Application of Emulsion Blocking Mechanism for Sealing the Near Wellbore Region
Bibliographic record
Abstract
Abstract Across Canada, thousands of abandoned gas wells leak gas to the surface. As a potential solution to this problem, we have done laboratory-scale, experiments relevant to a field-tested novel process that injects a viscous oil-in-water emulsion to plug the formation near the wellbore. In an earlier study, we observed that, for unconsolidated cores, the process is effective and may withstand large pressure gradients over long periods of time; however, the emulsion penetration depth was limited to a small fraction of the core's length. Now, we report results of subsequent experiments, our understanding of the emulsion blocking mechanism and criteria for controlling the distance into the formation to which an emulsion may penetrate. In these experiments, well-characterized emulsions were injected into an etched glass micromodel or a micromodel packed with well-sorted glass beads or sand grains to yield the desired permeability. Visualization experiments were done to observe the capture mechanisms of the emulsion droplets. We observed that, for a given pressure gradient, some droplets are too large to pass through a pore's throat (size exclusion), that other droplets attach to the pore's surface and coalesce with nearby droplets to accelerate the blockage process and that more viscous droplets are most effective in blocking pores. We found that the rate and extent of transfer of surfactant from the solution to the bead's surface must be important. In subsequent experiments, we flushed the micromodels with a surfactant solution to alter the wettability of the beads prior to injecting the emulsions. The results showed that the choice of surfactant, its concentration and the volume of its injected solution predictably affect the depth to which the oil-in-water emulsion may penetrate into a micromodel. In conclusion, this work characterizes the flow behaviour and breaking of an emulsion in a porous medium and examines parameters that may be adjusted to control the novel process. Introduction Crude oil emulsions are a broad area and several books have been written on the subject(1–3). Their occurrence in certain modes of crude oil production and their application as a blocking agent in underground porous media have received more attention recently. Emulsions can be encountered in almost all phases of oil production and processing(4, 5). Field emulsions can be generated within oil reservoirs and/or in the wellbore during production. The problem has been more pronounced recently due to the fact that many oil reservoirs are being watered out. Salty water has to be separated from the produced emulsions in order to meet crude specifications for transportation, storage and export, as well as avoid catalyst poisoning in the refineries. However, emulsion treatment is not an easy task and demands an application of various thermal, mechanical, chemical and/or electrical processes or their combination(4, 5). On the other hand, the application of emulsions as blocking agents in many secondary and tertiary oil recovery processes makes them of particular interest to many researchers. Laboratory studies have suggested that oil-in-water (O/W) emulsions can be used to obtain a deeper formation plugging resulting in better sweep efficiency and greater oil recovery(6–11).
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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 | 0.001 |
| 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".