A Parallel Simulator for Massive Reservoir Models Utilizing Distributed-Memory Parallel Systems

TL;DR

This paper develops scalable parallel computational methods for large-scale reservoir simulations, employing advanced linear and nonlinear solvers, preconditioning, and matrix strategies to efficiently utilize thousands of CPU cores.

Contribution

It introduces a multi-stage preconditioner and local reordering techniques tailored for massive reservoir models on distributed-memory parallel systems.

Findings

Methods are effective and scalable for large problems

Capable of using thousands of CPU cores

Applicable to various reservoir models

Abstract

This paper presents our work on developing parallel computational methods for two-phase flow on modern parallel computers, where techniques for linear solvers and nonlinear methods are studied and the standard and inexact Newton methods are investigated. A multi-stage preconditioner for two-phase flow is applied and advanced matrix processing strategies are studied. A local reordering method is developed to speed the solution of linear systems. Numerical experiments show that these computational methods are effective and scalable, and are capable of computing large-scale reservoir simulation problems using thousands of CPU cores on parallel computers. The nonlinear techniques, preconditioner and matrix processing strategies can also be applied to three-phase black oil, compositional and thermal models.

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