Efficient seismic history matching using ensemble-based methods with distance parameterization

A distance parameterization of flood fronts derived from time-lapse seismic anomalies was recently developed to facilitate incorporation of time-lapse seismic data into history matching workflows based on ensemble methods such as the ensemble Kalman filter (EnKF) and the ensemble smoother (ES). A number of advantages were demonstrated on synthetic data including a significant reduction in the number of data points and flexibility in the type of attribute from which the front information can be extracted. In order to enable the use of the proposed method in real-field history-matching cases, we first extended the applicability of the algorithm computing the distance between observed and simulated fronts from regular Cartesian grids to generic corner-pint grids. Secondly, we used concepts from image analysis to generalize the innovations from the distance parameterization used in the EnKF as a directed local Hausdorff distance (from simulated to observed fronts) whereby a further improvement was achieved by taking into account the reverse measure (from observed to simulated fronts) as well. The workflow was subsequently applied to a series of numerical experiments on synthetic realistically complex test cases with promising results. The next step is a comprehensive examination on real field data as an objective of the study supported by National IOR Centre Norway.

Yanhui Zhang (TNO)