Geostatistical Inversion for Petrophysical Properties Constrained Using Maturity Facies in Shale Reservoirs

Shale reservoirs are a key category of unconventional resources, with shale oil and gas playing a significant role in the global energy supply. The thermal maturity level, indicative of the degree of kerogen-to-hydrocarbon conversion, profoundly controls the rock properties, pore structure, and hydrocarbon distribution in organic-rich shales. The goal of seismic reservoir characterization is to estimate elastic and physical properties from seismic and well log data, based on elastic wave propagation and the rock-physics relations. In this study, we propose an inversion strategy and workflow to estimate petrophysical properties (clay volume, porosity, and TOC content) of shale reservoirs considering the maturity influence. This method integrates geostatistical simulation with geophysical modeling (including rock physics and convolutional models) to generate high-resolution models of petrophysical properties, conditioned by the maturity facies. By employing stochastic nonlinear inversion techniques, the approach enables sweet spot prediction of organic-rich shale reservoirs. We also compare the proposed approach with the conventional Bayesian two-step inversion. The results show that the proposed method in this paper has better consistency with the verification well in terms of reservoir location.