Assessing Uncertainty in Channelized Reservoirs Using Experimental Designs

Abstract

Summary It is well established that uncertainty exists in simulated recovery forecasts because of the ambiguity in the measurement and representation of the reservoir and geologic parameters. This is especially true for immature projects, such as deepwater reservoirs, where the high cost of data limits the information that is available to build reservoir models. We present two strategies based on Experimental Design (ED) to quantitatively assess this uncertainty in recovery predictions for primary and waterflood processes. We apply the ED methodology to channelized sandstone systems because of their relevance to many deepwater projects. We choose to study synthetic geological analogs of channelized systems that are built from a panoply of relevant parameters while taking into account the uncertainty that exists in the estimation of their ranges. We use the results of this study to generate type curves with neural networks. The trained neural networks can be used to predict reservoir performance rapidly where field data are very limited. We discuss applications of this methodology on field cases from western Africa.