Application of Emulsion Flow for Sealing Leaky Gas Wells
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 and which injects an oil-in-water emulsion through the well casing perforations to plug the cement and part of the formation near the well bore. Earlier we observed that for unconsolidated cores, the process is effective and may withstand large pressure gradients over long periods of time; however, examination of the cores showed that the emulsion broke upon entering the core and therefore did not penetrate to the desired depth. Now, we report results of subsequent experiments, our understanding of the process, and criteria for controlling the distance into the formation to which an emulsion may penetrate. In these experiments, well-characterized emulsions were injected into micro-models packed with well-sorted glass beads or sand grains to yield the desired permeability. Visualization experiments were done to observe emulsion droplets capture. 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 were most effective in blocking pores. We found that the rate and extent of transfer of surfactant from the solution to the beads' surface must be important. In subsequent experiments, we flushed the micro-models with 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 micro-model. In conclusion, this work characterizes the flow behavior 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 well bore 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.
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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 | 0.000 | 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 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".