We consider permeability as a random space function definedby its mean and covariance. The stochastic nature of the permeabilitydescription leads to uncertainty in flow-related quantitiessuch as pressure, saturation and production rate. We extendedour statistical moment equation (SME) approach to accommodateconditioning. The conditional stochastic momentequations (CSME) framework is a direct approach for quantifyingthe uncertainty in flow performance due to uncertainty inthe reservoir description. It is quite different from Monte CarloSimulation (MCS). In MCS, the performance uncertainty is obtainedthrough a statistical post-processing of flow simulations,one for each of a large number of equiprobable realizations ofthe reservoir description. We developed a CSME computationaltool for flow in heterogeneous domains. We employ an approachanalogous to the deterministic streamline-based methodin order to solve the equations that govern the first (mean) andsecond (variance and covariance) moments of interest. This numericalCSME tool allows for quantifying the value of existingand future information, and that helps evaluate existing projectsand steer future development plans. We present several examplesthat demonstrate how to choose the best sampling locationsto obtain maximum reduction in prediction uncertainty.We compare our results with high-resolution MCS.
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