Application of SAGD technology in the development of super heavy oil reservoir buried medium deep
Bibliographic record
Abstract
Abstract Continental super heavy oil reservoirs in China are characterized by deep burial depth, strong reservoir heterogeneity and high oil viscosity. The steam huff and puff technology are used to develop this type of reservoir, but in this development method, its oil recovery rate, oil steam ratio, and ultimate recovery are low. The benefit of the reservoir development is losses in this high cost per ton of oil. Many vertical wells injecting steam and one horizontal well-producing liquids pattern of SAGD technology (abbreviated as VERTIHORIONATAL pattern SAGD) were Innovated to solve the problems based on SAGD technology introduced from Canada. The SAGD technology introduced in Canada was made of double horizontal wells. The above horizontal well injects steam and the other horizontal well produces liquids continued (abbreviated as DOUBLE HORIZONTAL pattern SAGD). The VERTIHORIONATAL SAGD technology makes full use of the original well pattern of huff and puff and takes advantage of the thermal connectivity and underground temperature field formed by early development; At the same time, the VERTIHORIONATAL pattern SAGD technology can effectively solve the problem of steam cavity uneven expansion caused by reservoir heterogeneity by adjusting the location of steam injection vertical wells. It can also balance the liquid production in good horizontal sections and improve steam cavity formation speed by adjusting steam injection well sections and steam injection parameters. The technology has been successfully applied in the medium-deep super heavy oil reservoir in the Shu-1 area, Du84 block, Liaohe Oilfield. Seven oil wells with a daily output of 100 tons have been cultivated, and a stable production of million tons for four consecutive years. It has been built as China’s largest SAGD development demonstration area for medium-deep super-heavy oil.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".