A rock physics driven Bayesian inversion for TOC in the Fuling shale gas reservoir. (April 2019) - Qian, Keran; Ning, Junrui; Liu, Xiwu; Zhang, Yuanyin
Abstract: Total organic content (TOC) is an important indicator in the evaluation of the organic abundance and production potential of shale reservoirs. The conventional approach to TOC estimation is to correlate TOC with inverted elastic parameters in a linearized empirical equation. However, this approach ignores complex rock physics properties (such as complex mineralogy and pore space) in the shale while generating smooth, low resolution results. This paper proposes a rock physics-based method to forward model the seismic and elastic response of the shale reservoir and then invert using a Bayesian approach to derive key parameters, such as the TOC, total porosity and the fractional volume of brittle minerals, and to delineate the sweet-spots in an unconventional shale reservoir. Highlights: A linear rock physical model has been proposed to solves the under-determination problem. A two-stage linear Bayesian inversion workflow has been proposed to predict TOC. The proposed method has higher computational efficiency.