Prediction of gas hydrate saturation using machine learning and optimal set of well-logs
We report resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well...
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Online Access: | http://www.osti.gov/servlets/purl/1893637 https://www.osti.gov/biblio/1893637 https://doi.org/10.1007/s10596-020-10004-3 |
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ftosti:oai:osti.gov:1893637 2023-07-30T03:55:36+02:00 Prediction of gas hydrate saturation using machine learning and optimal set of well-logs Singh, Harpreet Seol, Yongkoo Myshakin, Evgeniy M. 2022-11-15 application/pdf http://www.osti.gov/servlets/purl/1893637 https://www.osti.gov/biblio/1893637 https://doi.org/10.1007/s10596-020-10004-3 unknown http://www.osti.gov/servlets/purl/1893637 https://www.osti.gov/biblio/1893637 https://doi.org/10.1007/s10596-020-10004-3 doi:10.1007/s10596-020-10004-3 58 GEOSCIENCES 2022 ftosti https://doi.org/10.1007/s10596-020-10004-3 2023-07-11T10:15:44Z We report resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well-specific calibration of empirical exponents in the electrical resistivity method, (ii) assumption of known pore morphology for gas hydrates in the acoustic velocity method, and (iii) presence of unknown mineralogy and bulk modulus terms in the acoustic velocity method. NMR-density porosity-derived gas hydrate saturation based on the analysis of the transverse magnetization relaxation time (T2) is considered the most precise method, but acquisition of NMR-based logs is limited at relatively recent drilled sites; additionally, its use in conventional oil and gas reservoirs is not that common due to higher cost and operational deployment limitations associated with acquiring NMR well-logs. This study proposes a new method that predicts gas hydrate saturation (S h ) for any well using porosity, bulk density, and compressional wave (P wave) velocity well-logs with neural network (or stochastic gradient descent regression) without any well-specific calibration and/or other aforementioned shortcomings of the existing methods. The method is developed by examining the underlying dependency between S h and different combinations of well-logs, chosen from 6 routine logs, with 12 different machine learning (ML) algorithms. The accuracy of the proposed method in predicting S h is ~ 84%, which is better than the accuracy of seismic and electrical resistivity methods (≤ 75%) per the results reported by three different studies. The robustness of the method in the specific case of permafrost-associated gas hydrates is demonstrated with well-log data from two wells drilled on the Alaska North Slope. Other/Unknown Material Alaska North Slope north slope permafrost Alaska SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Computational Geosciences 25 1 267 283 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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58 GEOSCIENCES |
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58 GEOSCIENCES Singh, Harpreet Seol, Yongkoo Myshakin, Evgeniy M. Prediction of gas hydrate saturation using machine learning and optimal set of well-logs |
topic_facet |
58 GEOSCIENCES |
description |
We report resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well-specific calibration of empirical exponents in the electrical resistivity method, (ii) assumption of known pore morphology for gas hydrates in the acoustic velocity method, and (iii) presence of unknown mineralogy and bulk modulus terms in the acoustic velocity method. NMR-density porosity-derived gas hydrate saturation based on the analysis of the transverse magnetization relaxation time (T2) is considered the most precise method, but acquisition of NMR-based logs is limited at relatively recent drilled sites; additionally, its use in conventional oil and gas reservoirs is not that common due to higher cost and operational deployment limitations associated with acquiring NMR well-logs. This study proposes a new method that predicts gas hydrate saturation (S h ) for any well using porosity, bulk density, and compressional wave (P wave) velocity well-logs with neural network (or stochastic gradient descent regression) without any well-specific calibration and/or other aforementioned shortcomings of the existing methods. The method is developed by examining the underlying dependency between S h and different combinations of well-logs, chosen from 6 routine logs, with 12 different machine learning (ML) algorithms. The accuracy of the proposed method in predicting S h is ~ 84%, which is better than the accuracy of seismic and electrical resistivity methods (≤ 75%) per the results reported by three different studies. The robustness of the method in the specific case of permafrost-associated gas hydrates is demonstrated with well-log data from two wells drilled on the Alaska North Slope. |
author |
Singh, Harpreet Seol, Yongkoo Myshakin, Evgeniy M. |
author_facet |
Singh, Harpreet Seol, Yongkoo Myshakin, Evgeniy M. |
author_sort |
Singh, Harpreet |
title |
Prediction of gas hydrate saturation using machine learning and optimal set of well-logs |
title_short |
Prediction of gas hydrate saturation using machine learning and optimal set of well-logs |
title_full |
Prediction of gas hydrate saturation using machine learning and optimal set of well-logs |
title_fullStr |
Prediction of gas hydrate saturation using machine learning and optimal set of well-logs |
title_full_unstemmed |
Prediction of gas hydrate saturation using machine learning and optimal set of well-logs |
title_sort |
prediction of gas hydrate saturation using machine learning and optimal set of well-logs |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1893637 https://www.osti.gov/biblio/1893637 https://doi.org/10.1007/s10596-020-10004-3 |
genre |
Alaska North Slope north slope permafrost Alaska |
genre_facet |
Alaska North Slope north slope permafrost Alaska |
op_relation |
http://www.osti.gov/servlets/purl/1893637 https://www.osti.gov/biblio/1893637 https://doi.org/10.1007/s10596-020-10004-3 doi:10.1007/s10596-020-10004-3 |
op_doi |
https://doi.org/10.1007/s10596-020-10004-3 |
container_title |
Computational Geosciences |
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25 |
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1 |
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267 |
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283 |
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