ANLISE TCNICO-ECONMICA DO PROCESSO DE RECUPERAO AVANADA POR DRENAGEM GRAVITACIONAL ASSISTIDA POR VAPOR (SAGD).
Bibliographic record
Abstract
The enhanced recovery methods are important to the petroleum industry because they work to remove the oil that remains in the reservoir after the primary recovery.The Steam Assisted Gravitational Drainage, SAGD, is a thermal method of reservoir recovery, which reduces the oil viscosity and improves the oil movement to the productions wells.This method has appeared in Canada and shows positive answers for heavy oils, although it has not been used in Brazil yet.The purpose of this paper is to evaluate the technical economic viability of the method application in heavy oil reservoirs of the Brazilian Northeast considering the continuous and alternating injection of fluids and different configurations of injector wells (horizontal and vertical) to find the most compatible system with the production conditions in this region.This analysis was made to using the STARS (Steam Thermal and Advanced Process Reservoir Simulator) module of CMG (Computer Modeling Group).The economic aspects were calculated with the costs before and during the oil production, relating to the revenue obtained.The Net Present Value (NPV) equation was used due to its high in this applicability in the oil industry.The results showed that the thermal methods with steam injection increase the oil recovery factor in the reservoirs, and then increase the NPV.The SAGD presents the highest NPVs calculated, once it has two horizontal wells (injector and producer), which can drainage a large reservoir area, and thus obtained an accumulated oil production significantly higher than the other methods.In conclusion, the SAGD-2V method, with two vertical injector wells, might be a good option when reuses wells already drilled and inoperative in oil fields.The water alternating steam injection technique, despite having lower oil recovery efficiency than the continuous steam injection, it was in some cases advantageous due to the lower costs associated with steam generation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".