Assessing how much geothermal energy lies beneath a given patch of Earth has long been a patchwork affair, with research teams around the world relying on disparate datasets, variable modelling assumptions and discipline-specific methodologies that make their findings nearly impossible to compare. A new study published in Geothermal Energy tackles that fragmentation head-on by proposing a standardized, multi-scale, multi-physics workflow for deep geothermal potential assessment that its authors say can be applied anywhere, whether in extensively explored regions or in areas where subsurface data are scarce.
The study, led by Gianluca Gola of Italy’s National Research Council alongside colleagues from institutions including Racine Basant, Valentina Cortassa, Antonio Galgaro, Thomas Nanni, Pawel Slupski, Magdala Tesauro and Adele Manzella, reviews the most widely used methods and open-source tools for underground characterization and geothermal potential estimation at regional to continental scales. The goal, the authors write, is to establish a workflow applicable everywhere, whether with extensive subsurface data in so-called brownfield settings or in less-explored or unexplored greenfield regions. By integrating multi-scale geological, petrophysical and geophysical constraints with robust thermal modelling strategies, the framework offers a consistent basis for estimating geothermal potential and facilitating comparative analysis across different regions.
The workflow is organized around three pillars. Two of them are evidence pillars comprising external observational constraints: geophysical evidence that delineates crustal and subcrustal architecture and defines the physical boundaries for subsurface modelling, and petrophysical-thermal evidence derived from laboratory and in situ measurements that supplies the quantitative parameters needed for accurate model parameterization. These direct-measurement anchors, the authors explain, are what keep the workflow tied to physical properties rather than modelling assumptions, reducing the uncertainty inherent in subsurface interpretation. The central pillar is an integrated model that synthesizes all available datasets into a coherent three-dimensional representation of the subsurface, encompassing the reconstruction of crustal geometries, thermal modelling based on petrophysical parameters and corrected temperature and heat-flow data, and geothermal potential assessment through approaches ranging from heat-in-place calculations to advanced thermo-hydraulic simulations.
Two innovative elements extend the workflow beyond established practice: a coupled well-reservoir modelling scheme and a thermodynamic module for simulating surface-plant performance. Together, the authors say, these additions create a continuous chain from subsurface characterization to surface energy conversion, enabling a realistic and internally consistent techno-economic assessment of geothermal resources. The framework also accommodates emerging geothermal concepts beyond conventional hydrothermal systems, noting that while enhanced geothermal systems may rely on methods similar to those used for hydrothermal systems, demonstrations of deep heat exchangers and underground heat storage require updating the methods used for potential assessment.
Reconstructing the subsurface begins with geological modelling that typically relies on integrating heterogeneous datasets including seismic reflection profiles, borehole information and cartographic material, much of it vintage data acquired during earlier hydrocarbon exploration phases that are increasingly becoming publicly accessible through national or regional data repositories. A critical first step is consistent spatial referencing of all available data, converting raster maps into suitable image formats and georeferencing them using identifiable control points, then transforming coordinate systems into a single, consistent spatial reference system. Borehole data provide the most direct and reliable information on subsurface geology and represent the primary calibration points for geological and geophysical models, with well reports typically including location, elevation, total depth, deviation data, lithological descriptions and, where available, geophysical logs. Seismic reflection data are then essential for extrapolating borehole information away from well control, with legacy scanned paper sections requiring conversion into digital seismic formats before interpretation. The workflow is explicitly software-agnostic, pointing to open-source tools such as GIMP for image preprocessing, the Kogeo Seismic Toolkit and WIGGLE2SEGY for raster-to-SEGY conversion, and OpendTect for interpretation alongside commercial alternatives.
Because borehole data directly sample the subsurface, they are generally considered to have the lowest geological uncertainty and therefore provide the primary reference for lithological interpretation and petrophysical characterization. The workflow addresses uncertainty explicitly, drawing on a data-driven approach that assigns confidence levels to data types and interpreted surfaces based on data origin, quality, resolution and spatial distribution. Seismic interpretations carry higher uncertainty than boreholes due to data quality limitations, velocity value assumptions and interpretational ambiguity, especially when derived from digitized vintage profiles. These uncertainty classes can be propagated through the modelling workflow and visualized as confidence ranges or maps, allowing identification of well-constrained versus poorly constrained areas and improving the robustness of the geothermal potential assessment.
Laboratory analysis of petrophysical properties is crucial for correctly interpreting geological and geophysical results and for populating thermal numerical models with realistic values. Characterization of thermal and hydro-mechanical properties including density, porosity and permeability, together with a petrographic study of reservoir rocks, provides additional data to improve the accuracy and reliability of geothermal models. Thermal properties such as thermal conductivity, thermal diffusivity, heat capacity and radiogenic heat production control how geothermal models predict subsurface temperatures and recoverable heat, with thermal conductivity exerting first-order control on simulated geothermal gradients, reservoir temperatures at depth and the spatial distribution of heat flow used to rank prospects. Radiogenic heat production, quantified by determining concentrations of uranium, thorium and potassium through techniques such as ICP-MS, ICP-OES and X-ray fluorescence, provides an internal volumetric heat source particularly relevant in felsic crustal rocks and can significantly modify the vertical distribution of heat flow and the curvature of the geothermal gradient. Hydraulic properties, especially porosity and permeability, largely determine whether subsurface heat is practically accessible and sustainable under production, governing achievable flow rates, pressure drawdown, required pumping power and the evolution of the temperature field during production.
