Christina Lake Early Rise Rate Solvent Aided Process Pilot
Bibliographic record
Abstract
Abstract Solvent Aided Process (SAP) combines benefit of using steam with solvents and has the potential to substantially improve SAGD performance with lower energy intensity and impact on the environment. Cenovus has been developing a SAP since 1996 and has conducted a few successful pilots (Senlac 2002, Christina Lake A0101 2004-2005, Christina Lake A0202 2009-2016). Cenovus has planned to implement a SAP in the Narrows Lake development when that project is sanctioned. The general practice for SAP is to co-inject solvent with steam after the SAGD peak rate is reached as steam is most effective for the vertical growth of steam chamber. However, by injecting solvent during the rise rate phase, there could be a large facility saving by not requiring an additional solvent line to each wellhead. Rather, solvent can be added to the steam header immediately after steam leaves the steam generation plant and distributed to all wells, regardless of their vintage. To de-risk the addition of solvent at the steam header, Cenovus implemented an early rise rate phase SAP pilot at Christina Lake A0201 well pair. The objective of this field pilot was to investigate the effects of butane injection during the SAGD early rise rate phase. This paper describes the implementation and results of the Christina Lake A0201 early rise rate SAP pilot. Presented in this paper are the results of this field test showing that no adverse effects on SAGD performance were observed when injecting solvent during the rise rate phase. This paper also shows the history match of field production data using CMG CMOST simulation. The findings of this investigation add to the knowledge base of information related to the optimal solvent injection timing in a SAP process. Insights into performing SAP history matches are also presented based on the simulation study that was undertaken.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".