Abstract. Well logs, aquired in the two scientific drill holes of the German Continental Deep Drilling Program (KTB), provide continuous records of physical and chemical data of the metamorphic rocks penetrated. The 4-km-deep pilot hole was almost completely cored, enabling the well logs to be calibrated with regard to rock composition and structural features derived from laboratory analysis of cores. The observed relationships were transferred to the 9101 m deep, nearly uncored, main hole to reproduce in detail the lithology and to estimate physical properties from the logs. Synthetic lithological profiles were constructed for the pilot hole and the main hole by applying the electrofacies concept adapted to the crystalline environment. These profiles provide information on lithostratigraphy, alteration, cataclastic overprint, and petrogenetic features. Cross-hole correlations of these profiles reveal identical rock sequences for large sections of the drilled, metamorphic basement in both holes, in which the primary differences between the protoliths are largely preserved. Multivariate statistical methods were used to determine porosity depth functions from log responses. Linear as well as multilinear regression yielded continuous porosity profiles for both boreholes. Factor analysis was used to extract a parameter interpreted as a fluid and fracture indicator. Comparison of the porosity profiles with lithological information from log, core, and cuttings data revealed two different origins of increased porosity. Rock porosity and permeability are not only related to discrete planar discontinuities such as faults and fractures but also to more extensive zones of intense rock alteration where considerable matrix porosity occurs.

Well logging has become a standard method in the oil industry for the investigation of subsurface geology. Accordingly, interpretation techniques have been mainly developed for use in sedimentary rocks, and the log responses of sediments are well known. However, this is not the case for igneous and metamorphic rocks. We present a compilation of log responses for mafic rocks from drill-holes in oceanic and continental basement. The holes cover a variety of mafic rocks: mid-ocean ridge basalt (MORB), gabbro, basalt and andesitic basalt from back-arc basins, flood basalt from large igneous provinces (LIPs), and continental metamorphic rocks. The comparison of log responses shows that rocks from the same geological setting have similar in situ physical properties. Differences in physical properties between rocks from different geological settings are mainly related to variations in the structure of the rocks, while variations in composition have only a minor effect on the in situ physical properties. In volcanic rocks, variations in fracturing and vesicularity related to cooling of the lava strongly influence log responses. Mafic rocks from continental drill-holes were enriched in radioactive elements during regional metamorphism, resulting in higher values in the total gamma-ray compared to the oceanic rocks.

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S U M M A R YQuantifying and minimizing uncertainty is vital for simulating technically and economically successful geothermal reservoirs. To this end, we apply a stochastic modelling sequence, a Monte Carlo study, based on (i) creating an ensemble of possible realizations of a reservoir model, (ii) forward simulation of fluid flow and heat transport, and (iii) constraining postprocessing using observed state variables. To generate the ensemble, we use the stochastic algorithm of Sequential Gaussian Simulation and test its potential fitting rock properties, such as thermal conductivity and permeability, of a synthetic reference model and-performing a corresponding forward simulation-state variables such as temperature. The ensemble yields probability distributions of rock properties and state variables at any location inside the reservoir. In addition, we perform a constraining post-processing in order to minimize the uncertainty of the obtained distributions by conditioning the ensemble to observed state variables, in this case temperature. This constraining post-processing works particularly well on systems dominated by fluid flow. The stochastic modelling sequence is applied to a large, steady-state 3-D heat flow model of a reservoir in The Hague, Netherlands. The spatial thermal conductivity distribution is simulated stochastically based on available logging data. Errors of bottom-hole temperatures provide thresholds for the constraining technique performed afterwards. This reduce the temperature uncertainty for the proposed target location significantly from 25 to 12 K (full distribution width) in a depth of 2300 m. Assuming a Gaussian shape of the temperature distribution, the standard deviation is 1.8 K. To allow a more comprehensive approach to quantify uncertainty, we also implement the stochastic simulation of boundary conditions and demonstrate this for the basal specific heat flow in the reservoir of The Hague. As expected, this results in a larger distribution width and hence, a larger, but more realistic uncertainty estimate. However, applying the constraining post-processing the uncertainty is again reduced to the level of the post-processing without stochastic boundary simulation. Thus, constraining post-processing is a suitable tool for reducing uncertainty estimates by observed state variables.

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