The Influence of Geological Factors and Transmission Fluids on the Exploitation of Reservoir Geothermal Resources: Factor Discussion and Mechanism Analysis
Bibliographic record
Abstract
The geothermal resources present within the reservoir post-oil production in the oil field have hitherto been overlooked and underdeveloped, constituting a novel energy supplement for the maintenance of energy security. The present study constructed a geothermal transmission and exploitation model for oil reservoirs based on the geological environment and the characteristics of geothermal transmission media. This model can be used to analyse the impact of different factors on the efficiency of reservoir geothermal resources. Concurrently, a molecular dynamics model was constructed to reveal the geothermal transmission mechanism at a microscopic perspective, which will facilitate the optimisation of geothermal mining technology. The findings indicate that fluid viscosity hinders geothermal transmission, and the transmission medium of 40mPa·s increases the diameter range of geothermal transmission by 9m compared with 30mPa·s. Furthermore, the deflection angle of reservoir fractures is also not conducive to reservoir geothermal transmission. It has been demonstrated that an increase in the deflection angle results in a reduction of the transmission capacity, owing to substantial fluid filtration. The utilisation of reservoir geothermal resources provides fundamental data support for the rational application of energy and the assurance of energy security.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".