Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering

TL;DR

This paper demonstrates the application of the ensemble Kalman filter for real-time seismic monitoring of CO2 plume dynamics in subsurface reservoirs, improving estimation accuracy over traditional methods.

Contribution

It introduces a scalable EnKF-based data assimilation approach tailored for high-dimensional CO2 reservoir monitoring with seismic data, providing guidance on hyperparameter selection.

Findings

EnKF yields more accurate CO2 saturation estimates than seismic or physics data alone.

The study offers practical hyperparameter tuning guidance for seismic CO2 monitoring.

Application to realistic seismic survey design demonstrates real-world feasibility.

Abstract

Monitoring carbon dioxide (CO2) injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO2 injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO2 plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO2 monitoring domain by applying this scalable DA algorithm to a high-dimensional CO2 reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO2 saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO2 reservoir monitoring.