Completions and Reservoir Quality are two key attributes that are used to characterize nonconventional hydrocarbon assets.This is because,for optimum exploitation of these unconventional assets,horizontal wells need t...Completions and Reservoir Quality are two key attributes that are used to characterize nonconventional hydrocarbon assets.This is because,for optimum exploitation of these unconventional assets,horizontal wells need to be drilled in“Sweet Spots”(i.e.,regions where Completions and Reservoir Quality are both superior).One way to quantify these qualities is to use reservoir and geomechanical properties.These properties can be estimated on a location basis from well logs,and then mapped over terrain using geostatistical modeling.This study presents a‘Sweet Spots’identification workflow based on three performance indexes(Storage Potential Index,Brittleness Index,and Horizontal Stress Index)that can be used to quantify CQ and RQ.The performance indexes are computed from petrophysical property volumes(of Young's Modulus,Bulk Modulus,Shear Modulus,Poisson's Ratio,Minimum Horizontal Stress,Volume of Shale,Total Organic Carbon,Thickness,and Porosity)which are in turn computed from well logs and geostatistical simulation.In the end,the study offers a method to compare the predicted“Sweet Spots”against available production data via their correlation coefficient.The resulting reasonable formation property maps,the successful identification of‘Sweet Spots’,and a correlation coefficient of 0.88(between the predicted“Sweet Spots”and well production data)point to the potential of the proposed effort.展开更多
文摘Completions and Reservoir Quality are two key attributes that are used to characterize nonconventional hydrocarbon assets.This is because,for optimum exploitation of these unconventional assets,horizontal wells need to be drilled in“Sweet Spots”(i.e.,regions where Completions and Reservoir Quality are both superior).One way to quantify these qualities is to use reservoir and geomechanical properties.These properties can be estimated on a location basis from well logs,and then mapped over terrain using geostatistical modeling.This study presents a‘Sweet Spots’identification workflow based on three performance indexes(Storage Potential Index,Brittleness Index,and Horizontal Stress Index)that can be used to quantify CQ and RQ.The performance indexes are computed from petrophysical property volumes(of Young's Modulus,Bulk Modulus,Shear Modulus,Poisson's Ratio,Minimum Horizontal Stress,Volume of Shale,Total Organic Carbon,Thickness,and Porosity)which are in turn computed from well logs and geostatistical simulation.In the end,the study offers a method to compare the predicted“Sweet Spots”against available production data via their correlation coefficient.The resulting reasonable formation property maps,the successful identification of‘Sweet Spots’,and a correlation coefficient of 0.88(between the predicted“Sweet Spots”and well production data)point to the potential of the proposed effort.
