A Novel Hybrid Solvent-Based Complex Fluid for Enhanced Heavy Oil Recovery
Bibliographic record
Abstract
Abstract This study designs a novel complex fluid (foam/emulsion) using as main components gas, low-toxicity solvents (green solvents) which may promote oil mobilization, and synergistic foam stabilizers (i.e. nanoparticles and surfactants) to improve sweep efficiency. This nanoparticle-enabled green solvent foam (NGS-foam) avoids major greenhouse gas emissions from the thermal recovery process and improves the performance of conventional green solvent-based methods (non-thermal) by increasing the sweep efficiency, utilizing less solvent while producing more oil. Surfactants and nanoparticles were screened in static tests to generate foam in the presence of a water-soluble/oil-soluble solvent and heavy crude oil from a Canadian oil field (1600 cp). The liquid phase of NGS-foam contains surfactant, nanoparticle, and green solvent (GS) all dispersed in the water phase. Nitrogen was used as the gas phase. Fluid flow experiments in porous media with heterogeneous permeability structure mimicking natural environments were performed to demonstrate the dynamic stability of the NGS-foam for heavy oil recovery. The propagation of the pre-generated foam was monitored at 10 cm intervals over the length of porous media (40 cm). Apparent viscosity, pressure gradient, inline measurement of effluent density, and oil recovery were recorded/calculated to evaluate the NGS-foam performance. The outcomes of static experiments revealed that surfactant alone cannot stabilize the green solvent foam and the presence of carefully chosen nanoparticles is crucial to have stable foam in the presence of heavy oil. The results of NGS-foam flow in heterogeneous porous media demonstrated a step-change improvement in oil production such that more than 60% of residual heavy oil was recovered after initial waterflood. This value of residual oil recovery was significantly higher than other scenarios tested in this study (i.e. GS- water and gas co-injection, conventional foam without GS, GS-foam stabilized with surfactant only and GS-waterflood). The increased production occurred because NGS-foam remained stable in the flowing condition, improves the sweep efficiency and increases the contact area of the solvent with oil. The latter factor is significant: comparing to GS-waterflood, NGS-foam produces a unit volume of oil faster with less solvent and up to 80% less water. Consequently, the cost of solvent per barrel of incremental oil will be lower than for previously described solvent applications. In addition, due to its water solubility, the solvent can be readily recovered from the reservoir by post flush of water and thus re-used. The NGS-foam has several potential applications: recovery from post-CHOPS reservoirs (controlling mobility in wormholes and improving the sweep efficiency while reducing oil viscosity), fracturing fluid (high apparent viscosity to carry proppant and solvent to promote hydrocarbon recovery from matrix while minimizing water invasion), and thermal oil recovery (hot NGS-foam for efficient oil viscosity reduction and sweep efficiency improvement).
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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 | 0.000 | 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".