Quantifying the Impact of Geological Uncertainty on Reservoir Performing Forecasts

Abstract

Abstract This study addresses the problem of transferring uncertainty in the geological model through the flow simulation model (the comprehensive simulator - CS) up to the reservoir production forecasts. The uncertainty in the geological model is characterized by the differences between many equally probable reservoir descriptions that honor available data and that are generated by geostatistical stochastic simulation. The uncertainty in the production forecasts can be characterized by the variability or spread in the responses derived from the flow simulation, or more precisely by the probability distribution of response variables, such as cumulative oil production, oil recovery, breakthrough time, cumulative water-oil ratio, etc. A new methodology to perform this transfer of uncertainty is summarized. Each reservoir description is first ranked using a fast simulator (FS) rather than the comprehensive flow simulator (CS). A few selected descriptions are then processed through the CS to generate an approximate probability distribution of the reservoir production response. This new methodology is applied with a coarse grid as the FS model to a two-phase, three-dimensional field scale problem with five producing wells, 8×10 6 STm3 original oil in place, and a very large and active aquifer. The impact of uncertainty in absolute permeability and porosity on the field cumulative oil production is considered over 7.8 years of production. Good results are achieved with a 85% to 93% reduction in the computer time over the use of the CS alone. The favorable results achieved show that extension of the proposed approach to other types of problems is worthwhile. Introduction A reservoir flow simulation study combines data from many different sources and processes the data through a complex nonlinear system of equations to generate the reservoir production forecast required for economic analysis. These data include geological data, seismic data, petrophysical data, well data, production data, etc. A problem with these simulations is that the data are not exact and only a small sample is usually available, thus giving the results inherent uncertainty. Uncertainty is the "lack of assurance about the truth of a statement or about the exact magnitude of an unknown measurement or number". Uncertainty involves the risk of making an incorrect decision because the estimates do not agree with reality. Thus, uncertainty is associated with the economic risk analysis of the reservoir production forecast and is a central concern in the decision making process. Because uncertainity in the production forecast results from the interaction of the uncertainties in all sources of data, as well as from the assumptions in the numerical flow model, a procedure to transfer uncertainty through the numerical flow model up to the production forecasts is highly desired. A general procedure is available, and some approximation have been proposed. However, the available general procedure requires such a large amount of computational work that it is not a viable option for most flow simulation studies. Moreover, the approximations proposed by previous investigators cannot handle all the non-linearities of a flow model or are too arbitrary to be of great value. A new systematic methodology for the transfer of uncertainty, requiring reasonable computational work, is applied on a three-dimensional field scale problem. All computer times reported are for a DEC-5810 with 32MB of memory and 1 processor. P. 47^