De-risking of faults associated with picking strategies: Implications for assessing fault growth and reactivation potential for CO2 storage in the Northern Horda Platform

Abstract

Structural de-risking of Carbon Capture and Storage (CCS) prospects are highly dependent on the effects of faults. Seismic scale faults will dictate how injected CO2 migrate within the subsurface. Depending on fault characteristics (e.g., strike, dip, and throw) faults can act either as conduits to CO2 migration or as baffles/ barriers. Locally increased pressure might also cause a fault to reactivate, making new migration pathways. To make any meaningful prediction on CO2 behavior after injection the quality of structural models are essential. The results from this study show that the strategy used (sampling interval and surface generating algorithms) when picking faults and fault cut-off lines impact the results of fault characteristics. Throw-Distance profiles, which is widely used in fault growth analysis failed to identify areas of possible linkage when faults were picked at coarser intervals. Near fault tips, coarser sampling intervals lead to missed data and fault surfaces that were considerable shorter than when picked at finer intervals. Increased sampling intervals also leads to an increase in the average fault stability, missing small areas of high reactivation potential. However, due to the nature of human error when picking faults from segment to segment, sampling intervals close to the seismic resolution resulted in fault surfaces being overly rugous, not honoring the seismic data. Picking faults at 100 m intervals identified all fault segments also identified at 25 m intervals. Considering time invested vs. details found, this study recommends 100 m intervals when picking the main body of the fault, for the creation of Throw-Distance profiles. To capture the whole length of faults sampling intervals close to the vertical resolution of the survey is recommended approaching fault tips. Assessing fault reactivation potential, interpreters are advised to be aware of how human error and triangulation methods influence surface rugosity and hence also geomechanical results. Optimum picking strategy will be a balance between smoothing over human error while still maintaining as much geological details as possible.