GeoProp: A thermophysical property modelling framework for single and two-phase geothermal geofluids
Bibliographic record
Abstract
The techno-economic evaluation of geothermal resources requires knowledge of the geofluid's thermophysical properties. While the properties of pure water and some specific brines have been studied extensively, no universally applicable model currently exists. This can result in a considerable degree of uncertainty as to how different geothermal resources will perform in practice. Geofluid modelling has historically been focused on two research fields: 1) partitioning the geofluid into separate phases, and 2) the estimation of the phases’ thermophysical properties. Models for the two fields have commonly been developed separately. Recognising their potential synergy, we introduce GeoProp , a novel geofluid modelling framework, which addresses this application gap by coupling existing state-of-the-art fluid partitioning simulators, such as Reaktoro , with high-accuracy thermophysical fluid property computation engines, like CoolProp and ThermoFun. GeoProp has been validated against field experimental data as well as existing models for some incompressible binary fluids. We corroborate GeoProp's efficacy at modelling the thermophysical properties of geothermal geofluids via a case study on the heat content of different geofluids. Our results highlight the importance of accurately characterising the thermophysical properties of geofluids in order to quantify the resource potential and optimise the design of geothermal power plants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".