Abstract: Embodiments of a method of pore type classification for petrophysical rock typing are disclosed herein. In general, embodiments of the method utilize parameterization of MICP data and/or other petrophysical data for pore type classification. Furthermore, embodiments of the method involve extrapolating, predicting, or propagating the pore type classification to the well log domain. The methods described here are unique in that: they describe the process from sample selection through log-scale prediction; PTGs are defined independently of the original depositional geology; parameters which describe the whole MICP curve shape can be utilized; and objective clustering can be used to remove subjective decisions. In addition, the method exploits the link between MICP data and the petrophysical characteristics of rock samples to derive self-consistent predictions of PTG, porosity, permeability and water saturation.

Abstract: Embodiments of a method of pore type classification for petrophysical rock typing are disclosed herein. In general, embodiments of the method utilize parameterization of MICP data and/or other petrophysical data for pore type classification. Furthermore, embodiments of the method involve extrapolating, predicting, or propagating the pore type classification to the well log domain. The methods described here are unique in that: they describe the process from sample selection through log-scale prediction; PTGs are defined independently of the original depositional geology; parameters which describe the whole MICP curve shape can be utilized; and objective clustering can be used to remove subjective decisions. In addition, the method exploits the link between MICP data and the petrophysical characteristics of rock samples to derive self-consistent predictions of PTG, porosity, permeability and water saturation.