Envisioning faults beyond the framework of fracture mechanics

Faults are complex structures that substantially influence the mechanical behavior and hydraulic connectivity of rock formations. Therefore, studying faults is important for a variety of disciplines such as geoscience, civil, geotechnical, reservoir engineering, and material science among others. Researchers from these disciplines have considered different aspects of faults, namely geometry, petrophysical properties and mechanics. Until now, these studies have evolved separately and at different scales, making it difficult to connect the geometric development of fault structure to its mechanics. The current understanding of fault geometry and growth is based on fracture mechanics and on many qualitative and quantitative studies on outcrop and seismic reflection sur-veys among other datasets. The application of fracture mechanics theory is mostly confined to simple geometries: elliptical models for a single fault plane and uniform properties. These applications predict the maximum displacement at the center of the fault, which is not in agreement with the new findings from 3D seismic and outcrop studies. These fracture mechanics models emphasize fault propagation along strike (in 2D). Although they can include the presence of a process zone at the fault tip, the models fail to explain the development of cross-fault damage zones and localization within the fault core as well as fault segmentation and displacement partitioning. Therefore, it is timely to revise the existing applications of fracture mechanics to simple fault ge-ometries and to develop a data-driven fault mechanics possessing closer agreement with real, observed sub-surface heterogeneity. This would allow better prediction of fault geometry, propagation, and growth in 3D. We suggest recent advances in non-destructive numerical characterization of faults and application of Deep Neural Networks (DNN) to map fault geometry and predict its properties from seismic data enable us for the first time to extract simultaneously faults' geometrical and mechanical properties at an unprecedented speed and accuracy, thus resolving the 3D fault shape and properties in ways that were unthinkable just a decade ago.

Automated summary

Studying faults is important for various disciplines due to their influence on rock formations' mechanical behavior and hydraulic connectivity. However, the current understanding of fault geometry and growth is limited by traditional fracture mechanics models. Recent advances in non-destructive numerical characterization and Deep Neural Networks enable the extraction of fault geometry and mechanical properties at an unprecedented speed and accuracy, resolving the 3D fault shape and properties in ways that were unthinkable just a decade ago.