Investigating the Pilot Point Ensemble Kalman Filter for geostatistical inversion and data assimilation

TL;DR

This paper thoroughly evaluates the pilot point ensemble Kalman filter (PP-EnKF) for geostatistical parameter estimation, demonstrating its superior performance over classical EnKF in accuracy and suppression of spurious correlations, especially with small ensembles.

Contribution

The paper provides a detailed formulation and extensive performance comparison of the PP-EnKF, highlighting its advantages over traditional EnKF methods in geostatistical data assimilation.

Findings

PP-EnKF ranks better than classical EnKF in model setups.

PP-EnKF estimates ensemble variance close to large-ensemble reference.

PP-EnKF better suppresses spurious correlations with small ensembles.

Abstract

Parameter estimation has a high importance in the geosciences. The ensemble Kalman filter (EnKF) allows parameter estimation for large, time-dependent systems. For large systems, the EnKF is applied using small ensembles, which may lead to spurious correlations and, ultimately, to filter divergence. We present a thorough evaluation of the pilot point ensemble Kalman filter (PP-EnKF), a variant of the ensemble Kalman filter for parameter estimation. In this evaluation, we explicitly state the update equations of the PP-EnKF, discuss the differences of this update equation compared to the update equations of similar EnKF methods, and perform an extensive performance comparison. The performance of the PP-EnKF is tested and compared to the performance of seven other EnKF methods in two model setups, a tracer setup and a well setup. In both setups, the PP-EnKF performs well, ranking better than the classical EnKF. For the tracer setup, the PP-EnKF ranks third out of eight methods. At the same time, the PP-EnKF yields estimates of the ensemble variance that are close to EnKF results from a very large-ensemble reference, suggesting that it is not affected by underestimation of the ensemble variance. In a comparison of the ensemble variances, the PP-EnKF ranks first and third out of eight methods. Additionally, for the well model and ensemble size 50, the PP-EnKF yields correlation structures significantly closer to a reference than the classical EnKF, an indication of the method's skill to suppress spurious correlations for small ensemble sizes.