Abstract

A combination technique of parallel simulation technology and upscaling algorithms is described. Based on our case studies, the results from the fine-scale model may appropriately be used to guide the upscaling procedure via a tuning procedure.This tuning procedure has been explored in the study to obtain results that are in close agreement with fine-scale simulation results. The combination of parallel simulation technology and upscaling algorithms can be used to provide a better estimation of the amount of uncertainty in predicted oil recovery for real fields.The concept of local global upscaling has been extended to include a technique that honors arbitrary faults in complex geological environments.

Several single-phase upscaling techniques are applied to an analog Gulf of Mexico reservoir model and a synthetic model.For the Gulf of Mexico reservoir model, we obtained results that were in close agreement with coarsened models obtained from the parallel simulations using the fine-scale model. In this case, the CPU time of one simulation run with 20 years of production time was significantly reduced from 22 hours, using a PC cluster with 8 processors computer, to 2 minutes, a single processor, applying upscaling methods. For the synthetic model, the agreement between coarse-scale model and the fine-scale model using the same upscaling method is within engineering accuracy.

Introduction

Upscaling of fluid through permeable media and parallel reservoir simulation are two important research areas in petroleum engineering. Despite several decades of study, many unsolved problems still exist, ranging from basic theories and methods through practical applications.

Reservoir characterization techniques have made possible geological reservoir models with multi-million cells populated with permeability, porosity, and fluid saturations. These geological models are often too large to be simulated because of computational limits. These computational limits mean that typical full-field reservoir simulation models are limited to fewer than one million cells - at least two orders of magnitude less than the geological models.Upscaling techniques have been used to bridge the gap between these geological models and flow simulation.Although there have been significant efforts in developing single-phase and two-phase upscaling algorithms, limited validation of the upscaling methods has been performed on a full-field basis.

In addition to upscaling techniques, parallel simulators have been developed to solve multi-million cell models with reasonable computational efficiency1. Parallel simulations take up to a few hours of CPU time instead of days to run multi-million cell models. However, when many simulations are to be performed over a large range of parameter values for uncertainty studies, parallel simulations again become prohibitive and upscaling must be employed.Upscaling can provide the large number of simulation results required for risk and uncertainty assessments with significantly reduced computational requirements. On the other hand, the results from these upscaled simulations must be validated with results from fine-scale simulations to give confidence on the reliability of the results. There is really no way of knowing how good the results of the upscaled simulations are unless we are able to perform the fine-scale simulations for validation. Parallel ultra-fine-scale simulations may provide the tool for this requirement.

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