Case study: Integrated interpretation of borehole acoustic televiewer (ATV) and radar data for imaging major fracture networks in crystalline rock

Characterizing fracture networks in deep (hundreds-of-meters) crystalline rock is a key requirement for assessing the suitability of a high-level radioactive waste repository site. Hydraulically conductive fracture networks may act as preferential groundwater pathways and therefore need to be identified for long-term safety assessment. In line with the geophysical exploration objective to investigate and visualize connected fracture networks in deep rock, this study proposes an integrated interpretation strategy combining borehole-based multi geophysical data systems. This work is part of a basic research project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) entitled “Development of Core Technologies for Characterization and Modeling of Fractured Rock in the Assessment of Site Suitability for High-Level Radioactive Waste Disposal”.

This study aims to identify and quantitatively characterize major fracture-network intervals around the borehole by integrating acoustic televiewer (ATV) borehole imaging logs with borehole radar data. ATV provides high-resolution structural information for fractures penetrating the borehole wall, including dip, dip direction, and aperture (where resolvable). However, because ATV observation is confined to the borehole wall image, it has limited capability to evaluate the continuity and spatial distribution of the fractures around the borehole. In contrast, while quantitative characterization of detailed fracture geometry (e.g., orientation and aperture) from borehole radar data alone is difficult, it can image relatively large and continuous fractures within approximately 10-15 m of the borehole that may be hydraulically conductive. Borehole radar method can also detect fractures that do not directly intersect the borehole. Using these complementary strengths, we propose the joint interpretation strategy to image major fracture-network candidates around borehole and to infer their dip, dip direction, aperture, and relative continuity. In addition, we demonstrate the workflow through a case study with field data.

The case study was conducted using ATV and borehole radar datasets acquired from a KIGAM borehole down to a depth of 100 m. First, based on the ATV log, fracture characteristics (e.g., dip, dip direction, and aperture) were analyzed, and major fracture-network intervals with clustered fractures were identified. Then, near-borehole (early-time) reflection events in the borehole radar data were analyzed to evaluate their relationship with the major fracture-network intervals derived from the ATV log. In particular, distinct reflectors observed at relatively later times (i.e., beyond the near-borehole) in radar data were matched to the major fracture-network intervals derived from ATV to estimate the relative continuity of them around the borehole. The proposed borehole-based integrated ATV and radar interpretation strategy enables characterization of major fracture networks and is expected to provide a practical approach for screening and visualizing deep fracture networks associated with the stability of repository sites.