Geometry-based finite volume methods for modelling transport on micro-CT images

Digital rock analysis has become increasingly popular for studying the microscopic structure of reservoir rocks. Direct numerical flow simulations are a common approach to compute petrophysical properties of rocks by modelling fluid flow on rock micro-Computed Tomography (CT) images. However, they are computationally demanding and complicated to include additional flow mechanisms. In this Thesis, a Pore-scale Finite Volume Solver (PFVS) is proposed that solves an elliptic diffusion equation to obtain the spatial pressure distribution on the entire micro-CT image. The flow results have 11% error compared to other solvers such as Stokes solver and Lattice-Boltzmann method. However, the computation times of PFVS are typically 5 times less compared to other solvers. PFVS is also capable of resolving the flow within the microporosity of rocks that cannot be captured by the previous solvers. PFVS is equipped with voxel agglomeration to merge pore voxels locally reducing the number of voxels in the system and the computation time by at least 59%. Furthermore, a Convolutional Neural Network (CNN) model is integrated into PFVS to predict the local conductivity of each voxel based on the training data, bypassing the iterative local largest inscribed radius algorithm. The method has a minor impact on flow results, while the computation time decreases significantly. Rock images commonly contain multiscale pores that are not fully resolved by the micro-CT scanners. A novel analytical formulation is introduced to include the effect of sub-resolution features in low resolution images on flow simulation. The results are validated against the flow directly simulated on corresponding high-resolution images where available. An extension of the PFVS is developed to approximate two-phase flow in porous media with the assumption that the viscous coupling can be ignored. Two fluids are simulated independently, and the results are compared against lattice Boltzmann simulations and determination of relative permeability curves are discussed. The results of this Thesis demonstrate the advantages of integrating innovative numerical methods for reliable simulation of fluid flow directly on rock images while minimising the computational requirements. This will contribute to improving capabilities of digital rock analysis to effectively and efficiently predict petrophysical properties of rocks.

Grantor dc:publisher

UNSW, Sydney

Year dc:date

2021

Author dc:creator

• Chung, Traiwit

• Finite volume
• Flow in porous media
• Pore-scale modeling

Statement dc:rights

• open access
• CC BY-NC-ND 3.0
• free_to_read

Licence

• https://purl.org/coar/access_right/c_abf2
• https://creativecommons.org/licenses/by-nc-nd/3.0/au/

Language dc:language

EN

Identifier

https://doi.org/10.26190/unsworks/22616

OAI identifier oai:identifier

oai:unsworks.library.unsw.edu.au:1959.4/70969