Drainage flows in oil and gas well plugging: Experiments and modeling
Bibliographic record
Abstract
This work studies the drainage flow of a heavy fluid from an inner pipe into an outer closed-end inclined pipe filled with an in-situ light fluid. This configuration represents the bailer drainage in the dump bailing method, which is a common technique for cement plug placement in plug and abandonment (P&A) operations of oil and gas wells, especially in Western Canada. Cement plugs are set as part of well decommissioning to prevent oil and gas leakages from hydrocarbon zones to different formations, freshwater underground resources, and the surface. The heavy fluid can be a Newtonian or viscoplastic fluid, while the light fluid is always a Newtonian fluid. The two fluids are miscible, and they have a density difference. Using experiments and modeling, the effects of the heavy fluid properties and flow parameters are examined on the drainage flow dynamics. In the experiments, high-speed imaging and non-intrusive measurement techniques are used to provide ample drainage flow characterizations. In particular, the experimental results show two distinct flow regimes inside the inner pipe, namely the slump-type and the center-type flow regimes, which are classified in a plane of the governing dimensionless numbers. The study further examines the onset of the heavy fluid drainage from the pipe based on the fluid’s yield stress and applied longitudinal buoyant stress. Once this onset is identified, a one-dimensional drainage model is developed, based on the energy balance to predict the drainage rate of the heavy fluid. The comparison between the experimental results and modeling predictions shows reasonable agreement, demonstrating that the proposed model can well present the heavy fluid drainage rate. The model also suggests that the drainage rate can be enhanced by reducing the viscosity and yield stress of the heavy fluid as well as increasing the density difference, pipes’ diameters, and inclination angle. The outcomes of this study can be helpful for improving the cementing processes in P&A operations of oil and gas wells.
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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".