Developing New Fields Using Probabilistic Reservoir Forecasting

Abstract

Abstract Limited and uncertain geologic and engineering data at the onset of any new field development are the bane of reservoir characterization and simulation. The problem stems from the uncertainty in various model-input variables, such as reservoir connectivity, fluid viscosity, and endpoint saturations, to name a few. Given this scenario, an ad-hoc, one-factor-at-a-time approach to earth and flow-simulation modeling cannot possibly yield unbiased information for making objective business decisions. This study presents three field cases where both engineering and earth model variables were varied in a systematic way to assess reservoir performance using the Experimental Design (ED) approach. Results of the field cases show that well requirements, both producers and injectors, turned out to be fewer than those thought initially. Equally important, one case study showed that laboratory measurements could minimize uncertainty surrounding oil viscosity and endpoint saturations. At the same time, we learned the preferred horizontal well orientation was marginally superior to vertical wells, in light of high reservoir anisotropy. In another case, stratigraphy, gas-oil contact, and aquifer strength became the primary variables for the full-factorial design, after the initial screening. Here, we proved that the project could proceed because it met the minimum reserves criterion. Perhaps most important, all studies showed how to obtain unbiased information in far fewer flow-simulation runs than one would do using an ad-hoc approach.