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Left: The Nesjavellir Geothermal Power Plant in ÃŽingvellir, Iceland (Foto: Gretar Ãvarsson, 2006; via wikimedia)
Right: Well-log based prediction of rock thermal conductivity (Fuchs & Förster, 2013)

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Thesis

Well-log based determination of rock thermal conductivity in the North German Basin

Author: Fuchs, S. (2013)

University of Potsdam (Germany) - Band , Heft , Seiten 137

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In sedimentary basins, rock thermal conductivity can vary both laterally and vertically, thus altering the basin ’ s thermal structure locally and regionally. Knowledge of the thermal conductivity of geological formations and its spatial variations is essential, not only for quantifying basin evolution and hydrocarbon maturation processes, but also for understanding geothermal conditions in a geological setting. In conjunction with the temperature gradient, thermal conductivity represents the basic input parameter for the determination of the heat-flow density; which, in turn, is applied as a major input parameter in thermal modeling at different scales. Drill-core samples, which are necessary to deter-mine thermal properties by laboratory measurements, are rarely available and often limited to previously explored reservoir formations. Thus, thermal conductivities of Mesozoic rocks in the North German Basin (NGB) are largely unknown. In contrast, geophysical borehole measurements are often available for the entire drilled sequence. Therefore, prediction equations to determine thermal conductivity based on well-log data are desirable. In this study rock thermal conductivity was investigated on different scales by (1) providing thermal-conductivity measurements on Mesozoic rocks, (2) evaluating and improving commonly applied mixing models which were used to estimate matrix and pore-filled rock thermal conductivities, and (3) developing new well-log based equations to predict thermal conductivity in boreholes without core control. Laboratory measurements are performed on sedimentary rock of major geothermal reservoirs in the Northeast German Basin (NEGB) (Aalenian sandstone, Rhaethian-Liassic Complex, Stuttgart Fm., and Middle Buntsandstein). Samples are obtained from eight deep geothermal wells that approach depths of up to 2,500 m. Bulk thermal conductivities of Mesozoic sandstones range between 2.1 and 3.9 W/(m·K), while matrix thermal conductivity ranges between 3.4 and 7.4 W/(m·K). Local heat flow for the Stralsund location averages 76 mW/m², which is in good agreement to values reported previously for the NEGB. For the first time, in-situ bulk thermal conductivity is indirectly calculated for entire borehole profiles in the NEGB using the determined surface heat flow and measured temperature data. Average bulk thermal conductivity, derived for geological formations within the Mesozoic section, ranges between 1.5 and 3.1 W/(m·K). The measurement of both dry- and water-saturated thermal conductivities allow further evaluation of different two-component mixing models which are often applied in geothermal calculations (e.g., arithmetic mean, geometric mean, harmonic mean, Hashin-Shtrikman mean, and effective-medium theory mean). It is found that the geometric-mean model shows the best correlation between calculated and measured bulk thermal conductivity. However, by applying new model-dependent correction, equations the quality of fit could be significantly improved and the error diffusion of each model reduced. The "corrected" geometric mean provides the most satisfying results and constitutes a universally applicable model for sedimentary rocks. Furthermore, lithotype-specific and model-independent conversion equations are developed permitting a calculation of water-saturated thermal conductivity from dry-measured thermal conductivity and porosity within an error range of 5 to 10 %. The limited availability of core samples and the expensive core-based laboratory measurements make it worthwhile to use petrophysical well logs to determine thermal conductivity for sedimentary rocks. In literature, several formulations are given to estimate thermal conductivity based on well-log data. However, they all show the typical limitations of statistically derived empirical prediction equations that limit such application to specific geological formations (represented by specific rock compositions) from which rock samples are implemented in the analysis. The approach followed in this study is based on the detailed analyses of the relationships between thermal conductivity of rock-forming minerals, which are most abundant in sedimentary rocks, and the properties measured by standard logging tools (i.e., gamma ray, density, sonic interval transit time, hydrogen index, and photoelectric factor). By using multivariate statistics separately for clastics, carbonates and evaporites, the findings from these analyses allow the development of prediction equations from large artificial data sets that predict matrix thermal conductivity within an error of 4 to 11 %, without being affected by the limitations mentioned above. These equations are validated successfully on a comprehensive subsurface data set from the NGB. In comparison to the application of earlier published approaches formation-dependent developed for certain areas, the new developed equations show a significant error reduction of up to 50 %. These results are used to infer rock thermal conductivity for entire borehole profiles. By inversion of corrected in-situ thermal-conductivity profiles, temperature profiles are calculated and compared to measured high-precision temperature logs. The resulting uncertainty in temperature prediction averages < 5 %, which reveals the excellent temperature prediction capabilities using the presented approach. In conclusion, data and methods are provided to achieve a much more detailed parameterization of thermal models, helping to understand the thermal structure of sedimentary basins in general and of the North German Basin in particular.