Effect of Mobile Water-Saturation on Thermal Efficiency of Steam-Assisted Gravity-Drainage Process
Bibliographic record
Abstract
Abstract The commercial viability of SAGD process is negatively affected by several undesirable reservoir features like pronounced heterogeneity, low vertical permeability, thick and areally extensive shale barriers and steam thief zones. The efficiency of SAGD projects is also affected by the presence of mobile water saturation in the target zone. Although the presence of small mobile water saturation is not considered harmful, reservoirs with high mobile water saturation may be poorly suited for the SAGD process. Nonetheless, SAGD remains the only practical technology for in situ extraction of oil from oil-sand reservoirs, even when mobile water is present. This raises the question of how much mobile water is a show stopper. To investigate the effect of mobile water saturation on SAGD performance, high pressure physical model experiments were carried out. Different levels of mobile water saturations were established in the model by modifying the packing and saturating techniques. SAGD experiments were conducted by injecting superheated steam at controlled rates and producing the oil from the production well at constant pressure. The injection rate was selected to keep the pressure difference between the injector and producer at a low level. The oil production behavior was analyzed to evaluate the effect of water saturation on the thermal efficiency of the process. Based on the results of low (immobile) and high (mobile) water saturation experiments it was observed that oil recovery factor droped by 7% and Cumulative Steam Oil Ratio increased by 50 percent when initial water saturation was increased from 14.7% to 32%.
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 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".