Reserve Growth an Examination of Infill Drilling and EOR/IOR in Canada
Bibliographic record
Abstract
Abstract Growth in reserves from existing reservoirs has been the primary contributor to reserve additions in most mature basins. Historically, infill drilling has been the main driver for reserves growth for the Western Canadian Sedimentary Basin (WCSB) and other parts of the world. This study examines the other why's of reserve growth in WCSB. Examination of historical trends in fields shows that injection processes (Enhanced Oil Recovery, waterflooding) dominate reserves growth in WCSB, and have been the reason for an increasing oil rate in the region. This paper will examine performance of both legacy production and infill well drilling programs and relate it to drive mechanism. Production trends in Canada will be presented as well as the incremental production and contribution to WCSB seen from the infill programs. Next, EOR development will be analyzed. Again, production trends in Canada will be presented and incremental oil contribution due to each individual EOR method will be shown. A brief look at USA EOR experience will also be analyzed. The influence of technology growth and oil price will be reviewed for both infill and EOR oil pools.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".