--> Quantification and 3-D Modeling of Architectural Variability and Controls in a Permian Carbonate Ramp, Last Chance Canyon, NM

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Quantification and 3-D Modeling of Architectural Variability and Controls in a Permian Carbonate Ramp, Last Chance Canyon, NM

Abstract

This study presents the integration of field-based geological data and drone-based photogrammetry into a 3D geological model of a carbonate ramp system. The study area in Last Chance Canyon (LCC) represents the outcrop analogue of extensive clastic-carbonate clinoform plays of the Upper San Andres formation in the subsurface of the Permian Basin. Goal of this study is to quantify the 3D facies distribution to improve the understanding of how external and internal controls affect geological variability and depositional rates in carbonate clinoform systems. The geological 3D model can be used as analogue model for existing subsurface models in the Permian Basin and for other hydrocarbon-bearing carbonate ramp systems worldwide. Furthermore, the quantitatively constrained ranges of facies distribution will help to decrease uncertainty in existing models of Upper San Andres reservoirs in the Permian Basin. Moreover, the model can reduce cost and minimize risks associated with field work by serving as tool to lead digital field trips. Hand samples, thin sections, geological measured sections, gamma ray profiles, and 2D photomosaics are integrated into a sequence stratigraphic framework. Petrographic descriptions of outcrop-based rock samples indicate a complex diagenetic history of multiple stages of cementation and dissolution with detrimental effect on porosity and permeability. Current work puts focus on the delineation of the effect of autogenic and allogenic controls on system architecture and rates of sediment volume progradation in 3D. Enhancing 2D outcrop information into a comprehensive 3D geological model represents a novel approach to the outcrop-based investigation of carbonate systems. The model allows for the quantification of facies distribution, depositional geometries and sediment volume progradation through time. An improved understanding of the interactions between external and internal controls on carbonate system architecture will help to more accurately predict their reservoir geometries and reservoir properties.