Chemical Additives and Foam Assisted SAGD Model Development
Bibliographic record
Abstract
Abstract Steam assisted gravity drainage (SAGD) is recognized as a profitable and stable approach to address the exploitation of heavy oil and oil sand resources. However, the efficiency of SAGD, a close relative of a sufficiently-expanded and uniformly-developed steam chamber, tends to be deteriorated by quick steam movement and high heterogeneity. Chemical additives and foam assisted SAGD (CAFA-SAGD) is a strategy proposed on this account. This study aims to analyze the mechanisms and phenomena involved. The injection of chemical additives to promote in-situ foam generation reduces gas relative permeability by slow-moving and stagnant bubbles trapping. Also, lamella resists bubbles flow and increases apparent gas viscosity. The restriction of steam mobility thus favors a sufficiently-expanded steam chamber and the nitrogen co-injected to stabilize bubbles works as a separator between steam and overburden to reduce heat loss. Simultaneously, the interfacial tension reduction due to surfactants injection at a water/oil interface may influence phase behavior, which further leads to the solubilisation of residual oil. CAFA-SAGD is thus likely to increase heat efficiency and add oil output. A homogeneous model is built to analyze CAFA-SAGD considering foam generation by snap-off and leave-behind, foam trapping in a porous medium and foam coalescence due to both the lack of surfactants and capillary suction. Besides, with the analysis of foam wall slip phenomena, a comprehensive foam property model is coupled to analyze shear thinning rheology and calculate lamella viscosity as a function of gas saturation and gas velocity. In addition, the influences generated by surfactant injection should be added. This study also develops an analytical FA-SAGD model based on Butler's finger rising model (1987) to show foam's effects on a steam chamber growth rate and shape. We derive the FA-SAGD model accounting for the retarded steam movement with higher steam viscosity and lower gas relative permeability. The foam viscosity is calculated as a function of gas saturation and a gas rate, and the modification of gas relative permeability is reflected with a higher gas residual saturation according to Bertin et al.'s foam property model (1998). After comparing, validating, and discussing the developed model against the SAGD model, we find that foam injection contributes to high production efficiency with less steam consumption. A lower steam mobility generated by stronger foam is more likely to have a lower SOR (steam-oil ratio). The results agree well with the published high-temperature steam foam experiments and pilot tests. Strong bubbles accumulate along the boundary of a steam chamber to restrict steam movement, while weak foam fills inside the chamber to enhance steam trap, contributing to a higher oil recovery factor and lower SOR.
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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 | 0.001 | 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".