Integrated, scalable MBS for flow through porous media

University Of Oklahoma Norman Campus, Norman OK

Investigators

Abstract

Fluid flow through porous materials is critical for understanding and predicting the behavior of systems as diverse in function and scale as hydrocarbon reservoirs, aquifers, separation tower and reactor units with packed beds, filters, membrane separators and even catalytic converters. Recently, there has been a thrust to incorporate more physics in reservoir simulations, as well as a call for substantial improvements in computational capability through the use of High Performance Computing (HPC), in order to improve reservoir management. This need has become particularly critical as oil and gas prices have fluctuated within one year from the lowest level of the past two decades to the highest. The goal of this project is to develop an integrated simulator for flow through heterogeneous porous materials using a hierarchy of simulations. Current approaches involve the use of simulations having a single physical scale. However, recent advances in HPC have made it possible to increase significantly the problem size and to use more sophisticated approaches. The challenge is to combine the individual simulations into an integrated multiscale system that will be able to include all physical scales and will self-adjust in accordance with the input data. Emphasis will be placed on the portability, scalability, efficiency and extensibility of the final product. The proposed simulator will be an improved prediction tool for hydrocarbon reservoir management and will be ready for use on integrated grid architectures, as they become available. Flow through porous media is a multi-scale phenomenon. Microscopic scale simulation, based on Lattice Boltzmann Methods, will be used for the direct simulation of flow through porous materials. Microtomographic digital images of rock samples will be used to realistically represent the spatial domain subjected to flow, taking advantage of the flexibility of Lattice Boltzmann Methods. At the mesoscopic scale, stochastic methods will be used for the systematic isolation and study of the effects of microscopic features of rock structure on the flow field. The stochastic approach will also be used to develop a method for rock property characterization. A macroscopic simulation, based on conventional finite difference methods, will be used to test the impact of modified flow models on hydrocarbon production at reservoir scale. The macroscopic simulation will incorporate the behavior of production/injection wells (which form singularities in a reservoir model) over the life of a reservoir. It will also incorporate coupling of flow with geomechanics (porosity-dependent permeability and non-Darcy coefficients). The education effort resulting from this project will emphasize the training of undergraduate students in the use of HPC resources. This research will: (i) improve our understanding of the fundamental flow mechanisms; (ii) update the model for non-Darcy flow through anisotropic porous materials; and (iii) integrate the presence of discontinuities, such as wells and fractures, in the simulation. The innovations of the proposed study include: (a) use of state-of-the-art simulations at different scales; (b) use of experimentally measured quantities to deduce the properties of the porous medium and to update flow models; (c) integration of the individual components of a set of prototype software into a seamless simulator for industrial use; and (d) application of a hybrid of shared-memory and distributed parallelism to achieve scalability on a variety of HPC architectures. The research project will be extremely valuable for the educational experience of the graduate students involved. Its educational aspect will also involve the incorporation of HPC applications in the undergraduate curricula of three Departments and the development of research projects for Research Experience for Undergraduates. It will, thus, prepare a large group of the technical workforce of our State to the useful aspects of HPC applications and to interact with HPC infrastructure.