How good is your seismic? A genetic algorithm to kickstart evaluation of CCS candidates

The information extracted from seismic data often constitutes the base of subsurface models which are used to make decisions for further exploration, appraisal, or development activities. With the current momentum in the carbon capture and storage area, new seismic data is often acquired, or old data revisited to assess the potential of candidates, with still limited time for analysis and interpretation. It is very difficult today for geoscientists to precisely quantify the changes in final characteristics of post-stack 3D seismic images. Depending on the person or company doing the processing and the workflow used, the results can be quite different. If this is a challenge for all disciplines, it becomes a particular one for screening CCS candidates and de-risk long term storage opportunities, and especially to analyze cap rock integrity or predict conformance. We will describe a new approach using a genetic algorithm (GA) to automatically extract information from the 3D seismic data in an unbiased manner and in record time and show how this approach can be used to compare seismic images which have undergone different processing. We will discuss an example using the publicly available data of the Sleipner CO2 storage site.