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

Bao, Ting; Burghardt, Jeffrey A.

doi:10.1007/s00603-022-02857-0

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

Journal Article · Thu May 12 00:00:00 EDT 2022 · Rock Mechanics and Rock Engineering

DOI:https://doi.org/10.1007/s00603-022-02857-0· OSTI ID:2474985

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Chongqing Univ. (China); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

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. Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy and Carbon Management (FECM)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2474985

Report Number(s):: PNNL-SA--167337

Journal Information:: Rock Mechanics and Rock Engineering, Journal Name: Rock Mechanics and Rock Engineering Journal Issue: 8 Vol. 55; ISSN 0723-2632

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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