Can We Generate Stable Pickering Emulsions Activating Naturally Occurring Nanoparticles in the Reservoir for Cost Effective Heavy-Oil Recovery?
Bibliographic record
Abstract
Abstract Activating naturally occurring nanoparticles in the reservoir (clays) to generate Pickering emulsions results in low-cost heavy oil recovery. In this study, we test the stability of emulsions generated using different types of clays and perform a parametric analysis on salinity, pH, water to oil ratio (WOR), and particle concentration; additionally, we report on a formulation of injected water used to activate the clays found in sandstones to improve oil recovery. First, oil-in-water (O/W) emulsions generated by different clay particles (bentonite and kaolinite) were prepared for both bottle tests and zeta potential measurements, then the stability of dispersion was measured under various conditions (pH and salinity). Heavy crude oils (50 to 170,000 cP) were used for all experiments. The application conditions for these clay types on emulsion generation and stability were examined. Second, sandpacks with known amounts of clays were saturated with heavy-oil samples. Aqueous solutions with various salinity and pH were injected into the oil-saturated sandpack with a pump. The recoveries were monitored while analyzing the produced samples; a systematic comparison of emulsions formed under various conditions (e.g., salinity, pH, WOR, clay type) was presented. Third, glass bead micromodels with known amounts of clays were also prepared to visualize the in-situ behavior of clay particles under various salinity conditions. The transparent mineral oil instead of opaque heavy oil was used in these micromodel tests for better visualization results. Recommendations were made for the most suitable strategies to enhance heavy oil recovery with and without the presence of clay in the porous medium; moreover, conditions and optimal formulations for said recommendations were presented. The bottle tests showed that 3% bentonite can stabilize O/W emulsions under a high WOR (9:1) condition. The addition of 0.04% of NaOH (pH=12) further improved the emulsion stability against salinity. This improvement is because of the activation of natural surfactant in the heavy oil by the added alkali—as confirmed by the minimum interfacial tension (0.17 mN/M) between the oil and 0.04% of the NaOH solution. The sandpack flood experiments showed an improved sweep efficiency caused by the swelling of bentonite when injecting low salinity fluid (e.g., DIW). The micromodel tests showed a wettability change to be more oil-wet under high salinity conditions, and the swelling of bentonite would divert incoming water flow to other unswept areas thus improving sweep efficiency. This paper presents new ideas and recommendations for further research as well as practical applications to generate stable emulsions for improved waterflooding as a cost-effective approach. It was shown that select clays in the reservoir can be activated to act as nanoparticles, but making them generate stable (Pickering) emulsions in-situ to improve heavy-oil recovery requires further consideration.
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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 | 0.001 | 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 | 0.001 | 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".