Acidizing—Lessons from the Past and New Opportunities
Bibliographic record
Abstract
Abstract The Canadian energy sector pioneered and developed industry-leading oil- and liquids-rich reservoir acidizing technology. This involved new acid additive chemistry and completion techniques. However, many of the newer technical professionals in the industry have not been exposed to this technology. The first section of this paper outlines acidizing technology, with a focus on application to current new opportunities. Many of the current oil- and liquids-rich plays involve naturally fractured carbonate reservoirs. Acid treatments designed to enhance the conductivity of the existing fracture system can provide more-effective reservoir drainage than proppant fracturing treatments. The second section of this paper discusses how new placement techniques can offer more-effective zonal isolation while reducing completion time and associated costs, and how acid pre-pads can also reduce breakdown pressures and help minimize near-wellbore (NWB) tortuosity effects in many shale and sandstone reservoirs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".