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Assessment of Identified Geothermal Resources

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Assessment of Identified Geothermal Resources ( assessment-identified-geothermal-resources )

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Although there is some direct evidence for fractal dimensions of properties that are relevant to permeability, such as fracture aperture, fracture length, and fracture density, the fractal dimensions for permeability may vary over a wide range (for example, Watanabe and Takahashi, 1995; Dreuzy and others, 2001). For the purpose of this analysis, the fractures of interest are those that contribute significant volume to flow in the well and thus span a permeability range of approximately two orders of magnitude (Bjornsson and Bodvarsson, 1990). These will be a relatively small subset of the total population of fractures with measureable permeability. This analysis also equates the productivity of individual fracture sets with their permeability, an approach consistent with observations in producing geothermal fields (for example, James and others, 1987). Records of flow from producing fractures in geothermal wells confirm the varying contribution of individual fractures or fracture sets to geothermal production (fig. 7), and also demonstrate large the range of fractal dimensions necessary to characterize the observed variations in flow. Figure 8 compares flow capacity/storage capacity curves from self-similar models for three different fractal dimensions with the Beowawe, Dixie Valley and uniform fracture model curves from figure 6. (For details see Williams, 2007.) The distribution of flow for the Dixie Valley field is consistent with the modeled distribution for d=1, and the distribution for the Beowawe field is consistent with the modeled distribution for d=0.667. The smaller value for d in the Beowawe field reflects the dominance of a single fracture or fracture system in the permeability tapped by the chemical tracer test. Like the uniform fracture model, the self-similar fracture flow models yield a range of values for Rg that depends both on average fracture spacing and on the dimensionality of the spatial distribution of fractures (fig. 9). These results indicate that the self-similar models for fracture permeability reproduce the behavior of producing geothermal reservoirs and provide a physically- based justification for the observed variation in Rg. Given the observed variability in fracture flow properties, the likelihood that most natural fractures will match these varied flow properties with diverse fracture spacings and orientations, and the range of recovery factors determined from production histories of geothermal reservoirs, it is not possible to assign a single value, or even a narrow range, for Rg for unexploited geothermal systems. Taking the above analysis as a guide, in the new resource assessment Rg for fracture- dominated reservoirs is estimated to range from 0.08 to 0.2, with a uniform probability over the entire range. For sediment-hosted reservoirs this range is increased from 0.1 to 0.25. Electric Power Estimates and Monte Carlo Uncertainty Analyses Equations 1 through 5 cover the basic relationships used to estimate electric power generation potential for a given geothermal system. Uncertainties in the estimates are accommodated through a Monte Carlo simulation approach, which is shown schematically in figure 10. For each system, USGS investigators determine most likely, 12

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