Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or ...Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or thin sections,face limitations associated with depth specificity.In this study,we introduce an innovative framework that leverages nuclear magnetic resonance(NMR)log data,encompassing clay-bound water(CBW),bound volume irreducible(BVI),and free fluid volume(FFV),to determine three PSDs(micropores,mesopores,and macropores).Moreover,we establish a robust pore size classification(PSC)system utilizing ternary plots,derived from the PSDs.Within the three studied wells,NMR log data is exclusive to one well(well-A),while conventional well logs are accessible for all three wells(well-A,well-B,and well-C).This distinction enables PSD predictions for the remaining two wells(B and C).To prognosticate NMR outputs(CBW,BVI,FFV)for these wells,a two-step deep learning(DL)algorithm is implemented.Initially,three feature selection algorithms(f-classif,f-regression,and mutual-info-regression)identify the conventional well logs most correlated to NMR outputs in well-A.The three feature selection algorithms utilize statistical computations.These algorithms are utilized to systematically identify and optimize pertinent input features,thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors.So,all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs.Subsequently,the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM),belonging to the category of DL algorithms and harnessing the computational power of GPUs,is employed for the prediction of CBW,BVI,and FFV logs.This prediction leverages the optimal logs identified in the preceding step.Estimation of NMR outputs was done first in well-A(80%of data as training and 20%as testing).The correlation coefficient(CC)between the actual and estimated data for the three outputs CBW,BVI and FFV are 95%,94%,and 97%,respectively,as well as root mean square error(RMSE)was obtained 0.0081,0.098,and 0.0089,respectively.To assess the effectiveness of the proposed algorithm,we compared it with two traditional methods for log estimation:multiple regression and multi-resolution graph-based clustering methods.The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches.This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs.Seven distinct PSCs within well-A employing actual NMR logs(CBW,BVI,FFV),in conjunction with an equivalent count within wells B and C utilizing three predicted logs,are harmoniously categorized leading to the identification of seven distinct pore size classification facies(PSCF).this research introduces an advanced approach to pore size classification and prediction,fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log.The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.展开更多
Deep coal reservoirs(buried depth>2000 m)represent a significant yet underexploited resource for coalbed methane(CBM)production.In these reservoirs,CBM primarily exists in adsorbed and free phase,with the pore stru...Deep coal reservoirs(buried depth>2000 m)represent a significant yet underexploited resource for coalbed methane(CBM)production.In these reservoirs,CBM primarily exists in adsorbed and free phase,with the pore structure playing a critical role in gas storage and migration.The Jiaxian block in the northeastern Ordos Basin,has emerged as a key area for deep CBM exploration due to its promising resource potential.However,the pore structure characteristics of the No.8 coal seam in Jiaxian block and their implications for gas storage and production remain poorly understood.A comprehensive characterization of the No.8 coal seam's pore structure is conducted in the study using multiple methods including high-pressure mercury injection,N2/CO_(2)adsorption experiments,and integration of measured core gas content data and production history.The study results reveal that the pores can be mainly classified as vesicles and cellular pores,and the fractures are mainly static pressure fractures.Micropores(pore diameter<10 nm)dominate the pore system(accounting for more than 99%of the total specific surface area),providing important adsorption sites for gas storage.Although mesopores(pore diameter of 100-1000 nm)and macropores(pore diameter>1000 nm)account for a small proportion,they feature effective storage spaces and interconnectivity,resulting in a high proportion of free gas.Therefore,the reservoirs shows great development potential after stimulation(such as hydraulic fracturing).These findings emphasize the feasibility of large-scale and long-term development of CBM in the Jiaxian block in terms of reservoir space,gas content and production characteristics.This study serves to lay a scientific basis for its efficient exploitation.展开更多
文摘Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or thin sections,face limitations associated with depth specificity.In this study,we introduce an innovative framework that leverages nuclear magnetic resonance(NMR)log data,encompassing clay-bound water(CBW),bound volume irreducible(BVI),and free fluid volume(FFV),to determine three PSDs(micropores,mesopores,and macropores).Moreover,we establish a robust pore size classification(PSC)system utilizing ternary plots,derived from the PSDs.Within the three studied wells,NMR log data is exclusive to one well(well-A),while conventional well logs are accessible for all three wells(well-A,well-B,and well-C).This distinction enables PSD predictions for the remaining two wells(B and C).To prognosticate NMR outputs(CBW,BVI,FFV)for these wells,a two-step deep learning(DL)algorithm is implemented.Initially,three feature selection algorithms(f-classif,f-regression,and mutual-info-regression)identify the conventional well logs most correlated to NMR outputs in well-A.The three feature selection algorithms utilize statistical computations.These algorithms are utilized to systematically identify and optimize pertinent input features,thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors.So,all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs.Subsequently,the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM),belonging to the category of DL algorithms and harnessing the computational power of GPUs,is employed for the prediction of CBW,BVI,and FFV logs.This prediction leverages the optimal logs identified in the preceding step.Estimation of NMR outputs was done first in well-A(80%of data as training and 20%as testing).The correlation coefficient(CC)between the actual and estimated data for the three outputs CBW,BVI and FFV are 95%,94%,and 97%,respectively,as well as root mean square error(RMSE)was obtained 0.0081,0.098,and 0.0089,respectively.To assess the effectiveness of the proposed algorithm,we compared it with two traditional methods for log estimation:multiple regression and multi-resolution graph-based clustering methods.The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches.This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs.Seven distinct PSCs within well-A employing actual NMR logs(CBW,BVI,FFV),in conjunction with an equivalent count within wells B and C utilizing three predicted logs,are harmoniously categorized leading to the identification of seven distinct pore size classification facies(PSCF).this research introduces an advanced approach to pore size classification and prediction,fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log.The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.
基金funded by the National Key R&D Program of China(2024YFC2909400)the National Natural Science Foundation of China(42402180,42202195)the tackling applied science and technology projects of China National Petroleum Corporation(2023ZZ18)。
文摘Deep coal reservoirs(buried depth>2000 m)represent a significant yet underexploited resource for coalbed methane(CBM)production.In these reservoirs,CBM primarily exists in adsorbed and free phase,with the pore structure playing a critical role in gas storage and migration.The Jiaxian block in the northeastern Ordos Basin,has emerged as a key area for deep CBM exploration due to its promising resource potential.However,the pore structure characteristics of the No.8 coal seam in Jiaxian block and their implications for gas storage and production remain poorly understood.A comprehensive characterization of the No.8 coal seam's pore structure is conducted in the study using multiple methods including high-pressure mercury injection,N2/CO_(2)adsorption experiments,and integration of measured core gas content data and production history.The study results reveal that the pores can be mainly classified as vesicles and cellular pores,and the fractures are mainly static pressure fractures.Micropores(pore diameter<10 nm)dominate the pore system(accounting for more than 99%of the total specific surface area),providing important adsorption sites for gas storage.Although mesopores(pore diameter of 100-1000 nm)and macropores(pore diameter>1000 nm)account for a small proportion,they feature effective storage spaces and interconnectivity,resulting in a high proportion of free gas.Therefore,the reservoirs shows great development potential after stimulation(such as hydraulic fracturing).These findings emphasize the feasibility of large-scale and long-term development of CBM in the Jiaxian block in terms of reservoir space,gas content and production characteristics.This study serves to lay a scientific basis for its efficient exploitation.
