How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue...How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workfl展开更多
The mechanisms causing quality variations and key control factors of submarine-fan reservoirs in the gas field X of the Rovuma Basin,East Africa are analyzed based on core and well-log data in this paper.Depositional ...The mechanisms causing quality variations and key control factors of submarine-fan reservoirs in the gas field X of the Rovuma Basin,East Africa are analyzed based on core and well-log data in this paper.Depositional fabric,lithofacies difference and characteristics of genetic units are the fundamental reasons of reservoir quality variations.In the case of weak cementation,porosity and permeability of submarine-fan reservoirs are controlled by grain sorting and clay content,respectively.Reservoir quality variations for 5 main lithofacies are related to variable depositional fabrics and calcite cementation.Among them,massive medium-coarse sandstones with weak cementation have the highest porosity and permeability,and coarser or finer sandstones have poorer reservoir quality.The existence of bottom current can develop laminated sandstones,improving the pore structure and physical properties greatly.Lithofacies vary among different types,locations and stages of genetic units,and they control the distribution patterns of submarine-fan reservoir quality:the physical properties of channel shaft or lobe main body are better than those of the channel or lobe edge.The sandstone sorting and physical properties are gradually improved from near-source to far-source.When multi-stage sand bodies are superimposed,the sand-mud ratio in the later stage is higher than that in the earlier stage,making the physical properties get better in the later stage.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workfl
文摘The mechanisms causing quality variations and key control factors of submarine-fan reservoirs in the gas field X of the Rovuma Basin,East Africa are analyzed based on core and well-log data in this paper.Depositional fabric,lithofacies difference and characteristics of genetic units are the fundamental reasons of reservoir quality variations.In the case of weak cementation,porosity and permeability of submarine-fan reservoirs are controlled by grain sorting and clay content,respectively.Reservoir quality variations for 5 main lithofacies are related to variable depositional fabrics and calcite cementation.Among them,massive medium-coarse sandstones with weak cementation have the highest porosity and permeability,and coarser or finer sandstones have poorer reservoir quality.The existence of bottom current can develop laminated sandstones,improving the pore structure and physical properties greatly.Lithofacies vary among different types,locations and stages of genetic units,and they control the distribution patterns of submarine-fan reservoir quality:the physical properties of channel shaft or lobe main body are better than those of the channel or lobe edge.The sandstone sorting and physical properties are gradually improved from near-source to far-source.When multi-stage sand bodies are superimposed,the sand-mud ratio in the later stage is higher than that in the earlier stage,making the physical properties get better in the later stage.
