An In-Depth Evaluation of Toe-To-Heel Air Injection Application in a Heavy Oil Reservoir Underlain by Bottom Water. Kerrobert Case
Bibliographic record
Abstract
Toe-To-Heel Air Injection (THAI) is an efficient in-situ combustion (ISC) process in which a horizontal producer is located close to the bottom of an oil layer with its toe close to a vertical air injection well. Once initiated, the ISC front propagates from the toe to the heel region of the horizontal section of the producer. THAI provides more control over the direction of the ISC front propagation (guided by the horizontal section of the producer), and it preserves the in-situ upgrading of the oil due to its short-distance oil displacement feature. Because of its controlled gas-liquid segregation, THAI is designed to mitigate the severe override experienced in conventional ISC processes. The Kerrobert THAI Project represented the second testing of THAI in the field, and it was designed and implemented by Petrobank Energy and Resources ("Petrobank"), based on the THAI patent (US Patent No. 5626191, 1997, Canada Patent No. 2176639, 2000). The patents basically describe two separate well applications: a direct line drive (DLD) configuration and a staggered line drive (SLD) configuration, for which birds-eye views are provided in Figure 1 and Figure 2, respectively. An illustrative cross-section of the process is shown in Figure 3; the start-up region is shown in all these pictures. The patents indicate that the vertical injector should be perforated high in the oil formation in both cases. The Kerrobert Project was designed to use DLD well configuration, but during the operation, SLD configuration was also tested in a few cases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".