Numerical study on the effects of thermoelastic and poroelastic parameters on the geomechanical behaviour of Hot Dry Rock geothermal reservoirs
Bibliographic record
Abstract
During the last two decades, energy production has been directed towards renewable resources, one of the most important of which is geothermal energy. Despite the reliability of geothermal energy as a resource, its effects on the surrounding environment have not been investigated in detail. This study focuses on reservoir deformation as one of the environmental concerns of geothermal energy extraction. A coupled thermo-hydro-mechanical (THM) model is employed for simulating the reservoir deformations. The validity of the model results is examined by comparing the numerical results with the analytical solution of a benchmark THM problem. The model is then utilised to study the behaviour of a Hot Dry Rock reservoir consisting of two wells. The obtained results show that in some cases the reservoir deformation is significant, making it an important factor in design considerations. Also, it is found that the deformation caused by thermal volume change is up to 6 times more significant compared to that of poroelastic effects. The conducted parametric study demonstrated that the coefficient of thermal expansion of rock and the production rate severely influence the reservoir deformation, while rock elasticity modulus, porosity and Biot-Willis coefficient only affect the behaviour of the geothermal reservoir to some extent.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".