Compressive time-lapse seismic monitoring of carbon storage and sequestration with the joint recovery model

TL;DR

This paper introduces a joint recovery model for time-lapse seismic monitoring of CO2 sequestration, enhancing image quality and detection without needing replicated surveys, demonstrated through a realistic synthetic case study.

Contribution

It proposes a novel joint inversion approach that leverages shared information across surveys, improving seismic monitoring of CO2 plumes without survey replication.

Findings

Improved seismic images of CO2 plumes using joint recovery model

Enhanced detection of time-lapse changes without survey replication

Demonstrated effectiveness on a realistic synthetic model

Abstract

Time-lapse seismic monitoring of carbon storage and sequestration is often challenging because the time-lapse signature of the growth of CO2 plumes is weak in amplitude and therefore difficult to detect seismically. This situation is compounded by the fact that the surveys are often coarsely sampled and not replicated to reduce costs. As a result, images obtained for different vintages (baseline and monitor surveys) often contain artifacts that may be attributed wrongly to time-lapse changes. To address these issues, we propose to invert the baseline and monitor surveys jointly. By using the joint recovery model, we exploit information shared between multiple time-lapse surveys. Contrary to other time-lapse methods, our approach does not rely on replicating the surveys to detect time-lapse changes. To illustrate this advantage, we present a numerical sensitivity study where CO2 is injected in a realistic synthetic model. This model is representative of the geology in the southeast of the North Sea, an area currently considered for carbon sequestration. Our example demonstrates that the joint recovery model improves the quality of time-lapse images allowing us to monitor the CO2 plume seismically.