Stratigraphic subdivision-based logging curves generation using neural random forests

Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhu, Weiyao [verfasserIn] Song, Tianru [verfasserIn] Wang, Mingchuan [verfasserIn] Jin, Wujun [verfasserIn] Song, Hongqing [verfasserIn] Yue, Ming [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Log generation Machine learning Stratigraphic subdivision Neural random forests

Übergeordnetes Werk:	Enthalten in: Journal of petroleum science and engineering - Amsterdam [u.a.] : Elsevier Science, 1987, 219
Übergeordnetes Werk:	volume:219

DOI / URN:	10.1016/j.petrol.2022.111086

Katalog-ID:	ELV008716846

Internformat


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520			\|a Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs.
650		4	\|a Log generation
650		4	\|a Machine learning
650		4	\|a Stratigraphic subdivision
650		4	\|a Neural random forests
700	1		\|a Song, Tianru \|e verfasserin \|4 aut
700	1		\|a Wang, Mingchuan \|e verfasserin \|4 aut
700	1		\|a Jin, Wujun \|e verfasserin \|4 aut
700	1		\|a Song, Hongqing \|e verfasserin \|0 (orcid)0000-0002-6642-3773 \|4 aut
700	1		\|a Yue, Ming \|e verfasserin \|0 (orcid)0000-0003-2192-6032 \|4 aut
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publishDate	2022
allfields	10.1016/j.petrol.2022.111086 doi (DE-627)ELV008716846 (ELSEVIER)S0920-4105(22)00938-X DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhu, Weiyao verfasserin aut Stratigraphic subdivision-based logging curves generation using neural random forests 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs. Log generation Machine learning Stratigraphic subdivision Neural random forests Song, Tianru verfasserin aut Wang, Mingchuan verfasserin aut Jin, Wujun verfasserin aut Song, Hongqing verfasserin (orcid)0000-0002-6642-3773 aut Yue, Ming verfasserin (orcid)0000-0003-2192-6032 aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 219 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:219 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 219
spelling	10.1016/j.petrol.2022.111086 doi (DE-627)ELV008716846 (ELSEVIER)S0920-4105(22)00938-X DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhu, Weiyao verfasserin aut Stratigraphic subdivision-based logging curves generation using neural random forests 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs. Log generation Machine learning Stratigraphic subdivision Neural random forests Song, Tianru verfasserin aut Wang, Mingchuan verfasserin aut Jin, Wujun verfasserin aut Song, Hongqing verfasserin (orcid)0000-0002-6642-3773 aut Yue, Ming verfasserin (orcid)0000-0003-2192-6032 aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 219 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:219 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 219
allfields_unstemmed	10.1016/j.petrol.2022.111086 doi (DE-627)ELV008716846 (ELSEVIER)S0920-4105(22)00938-X DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhu, Weiyao verfasserin aut Stratigraphic subdivision-based logging curves generation using neural random forests 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs. Log generation Machine learning Stratigraphic subdivision Neural random forests Song, Tianru verfasserin aut Wang, Mingchuan verfasserin aut Jin, Wujun verfasserin aut Song, Hongqing verfasserin (orcid)0000-0002-6642-3773 aut Yue, Ming verfasserin (orcid)0000-0003-2192-6032 aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 219 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:219 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 219
allfieldsGer	10.1016/j.petrol.2022.111086 doi (DE-627)ELV008716846 (ELSEVIER)S0920-4105(22)00938-X DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhu, Weiyao verfasserin aut Stratigraphic subdivision-based logging curves generation using neural random forests 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs. Log generation Machine learning Stratigraphic subdivision Neural random forests Song, Tianru verfasserin aut Wang, Mingchuan verfasserin aut Jin, Wujun verfasserin aut Song, Hongqing verfasserin (orcid)0000-0002-6642-3773 aut Yue, Ming verfasserin (orcid)0000-0003-2192-6032 aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 219 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:219 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 219
allfieldsSound	10.1016/j.petrol.2022.111086 doi (DE-627)ELV008716846 (ELSEVIER)S0920-4105(22)00938-X DE-627 ger DE-627 rda eng 660 DE-600 38.51 bkl 57.36 bkl Zhu, Weiyao verfasserin aut Stratigraphic subdivision-based logging curves generation using neural random forests 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs. Log generation Machine learning Stratigraphic subdivision Neural random forests Song, Tianru verfasserin aut Wang, Mingchuan verfasserin aut Jin, Wujun verfasserin aut Song, Hongqing verfasserin (orcid)0000-0002-6642-3773 aut Yue, Ming verfasserin (orcid)0000-0003-2192-6032 aut Enthalten in Journal of petroleum science and engineering Amsterdam [u.a.] : Elsevier Science, 1987 219 Online-Ressource (DE-627)303393076 (DE-600)1494872-2 (DE-576)259484024 nnns volume:219 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 219
language	English
source	Enthalten in Journal of petroleum science and engineering 219 volume:219
sourceStr	Enthalten in Journal of petroleum science and engineering 219 volume:219
format_phy_str_mv	Article
bklname	Geologie fossiler Brennstoffe Erdölgewinnung Erdgasgewinnung
institution	findex.gbv.de
topic_facet	Log generation Machine learning Stratigraphic subdivision Neural random forests
dewey-raw	660
isfreeaccess_bool	false
container_title	Journal of petroleum science and engineering
authorswithroles_txt_mv	Zhu, Weiyao @@aut@@ Song, Tianru @@aut@@ Wang, Mingchuan @@aut@@ Jin, Wujun @@aut@@ Song, Hongqing @@aut@@ Yue, Ming @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	303393076
dewey-sort	3660
id	ELV008716846
language_de	englisch
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author	Zhu, Weiyao
spellingShingle	Zhu, Weiyao ddc 660 bkl 38.51 bkl 57.36 misc Log generation misc Machine learning misc Stratigraphic subdivision misc Neural random forests Stratigraphic subdivision-based logging curves generation using neural random forests
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topic_title	660 DE-600 38.51 bkl 57.36 bkl Stratigraphic subdivision-based logging curves generation using neural random forests Log generation Machine learning Stratigraphic subdivision Neural random forests
topic	ddc 660 bkl 38.51 bkl 57.36 misc Log generation misc Machine learning misc Stratigraphic subdivision misc Neural random forests
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title	Stratigraphic subdivision-based logging curves generation using neural random forests
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title_full	Stratigraphic subdivision-based logging curves generation using neural random forests
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title_sort	stratigraphic subdivision-based logging curves generation using neural random forests
title_auth	Stratigraphic subdivision-based logging curves generation using neural random forests
abstract	Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs.
abstractGer	Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs.
abstract_unstemmed	Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs.
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title_short	Stratigraphic subdivision-based logging curves generation using neural random forests
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author2	Song, Tianru Wang, Mingchuan Jin, Wujun Song, Hongqing Yue, Ming
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doi_str	10.1016/j.petrol.2022.111086
up_date	2024-07-06T20:39:43.208Z
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Stratigraphic subdivision-based logging curves generation using neural random forests

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