Anisotropic seismic characterization of the Eagle Ford Shale: rock-physics modeling, stochastic seismic inversion, and geostatistics

Quantitative reservoir characterization using integrated seismic data and well log data is important in sweet spot identification, well planning, and reservoir development. The process includes building up the relations between rock properties and elastic properties through rock physics modeling, inverting for elastic properties from seismic data, and inverting for rock properties from both seismic data and rock physics models. Many quantitative reservoir characterization techniques have been developed for conventional reservoirs. However, challenges remain when extending these methods to unconventional reservoirs because of their complexity, such as anisotropy, micro-scale fabric, and thin beds issues. This dissertation focuses on developing anisotropic rock physics modeling method and seismic inversion method that are appliable for unconventional reservoir characterization. The micro-scale fabric, including the complex composition, shape and alignment of clay minerals, pore space, and kerogen, significantly influences the anisotropic elastic properties. I developed a comprehensive three-step rock-physics approach to model the anisotropic elastic properties, accounting for the micro-scale fabric. In addition, my method accounts for the different pressure-dependent behaviors of P-waves and S-waves. The modeling provides anisotropic stiffnesses and pseudo logs of anisotropy parameters. The application of this method on the Upper Eagle Ford Shale shows that the clay content kerogen content and porosity decrease the rock stiffness. The anisotropy increases with kerogen content, but the influence of clay content is more complex. Comparing the anisotropy parameter pseudo logs with clay content shows that clay content increases the anisotropy at small concentrations; however, the anisotropy stays constant, or even slightly decreases, as clay content continues to increase. Thin beds and anisotropy are two important limitation of the application of seismic characterization on unconventional reservoirs. I introduced the geostatistics into stochastic seismic inversion. The geostatistical models, based on well log data, simulate small-scale vertical variations that are beyond seismic resolution. This additional information compensates the seismic data for its band-limited nature. I applied this method on the Eagle Ford Shale, using greedy annealing importance sampling as inversion algorithm. The thin Lower Eagle Ford Formation, which cannot be resolved by conventional inversion method, is clearly resolved in the inverted impedance volume using my method. In addition, because anisotropy is accounted for in the forward modeling, the accuracy of inverted S-impedance is significantly improved.