Bibliographic record
Abstract
Abstract Use of vacuum-insulated tubing (VIT) in thermal (typically steam injection) wellbores dates back to at least the 1980s but, due to high cost and limited availability, its use until recently had been limited. While it has the potential to significantly reduce heat losses to overburden, thereby improving well operating economics, the correct application of VIT can be more of an art rather than science given the factors that impact its performance. These include understanding how VIT is manufactured and what design elements influence good long-term performance, what quality assurance is used during manufacture and on the finished product, how to confirm actual k-factor (insulation) values on delivered product in lieu of advertised values, and how to verify true performance once the VIT is installed in a well. Recent new global sources of VIT have provided additional product choices for operators, as well as more competitive pricing, allowing VIT to be more broadly considered in projects where downhole heat losses must be actively managed to achieve the recovery performance desired. Calculation of heat loss reduction can be done with several different programs, but careful attention must be paid to the way the computer model is built to ensure results reflect actual, expected, field conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".