Characterization of geothermal reservoirs’ parameters by inverse problem resolution and geostatistical simulations

BTES (borehole thermal energy storage)systems exchange thermal energy by conduction with the surrounding ground through borehole materials. The spatial variability of the geological properties and the space-time variability of hydrogeological conditions affect the real power rate of heat exchangers and, consequently, the amount of energy extracted from / injected into the ground. For this reason, it is not an easy task to identify the underground thermal properties to use when designing. At the current state of technology, Thermal Response Test (TRT) is the in situ test for the characterization of ground thermal properties with the higher degree of accuracy, but it doesn’t fully solve the problem of characterizing the thermal properties of a shallow geothermal reservoir, simply because it characterizes only the neighborhood of the heat exchanger at hand and only for the test duration. Different analytical and numerical models exist for the characterization of shallow geothermal reservoir, but they are still inadequate and not exhaustive: more sophisticated models must be taken into account and a geostatistical approach is needed to tackle natural variability and estimates uncertainty. The approach adopted for reservoir characterization is the “inverse problem”, typical of oil&gas field analysis. Similarly, we create different realizations of thermal properties by direct sequential simulation and we find the best one fitting real production data (fluid temperature along time). The software used to develop heat production simulation is FEFLOW 5.4 (Finite Element subsurface FLOW system). A geostatistical reservoir model has been set up based on literature thermal properties data and spatial variability hypotheses, and a real TRT has been tested. Then we analyzed and used as well two other codes (SA-Geotherm and FV-Geotherm) which are two implementation of the same numerical model of FEFLOW (Al-Khoury model).

[1]  Per Eskilson Thermal analysis of heat extraction boreholes , 1987 .

[2]  C. Willmott Some Comments on the Evaluation of Model Performance , 1982 .

[3]  K. Loague,et al.  Statistical and graphical methods for evaluating solute transport models: Overview and application , 1991 .

[4]  O. J. Zobel,et al.  Heat conduction with engineering, geological, and other applications , 1955 .

[5]  R. Al-Khoury,et al.  Efficient finite element formulation for geothermal heating systems. Part I: steady state , 2005 .

[6]  S. Gehlin Thermal response test : in situ measurements of thermal properties in hard rock , 1998 .

[7]  James G. Berryman,et al.  Thermal conductivity of porous media , 2004 .

[8]  P. Goovaerts Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall , 2000 .

[9]  Jan W. Hopmans,et al.  Optimization of Hydraulic Functions from Transient Outflow and Soil Water Pressure Data , 1993 .

[10]  A. Sahuquillo,et al.  Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory , 1997 .

[11]  L. Ferreira,et al.  Surface roughness effects on soil albedo , 2000 .

[12]  Hiroshi Akima,et al.  A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures , 1970, JACM.

[13]  Simon J. Rees,et al.  A transient two-dimensional finite volume model for the simulation of vertical U-tube ground heat exchangers , 1999 .

[14]  E.T. Castano,et al.  Statistical Functional Modeling of Quality Changes of Garlic under Different Storage Regimes , 2006, Journal of Data Science.

[15]  Sudhindra N. Panda,et al.  Field test of a soil water balance simulation model , 2003 .