For deeper structures, the workflow turns to geophysical modelling, integrating seismic tomography and gravity datasets to constrain structural features up to the upper mantle. Existing seismic tomography models help lower computational time and costs of inversions, examine uncertainties and reduce the non-uniqueness associated with geophysical inversions, with open-source models available for the Alpine chain and surrounding regions and global datasets accessible through the IRIS Earth Product Model Query. Gravity data image density anomalies that can be related to the size and location of different geological structures, with low-density anomalies identified in geothermal settings as clay cap rocks or alteration zones and high-density anomalies as intrusive regions. To model crustal density variations, the workflow first isolates the gravity effects of the mantle from the crust using wavelength-based filtering techniques, then inverts residual data using open-source tools such as GROWTH-23, jif3D, IGMAS+ and SimPEG. Joint geophysical interpretation, often aided by machine learning clustering techniques such as k-means and Fuzzy c-means, integrates multiple geophysical methods to reduce uncertainties, with a priori geological data used to further constrain the number of clusters and petrophysical or well log databases used for validation.
Reconstructing the subsurface natural thermal state requires integrating a quality-checked temperature dataset with a geometrically consistent subsurface model. Temperature data typically derive from exploratory geothermal and petroleum wells as bottom hole temperatures, continuous high-resolution thermal logs and drill stem test temperatures. Raw bottom hole temperatures consistently underestimate static formation temperatures because of the circulation of cooler drilling mud, and thermal recovery may require several months, so both datasets need careful analysis and adjustment using correction methods that range from analytical linear, spherical and cylindrical source models to graphical semi-analytical approaches widely used in industry. The authors emphasize that uncorrected bottom hole temperatures are unusable for quantitative thermal studies, and that applying an empirical correction provides the best available estimate of subsurface temperature when more rigorous transient data are lacking. Heat flow estimates derived from corrected temperatures are then adjusted for topography, paleoclimate and sedimentation or erosion effects, which can bias both heat-flow estimates and temperature extrapolations at large depths if neglected. Steady-state one-dimensional conductive geotherms can be reconstructed directly from corrected surface heat flow, while numerical heat-transport models coupling conduction and convection in porous and fractured media can be solved with simulation codes including the TOUGH suite, SHEMAT suite, CSMP++, MUFITS and MODFLOW, with stochastic approaches and tools such as the PEST Suite providing a framework for representing spatial variability and uncertainty in subsurface thermal fields.
Geothermal potential assessment itself has evolved from simplified static estimations toward integrated, physics-based and techno-economic workflows. Modern approaches recognize that geothermal potential is not a single quantity but a sequence of progressively refined metrics involving heat-in-place estimates, recoverable heat estimates, energy capacity estimates obtained by conversion and economically viable output. The volumetric or heat-in-place method, a cornerstone of geothermal resource assessment since the early 1970s, calculates recoverable thermal energy in porous and permeable rock volumes adjusted by recovery factors, conversion efficiency and plant life, and is primarily intended for regional screening and preliminary assessment rather than detailed project design or production forecasting. Recovery factors can be as high as 25 percent for hydrothermal systems, while in enhanced geothermal system reservoirs they are generally lower due to reduced permeability, with reported values of only 0.2 to 2 percent at research sites including Fenton Hill, Rosemanowes, Hijiori and Cooper Basin. Net thermal efficiencies for electricity generation generally range from 8 to 20 percent with a global mean of about 12 percent, constrained by the Carnot limit, parasitic loads and system losses, while direct thermal uses typically achieve much higher efficiencies of 70 to 90 percent. The most comprehensive open-source tools for techno-economic assessment at the concession scale are GEOPHIRES and genGEO, which differ substantially in scope and modelling philosophy, with GEOPHIRES suited for rapid scenario screening and genGEO offering greater physical detail and design flexibility. The workflow aligns with the United Nations Framework Classification for Resources, whose three independent axes of geological knowledge, field project feasibility and environmental-socioeconomic viability map onto subsurface characterization, dynamic production modelling and techno-economic evaluation respectively, though the authors note that environmental, social and regulatory factors require dedicated protocols beyond the technical domain addressed here.
Subject of Research: A standardized multi-scale, multi-physics workflow for deep geothermal potential assessment
Article Title: Toward standardized protocols for geothermal potential assessment: a multi-scale, multi-physics workflow for deep resources
Article References: Gola, G., Basant, R., Cortassa, V., Galgaro, A., Nanni, T., Slupski, P., Tesauro, M., & Manzella, A. (2026). Toward standardized protocols for geothermal potential assessment: a multi-scale, multi-physics workflow for deep resources. Geothermal Energy, 14(1), Article 21. https://doi.org/10.1186/s40517-026-00392-7
Image Credits: AI Generated
DOI: 10.1186/s40517-026-00392-7
Keywords: geothermal energy, deep geothermal, potential assessment, standardized workflow, petrophysics, seismic tomography, gravity data, thermal modelling, heat-in-place, open-source tools, UNFC, resource classification
News Source: Violet Maxwell. (October 30, 2026). Scientists Propose a Universal Workflow to Standardize Deep Geothermal Assessments. Scienmag.
