Bibliographic record
Abstract
Given the environmental impact and relatively high cost of steam-based recovery processes for oil sands reservoirs, there is a search for other recovery processes that yield greater efficiency and lower operating costs. Air injection based recovery processes offer potential for improved efficiency given that the heat is generated within the reservoir. However, industry has been reluctant to adopt air injection methods for oil sands reservoirs. In the research documented in this thesis, a detailed examination of the Kerrobert toe-to-heel air injection (THAI) process is conducted by using data analytics. The current operator of the facility has provided all of the data for the operation including injection and production rates, temperatures, pressures, gas compositions, and facility data. Four studies were conducted: 1. Detailed analysis of causal relationships between injectants and production rates, gas composition, and temperature rise within the reservoir through manual examination of the data, 2. Clustering analysis of operational variables and seek for optimal operating strategy to maximize production rate, 3. Lag time analysis between injection and production to explain the underlying production mechanisms in THAI, and 4. Understand the reaction systems in THAI using produced gas compositions through an inverse calculation approach.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Petroleum engineering dissertation on toe-to-heel air injection for heavy oil recovery.
It analyzes an oil-recovery process and its operational data, not research practice.
Petroleum engineering thesis on toe-to-heel air injection for heavy oil.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".