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 reservoirs especially for the self-generated and self-stored reservoirs. T...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yu, Hongyan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Using a bacterial fucose-rich polysaccharide as encapsulation material of bioactive compounds - Lourenço, Sofia C. ELSEVIER, 2017transfer abstract, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:179 ; year:2017 ; day:15 ; month:06 ; pages:269-277 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.coal.2017.06.011 |
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| Katalog-ID: |
ELV040320863 |
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| 245 | 1 | 0 | |a A new method for TOC estimation in tight shale gas reservoirs |
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| 520 | |a 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 reservoirs 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. | ||
| 520 | |a 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 reservoirs 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. | ||
| 700 | 1 | |a Rezaee, Reza |4 oth | |
| 700 | 1 | |a Wang, Zhenliang |4 oth | |
| 700 | 1 | |a Han, Tongcheng |4 oth | |
| 700 | 1 | |a Zhang, Yihuai |4 oth | |
| 700 | 1 | |a Arif, Muhammad |4 oth | |
| 700 | 1 | |a Johnson, Lukman |4 oth | |
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10.1016/j.coal.2017.06.011 doi GBVA2017005000009.pica (DE-627)ELV040320863 (ELSEVIER)S0166-5162(17)30368-3 DE-627 ger DE-627 rakwb eng 550 620 660 550 DE-600 620 DE-600 660 DE-600 540 VZ 570 VZ 570 610 VZ 58.30 bkl 50.22 bkl 44.09 bkl Yu, Hongyan verfasserin aut A new method for TOC estimation in tight shale gas reservoirs 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 reservoirs 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. 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 reservoirs 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. Rezaee, Reza oth Wang, Zhenliang oth Han, Tongcheng oth Zhang, Yihuai oth Arif, Muhammad oth Johnson, Lukman oth Enthalten in Elsevier Lourenço, Sofia C. ELSEVIER Using a bacterial fucose-rich polysaccharide as encapsulation material of bioactive compounds 2017transfer abstract Amsterdam [u.a.] (DE-627)ELV019980760 volume:179 year:2017 day:15 month:06 pages:269-277 extent:9 https://doi.org/10.1016/j.coal.2017.06.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.30 Biotechnologie VZ 50.22 Sensorik VZ 44.09 Medizintechnik VZ AR 179 2017 15 0615 269-277 9 045F 550 |
| spelling |
10.1016/j.coal.2017.06.011 doi GBVA2017005000009.pica (DE-627)ELV040320863 (ELSEVIER)S0166-5162(17)30368-3 DE-627 ger DE-627 rakwb eng 550 620 660 550 DE-600 620 DE-600 660 DE-600 540 VZ 570 VZ 570 610 VZ 58.30 bkl 50.22 bkl 44.09 bkl Yu, Hongyan verfasserin aut A new method for TOC estimation in tight shale gas reservoirs 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 reservoirs 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. 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 reservoirs 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. Rezaee, Reza oth Wang, Zhenliang oth Han, Tongcheng oth Zhang, Yihuai oth Arif, Muhammad oth Johnson, Lukman oth Enthalten in Elsevier Lourenço, Sofia C. ELSEVIER Using a bacterial fucose-rich polysaccharide as encapsulation material of bioactive compounds 2017transfer abstract Amsterdam [u.a.] (DE-627)ELV019980760 volume:179 year:2017 day:15 month:06 pages:269-277 extent:9 https://doi.org/10.1016/j.coal.2017.06.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.30 Biotechnologie VZ 50.22 Sensorik VZ 44.09 Medizintechnik VZ AR 179 2017 15 0615 269-277 9 045F 550 |
