A 3-Year Results of Application a Combined Scale Inhibition and Hydraulic Fracturing Treatments using a Novel Hydraulic Fracturing Fluid, Russia
Bibliographic record
Abstract
Abstract Scale deposits are a common problem in oil and gas wells and can have detrimental effects on well production. Depending on the severity, scaling can stop production entirely as scale forms anywhere in the well production system, including the formation, perforations, casing or tubular, and in or on the artificial lift equipment. There are several chemical and mechanical methods for removing scale deposits. However, to prevent scale deposition, the only solution is chemical inhibitors injected into the formation. The typical production system includes artificially lifted, stimulated wells (propped hydraulic fractures) placed in reservoirs where pressure maintenance is achieved by water flooding. The artificial lifting is typically accomplished through use of electric submersible pumps (ESPs). In reservoirs where produced fluids exhibit scaling tendencies, ESP run life is significantly shortened by scale formation on the pump elements restricting rotation. By treating the formation with chemical inhibitors, the life of the ESP can be extended. In this paper we provide approaches for improving a compatibility of a novel hydraulic fracturing fluid (used in Russia) and scale inhibitor. A 3-year campaign to combine scale inhibition with the hydraulic propped fracture effectively increased the average run life of ESPs in the Mayskoe and Snezhnoe oil fields.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".