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


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error