Investigation Of Hybrid SAGD Using Carbon Dioxide, Propane, Nitrogen and Methane
Bibliographic record
Abstract
This study investigates a hybrid SAGD (steam-assisted gravity drainage) process utilizing four gaseous solvents, namely, carbon dioxide, propane, nitrogen and methane that are co-injected with steam at different concentrations of 5.0, 7.5 and 10.0 wt%. The objectives are to evaluate and compare the effectiveness of non-condensable gases like methane, nitrogen and carbon dioxide with those of condensable hydrocarbons like propane; to evaluate the performance of hybrid SAGD applied to depleted, low-pressure oil reservoirs; and to numerically simulate the experimental results and obtain tuned relative permeability curves. For this purpose, rigorous experimentation is done using a laboratory-scale, cylindrical replica (i.e., physical model) of an oil reservoir with a set of parallel horizontal injection and production wells. A numerical process model is developed, simulated, and calibrated with the help of experimental data. The experimental setup incorporates i) an injection system designed to co-inject solvent and steam at the required injection temperature of 195°C and pressure of 1.45 MPa, gauge; ii) a production system designed to collect the produced fluids and measure the fractional flow of each phase while ensuring smooth operation with minimal variations in production pressure; and iii) control systems designed to precisely control the heaters temperatures. The experiments are performed at isothermal conditions with model permeability and porosity, respectively, 10.7 Darcy and 32%. It is observed that for low pressure reservoirs, oil recoveries with co-injected solvents are at least 18% more than that from steam alone. On an equal-weight-percentage basis, methane is found to be the best solvent, and results in the highest oil recovery of 50.7% of the original oil in place. Compared to non-condensable gases, propane has the highest solvent retention of up to 15%. The gases with higher solubility in heavy oil, like carbon dioxide and propane, show a reduction in oil recovery with an increase in feed solvent concentration. A numerical model of the process is developed and simulated using Computer Modelling Group’s (CMG) WinProp and STARS simulators. For the solvents that are found to be promising, the simulated oil, water and gas recoveries are history-matched with their experimental counterparts by adjusting the relative permeability curves. The resulting, calibrated model is able to predict oil, water and gas recovery in the hybrid SAGD process with less than 5% relative error.
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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 | 0.000 |
| 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 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".