Probabilistic Forecasting and Model Validation for the First-Eocene Large-Scale Pilot Steamflood, Partitioned Zone, Saudi Arabia and Kuwait

摘要

The First-Eocene heavy-oil reservoir (IE) in the Wafra field is a candidate for steamflooding because of its world-class resource base and low-estimated primary recovery. However, industry has little experience in steamflooding carbonate reservoirs, which has prompted the staging of several IE steamflooding tests, the latest of which is the large-scale pilot (LSP) started in 2009. To assist in facilities design, to help understand expected performance in a very heterogeneous reservoir, and to provide input to early-decision analyses, numerical thermal simulation was used to generate probabilistic forecasts. When adequate pilot history was available, the model was validated with probabilistic methods. The LSP model contained 1.5 million cells, which allowed the maintenance of adequate resolution and proper boundary conditions in the pilot area. Parallel computation enabled a probabilistic workflow to be implemented with this large thermal model. In this paper, we highlight the methodologies and inputs used to generate the probabilistic forecasts and validate the model. Major results of this work include the following: In contrast to many greenfield forecasts, the LSP forecasts were conservative, likely because of the unique aspects of the forecasting methodology, proper selection of uncertainty ranges, and the relatively high density of input data for model construction; wide variations in production metrics were forecast, indicative of a highly heterogeneous reservoir; results indicated that the validated model adequately captured the global or statistical pilot heterogeneity, enabling proper capture of steamflood flow/drainage mechanisms; and despite this heterogeneity, forecast oil-recovery levels were comparable with those observed in steamfloods in sandstone reservoirs.