Parallel Multi-Stage Preconditioners with Adaptive Setup for the Black Oil Model

TL;DR

This paper introduces a parallel multi-stage preconditioner with an adaptive setup for black oil models, significantly enhancing the efficiency and scalability of reservoir simulations on multi-core and GPU architectures.

Contribution

It develops a CPR-type preconditioner with an adaptive setup phase and a multi-color Gauss-Seidel algorithm for algebraic multigrid, improving parallel performance in reservoir simulations.

Findings

Parallel speedup over 6.5 with 16 threads on OpenMP

CUDA implementation achieves over 9.5x speedup

Maintains convergence behavior similar to single-threaded algorithms

Abstract

The black oil model is widely used to describe multiphase porous media flow in the petroleum industry. The fully implicit method features strong stability and weak constraints on time step-sizes; hence, commonly used in the current mainstream commercial reservoir simulators. In this paper, a CPR-type preconditioner with an adaptive "setup phase" is developed to improve parallel efficiency of petroleum reservoir simulation. Furthermore, we propose a multi-color Gauss-Seidel (GS) algorithm for algebraic multigrid method based on the coefficient matrix of strong connections. Numerical experiments show that the proposed preconditioner can improve the parallel performance for both OpenMP and CUDA implements. Moreover, the proposed algorithm yields good parallel speedup as well as same convergence behavior as the corresponding single-threaded algorithm. In particular, for a three-phase benchmark problem, the parallel speedup of the OpenMP version is over 6.5 with 16 threads and the CUDA version reaches more than 9.5.