Framework for Bayesian Assessment of Factors that Impact Rock Mechanical Response

The ability to accurately measure the static Young's modulus is crucial for understanding subsurface storage reservoirs. However, obtaining this data can be difficult and costly. Much previous research focused on the impact of one or two factors on geomechanical properties at a single scale, but a more comprehensive understanding is needed. A data-driven Bayesian approach was used to quantify the uncertainty of Young's modulus using cost-effective experimental measurements of six rock properties—porosity, clay content, permeability, the ratio of framework grain content to cement content (FGC/CC), mean grain size, and sample size. We further use the comprehensive geomechanical model to examine the impact of six rock properties on Young's modulus. We found that the pore abundance and the relative amount of framework grains and cements play significant and competing roles in rock's elastic properties. Furthermore, the surrogate model yields the minimum uncertainty and reflects nonlinear and non-monotonic trends between Young's modulus and secondary rock properties. The surrogate model can estimate Young's modulus distribution of common sedimentary rocks, reducing the cost associated with traditional laboratory testing. Overall, this work elucidates the elastic mechanical behavior of rocks at various core scales in response to other secondary rock properties in the deep subsurface.