Findings from a Solvent-Assisted SAGD Pilot at Cold Lake
Bibliographic record
Abstract
Abstract ExxonMobil and Imperial Oil Resources (Imperial) are conducting a Solvent Assisted - Steam Assisted Gravity Drainage (SA-SAGD) experimental pilot at Cold Lake in the Clearwater formation where up to 20% by volume of hydrocarbon solvent (diluent) has been injected along with dry steam in a dual horizontal well SAGD configuration. Experimental work performed by the Alberta Research Council (ARC) (Nasr, 2003) and Imperial indicated that addition of solvent to steam increases bitumen rates and decreases steam-oil ratios relative to the conventional SAGD process. The main objective of this pilot has been to produce high quality field data to definitively support these experimental conclusions. The pilot scope includes two horizontal well pairs (four wells), six observation wells, associated steam and diluent injection facilities, artificial lift, as well as, dedicated production measurement and testing facilities. The SA-SAGD pilot uses existing steam generation, water treatment, bitumen separation and processing facilities at Imperial's Mahkeses plant and existing steam distribution and production gathering systems. The main focus of this paper is to document the integrated approach taken to ensure that this multi-year pilot is successful and to provide information resulting from this multi-year pilot. Key surveillance products, such as, production/injection measurements, horizontal-well temperature logs, observation-well temperature and saturation logs, time-lapse 3D seismic, and the impact of a mid-pilot solvent switch will be discussed. In addition, this paper will review how these surveillance products are integrated with laboratory data and simulation efforts to improve our understanding of this process and increase confidence in go-forward predictions. Given the emerging importance of solvent-assisted thermal heavy-oil processes and the accelerated conversion of this technology from laboratory-scale to field-scale, data from a field pilot provides invaluable information in the quest to deploy this emerging in-situ recovery technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".