Bibliographic record
Abstract
Abstract This field study was based on 50 fracturing jobs at 42 wells that Halliburton pumped from 1977 to 2006 for Petro-Canada Oil and Gas Ltd., producing from the Viking formation in the Ricinus area. Four different fluid systems (Guar-borate, CO2, gelled oil and gelled CO2/oil) were investigated to compare their effects on the skin factor, effective fracture half-length, percentage of screenout jobs and production rate. All available fracturing and buildup data were collected and analyzed. Gelled CO2/oil fracture treatment showed the best results on the effective fracture half-length and production rate, and proved to be the best option among these four systems. This study was also focused on investigating the reasons for a high percentage of fracture jobs being screened out; 85% of the fractures investigated exhibited pressure-dependent leakoff (PDL) behaviour believed to be the primary characteristic of the unsuccessful jobs. Treating the formation at a pressure lower than the fissure opening pressure was considered to be a possible way to avoid screening out. A field case was used to investigate this possibility with the application of a fracture-simulation model, but it was not possible to pump at a rate low enough to prevent opening the natural fractures. Since July 17, 2002, the service company fractured all wells with gelled CO2/oil at high pumping rates and achieved a 100% success rate. This proved that pumping at a high rate is the only option so far to combat PDL and place the proppant successfully. Economic value to the customer (EVC) analysis showed $460,000 in savings on ten gelled CO2/oil high rate fracture jobs for Petro-Canada Oil and Gas Ltd. Fluid Systems Four fluid systems have been used to pump 50 fracture stimulation jobs in 42 Ricinus area wells producing from the Viking Formation. Figure 1 shows the area of these 42 wells. Among these 50 fracturing jobs, six used Guar-borate, 12 used gelled oil, 15 used CO2 and 17 used gelled CO2/oil. These fracture stimulation jobs were pumped since 1977. Figure 2 shows the percentage distribution for the four systems involved in this study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".