A Review of Parallel Computing for Large-scale Reservoir Numerical Simulation

Abstract

Reservoir numerical simulation is crucial for advancing research and development in petroleum engineering. To obtain high-precision spatial and temporal simulation results, a great amount of time and computational resources are needed. Parallel computing addresses this problem by distributing computational workloads and memory requirements across multiple processors. It enables large-scale and high-fidelity simulations and reduces time costs. In this paper, we review existing parallel computing for large-scale reservoir numerical simulation. The paper is achieved by conducting a systematic literature review published between 1990 and 2024. Using the PRISMA guideline, 134 supporting studies are selected for detailed extraction. The key contributions of this paper are threefold: (1) classification and analysis of numerical methods (including discretization methods, nonlinear methods, and linear iterative solvers and preconditioner methods); (2) an in-depth discussion on parallel techniques in high-performance computing (HPC), such as parallel programming models, load balancing, communication optimization, and GPU acceleration; and (3) an outline of software implementations, particularly solvers and reservoir simulators. In conclusion, developing efficient, robust, and scalable linear solving tools is key to reservoir simulation. We compare available preconditioner options and summarise the current state of the art in linear solving tools. Meanwhile, CPU and GPU parallel acceleration techniques have been rapidly developed. These emphases will provide a theoretical foundation and practical guidance for optimizing linear solution processes in the future.