An Improved Multi-Stage Preconditioner on GPUs for Compositional Reservoir Simulation

TL;DR

This paper introduces an improved multi-stage preconditioner optimized for GPU-based compositional reservoir simulations, enhancing parallel efficiency without sacrificing convergence performance.

Contribution

It presents a novel multi-stage preconditioner with adaptive setup and multi-color Gauss-Seidel for GPU-accelerated fully implicit reservoir modeling.

Findings

Achieves significant parallel speedup on GPUs.

Maintains convergence behavior comparable to sequential methods.

Improves efficiency of compositional flow simulations.

Abstract

The compositional model is often used to describe multicomponent multiphase porous media flows in the petroleum industry. The fully implicit method with strong stability and weak constraints on time-step sizes is commonly used in the mainstream commercial reservoir simulators. In this paper, we develop an efficient multi-stage preconditioner for the fully implicit compositional flow simulation. The method employs an adaptive setup phase to improve the parallel efficiency on GPUs. Furthermore, a multi-color Gauss-Seidel algorithm based on the adjacency matrix is applied in the algebraic multigrid methods for the pressure part. Numerical results demonstrate that the proposed algorithm achieves good parallel speedup while yields the same convergence behavior as the corresponding sequential version.