In this paper, we present a field example in which multiple reservoir descriptions were generated to capture uncertainties in reservoir performance; a streamline simulator was used to rank these multimillion-cell geostatistical realizations and to determine the optimum level of vertical upscaling.
During geostatistical reservoir characterization, it is a common practice to generate a large number of realizations of the reservoir model to assess the uncertainty in reservoir descriptions and performance predictions. However, only a small fraction of these models can be considered for comprehensive flow simulations because of the high computational costs. A viable alternative is to rank these multiple "plausible" reservoir models on the basis of an appropriate performance criterion that adequately reflects the interaction between heterogeneity and the reservoir flow mechanisms. One can generate thousands of geostatistical realizations with a minimal cost; however, the cost of ranking such realizations can be prohibitively expensive, even if fast streamline simulators are used. The objective is to generate a manageable number of realizations and represent the possible range of uncertainty in reservoir descriptions. Here, we propose a "hierarchical methodology" in designing uncertainties to be represented in reservoir descriptions.
In this paper, we also show how a streamline simulator can be used to design vertical upscaling of fine-scale reservoir descriptions. The biggest challenge of upscaling is to reduce model size without losing the heterogeneity level of the original geological model.
We use streamline time-of-flight connectivity derived from a streamline simulator. The time of flight reflects fluid-front propagation at various times, and its connectivity at a given time provides us with a direct measure of volumetric sweep efficiency for arbitrary heterogeneity and well configuration. The volumetric sweep efficiency is the simplest measure that reflects the interaction between heterogeneity and the flow field. It is a dynamic measure that can be updated easily to account for changing injection/production conditions.
Our field study involves a Middle Eastern carbonate reservoir under a moderate-to-strong aquifer influx. The reservoir is on primary depletion and has no injectors. In our streamline-simulation exercise, the aquifer pressure support is modeled by pseudoinjectors, and pressure updates are used to reflect changing field conditions.