Cementing Oil and Gas Wells by Dump Bailing Method: Effects of Injection Rate and Inclination Angle on Suspension Placement Efficiency
Bibliographic record
Abstract
Abstract Plug and abandonment (P&A) of oil and gas wells at the end of their operational life is one of the helpful techniques to reduce reservoir fluid leakage to upper geological formations and surface. This operation typically involves the placement of cement paste at targeted depths within the wellbore using various techniques to maximize cementing efficiency while limiting wellbore fluid-cement mixing. Among the different techniques, the dump-bailing method—a rapid, ringless, and economical approach—is extensively employed worldwide. In this process, cement slurry is injected from a bailer into the wellbore and settles on top of a permanent bridge plug. From a fluid mechanics perspective, many parameters, such as geometrical conditions, operational parameters, and fluid properties, may affect the efficiency of this process. In this work, we experimentally investigate the injection of heavy suspensions (representative of cement paste) under two different inclination angles into water, a common wellbore fluid in such processes. The experiments are performed within a closed-end pipe (simulating the wellbore and bridge plug) using a scaled-down setup. To examine the effects of injection rates and inclination angle, we employ advanced equipment to prepare and characterize the fluid, conduct experiments, and use non-intrusive techniques, such as a high-speed camera, to capture and analyze the flow dynamics. The results show that the dimensionless pipe filling time remains nearly constant across experiments, regardless of the injection rate or inclination angle. However, during the suspension placement, a higher injection rate increases mixing between the fluids, while a lower inclination angle reduces it. The outcome of this study is beneficial not only for cementing using the dump-bailing method but also for other cementing applications using different methods in oil and gas wells, including primary cementing, cementing during hydrocarbon production, and wellbore decommissioning.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".