Improving SAGD Efficiency in Carbonate Reservoirs by Combining Horizontal, Deviated and Vertical Wells
Bibliographic record
Abstract
Abstract The obtained experience of using in carbonates of the Permian - Carboniferous high viscous oil reservoir of the Usinsk field the "classical" options of thermal methods of enhanced oil recovery such as steam cycling and steamflooding injection in a system of vertical wells, and steam assisted gravity drainage in a system of parallel horizontal wells has worse efficiency in comparison with sandstones. First of all, this is due to the inadequate sweep of a massive fractured reservoir with the steam-thermal action. In this regard, the paper proposes a new combined technology of three-dimensional steam assisted gravity drainage based on the conjoint use of horizontal injection and production wells cross-drilled to each other with the deviated production wells and the vertical production wells surrounding them. The paper presents the actual results of the pilot demonstrating that the proposed technology is a real alternative to the "classical" options of the thermal development of the inner zone of the reservoir and the results of hydrodynamic calculations obtained on the sector model of the other pilot which allowed to study the mechanism of the proposed technology more deeply, to optimize the operating modes of wells and their location in the reservoir, as well as to predict the effectiveness of its application in the geological and physical conditions of the reservoir edge zone.
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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) | 0.001 | 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".