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10.1016/j.coal.2017.06.011 doi GBVA2017005000009.pica (DE-627)ELV040320863 (ELSEVIER)S0166-5162(17)30368-3 DE-627 ger DE-627 rakwb eng 550 620 660 550 DE-600 620 DE-600 660 DE-600 540 VZ 570 VZ 570 610 VZ 58.30 bkl 50.22 bkl 44.09 bkl Yu, Hongyan verfasserin aut A new method for TOC estimation in tight shale gas reservoirs 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 reservoirs 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. 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 reservoirs 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. Rezaee, Reza oth Wang, Zhenliang oth Han, Tongcheng oth Zhang, Yihuai oth Arif, Muhammad oth Johnson, Lukman oth Enthalten in Elsevier Lourenço, Sofia C. ELSEVIER Using a bacterial fucose-rich polysaccharide as encapsulation material of bioactive compounds 2017transfer abstract Amsterdam [u.a.] (DE-627)ELV019980760 volume:179 year:2017 day:15 month:06 pages:269-277 extent:9 https://doi.org/10.1016/j.coal.2017.06.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.30 Biotechnologie VZ 50.22 Sensorik VZ 44.09 Medizintechnik VZ AR 179 2017 15 0615 269-277 9 045F 550 |
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10.1016/j.coal.2017.06.011 doi GBVA2017005000009.pica (DE-627)ELV040320863 (ELSEVIER)S0166-5162(17)30368-3 DE-627 ger DE-627 rakwb eng 550 620 660 550 DE-600 620 DE-600 660 DE-600 540 VZ 570 VZ 570 610 VZ 58.30 bkl 50.22 bkl 44.09 bkl Yu, Hongyan verfasserin aut A new method for TOC estimation in tight shale gas reservoirs 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 reservoirs 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. 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 reservoirs 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. Rezaee, Reza oth Wang, Zhenliang oth Han, Tongcheng oth Zhang, Yihuai oth Arif, Muhammad oth Johnson, Lukman oth Enthalten in Elsevier Lourenço, Sofia C. ELSEVIER Using a bacterial fucose-rich polysaccharide as encapsulation material of bioactive compounds 2017transfer abstract Amsterdam [u.a.] (DE-627)ELV019980760 volume:179 year:2017 day:15 month:06 pages:269-277 extent:9 https://doi.org/10.1016/j.coal.2017.06.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.30 Biotechnologie VZ 50.22 Sensorik VZ 44.09 Medizintechnik VZ AR 179 2017 15 0615 269-277 9 045F 550 |
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10.1016/j.coal.2017.06.011 doi GBVA2017005000009.pica (DE-627)ELV040320863 (ELSEVIER)S0166-5162(17)30368-3 DE-627 ger DE-627 rakwb eng 550 620 660 550 DE-600 620 DE-600 660 DE-600 540 VZ 570 VZ 570 610 VZ 58.30 bkl 50.22 bkl 44.09 bkl Yu, Hongyan verfasserin aut A new method for TOC estimation in tight shale gas reservoirs 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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 reservoirs 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. 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 reservoirs 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. Rezaee, Reza oth Wang, Zhenliang oth Han, Tongcheng oth Zhang, Yihuai oth Arif, Muhammad oth Johnson, Lukman oth Enthalten in Elsevier Lourenço, Sofia C. ELSEVIER Using a bacterial fucose-rich polysaccharide as encapsulation material of bioactive compounds 2017transfer abstract Amsterdam [u.a.] (DE-627)ELV019980760 volume:179 year:2017 day:15 month:06 pages:269-277 extent:9 https://doi.org/10.1016/j.coal.2017.06.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 58.30 Biotechnologie VZ 50.22 Sensorik VZ 44.09 Medizintechnik VZ AR 179 2017 15 0615 269-277 9 045F 550 |
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Yu, Hongyan @@aut@@ Rezaee, Reza @@oth@@ Wang, Zhenliang @@oth@@ Han, Tongcheng @@oth@@ Zhang, Yihuai @@oth@@ Arif, Muhammad @@oth@@ Johnson, Lukman @@oth@@ |
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Traditional TOC estimation methods (such as Passey and Schmoker method) do not provide accurate TOC predictions in shale gas reservoirs 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. 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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 reservoirs 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. |
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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 reservoirs 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. |
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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 reservoirs 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. |
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