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Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs

Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining c...

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Autores principales: Siddig, Osama, Ibrahim, Ahmed Farid, Elkatatny, Salaheldin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448996/
https://www.ncbi.nlm.nih.gov/pubmed/34545282
http://dx.doi.org/10.1155/2021/7390055
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author Siddig, Osama
Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
author_facet Siddig, Osama
Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
author_sort Siddig, Osama
collection PubMed
description Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δt), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), uranium (Ur), and potassium (K). Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods' parameters were tested to assure the best possible accuracy in terms of correlation coefficient (R) and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with R values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions.
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spelling pubmed-84489962021-09-19 Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs Siddig, Osama Ibrahim, Ahmed Farid Elkatatny, Salaheldin Comput Intell Neurosci Research Article Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δt), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), uranium (Ur), and potassium (K). Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods' parameters were tested to assure the best possible accuracy in terms of correlation coefficient (R) and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with R values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions. Hindawi 2021-08-30 /pmc/articles/PMC8448996/ /pubmed/34545282 http://dx.doi.org/10.1155/2021/7390055 Text en Copyright © 2021 Osama Siddig et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Siddig, Osama
Ibrahim, Ahmed Farid
Elkatatny, Salaheldin
Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs
title Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs
title_full Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs
title_fullStr Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs
title_full_unstemmed Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs
title_short Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs
title_sort application of various machine learning techniques in predicting total organic carbon from well logs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448996/
https://www.ncbi.nlm.nih.gov/pubmed/34545282
http://dx.doi.org/10.1155/2021/7390055
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