Effect of Fracture Inclination on Oil Recovery by Steam Heating in Fractured Reservoirs
Bibliographic record
Abstract
The major objectives of this study are investigating the influences of fracture inclination on oil recovery by steam injection processes. The strongest recovery mechanism in steam injection is reduction of viscosity ratio. In this article, steam heating of a naturally fractured reservoir is modeled in terms of a block with a single fracture surrounded by steam. An analytical approach is used, which considers the transient temperature distribution within a single block. A heat integral method is used to obtain the unsteady-state temperature profile. The temperature distribution is used to calculate drainage rate under gravity flow. The solutions obtained are used to determine the effect of fracture inclination and some other parameters on the oil rate in steam injection. The results indicated that a single horizontal fracture in the direction of oil flow (zero inclination angle) recovered the highest oil, while a similar formation with a single vertical fracture perpendicular to oil flow direction (90° inclination angle) produced the lowest oil rate in steam heating. The increases of fracture inclination angle decreases oil recovery during steam heating. In addition, the recovery of oil for matrix with a single horizontal fracture is less dependent on average steam temperature, and this dependency becomes more as fracture inclination approaches to 90° (matrix with vertical fracture).
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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 | 0.000 | 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) | 0.000 | 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".