ES-SAGD Relative Permeability as a Function of Temperature and Solvent Concentrations
Bibliographic record
Abstract
Abstract Relative permeability plays a significant role in predicting oil rate and ultimate oil recovery factor. The literature reveals that same set of relative permeability is often used to predict the ES-SAGD performance regardless of not only the downhole operating temperature but also the injected solvent concentration. This can lead to significant errors when predicting the ES-SAGD performance and its economical feasibility. In paper SPE-180713-MS, SAGD relative permeability was presented as a function of temperature. In this paper, a series of realistic ES-SAGD relative permeability curves were developed based on experimental work combined with simulation studies. A typical Athabasca oil and sand were used to obtain the experimental data and construct relative permeabilities using unsteady-state method. First residual oil saturations were determined in presence of hot water and steam flooding under SAGD condition at a given temperature. Following that, a typical n-alkane (n-heaxane) solvent was co-injected with steam at different concentrations varying from one to 25 weight percent of the injected steam while the operating temperature was kept constant. The produced oil and water volumes were collected and measured during the hot water, steam injection, and solvent co-injection experiments. The data were utilized to construct the relative permeability curves at a given operating temperature (200 °C) and various solvent concentrations. Finally, a series of reservoir simulations were performed to history match the lab experiments and examine the accuracy of inferred relative permeability curves.
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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) | 0.001 | 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".