Beyond queues: Using grid computing for simulation studies

Reservoir simulators are computationally costly and produce diverse, voluminous results. These features motivate a need for a distributed computing systems with high-level methods to manage computation, data, and workflow: the Grid computing community is working to provide such tools. We begin by discussing the features of Grid computing environments, emphasizing features that enhance reliability and usability, and can contribute to decreased computing cost via a future market in Grid computing with high security. A Grid-based workflow for reservoir simulation is then outlined which uses software such as the Condor and Globus Toolkit to build and manage workflows, the Grid Security Infrastructure (GSI) for security, GridSphere for portal creation and management, and Cactus for parallelization. The workflow is demonstrated for a geostatistical study of three-dimensional displacements in heterogeneous reservoirs using a parallel IMPES reservoir simulator. A suite of 5,120 simulations assesses the effects of geostatistical methods. Much of the pre- and post-processing is automated in this workflow, which is based on experimental design. Multiple runs are simultaneously executed using Grid computing. Grid services manage security, data acquisition, resource brokerage and allocation, response analysis, and visualization; the reservoir engineer is freed from micromanaging these workflow components. Flow response analyses indicate that efficient, widely-used sequential geostatistical simulation methods may overes-timate flow response variability, compared to more computationally costly direct methods such as LU decomposition. The workflow is extended to automatic history matching using Ensemble Kalman Filters in the Grid environment. Users only need to choose parameters for stochastic simulations in a Web-based Grid portal. A two-dimensional problem with one injector and four producers is created to investigate the performance of the Grid ensemble Kalman filter. Grid computing offers a promising path toward effective use of computing resources within and between organizations. Existing tools support parallelization of code and construction of workflows. The software developed here can be adapted to drive other simulation tools, and is available for downloading.