Calculation of effective properties of geocomposites based on computed tomography images

Abstract

Biot’s parameter is included in the formula for calculating effective stresses and should be taken into account when assessing the stress-strain state of a water-saturated rock mass. A method for calculating Biot’s tensor parameter based on asymptotic averaging of the equilibrium equation for a fluid-saturated porous medium is proposed. Calculations of elastic properties and Biot’s coefficient were carried out on various types of rocks - limestone, dolomite, hyaloclastite, basalt. The calculations were carried out using 3D models of geocomposites built from X-ray computed tomography images. The results of 3D calculations of Young's modulus and Biot’s coefficient coincided with the results of experimental determinations of these properties by the ultrasonic method. This fact shows the expediency of using a computational approach that implements asymptotic averaging to estimate the effective properties using 3D models of the real rock structure. The results of 3D and 2D simulation of effective properties of geocomposites are compared. 2D models were built using photographs of rock sections. It was found that the values of Young's modulus and Biot’s parameter for 2D models differ from the corresponding experimental and 3D calculation results by 20-30 %. Therefore, 2D modeling is not suitable for evaluating the effective properties of porous geomaterials. Based on the results of calculations and experiments, the dependences of Young's modulus and Biot’s coefficient on porosity were obtained and studied. These dependencies are used in non-linear numerical simulation of the consolidation of water-saturated soils. The results of the study showed that Biot’s coefficient does not depend on Young's modulus of the rock skeleton material. The influence of the pore shape on Young's modulus and Biot’s coefficient was studied using 2D calculations. A method for predicting the shape of pores from the values of porosity and Young's modulus using neural networks is proposed. Using this method, a specific algorithm for predicting the shape of pores in hyaloclastites has been implemented and studied.