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Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Author

Listed:
  • Torsten Clemens

    (OMV Energy, Trabrennstr. 6–8, 1020 Vienna, Austria)

  • Maria-Magdalena Chiotoroiu

    (OMV Energy, Trabrennstr. 6–8, 1020 Vienna, Austria)

  • Anthony Corso

    (Aeronautics and Astronautics, Stanford University, 496 Lomita Mall, Stanford, CA 94305, USA)

  • Markus Zechner

    (Earth and Planetary Sciences, Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, USA)

  • Mykel J. Kochenderfer

    (Aeronautics and Astronautics, Stanford University, 496 Lomita Mall, Stanford, CA 94305, USA)

Abstract

Low-enthalpy geothermal energy can make a major contribution towards reducing CO 2 emissions. However, the development of geothermal reservoirs is costly and time intensive. In particular, high capital expenditures, data acquisition costs, and long periods of time from identifying a geothermal resource to geothermal heat extraction make geothermal field developments challenging. Conventional geothermal field development planning follows a linear approach starting with numerical model calibrations of the existing subsurface data, simulations of forecasts for geothermal heat production, and cost estimations. Next, data acquisition actions are evaluated and performed, and then the models are changed by integrating the new data before being finally used for forecasting and economics. There are several challenges when using this approach and the duration of model rebuilding with the availability of new data is time consuming. Furthermore, the approach does not address sequential decision making under uncertainty as it focuses on individual data acquisition actions. An artificial intelligence (AI)-centric approach to field development planning substantially improves cycle times and the expected rewards from geothermal projects. The reason for this is that various methods such as machine learning in data conditioning and distance-based generalized sensitivity analysis assess the uncertainty and quantify its potential impact on the final value. The use of AI for sequential decision making under uncertainty results in an optimized data acquisition strategy, a recommendation of a specific development scenario, or advice against further investment. This approach is illustrated by applying AI-centric geothermal field development planning to an Austrian low-enthalpy geothermal case. The results show an increase in the expected value of over 27% and a reduction in data acquisition costs by more than 35% when compared with conventional field development planning strategies. Furthermore, the results are used in systematic trade-off assessments of various key performance indicators.

Suggested Citation

  • Torsten Clemens & Maria-Magdalena Chiotoroiu & Anthony Corso & Markus Zechner & Mykel J. Kochenderfer, 2024. "Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning," Energies, MDPI, vol. 17(8), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1887-:d:1376384
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    References listed on IDEAS

    as
    1. Willems, C.J.L. & M. Nick, H., 2019. "Towards optimisation of geothermal heat recovery: An example from the West Netherlands Basin," Applied Energy, Elsevier, vol. 247(C), pages 582-593.
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    3. Babaei, Masoud & Nick, Hamidreza M., 2019. "Performance of low-enthalpy geothermal systems: Interplay of spatially correlated heterogeneity and well-doublet spacings," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Soltani, M. & Moradi Kashkooli, Farshad & Souri, Mohammad & Rafiei, Behnam & Jabarifar, Mohammad & Gharali, Kobra & Nathwani, Jatin S., 2021. "Environmental, economic, and social impacts of geothermal energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
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