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Fracture Modeling From Virtual Outcrops


Outcrop analogues represent one of the main source of data to constrain fracture network properties and heterogeneity and to relate these to sub-surface detectable feautures, such as faults and facies changes. The introduction and use of Virtual Outcrop technologies in geological analysis has created the opportunity to extend facture studies over vast and inaccessible areas and the possibility to generate tools and workflows that allow automated fracture extraction. The use of VO technologies together with automated fracture picking methods reduces time and costs for fieldwork and fracture interpretation, uncertainties due to operator and accessibility bias. In this contribution we propose a ‘best practice’ workflow for improved fracture modeling from outcrop analogues which combine and integrate data extracted from automated virtual outcrop analysis and field data. Automated fracture picking technologies for LiDAR- and satellite image-derived Virtual Outcrops were applied to ground-based LiDAR-derived virtual outcrops of the Yates Fm. G23 HFS of the Primative Road member, NW shelf, Rattlesnake Canyon, Capitan Platform, Texas. Automated fracture picking data were compared to fieldwork and manual picking data. Each dataset was used to build a fracture model and these were compared to assess limitations/pitfalls of the different data types. Fracture orientation, size and spatial distribution derived from automated fracture picking are very comparable to results obtained through field work and manual picking. Each dataset have limitations that are reflected into DFNs and upscaled static properties. It is the combination and integration of automated fracture picking and field work data that provide the best results in the shortest time. VO technologies and automated fracture picking methods complement and extend field-based observations and can significantly improve fracture characterization from outcrop analogues also reducing risks and costs associated to field work.