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. Introduction Availability of very limited data with large uncertainty presents significant challenges to any new field development. Stakes are high when deepwater prosepects are evaluated. With advances in 3D seismic, the reservoir surface may be mapped with certain degree of confidence. However, a few exploratory wells cannot provide detailed information about the reservoir's internal architecture, particularly with respect to flow barriers or baffles. In short, we are confronted with large uncertainty in reservoir's flow and, sometimes, fluid properties. Given limited and uncertain data, questions arise how to proceed with a field development plan. Historically, we have used reservoir simulation and sensitivity analyses as tools for predicting various scenarios, followed by economic analysis. But the approach has been less than satisfactory because of the ad-hoc nature of the exercise, meaning changing one variable at a time. In essence, this approach relies on setting one variable at the p-10 or p-90 level, while keeping others at the p-50 level in a simulation run. Subsequent ranking of the independent variables can conceivably be biased. Potentially, this bias stems from two sources. First, simulations may not contain independent information in the resultant 2n+1 simulations, where n represents the number of variables. Second, the method relies on comparing solutions where all but one varaible is set at the p-50 level. In addition, the traditional tornado chart, used for ranking the variables, does not provide any information pertaining to "statistical significance" of the independent variable effects on the dependent variable. However, systematic approaches1–9 have emerged to account for uncertainty associated with various input variables, based on experimental design (ED). For instance, Chewaroungroaj et al.5 demonstrated the use of ED with a series of dimensionless variables to allow extension of the results to similar systems. On the other hand, Corré et al.7 used ED to integrate data from diverse sources, including seismic, in their effort to quantify uncertainty. Application of ED has also been extended to history-matched reservoirs.8