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Geothermal aquifer performance assessment for direct heat production – Methodology and application to Rotliegend aquifers

Published online by Cambridge University Press:  24 March 2014

J.-D. van Wees*
Affiliation:
TNO – Geological Survey of the Netherlands, P.O. Box 80015, 3508 TA Utrecht, the Netherlands Utrecht University, Tectonics group, Faculty of Earth Sciences, PO Box 80021, 3508 TA, Utrecht, the Netherlands
M. van Putten
Affiliation:
TNO – Geological Survey of the Netherlands, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
M.P.D. Pluymaekers
Affiliation:
TNO – Geological Survey of the Netherlands, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
H. Mijnlieff
Affiliation:
TNO – Geological Survey of the Netherlands, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
P. van Hooff
Affiliation:
TNO – Geological Survey of the Netherlands, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
A. Obdam
Affiliation:
TNO – Geological Survey of the Netherlands, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
L. Kramers
Affiliation:
TNO – Geological Survey of the Netherlands, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
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Abstract

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In this paper we present a probabilistic fast model for performance assessment of geothermal doublets for direct heat applications. It is a simple yet versatile and multipurpose tool. It can be well applied in better understanding the sensitivity of performance to key subsurface parameters and depth trends therein, and for assessing the probability of success for geothermal projects under technical and financial constraints.

The underlying algorithms deliver a sensible accuracy given the uncertainties associated with geothermal projects at exploration state. A public release of the software, available under the name of DoubletCalc, is easy to handle and requires a limited set of input parameters. Thanks to an open source code, DoubletCalc can be implemented in other software applications and extended as it has been implemented for the integration into the national geothermal information system in the Netherlands (ThermoGIS, 2011).

Apart from its application for site assessments, the tool can be integrated into automated workflows processing faster representations of key aquifer properties and capable to produce indicative maps for predicted doublet power, economic feasibility and prediction of cumulative amount of heat that can be recovered. These capabilities are specifically important for decision support for policymakers while assessing the effects of particular insurance schemes and funding mechanisms.

DoubletCalc cannot and is not intended to substitute geologic exploration approaches. As exploration measures, such as seismic surveys are cost intensive, DoubletCalc can be used to focus geothermal exploration on areas and sites where an enhanced probability of success can be expected.

Type
Research Article
Copyright
Copyright © Stichting Netherlands Journal of Geosciences 2013

References

Batzle, M. & Wang, Z., 1992. Seismic properties of pore fluids. Geophysics 57: 13961408.Google Scholar
Beardsmore, G.R., Rybach, L., Blackwell, D. & Baron, C., 2010. A protocol for estimating and mapping the global EGS potential. GRC Transactions 34: 301312.Google Scholar
Beggs, H. & Brill, J., 1973. A study of two-phase flow in inclined pipes. Journal of Petroleum Technology 25: 607617.Google Scholar
Dake, L., 1978. Fundamentals of reservoir engineering. Elsevier (Heidelberg, London, New York).Google Scholar
Ehrenberg, S.N., Nadeau, P.H. & Steen, Ø., 2009. Petroleum reservoir porosity versus depth: Influence of geological age. American Association of Petroleum Geologists Bulletin 93: 12811296.CrossRefGoogle Scholar
Etherington, J.R. & Ritter, J.E., 2007. Reserves and Resources Classification, Definitions, and Guidelines: Defining the Standard. Hydrocarbon Economics and Evaluation Symposium (Dallas, Texas, USA).Google Scholar
Fahrshad, F. & Rieke, H., 2006. Surface-Roughness Design Values for Modern Pipes. SPE Drilling & Completion 21: 212215.Google Scholar
Fesitel, R. & Marion, G., 2007. A Gibbs-Pitzer function for high-salinity seawater thermodynamics. Progress in Oceanography. DOI:10.1016/j.pocean.2007.04.020.Google Scholar
Garcia-Gutierrez, A., Espinosa-Paredes, G. & Hernandez-Ramirez, I., 2001. Study on the flow production chrarcteristics of deep geothermal wells. Geothermics 31: 141167.Google Scholar
Gringarten, A.C., 1978. Well testing in two-phase geothermal wells. 53rd Annual Fall Technical Conference Exhibition (Houston, Texas).Google Scholar
Grunnberg, L., 1970. Properties of sea water concentrations. Third Interantional Symposium on Fresh Water from the Sea, Vol. 1.Google Scholar
Intergovernmental Panel on Climate Change, 2011. Special Report on Renewable Energy Sources and Climate Change Working Group III – Mitigation of Climate Change, 50 pp.Google Scholar
Kramers, L., Van Wees, J.-D., Pluymaekers, M.P.D, Kronimus, A. & Boxem, T., 2012. Direct heat resource assessment and subsurface information systems for geothermal aquifers; the Dutch perspective. Netherlands Journal of Geosciences 91–4: 637649, this issue.Google Scholar
Markowitz, H., 1952. Portfolio Selection. Journal of Finance 7: 7791.Google Scholar
Muffler, L.P.J & Cataldi, R., 1978. Methods for regional assessment of geothermal resources. Geothermics 7: 5389.Google Scholar
Pluymaekers, M.P.D, Kramers, L., Van Wees, J.-D., Kronimus, A., Nelskamp, S., Boxem, T. & Bonté, D., 2012. Reservoir characterisation of aquifers for direct heat production: Methodology and screening of the potential reservoirs for the Netherlands. Netherlands Journal of Geosciences 91–4: 621636, this issue.Google Scholar
Sharpe, W.F., 1964. Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance 19: 425442.Google Scholar
Van Everdingen, E.F., 1953. The Skin effect and its influence in the productive capacity of a well. Transactions, AIME 198: 171176.Google Scholar
Van Wees, J.D., Kramers, L., Kronimus, A., Pluymaekers, M.P.D, Mijnlieff, H.F. & Vis, G.-J., 2010. ThermoGISTM V1.0, Part II: Methodology: www.thermogis.nl/downloads/ThermoGISmanual_partII.pdf.Google Scholar
Verruijt, A., 1970. Theory of Groundwater Flow. Macmillan (London), 190 pp.Google Scholar
Warmte Atlas, 2011. Agentschap NL, Dutch Ministry of Economic Affairs.agentschapnl.kaartenbalie.nl/gisviewer/viewer.do?code=311bc9828c8015a87e9d3dd8fd179ed8.Google Scholar