AI for Geoscience & Energy

Together with Füsun Servin Tut Haklıdır, I have worked on data-driven methods for geothermal energy systems: fuzzy control of calcium carbonate and silica scaling (Geothermics, 2017), machine-learning prediction of reservoir temperatures from hydrogeochemical data (Natural Resources Research, 2020), deep-learning prediction of geothermal boron contamination (Environmental Earth Sciences, 2020), synthetic hydrogeochemical data generation with generative adversarial networks (Stanford Geothermal Workshop, 2025) and rank–Gaussian copulas (Geothermics, 2026), prediction of dissolved lithium in geothermal fluids with calibrated uncertainty quantification (Earth Science Informatics, 2026), agent-based reinforcement learning for geochemical control (2026) and agentic AI for dynamic reservoir management (World Geothermal Congress 2026).

Earlier papers explored hydrogen production from geothermal sources and from H₂S in the Black Sea.

Publications
(2026). Agent-Based Reinforcement Learning for Geochemical Control in Geothermal Energy Systems. GSL AI in the Geosciences.
(2024). Artificial Intelligence Approaches for Sustainable Geothermal Energy Systems: With a Case Study. Stanford Geothermal Workshop 2024.
(2017). Fuzzy control of calcium carbonate and silica scales in geothermal systems. Geothermics, 70, 230-238.
DOI
(2010). Hydrogen Production from Geothermal Sources in Turkey. WGC 2010.
(2009). The possibilities for hydrogen production from H2S and storage in the Black Sea. International Journal of Nuclear Hydrogen Production and Applications, 2(1), 78-85.
(2007). Hydrogen Production from Geothermal Sources. IHEC 2007.
(2006). Possibilities of Production and Storage of Hydrogen in the Black Sea. WHEC 2006.
(2005). Black Sea, a Hydrogen Source. IHEC 2005.