Maintaining Fracture Performance Through Active Scale Control
Bibliographic record
Abstract
Abstract Hydraulic fracturing using proppants is a well established technique for increasing well productivity. However, uncontrolled mineral scale deposition within the proppant pack can result in reduced conductivity and fracture performance, thereby devaluing the initial investment in the fracture. To protect this investment an active scale control strategy is required especially when executed in the presence of a water flood. A number of different techniques have been proposed and used historically, however they have all suffered with one or more drawbacks. These drawbacks have included excessive volumes, poor placement control, short treatment life and / or the potential for fines and sand generation and pack instability. The ability to place scale inhibitor within the voids of a porous proppant offers a robust technique for placing a large amount of scale inhibitor throughout the proppant pack while its controlled released protects the productivity of the fracture. Previous papers have described the development of porous, scale inhibitor impregnated proppants and highlighted the initial returns from field trials performed on land wells on the North Slope of Alaska. The impregnated proppant technology has now been further developed and two treatments have recently been deployed in the North Sea. The treatments were both designed to stimulate production and to protect the fracture against future scaling scenarios. This paper will describe the design criteria used to select this method of protecting the future performance of the fracture. In addition this paper will describe the design and execution of the treatments while highlighting the fracture performance and scale inhibitor return profiles generated by recent treatments performed in the British and Norwegian sectors of the North Sea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".