Fracture characterization and delineation have emerged as one of the key parameters in studying reservoir properties and compartmentalization in both conventional and unconventional energy resources. Fractures could also play a significant role in the assessment of subsurface carbon capture storage. Subsurface fracture is often challenging to delineate and map due to its complex origin and paragenesis. This is compounded by time consuming and biased-prone fracture analysis when using conventional methods.
In this study, we propose a new approach by coupling simple image binarization and advanced image-to-image translation with Conditional Generative Adversarial networks (CGAN) to fully automate and optimize fracture characterization and delineation processes. Here, fracture maps were generated from both outcrop and subsurface examples and the results show the proposed method is significantly more superior than conventional methods reaching up to 94% in mean accuracy. We further extends the application of our newly proposed method to solve inverse problems in fracture reservoir by generating realistic fracture map only from a simple sketch. This work further highlights the enormous potential of deep learning-assisted analysis on different geological problems, such as fracture characterization and delineation. Understanding reservoir fracture distribution and properties in both 2D and 3D spaces would help to optimize exploration, production, and reservoir monitoring of energy resources.
