Geothermal energy is a renewable, sustainable, and low-emission energy source derived from the Earth's subsurface layers through natural heat sources, such as rock formation and radioactive decay. It is used for heating, cooling, and power generation due to its cost-effectiveness, stable supply, and high-capacity factors throughout the year. The development of power generation from hydrothermal reservoirs started in 1913 and has since expanded to include various technologies such as flash and dry steam plants for high-temperature resources and binary cycle technologies for medium-temperature resources. Global electricity generation from geothermal energy grew from 69.8 GW·h in 2011 to 95.3 GW·h in 2021, providing a significant share of electricity demand in countries, such as El Salvador, New Zealand, Kenya, and the Philippines, and more than 90% of heating demand in Iceland (International Renewable Energy Agency, 2023).

The development of geothermal energy takes place in successive stages, starting with surface surveys, followed by exploration drilling for resource realization. If the resource is proven, delineation drilling follows to confirm the extent of the reservoir's productivity and its development plan. Production drilling and power plant construction can commence once the resource has been confirmed and financial viability has been established. Preliminary studies, exploration, and delineation drilling require significant investment and involve high financial risk, which can hinder resource evaluation plans. For example, in a recent appraisal study for geothermal exploration in Indonesia, the World Bank estimated that the predevelopment program would cost approximately USD 30 million, assuming a minimum of three wells for greenfield development and at least two wells producing an acceptable level of steam for site exploration to provide satisfactory evidence or resource availability (The World Bank, 2012). Figure 1 illustrates the geothermal development project stages, the level of risk at each stage, and the associated percentage of cumulative project cost (Gehringer & Loksha, 2012; ©World Bank; The World Bank et al., 2012).

           

Many countries are exploring hidden or blind geothermal resources (hydrothermal resources without surface manifestations), which require detailed knowledge of subsurface features (including hydrological, geophysical, geological, geomechanical, geochemical, and thermal characteristics) to assess their commercial potential (Pandey et al., 2018). Traditional methods of subsurface feature analysis rely heavily on expert knowledge for resource evaluation and reserve estimation, leading to uncertainties in the discovery of hidden geothermal resources. Advances in data-driven models have led to the use of artificial intelligence (AI) to replace traditional expert-based and statistical methods, where AI can uncover hidden patterns and develop predictive models from large multivariate datasets, thus enhancing exploration outcomes by reducing uncertainty and improving prediction accuracy. With the rapid increase in the creation of data repositories for the preservation, processing, and management of subsurface data, data-driven models offer an efficient and cost-effective approach to identifying key features of hidden geothermal resources (He et al., 2019). This supports resource evaluation, problem solving, and decision-making while reducing predevelopment costs in the geothermal industry.