In a recent First Break publication, PGS authors describe a CCS workflow over a proof-of-concept area to assess CCS storage capacity and containment at a candidate saline aquifer site. This successful testing of the workflow enables a move towards automation and the ability to extend the area of focus for efficient ranking and evaluation of potential CCS sites to a much larger area.
Advancements in technology including machine learning and imaging techniques applied to seismic and well data have enabled a step towards the automation of a CCS site selection workflow. This allows for turnaround improvements and could open up opportunities for the screening of large areas and efficient site ranking prior to selection.
"PGS is committed to supporting the energy transition, and that means going beyond the traditional provision of seismic data when it comes to carbon storage site identification and screening. This workflow is one example of our commitment to enable the energy transition through the development of products that can rapidly and effectively maximize the use of existing datasets to identify and screen the best sites." Nick Lee, Subsurface Manager, New Energy.
Overall CCS Project Lifecycle Workflow
Prior to CO2 injection at a specific site an integrated front-end project will be undertaken, following a similar path to a conventional oil or gas field assessment. The illustration below represents a high-level view of the carbon storage project lifecycle focusing on the early part of the project and encompassing elements such as petrophysics, rock physics, seismic data analysis, interpretation, and integration to a risk assessment matrix.
A New, Efficient, Scalable and Flexible CCS Workflow
The workflow described in the First Break paper illustrates the integration of high-quality broadband seismic data, well information, their derivative products, and several reservoir geoscience analysis tools to characterize key CCS site evaluation components: capacity, containment and monitorability.
The interpretation stage on its own provides geological understanding: sediment distribution, faulting, layer geometry, and depositional environment relevant to the suitability and expected performance of a site.
The petrophysical and rock physics analysis is the bridge linking elastic properties (AI and Vp/Vs) to reservoir properties (PhiT and Vclay). The well-to-seismic tie augments confidence in the reliability of the lithology prediction and reservoir property estimation away from the wells.
Finally, the calibration of seismic velocities improves the time to depth transform for the structure of the storage reservoir and its thickness which is crucial for the capacity volumetrics.
As the implemented workflow is mainly data-driven it can be easily extended over large areas or other areas for CCS site screening and characterization purposes. Ranking and evaluation of various CCS sites can be done using a developed risk evaluation matrix provided in the paper.
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