Liquid Addition to Steam for Enhancing Recovery (LASER) of Bitumen with CSS: Evolution of Technology from Research Concept to a Field Pilot at Cold Lake
Bibliographic record
Abstract
Abstract This paper describes the process of injecting a liquid (C5+) hydrocarbon as a steam additive in a CSS mode of operations. The process has been termed LASER, for "Liquid Addition to Steam for Enhancing Recovery". The process concept was first tested in a 3D physical model apparatus using Cold Lake bitumen. A sustained uplift in bitumen production was observed in later CSS cycles when compared to other tests conducted without liquid addition. Based on numerical simulations, these effects can be attributed to additional viscosity reduction of heated bitumen when contacted with solvent. For bitumen-diluent mixtures, Shuh’s method of viscosity prediction of bitumen with liquid hydrocarbons is adequate to make realistic viscosity predictions based on actual measurements. Field-scale simulations were used to support LASER performance trends from the physical model and establish the optimal timing for applying the technology in the field. The key recovery performance indicators for LASER technology are (1) bitumen uplift over continued CSS performance and (2) fractional recovery of the injected diluent. A field pilot has been designed based on expectations of (1) an improvement of 33% in the cycle Oil-Steam Ratio (OSR) and (2) diluent recovery of 66% using 6% v/v of diluent injection with steam. The pilot location was chosen based on various factors, including improved characterization of historical performance within and around the pilot location. This was achieved by developing a novel multivariate analysis technique to correlate current OSR field performance and reduce associated background noise. An extensive monitoring program has been developed for the pilot. This program is critical for developing a reliable characterization of the diluent recovery. Diluent injection began in April 2002, and the pilot is expected to last approximately 2 years, corresponding to the average length of CSS cycle 7 at Cold Lake.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".