Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin

Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhao, Xiaobo [verfasserIn] Chen, Xiaojun [verfasserIn] Huang, Qiao [verfasserIn] Lan, Zhangjian [verfasserIn] Wang, Xinguang [verfasserIn] Yao, Guangqing [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Permeability prediction Low-permeable sandstones Machine learning Shapley additive explanations (SHAP) Pattern visualization Wenchang A Sag

Übergeordnetes Werk:	Enthalten in: Journal of petroleum science and engineering - Amsterdam [u.a.] : Elsevier Science, 1987, 214
Übergeordnetes Werk:	volume:214

DOI / URN:	10.1016/j.petrol.2022.110517

Katalog-ID:	ELV00791072X

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245	1	0	\|a Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin
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520			\|a Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones.
650		4	\|a Permeability prediction
650		4	\|a Low-permeable sandstones
650		4	\|a Machine learning
650		4	\|a Shapley additive explanations (SHAP)
650		4	\|a Pattern visualization
650		4	\|a Wenchang A Sag
700	1		\|a Chen, Xiaojun \|e verfasserin \|0 (orcid)0000-0002-9348-8345 \|4 aut
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700	1		\|a Lan, Zhangjian \|e verfasserin \|4 aut
700	1		\|a Wang, Xinguang \|e verfasserin \|4 aut
700	1		\|a Yao, Guangqing \|e verfasserin \|4 aut
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allfields	10.1016/j.petrol.2022.110517 doi (DE-627)ELV00791072X (ELSEVIER)S0920-4105(22)00397-7 DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhao, Xiaobo verfasserin aut Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones. Permeability prediction Low-permeable sandstones Machine learning Shapley additive explanations (SHAP) Pattern visualization Wenchang A Sag Chen, Xiaojun verfasserin (orcid)0000-0002-9348-8345 aut Huang, Qiao verfasserin aut Lan, Zhangjian verfasserin aut Wang, Xinguang verfasserin aut Yao, Guangqing verfasserin aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 214 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:214 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.51 Geologie fossiler Brennstoffe 57.36 Erdölgewinnung Erdgasgewinnung AR 214
spelling	10.1016/j.petrol.2022.110517 doi (DE-627)ELV00791072X (ELSEVIER)S0920-4105(22)00397-7 DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhao, Xiaobo verfasserin aut Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones. Permeability prediction Low-permeable sandstones Machine learning Shapley additive explanations (SHAP) Pattern visualization Wenchang A Sag Chen, Xiaojun verfasserin (orcid)0000-0002-9348-8345 aut Huang, Qiao verfasserin aut Lan, Zhangjian verfasserin aut Wang, Xinguang verfasserin aut Yao, Guangqing verfasserin aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 214 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:214 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.51 Geologie fossiler Brennstoffe 57.36 Erdölgewinnung Erdgasgewinnung AR 214
allfields_unstemmed	10.1016/j.petrol.2022.110517 doi (DE-627)ELV00791072X (ELSEVIER)S0920-4105(22)00397-7 DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhao, Xiaobo verfasserin aut Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones. Permeability prediction Low-permeable sandstones Machine learning Shapley additive explanations (SHAP) Pattern visualization Wenchang A Sag Chen, Xiaojun verfasserin (orcid)0000-0002-9348-8345 aut Huang, Qiao verfasserin aut Lan, Zhangjian verfasserin aut Wang, Xinguang verfasserin aut Yao, Guangqing verfasserin aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 214 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:214 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.51 Geologie fossiler Brennstoffe 57.36 Erdölgewinnung Erdgasgewinnung AR 214
allfieldsGer	10.1016/j.petrol.2022.110517 doi (DE-627)ELV00791072X (ELSEVIER)S0920-4105(22)00397-7 DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhao, Xiaobo verfasserin aut Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones. Permeability prediction Low-permeable sandstones Machine learning Shapley additive explanations (SHAP) Pattern visualization Wenchang A Sag Chen, Xiaojun verfasserin (orcid)0000-0002-9348-8345 aut Huang, Qiao verfasserin aut Lan, Zhangjian verfasserin aut Wang, Xinguang verfasserin aut Yao, Guangqing verfasserin aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 214 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:214 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.51 Geologie fossiler Brennstoffe 57.36 Erdölgewinnung Erdgasgewinnung AR 214
allfieldsSound	10.1016/j.petrol.2022.110517 doi (DE-627)ELV00791072X (ELSEVIER)S0920-4105(22)00397-7 DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhao, Xiaobo verfasserin aut Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones. Permeability prediction Low-permeable sandstones Machine learning Shapley additive explanations (SHAP) Pattern visualization Wenchang A Sag Chen, Xiaojun verfasserin (orcid)0000-0002-9348-8345 aut Huang, Qiao verfasserin aut Lan, Zhangjian verfasserin aut Wang, Xinguang verfasserin aut Yao, Guangqing verfasserin aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 214 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:214 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.51 Geologie fossiler Brennstoffe 57.36 Erdölgewinnung Erdgasgewinnung AR 214
language	English
