Outcrop analogue studies play an important role in advancing our comprehension of reservoir architectures,offering insights into hidden reservoir rocks prior to drilling,in a cost-effective manner.These studies contri...Outcrop analogue studies play an important role in advancing our comprehension of reservoir architectures,offering insights into hidden reservoir rocks prior to drilling,in a cost-effective manner.These studies contribute to the delineation of the three-dimensional geometry of geological structures,the characterization of petro-and thermo-physical properties,and the structural geological aspects of reservoir rocks.Nevertheless,several challenges,including inaccessible sampling sites,limited resources,and the dimensional constraints of different laboratories hinder the acquisition of comprehensive datasets.In this study,we employ machine learning techniques to estimate missing data in a petrophysical dataset of fractured Variscan granites from the Cornubian Batholith in Southwest UK.The utilization of mean,k-nearest neighbors,and random forest imputation methods addresses the challenge of missing data,thereby revealing the effectiveness of random forest imputation in providing realistic estimations.Subsequently,supervised classification models are trained to classify samples according to their pluton origins,with promising accuracy achieved by models trained with imputed values.Variable importance ranking of the models showed that the choice of imputation method influences the inferred importance of specific petrophysical properties.While porosity(POR)and grain density(GD)were among important variables,variables with high missingness ratio were not among the top variables.This study demonstrates the value of machine learning in enhancing petrophysical datasets,while emphasizing the importance of careful method selection and model validation for reliable results.The findings contribute to a more informed decision-making process in geothermal exploration and reservoir tion characteriza-efforts,thereby demonstrating the potential of machine learning in advancing subsurface characterization techniques.展开更多
文摘Outcrop analogue studies play an important role in advancing our comprehension of reservoir architectures,offering insights into hidden reservoir rocks prior to drilling,in a cost-effective manner.These studies contribute to the delineation of the three-dimensional geometry of geological structures,the characterization of petro-and thermo-physical properties,and the structural geological aspects of reservoir rocks.Nevertheless,several challenges,including inaccessible sampling sites,limited resources,and the dimensional constraints of different laboratories hinder the acquisition of comprehensive datasets.In this study,we employ machine learning techniques to estimate missing data in a petrophysical dataset of fractured Variscan granites from the Cornubian Batholith in Southwest UK.The utilization of mean,k-nearest neighbors,and random forest imputation methods addresses the challenge of missing data,thereby revealing the effectiveness of random forest imputation in providing realistic estimations.Subsequently,supervised classification models are trained to classify samples according to their pluton origins,with promising accuracy achieved by models trained with imputed values.Variable importance ranking of the models showed that the choice of imputation method influences the inferred importance of specific petrophysical properties.While porosity(POR)and grain density(GD)were among important variables,variables with high missingness ratio were not among the top variables.This study demonstrates the value of machine learning in enhancing petrophysical datasets,while emphasizing the importance of careful method selection and model validation for reliable results.The findings contribute to a more informed decision-making process in geothermal exploration and reservoir tion characteriza-efforts,thereby demonstrating the potential of machine learning in advancing subsurface characterization techniques.
