Pushing the Limits of Tractor Technology to Rejuvenate Steam Injection Wells in Canada
Bibliographic record
Abstract
Abstract Using steam injection to produce wells has been a common practice in Canada for several decades. Steam injected into the well causes heavy oils to flow more freely, increasing or even enabling production. There are several methods for stimulating a well with steam. A common practice is to drill two horizontal wells on a vertical plane and inject steam into the top well. This promotes oil flow to the bottom well, where it can be produced. The horizontal wells can have extremely high temperatures. As wells age, it may be necessary to revisit the steam injection strategy. Cost efficiency plays a role in these wells; thus, wireline is a good operations candidate. However, performing wireline measurements and services, such as perforating, in these wells can be difficult due to the high temperatures and challenging environments, such as in horizontal wells. New tractor technology enables wireline tractors to go into uncharted territory. Several jobs have been performed at temperatures above 175°C, which is the temperature rating for standard industry tractors. In some jobs, temperatures above 210°C have been reported. Logging while tractoring can obtain a temperature baseline as the tools are run in hole. From these temperature logs, injection profiles can be obtained for optimization. Pushing the limits of tractor technology facilitates the optimization of steam injection wells. High-temperature tractoring and logging while tractoring provide critical information for changing the steam injection strategy of older wells. The ability to perforate at high temperature is also advantageous because the steam injection does not have to be shut down for very long, if at all. The result is increased production efficiency and reduced downtime. This paper will describe a tractor concept that has been used successfully in steam injection wells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".