A Bayesian Approach for In-Situ Stress Prediction and Uncertainty Quantification for Subsurface Engineering.

Many subsurface engineering applications require accurate knowledge of the in-situ state of stress for their safe design and operation. Existing methods to meet this need primarily include field measurements for estimating one or more of the principal stresses from a borehole, or optimization methods for constructing a 3D geomechanical model in terms of geophysical measurements. These methods, however, often contain considerable uncertainty in estimating the state of stress. In this paper, we build on a Bayesian approach to quantify uncertainty in stress estimations for subsurface engineering applications. This approach can provide an estimate of the 3D distribution of stress throughout the volume of interest and provide an estimate of the uncertainty arising from the stress measurement, the rheology parameters, and a paucity of measurements. The value of this approach is demonstrated using stress measurements from the In Salah carbon storage site, which was one of the world's first industrial carbon capture and storage projects. This demonstration shows the application of this Bayesian approach for estimating the initial state of stress for In Salah and quantifying the uncertainty in the estimated stress. Also, an assessment of a maximum injection pressure to prevent geomechanical risks from CO2 injection pressures is provided in terms of the probability distribution of the minimum principal stress quantified by the approach. With the In Salah case study, this paper demonstrates that using the Bayesian approach can provide additional insights for site explorations and/or project operations to make informed-site decisions for subsurface engineering applications. Highlights: This study proposes a Bayesian approach to quantify uncertainty in estimates of initial 3D stress distributions arising from different sources. The approach provides the joint probability of the two horizontal principal stresses, instead of only the mean stress state of each. Adding regional geologic information and stress-related informative priors can reduce uncertainty in estimates of stress and modeling parameters. The approach helps make more reliable geomechanical decisions for subsurface engineering applications compared to a deterministic method. [ABSTRACT FROM AUTHOR]