Recent technological advances in seismic acquisition and processing have proved valuable for CCS site screening projects. TGS authors describe a number of case studies from offshore North-West Europe in the June issue of First Break.
First Break June 2024 | Seismic-led exploration and characterization of carbon storage sites
The case studies described in our First Break paper fall into four categories:
The SNS Vision project, initiated by TGS in late 2022, exemplifies how the seismic imaging quality from rejuvenated multi-survey vintage data, can be enhanced to provide new products fit for both O&G exploration and CCS screening.
The common final Kirchhoff pre-stack depth migrated (KPSDM) product covers 26 legacy seismic surveys acquired between 1988 and 2006. It consists of approximately 12 000 sq. km of 3D seismic data predominantly located in the UK sector, with parts in the Dutch sector and provides a regionally consistent dataset for both structural, stratigraphic and quantitative interpretation purposes. Both pre- and post-salt intervals, whose depths vary greatly, were the main objectives of the reprocessing including an extensive velocity model building (VMB) sequence.
Quality improvements were significant at all depths, notably in the modeling and imaging of intra-salt heterogeneities and base salt anhydrites/dolomites, as well as at the deeper pre-salt Rotliegend which is mainly a target for gas exploration. The step change is even more significant in the post-salt section where a significant focus for CCS ventures is currently put on the Triassic Bunter sandstones and the overburden. Indeed, those intervals were historically poorly focused and therefore insufficiently imaged. The latest improvements are further emphasized by greater clarity of faults, some of which extend from top salt to near surface, a component that is critical for fully assessing containment risks.
The main KPSDM results provide reliable amplitudes for quantitative interpretation which still proves to be a challenge in this post-salt environment due to the scarcity of available well data. Fortunately, a novel approach based on machine learning allowed the reconstruction of missing overburden well information to support the generation of reliable rock properties, in particular for the Triassic Bunter Sandstone Formation BC28, as illustrated below.