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Ojito Amphitheater: Combining Terrestrial Lidar and Photogrammetric Techniques Into Physically Plausible Training Images for Multiple-Point Geostatistical Representations of Subsurface Reservoirs


For the past two decades, subsurface heterogeneity studies have focused on modeling sedimentary structure and facies distributions using a variety of standard geostatistical approaches; however, due to limitations in parameterization data and dimensionality these approaches may not capture realistic facies geometries and subsequent fluid flow paths. Multiple-point statistics (MPS) has shown promise in portraying these complex geometries more realistically; however, these realizations are limited by the realism of their foundation, the Training Image (TI). In an attempt to more accurately characterize subsurface reservoirs, a quantitative outcrop analog-based approach utilizing terrestrial lidar and high-resolution, calibrated digital photography is combined with lithofacies analysis to create realistic TI's. In the summer of 2011, terrestrial lidar scans and high-resolution digital imagery of approximately 15,000 m2 of contiguous cliff face were acquired of a Westwater Canyon Member, Morrison Formation outcrop in Ojito Wilderness, New Mexico, USA. The resulting ∼ 400 points/m2 3D lidar point cloud was used to develop a mesh of ∼ 2.5 cm resolution. The digital images were color corrected and geometrically calibrated prior to being processed through a series of photogrammetric techniques including edge detection and textural filters to delineate different facies and sedimentary structures. The resulting classified images were projected onto the high-resolution mesh creating a highly realistic virtual outcrop model (VOM), portions of which were used as TI's for a MPS reservoir simulation.