Analysis of the technology for increasing the recovery and intensification of hydrocarbon production
Bibliographic record
Abstract
Abstract The analysis and improvement of the method of additional stimulation of horizontal wells – carrying out multi-zone hydraulic fracturing, which allows to significantly increase the productivity of the well, increase the drainage area, which is especially important in formations with low permeability. In this paper, the oil recovery methods used at the Yuzhnoye field, the method of modeling hydraulic fracturing were considered, and hydraulic fracturing of a highly permeable formation was simulated for two wells 1 and 3 (double-wellbore) in this field. The optimization of the hydraulic fracture design is aimed at creating a fracture of maximum conductivity and minimum length. Since neocom is a highly permeable formation, the TSO (tipscreenout) technology is proposed for hydraulic fracturing – this is end screening. This TSO technology is successfully applied at the Prudhoe Bay field (USA), in the Gulf of Mexico, Indonesia, the North Sea, Canada, Brazil, Venezuela, Vietnam, Saudi Arabia. In Russia, an example of successful design and execution is the hydraulic fracturing operation with TSO in well 4370 of the Muravlenkovskoye field, Noyabrskneftegaz company. The hydraulic fracturing method will increase the vertical permeability and unite the disparate parts of the reservoir, which will make it possible to more efficiently develop the reserves of the Neocomian deposits in the field, system for electricity consumption for the purpose of short-term forecasting of electricity consumption.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".