Assessing Uncertainty in Channelized Reservoirs Using Experimental Designs

Abstract

Abstract It is well established that uncertainty exists in simulated recovery forecasts due to the ambiguity in the measurement and representation of the reservoir and geologic parameters. This is especially true for immature projects, such as deep-water 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, to quantitatively assess this uncertainty in recovery predictions for primary and waterflood processes. We apply the Experimental Design methodology to channelized sandstone systems because of their relevance to many deep-water projects. We choose to study synthetic geological analogs of channelized systems that are built from 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 rapidly predict reservoir performance where field data is very limited. We discuss applications of this methodology on field cases from western Africa.