Silica-Based Nanofluid Heavy Oil Recovery A Microfluidic Approach
Bibliographic record
Abstract
Abstract Heavy oil reservoirs form one of the primary unconventional fossil fuel resources to meet the growing demand for global energy. Hydrocarbon recovery yields from these reservoirs is often low or requires energy intensive thermal processes. For instance, in the case of waterflooding in heavy oil reservoirs, high oil viscosity results in early breakthrough and poor sweep efficiencies. Polymers have been used to increase the displacing water viscosity for conformance control and viscous fingering attenuation. However, polymer degradation and entrapment inside the reservoir make them less attractive. Recent experiments using conventional oil samples showed that the incorporation of silica nanoparticles in the injected solution (i.e. nanofluid) can dramatically enhance oil production. Nanofluids are more stable than polymers in harsh reservoir conditions, also they can modify the interfacial properties between oil and water, and thereby nanofluids may provide capabilities for heavy oil recovery. In this study, a microfluidic platform was utilized to monitor the process of nanofluid-based heavy oil recovery for a representative Alberta heavy oil sample. To create a reference, recovery experiments were repeated with waterflooding and surfactant flooding process. Consistent with coreflood experiments for conventional oil samples, nanofluid injection increases the oil recovery compared to the waterflooding. Microscale visualization revealed that emulsion formation during heavy oil displacement with chemicals is the main factor in incremental recovery. The developed microfluidic approach is a powerful mimetic model for the real-time visualization of the chemical-based heavy oil recovery process in micro/nano scale. Considering the time-consuming and expensive nature of coreflood experiments, this method provides an attractive alternative for rapid and low-cost chemical-enhanced oil recovery (EOR) screening studies. Results demonstrate the strength of nanofluid-EOR as an efficient recovery method for Alberta heavy oil reservoirs.
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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".