The ambition of the geothermal industry is to build 700+ geothermal energy projects in the Netherlands by 2050. This means that hot sedimentary aquifers that are currently used have to be exploited in adjacent locations and that similar reservoirs have to be found in more distant areas or deeper in the subsurface, where significantly less data are available. Moreover, geomechanical and thermal properties of target reservoirs are poorly known. Both of these properties are needed for accurate reservoir character prediction and to ensure safe and economic project performance.
The ambition of this project is to significantly enhance the knowledge, mapping, and prediction of the geological character of Dutch geothermal plays, focussing on thermal and geomechanical properties. The aim is to improve fundamental understanding of the geological causes behind rock properties, the understanding and quantification of the conversion of downhole log properties into rock properties, the statistical approaches to study these properties, and the implementation of rock properties into reservoir models. The study targets lower exploration costs for geothermal operators, lower uncertainties concerning production prediction and risk assessment, and improved production due to more optimal well placement and production strategies. The fully open-access results should be applicable to all geothermal targets in the Netherlands and thereby be able to calibrate reservoir, geomechanical and thermal reservoir models with the ultimate goal to optimise the exploitation of geothermal heat in the Netherlands in a sustainable and safe way.
The PhD candidate focusses on measuring and correlation components of the project. Geomechanical, mineralogical, and thermal properties are to be measured from nm to dm scales in different laboratories involved and using existing and new techniques. Data production and analysis will occur in continuous collaboration with specialists in each field. Geological understanding of rock properties is important for upscaling and uncertainty quantifications. The PhD will further attempt to link rock properties to petrophysical log data using traditional and innovative means, including machine learning. Upscaling and validation within the project are lead by industry partners, but the candidate will continuously interact on the width of the project with all six project partners to ensure optimal data and model exchange within the project. Outreach meetings with geothermal operators are foreseen to be organized by the PhD student.
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