Global energy systems are transforming to meet the dual imperatives of decarbonization and rising energy demand. Geothermal energy is uniquely positioned within this transition as a renewable, dispatchable, and low-carbon resource capable of supplying baseload electricity and direct-use heat. Its high-capacity factor and minimal greenhouse gas emissions make it a strong complement or replacement to intermittent renewables such as wind and solar (Tester et al., 2006). Policymakers and investors are recognizing the value of geothermal energy, which has instigated renewed global exploration and development interest and investment (International Energy Agency, 2021; US Department of Energy, 2023).

Worldwide installed geothermal electricity capacity reached approximately 16 GWe in 2023, with major producers including the United States, Indonesia, the Philippines, Turkey, New Zealand, and Kenya (International Renewable Energy Agency, 2024). Installed geothermal electricity capacity in the United States was 3.9 GWe, which represents ∼0.4% of total national electricity generation, with all that generation centered in a few states in the Western United States (Robertson-Tait et al., 2023).

Direct-use geothermal, spanning applications such district heating, greenhouse agriculture, aquaculture, and industrial processes, accounts for more than 100 GWth globally, with particularly high adoption in China, Turkey, Iceland, and parts of Europe (Lund and Boyd, 2016). Despite this progress, global geothermal potential remains vastly underutilized: currently tapped hydrothermal resources represent only a small fraction of the viable heat in the upper crust (Tester et al., 2006; Augustine, 2016).

Next-generation geothermal technologies aim to overcome the geographic and geological limitations of conventional hydrothermal systems. Enhanced geothermal systems (EGS), closed-loop heat exchangers, superhot rock development, and subsurface thermal energy storage are extending potential deployment beyond tectonically active areas (US Department of Energy, 2022). These approaches seek to engineer permeability or utilize conductive heat in deep, hot crystalline rock or sedimentary basins, enabling geothermal expansion into new regions and resource types. The convergence of advanced drilling, reservoir stimulation, fiber-optic monitoring, and data analytics, many adapted from the petroleum industry, is reducing exploration and development risk while opening new market opportunities.

The overlap in subsurface science, reservoir and drilling engineering, and exploration workflows between geothermal and petroleum geoscience has never been more relevant. Both rely on structural geology, reservoir characterization, and geophysics. Methods like play fairway analysis, three-dimensional seismic interpretation, and well log integration, longstanding in hydrocarbon exploration, are increasingly applied to geothermal well targeting, especially in next-generation geothermal developments. Drilling, completions, and stimulation technologies developed for unconventional hydrocarbons are being adapted for EGS and deep sedimentary geothermal plays. As the petroleum industry diversifies toward low-carbon energy, cross-disciplinary collaboration is accelerating geothermal innovation and investment. This special issue attempts to introduce readers to the breadth of research in both conventional and next-generation geothermal geoscience and bridge the gap between petroleum and geothermal geoscientists.

Holmes et al. (2025, this issue) critically assess the power density (PD) method, a widely used analogue-based approach for estimating geothermal resource capacity from reservoir temperature, production area, and tectonic setting. Using global data sets, they find significant overlap among tectonic classification categories and large scatter in PD–temperature relationships, especially above ∼250°C. The authors integrate expanded open-source and proprietary data sets, apply statistical tests, and compare PD performance to machine learning regressions trained on richer feature sets. Machine learning models substantially outperform the PD method in predictive accuracy, particularly at the power-plant scale, highlighting the importance of multidimensional data integration over deterministic, map-based estimation. The study concludes that PD curves should be reconsidered and potentially replaced with transparent, data-driven models in early-stage geothermal resource assessment.

Kraal et al. (2025, this issue) present a conceptual model for the northern Granite Springs Valley blind geothermal prospect in Nevada. The paper presents a traditional exploration workflow for a blind hydrothermal system that is representative of the most common type of resource currently being developed in the Great Basin region. Integrating geologic mapping, geophysics, temperature measurements, fluid geochemistry, and paleo-geothermal deposits, they delineate a large thermal anomaly tied to a complex fault network. They use silica geothermometers to predict deep reservoir temperatures that are in the commercial temperature range. Resource capacity estimates range from 3.7 to 35.6 MWe depending on scenario assumptions, underscoring the value of structural and thermal integration in blind system exploration.

Morgan et al. (2025, this issue) investigate the reservoir potential of Mesozoic siliciclastic strata along the Colorado Plateau–Basin and Range transition in Utah as analogues for basin-centered sedimentary geothermal systems. Through detailed outcrop sampling and laboratory analysis, they identify the Moenave, Kayenta, and Navajo Formations as promising targets, with median porosities of 10% to 16% and permeabilities from submillidarcy to nearly 400 md. Their findings show a consistent porosity–permeability relationship across formations, but with significant variability driven by facies heterogeneity, compaction, and diagenetic cementation. They conclude that reservoir quality is controlled as much by diagenetic and burial history as by depositional environment, emphasizing the need for detailed reservoir characterization approaches in assessing sedimentary geothermal prospects.

Bhattacharya et al. (2025, this issue) conduct an integrated geologic, geophysical, and techno-economic assessment of geothermal potential in Presidio County, Texas—a region with limited deep subsurface data and complex Basin and Range structural geology. Using high-resolution gravity surveys, borehole temperature and petrophysical logs, and core analyses, they divide the study area into three regions with distinct thermal and structural characteristics. Monte Carlo heat-in-place calculations and techno-economic simulations suggest viable potential for both power generation (including EGS and closed-loop systems) and high-efficiency direct-use applications. Economic outcomes vary by location, reservoir temperature, depth, and technology, with one region offering the most favorable combination of high gradients, structural permeability, and grid access. The study demonstrates a transferable workflow for resource assessment in underexplored, data-limited terrains.

Together, the contributions in this special issue highlight the breadth of innovation underway in geothermal geoscience. From reassessing long-standing resource estimation methods (Holmes et al., 2025, this issue), to integrated conceptual modeling of blind systems (Kraal et al., 2025, this issue), to reservoir characterization of sedimentary targets (Morgan et al., 2025, this issue), and holistic techno-economic assessments in frontier basins (Bhattacharya et al., 2025, this issue), these studies underscore how advances in data integration, subsurface characterization, and cross-disciplinary workflows are reshaping the geothermal landscape. Collectively, they demonstrate that geothermal energy is not only an essential tool for decarbonization but also a rapidly evolving field where petroleum geoscience expertise continues to play a central role.