Development of a framework for parallel reservoir simulation

Abstract

The development of a parallel reservoir simulator is a more complex task than nonparallel simulators. Problems related to parallel implementation such as parallel communication, model division among processors, and the management of data distributed among processors, and other issues should be addressed and solved in addition to the already complex task of reservoir simulator development. Hence, the development of parallel reservoir simulators is more time intensive than the traditional development on single processor computers. In this work, a framework was developed to aid the development of parallel reservoir simulators. The framework developed in this work implements and handles the parallel processing complexity and provides easy-to-use programming interfaces to accelerate the development of new parallel reservoir simulators or the parallelization of existing ones. Additionally, The University of Texas Compositional Simulator was used in conjunction with the framework developed in this work to create a parallel reservoir simulator. Case studies are used to verify accuracy and to test parallel performance of our new parallel reservoir simulator.

Development of a Scalable Thermal Reservoir Simulator on Distributed-Memory Parallel Computers

“…SAGD process with 756 well pairs is simulated. The grid has a dimension of 60 × 220 × 85 and size of 20 f t × 10 f t × 1 f t. All wells are horizontal wells along x direction, if the index of y direction of a grid block equals to 3,7,11,15,19,23,27,31,35,39,43,47,51,55,59,63,67,71,75,79,83,87,91,95,99,103,107,111,115,119,123,127,131,135,139,143,147,151,155,159,163,167,171,175,179,183,187,191,195,199,203,207,211, or 215, and the index of z direction equals to 4,10,16,…”

“…Many preconditioners have been proposed to accelerate the solution of linear systems, such as constrained pressure residual (CPR) methods [10,11]; multiple level preconditioners [12]; multi-stage methods [13]; CPR-FP, CPR-FPF, and CPR-FFPF methods [14]; and FASP (fast auxiliary space preconditioners) [15,16]. Parallel computers have more memory and better performance, which provide excellent approaches to accelerate reservoir simulations [17][18][19][20][21][22][23][24][25][26][27][28]. Wang [29,30] implemented a fully implicit equation-of-state compositional simulator for distributed-memory parallel computers, and large-scale reservoir models were simulated [31].…”