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• W2144963265 abstract "Modeling naturally fractured reservoirs requires a detailed understanding of the three-dimensional (3D) fracture-network characteristics, whereas generally only one-dimensional (1D) data, often suffering from sampling artifacts, are available as inputs for modeling. Additional fracture properties can be derived from outcrop analogs with the scanline method, but it does not capture their full two-dimensional (2D) characteristics. We propose an improved workflow based on a 2D field-digitizing tool for mapping and analyzing fracture parameters as well as relations to bedding. From fracture data collected along 11 vertical surface outcrops in a quarry in southeast France, we quantify uncertainties in modeling fracture networks. The fracture-frequency distribution fits a Gaussian distribution that we use to evaluate the intrinsic fracture density variability within the quarry at different observation scales along well-analog scanlines. Excluding well length as a parameter, we find that 30 wells should be needed to fully (i.e., steady variance) capture the natural variability in fracture spacing. This illustrates the challenge in trying to predict fracture spacing in the subsurface from limited well data. Furthermore, for models with varying scanline orientations we find that Terzaghi-based spacing corrections fail when the required correction angle is more than 60°. We apply the 1D well analog data to calculate 3D fracture frequency using stereological relations and find that these relations only work for cases in which the orientation distribution is accurately described, as results greatly vary with small changes in the orientation distribution." @default.
• W2144963265 created "2016-06-24" @default.
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• W2144963265 date "2014-07-01" @default.
• W2144963265 modified "2023-10-03" @default.
• W2144963265 title "Calibrating discrete fracture-network models with a carbonate three-dimensional outcrop fracture network: Implications for naturally fractured reservoir modeling" @default.
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• W2144963265 doi "https://doi.org/10.1306/02031413060" @default.
• W2144963265 hasPublicationYear "2014" @default.
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