Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data
Water saturation determination is among the most challenging tasks in petrophysical well-logging, which directly impacts the decision-making process in hydrocarbon exploration and production. Low-field nuclear magnetic resonance (LF-NMR) measurements can provide reliable evaluation. However, quantif...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/89526 |
| _version_ | 1848765235960217600 |
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| author | Markovic, Strahinja Bryan, J.L. Rezaee, Reza Turakhanov, A. Cheremisin, A. Kantzas, A. Koroteev, D. |
| author_facet | Markovic, Strahinja Bryan, J.L. Rezaee, Reza Turakhanov, A. Cheremisin, A. Kantzas, A. Koroteev, D. |
| author_sort | Markovic, Strahinja |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Water saturation determination is among the most challenging tasks in petrophysical well-logging, which directly impacts the decision-making process in hydrocarbon exploration and production. Low-field nuclear magnetic resonance (LF-NMR) measurements can provide reliable evaluation. However, quantification of oil and water volumes is problematic when their NMR signals are not distinct. To overcome this, we developed two machine learning frameworks for predicting relative water content in oil-sand samples using LF-NMR spin–spin (T2) relaxation and bulk density data to derive a model based on Extreme Gradient Boosting. The first one facilitates feature engineering based on empirical knowledge from the T2 relaxation distribution analysis domain and mutual information feature extraction technique, while the second model considers whole samples’ NMR T2-relaxation distribution. The NMR T2 distributions were obtained for 82 Canadian oil-sands samples at ambient and reservoir temperatures (164 data points). The true water content was determined by Dean-Stark extraction. The statistical scores confirm the strong generalization ability of the feature engineering LF-NMR model in predicting relative water content by Dean-Stark—root-mean-square error of 0.67% and mean-absolute error of 0.53% (R2 = 0.90). Results indicate that this approach can be extended for the improved in-situ water saturation evaluation by LF-NMR and bulk density measurements. |
| first_indexed | 2025-11-14T11:32:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-89526 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | eng |
| last_indexed | 2025-11-14T11:32:02Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-895262023-01-18T07:12:24Z Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data Markovic, Strahinja Bryan, J.L. Rezaee, Reza Turakhanov, A. Cheremisin, A. Kantzas, A. Koroteev, D. Canada Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Rapeseed Oil Sand Water saturation determination is among the most challenging tasks in petrophysical well-logging, which directly impacts the decision-making process in hydrocarbon exploration and production. Low-field nuclear magnetic resonance (LF-NMR) measurements can provide reliable evaluation. However, quantification of oil and water volumes is problematic when their NMR signals are not distinct. To overcome this, we developed two machine learning frameworks for predicting relative water content in oil-sand samples using LF-NMR spin–spin (T2) relaxation and bulk density data to derive a model based on Extreme Gradient Boosting. The first one facilitates feature engineering based on empirical knowledge from the T2 relaxation distribution analysis domain and mutual information feature extraction technique, while the second model considers whole samples’ NMR T2-relaxation distribution. The NMR T2 distributions were obtained for 82 Canadian oil-sands samples at ambient and reservoir temperatures (164 data points). The true water content was determined by Dean-Stark extraction. The statistical scores confirm the strong generalization ability of the feature engineering LF-NMR model in predicting relative water content by Dean-Stark—root-mean-square error of 0.67% and mean-absolute error of 0.53% (R2 = 0.90). Results indicate that this approach can be extended for the improved in-situ water saturation evaluation by LF-NMR and bulk density measurements. 2022 Journal Article http://hdl.handle.net/20.500.11937/89526 10.1038/s41598-022-17886-6 eng http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Canada Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Rapeseed Oil Sand Markovic, Strahinja Bryan, J.L. Rezaee, Reza Turakhanov, A. Cheremisin, A. Kantzas, A. Koroteev, D. Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data |
| title | Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data |
| title_full | Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data |
| title_fullStr | Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data |
| title_full_unstemmed | Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data |
| title_short | Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data |
| title_sort | application of xgboost model for in-situ water saturation determination in canadian oil-sands by lf-nmr and density data |
| topic | Canada Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Rapeseed Oil Sand |
| url | http://hdl.handle.net/20.500.11937/89526 |