Parallel history matching and associated forecast at the center for interactive smart oilfield technologies
We have developed a parallel and distributed computing framework to solve an inverse problem, which involves massive data sets and is of great importance to petroleum industry. A Monte Carlo method, combined with proxies to avoid excessive data processing, is employed to identify reservoir simulation models that best match the oilfield production history. Subsequently, the selected models are used to forecast future productions with uncertainty estimates. The parallelization framework combines: (1) message passing for tightly coupled intra-simulation decomposition; and (2) scheduler/Grid remote procedure calls for model parameter sweeps. A preliminary numerical test has included 3,159 simulations on a 256-processor Intel Xeon cluster at the USC-CACS. The results provide uncertainty estimates of unprecedented precision.
smart oilfield parallel and distributed computing inverse problem massive data sets monte carlo method history matching and forecast