Optimization of SAGD in Conductive Fractured Reservoir
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage is a successful process that has been applied to extract heavy oil and bitumen mostly in Canada. Conductive fractures as reservoir heterogeneity are spaced a few meters from each other and differ from network fractures. There are a few studies investigating the impact of such fractures on SAGD performance. This work is a numerical study examining the relative location and configuration of wells to conductive fractures as an attempt to optimize SAGD process in three types of conductive fractures including horizontal, vertical and oriented fractures. While vertical fractures located above the well pair enhance the oil recovery rate at early time, those located far from the well pair do not affect the process performance. Consequently, to optimize the process, the wells should be applied beneath the vertical fracture. For the vertical conductive fractures locating at bottom of the reservoir, higher well spacing results in more desirable performance if the wells are drilled at the place of the fracture. Sensitivity analysis of injector-producer well spacing illustrated that for horizontal fractures locating around 5 m from the reservoir base, the injector should be drilled above the fracture to enhance the process performance much more than the case of having the injection well below the fracture. Also it is showed that the well pair should be located in a manner that oriented fractures with positive slope place at right hand side and near the wells. Moreover, Off-setting the wells in the fracture direction resulted in enhanced behavior of the process. In low permeability tar sands, hydraulic fracturing can mimic vertical conductive fractures which improve steam chamber expansion, hence resulting in better performance. This study suggests some screening criteria for the well placement design to enhance the oil recovery in conductive fractured reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".