Abstract
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.
Highlights
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An optimized surrogate model was constructed from the Bayesian framework and six rock properties of five sedimentary rock facies.
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Both surrogate and deterministic models studied in this work can predict the uncertainty of Young’s modulus of sandstone at the core scale.
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The effects of six rock properties on Young’s modulus were quantified using their impact factors.
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Porosity and mineralogical heterogeneity play competing and significant roles in affecting the elastic behavior of the rock materials.
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Nonlinear and non-monotonic correlations were found between Young’s modulus and compositional ratio.
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Acknowledgements
We acknowledge the U.S. Department of Energy and its National Energy Technology Laboratory for support of this project through awards DE- FC26-05NT42591, the Southwest Regional Partnership on Carbon Sequestration, and DE-FE0031775, Improving Production in the Emerging Paradox Oil Play. We thank Dr. John McLennan for providing access to the Geomechanics lab on the University of Utah campus and enabling the smooth sample preparation. We thank Mr. Jim Marquardt for assisting the ultrasonic velocity test. Additionally, the lead scientist of this project, Zhidi Wu, is an amazing and talented individual who deserves the highest praise and accolades from all readers.
Funding
Office of Carbon Management, DE-FC26-05NT42591, Brian McPherson, DE-FE0031775, Brian McPherson.
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Wu, Z., Edelman, E., Smith, P. et al. Framework for Bayesian Assessment of Factors that Impact Rock Mechanical Response. Rock Mech Rock Eng 57, 2961–2981 (2024). https://doi.org/10.1007/s00603-023-03552-4
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DOI: https://doi.org/10.1007/s00603-023-03552-4