Simulation Analysis of the Effects of Fractures? Geometrical Properties on the Performance of Vapour Extraction Process
Bibliographic record
Abstract
Summary Vapour extraction (VAPEX), a newly developed heavy oil recovery (HOR) method, has been extensively studied, both theoretically and experimentally, in conventional sandstone models. However, the applicability of this process to naturally fractured reservoirs has not yet been addressed. The objective of this work is to evaluate the effects of fractures' geometrical properties, such as orientation, length of extension, discontinuity in both the upper well region (UWR) and the far well region (FWR) and vertical fracture location on VAPEX performance. Additionally, the effects of horizontal and vertical fracture dispersion in various density, dispersion scheme and networking on the performance of the VAPEX process are assessed. A fracture's discontinuity effect has also been scrutinized in the presence of parallel-piped networked fractures, which confirmed the results in the case of either horizontal or vertical fracture systems. It was found that the VAPEX process enhances performance in the case of longer vertical fractures, lower horizontal fracture dispersion, shorter horizontal fracture extension and lower vertical fracture discontinuity. Vertical fracture dispersion scheme also affects performance. The ultimate oil recovery, in the case of networked fractures, will be higher than the conventional model if the horizontal fractures have poor connectivity to each other.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".