A new method for TOC estimation in tight shale gas reservoirs
Total organic carbon (TOC) estimation is significantly crucial for shale reservoir characterization. Traditional TOC estimation methods (such as Passey and Schmoker method) do not provide accurate TOC predictions in shale gas r eservoirs especially for the self-generated and self-stored reservoirs....
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/57746 |
| _version_ | 1848760087070375936 |
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| author | Yu, H. Rezaee, M. Reza Wang, Z. Han, T. Zhang, Yihuai Arif, Muhammad Johnson, Lukman |
| author_facet | Yu, H. Rezaee, M. Reza Wang, Z. Han, T. Zhang, Yihuai Arif, Muhammad Johnson, Lukman |
| author_sort | Yu, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Total organic carbon (TOC) estimation is significantly crucial for shale reservoir characterization. Traditional TOC estimation methods (such as Passey and Schmoker method) do not provide accurate TOC predictions in shale gas r eservoirs especially for the self-generated and self-stored reservoirs. This study proposes, for the first time, a new TOC prediction method based on Gaussian Process Regression (GPR) bridging geostatistics and machine learning technique. The method utilizes a non-parametric regression approach in shale TOC predictions, and not only provides the expert solutions in high-dimension processing, small samples and non-linear problems, but also has a better adaptation and generalization ability compared with other machine learning methods. The approach accounts for all the well logging attributes and chooses the relevant logs to build TOC estimation model, and 7 different kernel functions and 5 attributes groups are analyzed to get the optimized hyperparameters in practice. Application of the developed model to two shale gas reservoirs showed that the model predicted TOC matched well with that from the laboratory measurements. The proposed model based on GPR method provides an accurate way for the TOC prediction in the tight shale gas reservoirs. |
| first_indexed | 2025-11-14T10:10:12Z |
| format | Journal Article |
| id | curtin-20.500.11937-57746 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:10:12Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-577462019-07-02T03:47:47Z A new method for TOC estimation in tight shale gas reservoirs Yu, H. Rezaee, M. Reza Wang, Z. Han, T. Zhang, Yihuai Arif, Muhammad Johnson, Lukman Total organic carbon (TOC) estimation is significantly crucial for shale reservoir characterization. Traditional TOC estimation methods (such as Passey and Schmoker method) do not provide accurate TOC predictions in shale gas r eservoirs especially for the self-generated and self-stored reservoirs. This study proposes, for the first time, a new TOC prediction method based on Gaussian Process Regression (GPR) bridging geostatistics and machine learning technique. The method utilizes a non-parametric regression approach in shale TOC predictions, and not only provides the expert solutions in high-dimension processing, small samples and non-linear problems, but also has a better adaptation and generalization ability compared with other machine learning methods. The approach accounts for all the well logging attributes and chooses the relevant logs to build TOC estimation model, and 7 different kernel functions and 5 attributes groups are analyzed to get the optimized hyperparameters in practice. Application of the developed model to two shale gas reservoirs showed that the model predicted TOC matched well with that from the laboratory measurements. The proposed model based on GPR method provides an accurate way for the TOC prediction in the tight shale gas reservoirs. 2017 Journal Article http://hdl.handle.net/20.500.11937/57746 10.1016/j.coal.2017.06.011 fulltext |
| spellingShingle | Yu, H. Rezaee, M. Reza Wang, Z. Han, T. Zhang, Yihuai Arif, Muhammad Johnson, Lukman A new method for TOC estimation in tight shale gas reservoirs |
| title | A new method for TOC estimation in tight shale gas reservoirs |
| title_full | A new method for TOC estimation in tight shale gas reservoirs |
| title_fullStr | A new method for TOC estimation in tight shale gas reservoirs |
| title_full_unstemmed | A new method for TOC estimation in tight shale gas reservoirs |
| title_short | A new method for TOC estimation in tight shale gas reservoirs |
| title_sort | new method for toc estimation in tight shale gas reservoirs |
| url | http://hdl.handle.net/20.500.11937/57746 |