of machine learning (ML); artificial neural networks (ANN), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) tools to estimate the contact angle, a key indicator of shale wettability, providing a more efficient alternative to conventional laboratory methods. A dataset comprising various shale samples under different conditions was collected to predict shale-water-CO(2) wettability by considering shale properties, operating pressure and temperature, and brine salinity. Pearson’s correlation coefficient (R) was utilized to assess the linearity between the contact angle (CA) value and other input parameters. Initial data analysis showed that the elements affecting the shale wettability are primarily reliant on the pressure and temperature at which it operates, the total organic content (TOC), and the mineral composition of the rock. Between the different ML models, the artificial neural network (ANN) model performed the best, achieving a training R(2) of 0.99, testing R(2) of 0.98 and a validation R(2) of 0.96, with an RMSE below 5. The adaptive neuro-fuzzy inference system (ANFIS) model also accurately predicted the contact angle, obtaining a training R(2) of 0.99, testing R(2) of 0.97 and a validation R(2) of 0.95. Conversely, the support vector machine (SVM) model displayed signs of overfitting, as it achieved R(2) values of 0.99 in the training dataset, which decreased to 0.94 in the testing dataset, and 0.88 in the validation dataset. To avoid rerunning the ML models, an empirical correlation was developed based on the optimized weights and biases obtained from the ANN model to predict contact angle values using input parameters and the validation data set revealed R(2) of 0.96. The parametric study showed that, among the factors influencing shale wettability at a constant TOC, pressure had the most significant impact, and the dependency of the contact angle on pressure increased when TOC values were high. Nature Publishing Group UK 2023-06-22 /pmc/articles/PMC10287739/ /pubmed/37349517 http://dx.doi.org/10.1038/s41598-023-37327-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
Data-driven models to predict shale wettability for CO(2) sequestration applications
title Data-driven models to predict shale wettability for CO(2) sequestration applications
title_full Data-driven models to predict shale wettability for CO(2) sequestration applications
title_fullStr Data-driven models to predict shale wettability for CO(2) sequestration applications
title_full_unstemmed Data-driven models to predict shale wettability for CO(2) sequestration applications
title_short Data-driven models to predict shale wettability for CO(2) sequestration applications
title_sort data-driven models to predict shale wettability for co(2) sequestration applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287739/
https://www.ncbi.nlm.nih.gov/pubmed/37349517
http://dx.doi.org/10.1038/s41598-023-37327-2
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