Multiscale Poromechanical Modeling of Shales Incorporating Microcracks Academic Article uri icon

abstract

  • © 2019, Springer-Verlag GmbH Austria, part of Springer Nature. A probabilistic multiscale model for poroelastic properties of organic-rich shale is extended to include the effect of microcracks. The approach is based on physics-based modeling of poroelastic properties and uncertainty propagation from nano- to macro-scale using homogenization schemes. The multiscale thought model used in this work improves on an existing thought model by adding another level to model embedded microcracks. From the scale of single clay particle to the scale of embedded microcracks, the model parameters are calibrated using experimental characterization at different length scales. To quantify the crack density and microcrack orientations, an inverse optimization approach is utilized which uses the UPV measurements at the macro-scale. Major model parameters representing compositional and mechanical properties at each length scale are modeled as a random variable. Probabilistic description of both scalar random variables and matrix-valued random variables is constructed using the maximum entropy principle (MaxEnt). Using such statistical description of model input parameters with Monte Carlo simulation, probabilistic descriptions of model outputs are obtained at each length scale. The results of stochastic upscaling are validated against available experimental values. Finally, a global sensitivity analysis is performed to identify the model input parameters which are most influential to the statistical fluctuation of upscaled poroelastic properties. The presented multiscale framework provides an enhanced understating regarding the effect of uncertainties associated with microcrack density and orientation, and other subscale features on homogenized poroelastic properties, thus further improves predictive capability for shale multiscale behavior.

author list (cited authors)

  • Dubey, V., Mashhadian, M., Abedi, S., & Noshadravan, A.

citation count

  • 2

publication date

  • May 2019