Assisted history matching for petroleum reservoirs

PhD Candidate Tarek Diaa-Eldeen

Main Supervisor Morten Hovd

Sponsor: Equinor

This research project aims at introducing and implementing novel methods to address the computational challenges in the ensemble-based History Matching (HM), with the purpose of
reducing uncertainty in the model and, therefore, increasing the forecasting accuracy and production control efficiency. HM is the inverse modelling problem where an initial reservoir mathematical model is iteratively, manually or automatically, updated to match the production data. Ensemble methods in general, and particularly the Ensemble Kalman Filter (EnKF), have been widely used in HM problems. This research project is designed to introduce new and improve existing methods, such as the localization and covariance inflation, to enhance the EnKF’s performance in HM problems.

BRU21 Conference 2022