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Remote Sensing of Thin-Bedded Reservoir Analogs in an Ancient Delta Using High-Resolution, Ground-Based, Hyperspectral and LiDAR Technologies, Cretaceous Notom Delta, Utah


Facies heterogenity in deltaic deposits is important in exploration and recovery of hydrocarbons, but conventional well- log and seismic data are often too sparse or too low resolution to resolve fine-scale features such as mud drapes, which may have dramatic effects on reservoir behavior, especially in thin-bedded reservoirs. Outcrop analysis provides key analog data that can be used to generate more detailed reservoir models. In this study, portable high-resolution hyperspectral and LiDAR sensors are used to image fine-scale heterogeneity in a fluvial-deltaic reservoir analog of the Cretaceous Ferron Notom Deltaic sandstone in central Utah. Light Detection and Ranging (LiDAR) is a laser scanning technology used to create an extremely accurate, millimeter-scale high-resolution, digital representation of the outcrop in 3D. Hyperspectral sensors record electromagnetic radiation reflecting off the outcrop in numerous contiguous bands, which are then used to generate a spectral signature for each pixel sampled. The spectral signatures are a function of mineralogy, chemistry, grain-size, and cements and are used to distinguish thin mudstones from sandstone facies. Comparison between the spectral signatures recorded from the outcrop and those of reference materials, and with previous facies architectural studies, enables lithofacies to be accurately mapped. Hyperspectral data is then draped over the LiDAR model to generate a spatially-accurate detailed 3D geologic map of the heterogeneity. The focus of this study is on previously mapped deltaic parasequences 5 and 6, particularly heterolithic thin-bedded facies of the distal delta front. Facies are locally highly variable, ranging from mudstone to interbedded planar-bedded to hummocky cross-stratified sandstone as well as heterolithic facies deposited within storm-influenced, river-dominated delta fronts. Coupling ground-based remote sensing technologies with traditional geological techniques provides continuous maps of grain size and lithology, as well as calibration of depositional gradients. Previous studies approximated these features using more traditional field methods, such as linear interpolation between measured sections (control points). The rapid data collection possible with ground-based hyperspectral and LiDAR allows more efficient and more complete reservoir characterization data sets to be obtained, especially in thin-bedded reservoir analogs that lack reservoir characterization studies.