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Quantifying 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 post-processing 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.
Various models have been proposed for the prediction of the necessary support pressure at the face of a shallow tunnel. To assess their quality, the collapse of a tunnel face was modelled with small-scale model tests at single gravity. The development of the failure mechanism and the support force at the face in dry sand were investigated. The observed displacement patterns show a negligible influence of overburden on the extent and evolution of the failure zone. The latter is significantly influenced, though, by the initial density of the sand: in dense sand a chimney-wedge-type collapse mechanism developed, which propagated towards the soil surface. Initially, loose sand did not show any discrete collapse mechanism. The necessary support force was neither influenced by the overburden nor the initial density. A comparison with quantitative predictions by several theoretical models showed that the measured necessary support pressure is overestimated by most of the models. Those by Vermeer/Ruse and Léca/Dormieux showed the best agreement to the measurements.
This paper proposes an approach to the choice and evaluation of engineering models with the aid of a typical application in geotechnics. An important issue in the construction of shallow tunnels, especially in weak ground conditions, is the tunnel face stability. Various theoretical and numerical models for predicting the necessary support pressure have been put forth in the literature. In this paper, we combine laboratory experiments performed at the University of Innsbruck with current methods of uncertainty and sensitivity analysis for assessing adequacy, predictive power and robustness of the models. The major issues are the handling of the twofold uncertainty of test results and of model predictions as well as the decision about what are the influential input parameters.