Assessing the impact of hierarchical geological heterogeneities on geothermal energy production.
Bibliographic record
Abstract
Summary Energy derived from geothermal systems is essential to the energy transition. Inherent geological and a lack of data requires the use of computer-driven modelling and simulation to aid decision-making. To make sound decisions, many reservoir models that encapsulate different geological scenarios should be analysed such that the impact of geological uncertainty on geothermal energy production can be evaluated adequately. Current geomodelling workflows, however, are too time consuming to build and explore different contrasting geological scenarios at various scales. In this study we used the open-source Rapid Reservoir Modelling (RRM) software to design different geological scenarios of a shallow marine succession hosting a potential geothermal reservoir and analyse how multi-scale geological features impact reservoir flow. RRM allows users to quickly create and explore realistic 3D geological models from intuitive 2D sketches. Models arecreated in minutes while flow diagnostics allow us to analyse fluid-flow behavior in real-time. Models are then imported into commercial reservoir simulation packages to investigate the effect of heterogeneity and scale on geothermal energy production. We show how we can quickly evaluate how different scales of heterogeneity impact geothermal production estimates and which heterogeneities must be represented in reservoir models to obtain reliable results about the possible reservoir behaviours.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".