Efficiency Analysis of Steam-Over-Solvent Injection in Fractured Reservoirs (SOS-FR) Method for Heavy-Oil Recovery
Bibliographic record
Abstract
Abstract Heavy-oil recovery from fractured carbonates is a real challenge, yet no proven technology exists as an efficient solution. Reservoir heating is generally inevitable and steam injection is the only effective way to heat heavy-matrix oil in such reservoirs using the steam distributed through fracture network. We propose a new method minimizing heat needed for efficient heavy-oil recovery from oil-wet fractured rocks by adding solvent component. Efficiency is a critical issue in this process due to potentially high cost of the process. A new technique we proposed previously called Steam-Over-Solvent in Fractured Reservoirs (SOS-FR) consists of a cyclic injection of steam and solvent in the following manner: Phase-1: Steam injection to heat up the matrix and recover oil mainly by thermal expansion, Phase-2: Solvent injection to produce matrix oil through diffusion-imbibition-drainage processes, and Phase-3: Steam injection to retrieve the injected solvent and recover more heavy-oil. Laboratory scale static and dynamic experiments had shown that, under very unfavorable conditions (oil-wet matrix, 4,000cp crude), oil recovery at the end of Phase-3 was around 85-90% OOIP with 80-85% solvent retrieval. In this paper, the experimental results obtained earlier were matched to a single matrix/single fracture numerical model and parameters needed for field scale simulation (matrix-fracture thermal diffusion, solvent diffusion and dispersion coefficients) were obtained. Using the data obtained through matching, field scale simulations were performed for efficiency analysis and to identify the optimal injection schemes (soaking time for cyclic and injection rate for continuous injection) and durations, and surface steam quality. Specific conclusions as to how to apply this technique efficiently in the field considering the cost of the process were reported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".