Innovative Gas Shutoff Method Using Heavy Oil-in-Water Emulsion
Bibliographic record
Abstract
Abstract Laboratory investigations were conducted to examine the effectiveness of heavy oil-in-water emulsion in plugging the near wellbore matrix, thereby reducing gas (and water) coning or eliminating gas leakage to the surface. Experiments at micro- and macro-scale levels were performed to: a) provide a detailed understanding of emulsion flow and blocking mechanism, b) set criteria for controlling an emulsion penetration depth before it breaks down and seals a porous medium. In these experiments, well-characterized oil-in-water emulsions were injected into etched-glass micro-models and micro-models packed with glass beads. The effect of droplet-to-pore size ratio, droplet stability, oil and surfactant type and concentration were studied through visualization experiments. It was observed that blockage happened because of size exclusion. Also, the blockage was accelerated due to droplets coalescence as a result of high shear rate or surfactant adsorption on the porous medium. Furthermore, emulsion droplet size distribution, emulsion viscosity and oil droplets-to-water interfacial tensions increased as the surfactant content decreased, resulting in higher capillary pressure across the trapped oil droplet. The effect of oil type, rock permeability, injection velocity, and wettability alteration were also studied. The results showed that emulsions carrying more viscous oils could resist higher pressures due to the combined effects of capillarity and viscosity. Also, conditioning the medium with pre-flush solutions predictably affected the depth to which an emulsion may penetrate into a porous medium. Surfactant and alkaline-based pre-flush solutions may enhance an emulsion penetration depth significantly. However, the emulsion may break down and emplace at a desired depth within a porous medium as a result of applying low pH solutions. Unconsolidated cores withstood 42,500 kPa/m (1,880 psi/ft) for a long period of time. Emulsions were optimized to seal cores with different permeabilities for the purpose of field application. A novel cost-effective sealant that uses heavy oil-in-water emulsion to block the near wellbore region has been developed. Emulsion flow behavior and methods controlling its propagation rate into a porous medium will be presented.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".