Visual Support for Heavy-Oil Emulsification and its Stability for Cold-Production using Chemical and Nano-Particles
Bibliographic record
Abstract
Abstract The performance of non-thermal, cold, heavy oil production methods, such as waterflooding or gas injection (foamy oil) applications, is predominantly limited. As an alternative, efficient chemical flooding has been recommended and tested around the world (mainly in Canada and China). Cost aside, the main issue with this application is the compatibility of the chemicals used in terms of rock type, salinity, temperature, and emulsion generation and stability. Low-cost materials with strong emulsion stability capability have been tested previously in our research group. As an ongoing part of our past studies on the extensive chemical flooding applications in enhanced heavy oil recovery, we visualize directional motion, patterns, and deformation of fingers observed in Hele-Shaw cells with different oil types (heavy oil of 13,850 cP at 21°C from western Canada, heavy mineral oil of 649.9 cP at 20°C. Macroscopic and microscopic visualizations allow us to gain insights into important and fundamental physical flow mechanisms such as the Saffman-Taylor instabilities due to the viscosity ratio, and the Marangoni effect due to the surface tension gradient, wetting, dewetting, and superspreading behaviors. Hele-Shaw visualization studies in the past have mainly focused on weakening or eliminating the fingering instabilities. In this study, we attempt to categorize the observed finger types which appear during the displacement, identify the finger types responsible for heavy oil-in-water emulsification, and relate the visualization results to final enhanced heavy oil recovery. We observe both miscible and immiscible flow behavior and in the case of immiscible flow, and we investigate the impact of the capillary number on finger growth and ramification patterns by manipulating the flow rates. There are a plethora of factors that may impact the visualization of heavy-oil emulsification including the fixed chemical properties, chemical compatibility, heterogeneous (or non-heterogeneous) chemical reaction, capillary number effect, mobility ratio, IFT gradient, chemical concentration, liquid-substrate wettability, pH of liquids, precipitation, and brine conditions. To investigate such impact, we investigated a large series of in-situ heavy oil-in-water emulsifications at various conditions using emulsifiers such as anionic surfactants, cationic surfactants, and NaOH. And for the stabilization of the emulsions formed with the emulsifiers, we tested nanofluids (silica, cellulose nanocrystal, zirconia, alumina) and polymer (Xanthan Gum and an anionic polyacrylamide-based polymer). The results displayed that there exist finger types which are responsible for stable Winsor type 4 heavy oil-in-water emulsification. By the method of controlling the infrastructure of emulsion droplets and correlating observed multiple finger interactions to the material designs, we enable the selection of both novel and cost-effective designs for heavy oil recovery as well as displacement mechanisms.
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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 | 0.001 | 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) | 0.006 | 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".