Electromagnetic prediction of rock thermal properties beyond boreholes: Soultz-sous-Forets (France) case study

Abstract

Estimation of deep temperature, thermo-hydro-dynamic and basin simulation, heat exchange and geothermal resource assessment require knowledge of the rock thermal properties beyond boreholes. At the absence of suitable methods and instruments for such estimates we propose a new approach to prediction of thermal conductivity, specific heat capacity and heat flow density based on results of electromagnetic sounding of the study area. Artificial neural network used to this end is taught by correspondence between laboratory measurements on cores from the drilled boreholes and adjacent electrical conductivity profiles determined by inversion of the electromagnetic sounding data collected in their vicinity. This approach is used to estimate rock thermal properties along NW-SE magnetotelluric profile crossing Soultz-sous-Forêts geothermal area, Alsace, France. We demonstrate that accuracy of such prediction both in depths below the boreholes and in the interwell space is comparable with that usually achievable in the borehole scale. In particular, it is shown that relative errors of thermal conductivity prediction at depths twice as large as the borehole depth are as small as 2%, and the accuracy of cross-well thermal conductivity prediction is 6% and 10% for dry and wet rocks, respectively. On the other hand, the prediction accuracy for matrix thermal conductivity of a rock from the known profile of dry thermal conductivity is about 8%. Based on the results of our studies, we constructed dry and wet thermal conductivity sections from the electrical conductivity model determined earlier by 2-D inversion of magnetotelluric data. The temperature model of the study area built along the same profile by means of electromagnetic geothermometer and the obtained thermal conductivity sections enabled estimating the profiles of the vertical component of heat flow density at the surface from dry and wet thermal conductivity sections. They could be considered as minimum and maximum constraints, respectively, bounding the area of its possible values, which, in turn, depend on in situ thermal conductivity of fluids filling the rock pores. The range of possible vertical heat flow density variations (100–150 ± 10% mW·m− 2) agrees well with heat flow estimates in the Upper Rhine graben and, in particular, in Soultz-sous-Forêts geothermal area roughly estimated earlier from borehole measurements. Using the artificial neural network trained on the correspondence between the specific heat capacity determined in the borehole and adjacent electrical conductivity profile, we built the model of specific heat capacity. Against the background, a zone of enhanced specific heat capacity ranging between 2.05 and 2.15 MJ·m− 3·K− 1 was observed in the domain located at the base of the sedimentary cover and upper part of the granitic basement.