# 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.

## Key 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.