Innovative Borehole Treatment Utilising Inflatable Packer Straddle System Technology
Bibliographic record
Abstract
Abstract Open hole fracturing and acid stimulation utilizing the traditional cemented liner and hydro jet perforations or mechanical packers with ball activated frac sleeves have been deployed successfully in the Unites States and Canada for years. One of the primary concerns about the conventional liner methods is assurance of knowing where the fracture or acid is placed. There is no way to determine if there was adequate annular isolation to ensure the planned treatments were placed in the zone of interest. In an effort to effectively place the fracture in a known location, an inflatable packer straddle system was introduced and utilized to treat the Bakken, Midale, Spearfish and Torquary formations in the southeast Saskatchewan region of Canada. Knowing the location of the fracture provides clarity to understanding the production results. Producing as a barefoot completion may also add incremental production from naturally fractured intervals that may not be exposed to newly-created fractures and also provides flexibility for later interventions or adding laterals. Improving the procedure, a setting head was developed to drastically reduce time spent between stages reducing water consumption by eliminating the need to drop and displace setting balls to activate the inflatable system at each setting interval. Using this system, the operator can monitor annular pressure in real time to understand when the fracture is starting to break around the upper packer allowing the operator to make adjustments to the fracture treatment of subsequent zones during the job. This paper will address main considerations of tool operation, case histories highlighting job procedures and lessons learned after several jobs. It will also address use of the straddle system for entering existing wells to re-fracture the formations to improve production and extend the life of the well.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".