Moving the industry forward

This issue of CJEG features a three-part series titled ‘Theseus 24D Optimized Seismic CCS MMV Method,’ which shines a critical light on the monitoring, measurement, and verification (MMV) in carbon storage. The work identifies the unique characteristics of onshore CCS MMV, discards monitoring elements that are determined to be unnecessary, redefines imaging areas for monitoring, and challenges the problem from a whole multi-decade repeat seismic perspective. The first part introduces a novel MMV design strategy aimed at enhancing capital efficiency within onshore CCS MMV programs. It focuses on the monitoring areas of repeat surveys, departing significantly from the National Energy Laboratory (NETL) recommendations in favor of something more apt for onshore carbon storage, and introduces the idea of ‘24D’, an integrated repeat 2D and 3D concept.

Part one of our three part Theseus 24D presentation series discussing a new seismic MMV method for CCS that is more efficient.

Part two of our three part Theseus 24D presentation series discussing a new seismic MMV method for CCS that is more efficient. This part focuses on an integrated 2D-3D time laspes method called 24D.

Part three of our three part Theseus 24D presentation series discussing a new seismic MMV method for CCS that is more efficient. This part focuses on an experiment to demonstrate the best 3D design for our integrated 2D-3D time laspes method called 24D.

A humorous journey through controversies in CCS

All aspects of a CCS project are tied back to risk management; value in the geosciences, including seismic, are directly related to their role in reducing project risk.

Lee, Eric, and Graham's article on using analogic reasoning to leverage known skills into the new field of CCS.

In 2022, the Government of Alberta (GOA) entered into negotiations with 25 project teams to potentially approve storage hubs for the timely and safe permanent underground storage of carbon dioxide (CCS).

Machine learning, data mining, deep learning and artificial intelligence (AI) are technical approaches to automated analysis and problem solving that are being both employed and marketed in the geosciences and beyond.

We come to conventions and Symposia to hear about success and bask in the glory of our awesome, successful colleagues. But mistakes may also lead to useful learning opportunities.