Sampling: The Key Piece for a Successful Steam-Solvent Pilot
Bibliographic record
Abstract
Abstract A solvent enhanced steam drive pilot in the Peace River area in Canada was executed over a period of two years on an inverted 5-spot pattern to evaluate bitumen uplift and solvent recovery. Data quality and uncertainty management were used to assure conclusive results for the pilot; in particular, this paper focuses on how sampling played a key role in providing conclusive results. Especial focus is provided to the design, performance, execution, and learnings from the use of the automatic proportional samplers which was particularly challenging, considering their novel use on heavy oil production containing abrasive material such as sand and H2S. Moreover, to determine solvent production, several newly developed algorithms were tested to split the solvent and bitumen compositions, since they overlap over a wide range of components. Results from the pilot show that significant errors and misinterpretations can occur while evaluating solvent recovery whenever the assumptions under the solvent-bitumen split algorithms are not checked against frequent sampling data. The pilot also provided best practices for the utilization of proportional auto samplers in heavy oil production in the presence of abrasive material inherent to in-situ production, and the sampling of gas streams with heavy condensate for the accounting of solvent production through casing vent gas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".