Field Scale Application of the SOS-FR (Steam-Over-Solvent Injection in Fractured Reservoirs) Method: Optimal Operating Conditions
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Bibliographic record
Abstract
Abstract Heavy oil reserves are considered to be the upcoming hydrocarbon resource. Yet, more efficient methods are needed as there are substantial economical and environmental drawbacks to sole injection of steam and solvents. A combined application of these yielded promising results in the laboratory experiments. But, optimal application conditions and cost lowering options need to be determined. Steam-over-solvent injection in fractured reservoirs (SOS-FR) is a recently proposed method which consists of an alternate injection of steam and hydrocarbon solvents to improve oil recovery over steam injection and accelerate the solvent retrieval rate. The initial tests were done for hot-water conditions instead of steam and liquid solvents for simplicity (Al-Bahlani and Babadagli, 2008; 2009a-b; 2011b). In our modification to this method, we introduced CO2 as an alternative to hydrocarbon solvents for only one pressure and temperature condition (Naderi and Babadagli, 2012). Initial results out of this study showed a moderate recovery of 50% OOIP in average for unfavorable matrix conditions (oil wet). In the present study, the SOS-FR applications with CO2 were tested at various conditions numerically and with different timings to improve the recovery. First, the effect of different parameters was studied to obtain the best match between the simulation and experimental results. This exercise not only provided data for field scale simulations (relative permeability and diffusion coefficients) but also clarify the impact of different rock and fluid properties on the mechanics of the proposed EOR technique. Finally, an optimization scheme was suggested for field scale applications. In this exercise, a field scale numerical model of experiments was performed based on experimentally validated core scale model and the optimal conditions (solvent type, application pressure and temperature, duration of cycles) were determined to maximize the recovery.
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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) | 0.000 | 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 it