New Frontiers in Large Scale Reservoir Simulation

Abstract

Giant reservoirs of Middle East contain substantial portion of the world's total hydrocarbons. Accurate simulation of these reservoirs requires as many as billion cells. A billion-cell parallel reservoir simulator was first presented in 2009. This paper discusses the progress in the past two years and future projections. In addition to black oil models, paper presents a large full field compositional model involving over billion unknowns. Throughout the paper grid size effects, computational work distribution and parallel scalability are discussed and illustrated. Paper also presents futuristic technologies for building and analyzing large simulation models.

Geomodeling Insights for Numerical Simulations of Naturally Fractured Reservoirs

“…( 2)). To avail acceptable simulation runtimes for models of comparable size and complexity it is recommended to utilize parallel and highly scalable simulators [10], deployed on high-performance computing (HPC) platforms.…”

A survey of sparse matrix-vector multiplication performance on large matrices

“…One of the main sources of sparse matrices is the discretization of partial differential equations that govern continuumphysics phenomena such as fluid flow and transport, phase separation, mechanical deformation, electromagnetic wave propagation, and others. Recent advances in high-performance computing area have been enabling researchers to tackle increasingly larger problems leading to sparse linear systems with hundreds of millions to a few tens of billions of unknowns, e.g., [5,6]. Iterative linear solvers are popular in large-scale computing as they consume less memory than direct solvers.…”

Comparison of Reproducible Parallel Preconditioned BiCGSTAB Algorithm Based on ExBLAS and ReproBLAS

“…In many scientific and engineering calculation fields, such as nuclear reactor simulation, radiation (magneto) hydrodynamics, radiation diffusion problems, oil and gas resource exploration, numerical weather prediction, etc. [26][27][28], differential equations are often used as mathematical models to describe problems. In order to simulate on a computer, it is necessary to discretize the differential equations to obtain a set of linear equations.…”