A Comprehensive Review of Enhanced Oil Recovery Projects in Canada and Recommendations for Planning Successful Future EOR projects
Bibliographic record
Abstract
Abstract A database is developed from multiple sources to comprehensively present and evaluate enhanced oil recovery (EOR) projects in Canada. The datasets comprising of in-situ reserves, corresponding production and EOR information of Canada and worldwide EOR projects are made publicly available on a custom-built interactive data analysis platform TIBCO Spotfire. EOR projects for light, medium and heavy oils in Canada are classified into 65 solvent flooding, 6 polymer flooding and 13 Alkali-Surfactant-Polymer (ASP) flooding. Thermal methods, particularly SAGD & CSS are conducted for extra heavy oil and bitumen in oil sands. Another 31 of immiscible flooding projects are also identified. Factors contributing to success of these projects in respective fields are evaluated based on reservoir properties and EOR parameters such as miscibility, wettability, mobility ratio, capillary number, minimum miscibility pressure. With detailed technical analysis, EOR screening criterion for each method is updated and validated with world EOR data as well as Alberta oil pools data of 2019. Primary and secondary flooding projects that have potential for tertiary recovery are matched with historic EOR data to estimate future enhanced production. It has been observed that 257 pools currently employing water flooding have some similarities to fields that have seen success with EOR. Upgrading these primary or secondary projects to enhanced recovery has the potential of increasing daily production by as much as 14%.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".