Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO₂ storage site assessment

Abstract

Generating an accurate model of the subsurface for the purpose of assessing the feasibility of a CO2 storage site is crucial. In particular, how faults are interpreted is likely to influence the predicted capacity and integrity of the reservoir; whether this is through identifying high risk areas along the fault, where fluid is likely to flow across the fault, or by assessing the reactivation potential of the fault with increased pressure, causing fluid to flow up the fault. New technologies allow users to interpret faults effortlessly, and in much quicker time, utilizing methods such as Deep Learning. These Deep Learning techniques use knowledge from Neural Networks to allow end-users to compute areas where faults are likely to occur. Although these new technologies may be attractive due to reduced interpretation time, it is important to understand the inherent uncertainties in their ability to predict accurate fault geometries. Here, we compare Deep Learning fault interpretation versus manual fault interpretation, and can see distinct differences to those faults where significant ambiguity exists due to poor seismic resolution at the fault; we observe an increased irregularity when Deep Learning methods are used over conventional manual interpretation. This can result in significant differences between the resulting analyses, such as fault reactivation potential. Conversely, we observe that well-imaged faults show a close similarity between the resulting fault surfaces when both Deep Learning and manual fault interpretation methods are employed, and hence we also observe a close similarity between any attributes and fault analyses made.

How displacement analysis may aid fault risking strategies for CO2 storage

“…An optimum fault picking strategy has been identified (Michie et al, 2021(Michie et al, , 2022 and has been utilised for the interpretation of the TFZ and the VFZ. Specifically, the sizeable half-graben bounding faults that extend for several kilometres require a picking strategy that best captures all detail, but not too high resolution to generate irregular fault surfaces that are not an accurate representation of the faults due to human (Faleide et al, 2021) and triangulation errors.…”