source	Enthalten in Journal of petroleum science and engineering 214 volume:214
sourceStr	Enthalten in Journal of petroleum science and engineering 214 volume:214
format_phy_str_mv	Article
bklname	Geologie fossiler Brennstoffe Erdölgewinnung Erdgasgewinnung
institution	findex.gbv.de
topic_facet	Permeability prediction Low-permeable sandstones Machine learning Shapley additive explanations (SHAP) Pattern visualization Wenchang A Sag
dewey-raw	660
isfreeaccess_bool	false
container_title	Journal of petroleum science and engineering
authorswithroles_txt_mv	Zhao, Xiaobo @@aut@@ Chen, Xiaojun @@aut@@ Huang, Qiao @@aut@@ Lan, Zhangjian @@aut@@ Wang, Xinguang @@aut@@ Yao, Guangqing @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	303393076
dewey-sort	3660
id	ELV00791072X
language_de	englisch
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author	Zhao, Xiaobo
spellingShingle	Zhao, Xiaobo ddc 660 bkl 38.51 bkl 57.36 misc Permeability prediction misc Low-permeable sandstones misc Machine learning misc Shapley additive explanations (SHAP) misc Pattern visualization misc Wenchang A Sag Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin
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topic_title	660 DE-600 38.51 bkl 57.36 bkl Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin Permeability prediction Low-permeable sandstones Machine learning Shapley additive explanations (SHAP) Pattern visualization Wenchang A Sag
topic	ddc 660 bkl 38.51 bkl 57.36 misc Permeability prediction misc Low-permeable sandstones misc Machine learning misc Shapley additive explanations (SHAP) misc Pattern visualization misc Wenchang A Sag
topic_unstemmed	ddc 660 bkl 38.51 bkl 57.36 misc Permeability prediction misc Low-permeable sandstones misc Machine learning misc Shapley additive explanations (SHAP) misc Pattern visualization misc Wenchang A Sag
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title	Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin
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title_full	Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin
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title_sort	logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: a case study in wenchang a sag, pearl river mouth basin
title_auth	Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin
abstract	Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones.
abstractGer	Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones.
abstract_unstemmed	Permeability is a crucial analytical variable in petrophysical parameters of reservoir rocks, which is highly related to geo-energy exploration and evaluation. Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones.
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title_short	Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin
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author2	Chen, Xiaojun Huang, Qiao Lan, Zhangjian Wang, Xinguang Yao, Guangqing
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up_date	2024-07-06T17:53:28.044Z
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Conventional physics-based models and data-driven permeability estimation methods using pore-structure parameters and image parameters as input both highly depend on core-experimental parameters and have potential limitations in uncored area. Using logging data as input, seven machine learning methods in total, including Linear Regression, Back Propagation Neural Network Regression, K-Neighbors Regression, Random Forest Regression, Support Vector Machine Regression, Gradient Boosting Decision Tree Regression, and Extreme Gradient Boosting (XGBoost) Decision Tree Regression were trained to predict the permeability of low-permeable sandstones of Zhuhai Formation in Wenchang A Sag, Pearl River Mouth Basin. In the seven well-trained models, XGBoost showed the optimal performance in permeability prediction with the coefficient of determination (R 2) of 0.91088 and mean squared error (MSE) of 0.135. Moreover, the continuous permeability profile of one well was predicted successfully using the optimal model of XGBoost. Furthermore, in order to interpret the black box of machine learning of XGBoost model, SHAP (Shapley additive explanations) was introduced to interpret the well-trained model of XGBoost. The results indicated that acoustic transit time and corrected compensated neutron log in the logging series contributed the most to the permeability prediction in the low-permeability reservoir rocks. And each output result could be expressed as the sum of contributions of five input logging parameters in the XGBoost model. This work gains insight into the rapid prediction of permeability of underground aquifers and hydrocarbon-bearing layers using easily available logging data without coring and testing, and provides pattern visualization experience for permeability prediction in low-permeable sandstones.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Permeability prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Low-permeable sandstones</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Shapley additive explanations (SHAP)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pattern visualization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wenchang A Sag</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, 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Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin

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