Permeability prediction from mercury injection capillary pressure curves by partial least squares regression method in tight sandstone reservoirs

Abstract

Permeability is an essential petrophysical parameter for reservoir modeling, reservoir classification, and productivity prediction in tight sandstone reservoirs. In this study, multiple parameters are extracted from the mercury injection capillary pressure (MICP) curves and the degree of multicollinearity between these parameters is analyzed. The partial least squares regression (PLSR) method is used for establishing the permeability prediction model and the optimal number of latent variables of the model is determined by the leave-one-out cross-validation (LOOCV) method. A comparison of the existing empirical models, the permeability prediction model by ordinary least square (OLS) method, and the permeability prediction model by PLSR method based on the MICP curves indicates that the permeability prediction model by PLSR method is superior to the other models for tight sandstone reservoirs.