Solvent Enhanced Steam Drive: Experiences from the First Field Pilot in Canada
Bibliographic record
Abstract
Abstract In recent years, the addition of a hydrocarbon condensate (C4 to C20) to steam operations (such as CSS and SAGD) in heavy oil and bitumen reservoirs has emerged as potential technology to improve not only oil recovery and but also energy efficiency. Shell has extended the idea of solvent addition to a steam drive process, applied it for the first time in the Peace River area in Canada, and obtained evidence of oil uplift in the patterns where solvent was injected. However, piloting this new technology in a brown field had many challenges, especially when evaluating its main economic factors: production increase and solvent recovery. To overcome these challenges, emphasis was put on experimental design, data acquisition and quality, and production surveillance. The pilot conditions were designed to increase the probability of success on the two economic factors aforementioned within a short period of time. The assessment of the pilot required that all production streams (emulsion and casing vent gas) were metered and frequently sampled to measure their respective compositions. Cross calibration of metered and sampled water cuts was essential in obtaining conclusive production uplift data. Automatic proportional samplers were successfully deployed under these challenging conditions to obtain representative samples. Due to the overlap of solvent and bitumen components, special attention was taken to allocate hydrocarbon production into bitumen and solvent. New in-house developed algorithms were tested to accurately calculate this split. The addition of a 4 month concentrated slug of solvent in two steam drive patterns resulted in a significant production uplift when compared to two adjacent patterns with steam-only injection. Solvent recovery is still ongoing and exceeds original expectations. Frequent sampling allowed the detection of several trends, including bitumen composition changes during solvent injection and solvent fractionation in the reservoir.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".