Heavy Oil-in-Water Emulsion as a Novel Sealant in the Near Well Bore Region
Bibliographic record
Abstract
Abstract This experimental study was designed to provide a detailed understanding of the blocking mechanism of heavy oil-in-water emulsions injected into a porous medium. The process in mind is one where a created emulsion will break near the well bore or at some pre-determined distance from it, in order to provide an effective, stable plug against, for instance, water or gas coning. Well-characterized heavy oil-in-water emulsions were injected into micro-models and their behavior was recorded in visualization experiments. The effect of droplet-to-pore size ratio, droplet stability, and surfactant type and concentration were studied. It was observed that blockage took place because of size exclusion. Droplets may coalesce and produce a larger droplet due to the local high shear rate or to surfactant adsorption on the porous medium. Also, emulsion droplet size distribution, emulsion viscosity and oil/water interfacial tension increased as the surfactant content decreased, resulting in higher capillary pressure across the trapped droplets. The effect of oil type, rock permeability, injection velocity, and wettability alteration were also studied. The experiments showed that an oil-in-water emulsion was effective in sealing unconsolidated cores for long periods of time. Emulsions carrying more viscous oils could resist higher pressures. 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 as a result of using low pH solutions. A novel sealant that uses heavy oil-in-water emulsion to block the near well bore matrix has been developed. Stable reduction in permeability to other fluids was observed as the plug withstood 42,500 kPa/m (about 1,800 psi/ft) pressure gradients. Criteria are defined for field application of this blockage phenomenon. Introduction An emulsion is a dispersion of two mutually insoluble liquids, such as water and oil. One of these two components is present in the form of finely dispersed spherical droplets in the second, which is the continuous phase. If oil is dispersed in water, the emulsion is referred to as an oil-in-water (O/W) emulsion which has a lower viscosity than the oil constituents; the reverse case is a water-in-oil emulsion (W/O). Emulsions can be encountered in almost all phases of oil production and processing [1 – 3]. Most of the world's crude oil is produced in emulsion form because natural emulsifiers exist in petroleum reservoirs. These natural emulsifiers, found in (heavy) crude oil, can be formed from asphaltenes, asphaltic and resinous materials, oil-soluble organic acids such as naphthenic acids, fatty acids or aromatic acids, or cyclic compounds (cyclic aromatics) such as toluene, benzene, decalin, methylcyclohexane, and cyclo-octane. Also, emulsions can be formed in-situ in many enhanced oil recovery processes such as chemical flooding, carbon dioxide flooding, steam flooding and fire flooding. Many laboratory studies [4 – 14] have been conducted to understand both the qualitative and the quantitative behavior of mulsions and their flow mechanisms through porous media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".