Integration of deep-learning fault segmentation, horizontal transverse isotropy analysis, and ambient microseismic methods to enhance fracture prediction in the Crisol Anticline, Colombia

Abstract

The analysis of fault and fracture systems in the subsurface is crucial for identifying potential reservoirs and optimizing production. However, conventional fault interpretation methods have limitations due to the complexity and amount of data involved. In this study, a multimethod approach was used to analyze fault and fracture systems in the Crisol area of the Valley Middle Magdalena basin. We used deep-learning fault segmentation, horizontal transverse isotropy (HTI), and ambient microseismic methods to identify and characterize fault and fracture systems in the study area. The deep-learning fault segmentation method helped to identify complex fault systems, whereas the HTI method identified areas and orientations of intense fracturing that occurred at any prior period of stress in geologic time. Meanwhile, the ambient microseismic method measured active rock movements and fracturing in the subsurface that occurred during the microseismic trace recording. The application of multiple methods for analyzing fault and fracture systems in the subsurface is demonstrated to offer a more comprehensive understanding of the geology and enhance the accuracy of well placement and production forecasts.