Numerical evaluation of mean-field homogenisation methods for predicting shale elastic response

Goodarzi, M.; Rouainia, M.; Aplin, A.C.

Abstract

Homogenisation techniques have been successfully used to estimate the mechanical response of synthetic composite materials, due to their ability to relate the macroscopic mechanical response to the material microstructure. The adoption of these mean-field techniques in geocomposites such as shales is attractive, partly because of the practical difficulties associated with the experimental characterisation of these highly heterogeneous materials. In this paper, numerical modelling has been undertaken to investigate the applicability of homogenisation methods in predicting the macroscopic, elastic response of clayey rocks. The rocks are considered as two-level composites consisting of a porous clay matrix at the first level and a matrix-inclusion morphology at the second level. The simulated microstructures ranged from a simple system of one inclusion/void embedded in a matrix to complex, random microstructures. The effectiveness and limitations of the different homogenisation schemes were demonstrated through a comparative evaluation of the macroscopic elastic response, illustrating the appropriate schemes for upscaling the microstructure of shales. Based on the numerical simulations and existing experimental observations, a randomly distributed pore system for the micro-structure of porous clay matrix has been proposed which can be used for the subsequent development and validation of shale constitutive models. Finally, the homogenisation techniques were used to predict the experimental measurements of elastic response of shale core samples. The developed methodology is proved to be a valuable tool for verifying the accuracy and performance of the homogenisation techniques.

Citation

Goodarzi, M., Rouainia, M., & Aplin, A. (2016). Numerical evaluation of mean-field homogenisation methods for predicting shale elastic response. Computational Geosciences, 20(5), 1109-1122. https://doi.org/10.1007/s10596-016-9579-y

• Journal Article Type: Article
• Acceptance Date: May 25, 2016
• Online Publication Date: Jun 11, 2016
• Publication Date: Jun 11, 2016
• Deposit Date: Nov 18, 2016
• Publicly Available Date: Jun 11, 2017
• Journal: Computational Geosciences
• Print ISSN: 1420-0597
• Electronic ISSN: 1573-1499
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 5
• Pages: 1109-1122
• DOI: https://doi.org/10.1007/s10596-016-9579-y
• Public URL: https://durham-repository.worktribe.com/output/1400510