Underground Upgrading of Heavy Oil Using THAI—''Toe-to-Heel Air Injection''
Bibliographic record
Abstract
Abstract The expansion of heavy oil and bitumens production is limited by the lack of advanced upgrading facilities and technology. Surface processing, mainly by delayed coking and some hydroprocessing, is very capital intensive. This cost can be more than that for the reservoir engineering facilities. The THAI process achieves substantial upgrading of heavy crude oil directly in the reservoir, via thermal cracking and associated reaction transformations. It captures the underground upgrading because the horizontal producer well process operates via a ‘short-distance displacement mechanism’, similar to that which occurs in the SAGD process. The results of a 3-D combustion cell test performed on Wolf Lake heavy oil are presented in the paper. Upgrading commenced as soon as the combustion front became anchored on the horizontal producer well. The produced oil viscosity was dramatically reduced, from 80,000 cSt, down to 50 cSt (average). The corresponding API gravity was increased from 10.1 to 20.4 °API (average). The quality of the produced oil was determined from a number of specific analyses, including TAN, Bromine number, SARA, and also water and gas analyses. The first field pilot of the THAI process is to be conducted by WHITESANDS INSITU Ltd, the heavy oil division of Petrobank Energy and Resources Ltd., at Christina Lake, Alberta, Canada 1. The pilot is scheduled to start early in 2006, and if the project is successful, the new THAI technology could revolutionize heavy oil recovery and upgrading.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".