Estimation of the porosity and pore aspect ratio of the Haynesville Shale using the Self-Consistent Model and a Grid Search Method

Abstract

Porosity and pore aspect ratio are two important parameters for reservoir characterization of unconventional gas shales. Porosity estimation helps to determine gas capacity, as well as the bulk density of shales. The pore aspect ratio estimation helps to understand where the stiffest or softest intervals are, and along with density, more favorable for hydraulic fracturing. This work introduces an algorithm to estimate the porosity distribution and an algorithm to estimate the pore aspect ratio distribution of the Haynesville Shale. Both algorithms are based on the self-consistent model and a grid search method. For the porosity estimation, we first calibrated a specific self-consistent model that contains a representative composition assemblage and pore aspect ratio distribution. Then a grid search method was combined with this specific self-consistent model to generate a probabilistic estimation of porosity. The estimated porosity matched with the observed porosity. For the pore aspect ratio estimation, we first generated a group of self-consistent models that contained all plausible pore aspect ratios. Then a grid search based on P-impedance and porosity was applied to the self-consistent models and provided the matching pore aspect ratios. When seismic data from a 3D volume is involved, 3D distributions of porosity and pore aspect ratio can be characterized.