Semi-dimensionless approach for simulating heat transfer of wellbore to optimize the temperature drop
Bibliographic record
Abstract
A reliable prediction of temperature changes in a wellbore is crucial for maximizing the efficacy of the geothermal energy extraction. Modeling such changes across a wellbore is a complex task that poses numerous challenges which require sophisticated numerical models and advanced computational tools. To simulate the real condition of a well, high fidelity simulations are needed due to the large well length to well radius ratio. This limitation causes high computational cost for each run. This study aims to develop and validate a computational model to optimize temperature predictions in geothermal wellbores while reducing computational costs. To reduce such time complexity while keeping calculation error below a reasonable bound, a novel approach is proposed in this paper. To validate the proposed model, an experimental setup of a closed loop system was designed. The experimental data and results obtained from simulations were in a good agreement. Based on the validated model, different controlling parameters of a wellbore were investigated to maximize the heat recovery from a geothermal well. Under two different scenarios from tubing or annulus space, different wellbore depths, and tubing to annulus size ratios, the extracted thermal energies were calculated. The study analyzed a range of injection rates from 0.1 kg/s to 100 kg/s, revealing the intricate relationship between injection rate, heat transfer, and heat loss in fluid-casing systems. The research also considered geothermal power generation systems to assess the potential of generated energy under various operating conditions. Annulus injection consistently resulted in higher outlet temperatures than tubing injection, especially at lower injection rates and deeper wells. The impact of tubing insulation and the tubing-to-annulus area ratio was also analyzed, showing that insulating the tubing significantly increased outlet temperatures by reducing heat loss. • A novel heat transfer simulation for geothermal wellbores effectively reduces computational costs. • The model was experimentally validated, showing high accuracy against real-world data. • Annulus injection consistently yields higher outlet temperatures than tubing injection. • Smaller tubing-to-annulus area ratios yield higher energy output at moderate to high flow rates. • Low injection rates enhance outlet temperatures due to longer fluid residence time but result in reduced energy extraction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".