Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)

Water saturation (Sw) is one of the significant parameters in hydrocarbon volume estimation. However, accurate estimation of this parameter is always difficult due the presence of clay minerals in the formation, which has a direct impact not only on the well log but also the core analysis data. The...

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Published in:AIP Conference Proceedings
Main Author: Wan Z.; Dollah M.R.; Khalid N.S.A.
Format: Conference paper
Language:English
Published: American Institute of Physics 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203973374&doi=10.1063%2f5.0230422&partnerID=40&md5=dd1819ebdd9a47580961ac17f66909c1
id 2-s2.0-85203973374
spelling 2-s2.0-85203973374
Wan Z.; Dollah M.R.; Khalid N.S.A.
Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)
2024
AIP Conference Proceedings
3161
1
10.1063/5.0230422
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203973374&doi=10.1063%2f5.0230422&partnerID=40&md5=dd1819ebdd9a47580961ac17f66909c1
Water saturation (Sw) is one of the significant parameters in hydrocarbon volume estimation. However, accurate estimation of this parameter is always difficult due the presence of clay minerals in the formation, which has a direct impact not only on the well log but also the core analysis data. The excess conductivity of the clay minerals would diminish the resistivity log data in the hydrocarbon reservoir leading to overestimation of Sw. In the experimental works of core samples, the clay conductivity would influence the determination of cementation factor and saturation exponent. All the aforementioned factors together with porosity would compromise the accuracy in Sw determination if mathematical model in conventional technique is used. With the evolution of artificial intelligent (AI) in the oil and gas industry, many had benefited from this approach by implementing data prediction from a single input parameter. This would reduce the impact of uncertainties from several input parameters such as in the case of Sw determination. In this paper, a new technique for estimating Sw using AI and integrated petrophysical analysis is introduced. A program code written in Python was established using Extreme Gradient Boosting (XGBoost) method with resistivity index (RI) from core analysis as input for model training and well log resistivity data for Sw prediction. Performance prediction was evaluated using mean squared error (MSE), root mean square error (RMSE) and R2. © 2024 Author(s).
American Institute of Physics
0094243X
English
Conference paper

author Wan Z.; Dollah M.R.; Khalid N.S.A.
spellingShingle Wan Z.; Dollah M.R.; Khalid N.S.A.
Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)
author_facet Wan Z.; Dollah M.R.; Khalid N.S.A.
author_sort Wan Z.; Dollah M.R.; Khalid N.S.A.
title Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)
title_short Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)
title_full Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)
title_fullStr Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)
title_full_unstemmed Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)
title_sort Reservoir characterization through integrated petrophysical approach with the application of extreme gradient boosting (XGBoost)
publishDate 2024
container_title AIP Conference Proceedings
container_volume 3161
container_issue 1
doi_str_mv 10.1063/5.0230422
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203973374&doi=10.1063%2f5.0230422&partnerID=40&md5=dd1819ebdd9a47580961ac17f66909c1
description Water saturation (Sw) is one of the significant parameters in hydrocarbon volume estimation. However, accurate estimation of this parameter is always difficult due the presence of clay minerals in the formation, which has a direct impact not only on the well log but also the core analysis data. The excess conductivity of the clay minerals would diminish the resistivity log data in the hydrocarbon reservoir leading to overestimation of Sw. In the experimental works of core samples, the clay conductivity would influence the determination of cementation factor and saturation exponent. All the aforementioned factors together with porosity would compromise the accuracy in Sw determination if mathematical model in conventional technique is used. With the evolution of artificial intelligent (AI) in the oil and gas industry, many had benefited from this approach by implementing data prediction from a single input parameter. This would reduce the impact of uncertainties from several input parameters such as in the case of Sw determination. In this paper, a new technique for estimating Sw using AI and integrated petrophysical analysis is introduced. A program code written in Python was established using Extreme Gradient Boosting (XGBoost) method with resistivity index (RI) from core analysis as input for model training and well log resistivity data for Sw prediction. Performance prediction was evaluated using mean squared error (MSE), root mean square error (RMSE) and R2. © 2024 Author(s).
publisher American Institute of Physics
issn 0094243X
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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