Laboratory and Field Scale Analysis of Steam Over Solvent Injection in Fractured Reservoirs (SOS-FR) for Heavy-Oil Recovery
Bibliographic record
Abstract
Abstract Tapping heavy-oil from fractured carbonates is a real challenge due to unfavorable rock and reservoir characteristics. We introduced a new technique called Steam-Over-Solvent in Fractured Reservoirs (SOS-FR) for efficient heavy-oil recovery from fractured reservoirs, more specifically carbonates. The process consists of cyclical 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 injected solvent and recover more heavy-oil. Our preliminary experiments under static (SPE 117626) and dynamic (SPE 123568) conditions showed that, under very unfavorable conditions (oil-wet carbonate, ~4,500 cp crude), oil recovery at the end of Phase-3 could be as high as 85-90% OOIP with 80-85% solvent retrieval. This paper presents numerical modeling of the dynamic experiments and an upscaling study for reservoir size matrix. Heptane was selected as the solvent to inject through a single-matrix/single-fracture oil-wet Berea sandstones saturated with ~4,500 cp oil. The experimental results were matched to a single matrix/single fracture numerical model and parameters needed for larger scale simulation (matrix-fracture interaction parameters such as thermal diffusion, solvent diffusion and dispersion coefficients) were obtained. The main focus was the matrix size and first an up-scaling study to field conditions was performed. Specific observations and conclusions as to the applicability of this technique in the field effectively were reported. It is hoped this new technique will be an alternative for tapping heavy matrix oil from oil-wet, fractured, deep, carbonate fields.
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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 | 0.000 | 0.000 |
| 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 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".