Using Population-Based Incremental Learning Algorithm to Quantify the Uncertainty in Model Parameters

H036 Using Population-Based Incremental Learning Algorithm to Quantify the Uncertainty in Model Parameters I. Petrovska* (Imperial College London) & J.N. Carter (Imperial College London) SUMMARY Reservoir modelling is widely used in the petroleum industry to quantify the risk associated with alternative production scenarios. However reservoir models themselves still contain a high level of uncertainty due to the typically very limited sparse and multi-scaled field knowledge available. History matching reduces this uncertainty by constraining the reservoir model to the available field data. History matching represents a typical non-linear inverse problem which yields the existence of not one but multiple solutions. Monte