TY  - JOUR
A1  - Vogt, C.
A1  - Mottaghy, Darius
A1  - Rath, V.
A1  - Marquart, G.
A1  - Dijkshoorn, L.
A1  - Wolf, A.
A1  - Clauser, C.
T1  - Vertical variation in heat flow on the Kola Peninsula: palaeoclimate or fluid flow?
JF  - Geophysical Journal International
N2  - Following earlier studies, we present forward and inverse simulations of heat and fluid transport of the upper crust using a local 3-D model of the Kola area. We provide best estimates for palaeotemperatures and permeabilities, their errors and their dependencies. Our results allow discriminating between the two mentioned processes to a certain extent, partly resolving the non-uniqueness of the problem. We find clear indications for a significant contribution of advective heat transport, which, in turn, imply only slightly lower ground surface temperatures during the last glacial maximum relative to the present value. These findings are consistent with the general background knowledge of (i) the fracture zones and the corresponding fluid movements in the bedrock and (ii) the glacial history of the Kola area.
Y1  - 2014
U6  - http://dx.doi.org/10.1093/gji/ggu282
SN  - 1365-246X
VL  - 199
IS  - 2
SP  - 829
EP  - 843
PB  - Oxford University Press
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Vogt, C.
A1  - Mottaghy, Darius
A1  - Wolf, A.
A1  - Rath, V.
A1  - Pechnig, R.
A1  - Clauser, C.
T1  - Reducing temperature uncertainties by stochastic geothermal reservoir modelling
JF  - Geophysical Journal International
N2  - 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.
Y1  - 2010
U6  - http://dx.doi.org/10.1111/j.1365-246X.2009.04498.x
SN  - 1365-246X
VL  - 181
IS  - 1
SP  - 321
EP  - 333
PB  - Oxford University Press
CY  - Oxford
ER  -