Sequentially optimized data acquisition for a geothermal reservoir

Given the high energy demands for heating and cooling, and the currently limited use of renewable sources, there is a pressing need for efficient and economically viable geothermal development. However, the success of new geothermal reservoir projects hinges on numerous uncertain geological and economic factors. Prior to development, the project uncertainty can be reduced by performing costly data acquisition campaigns. The central question in these campaigns is: which data should be acquired in which order to determine the viability of the proposed project? Traditional methods based on value of information and other heuristics are insufficient for assessing large-scale multi-well geothermal projects. Our approach reformulates geothermal assessment as a partially observable Markov decision process (POMDP), employing algorithmic decision-making techniques to devise an approximately optimal data acquisition strategy. Applied to a real-world low-enthalpy geothermal project in Austria, our methodology increased the expected net present value of the project by nearly 30% over baseline methods (including human experts) by minimizing the risk of developing a non-profitable project. This advancement not only promises a more efficient path towards large-scale geothermal energy production but also sets a precedent for applying sophisticated decision-making frameworks in renewable energy projects.