Fluid Placement in a Closed-end Pipe with Application in the Plug and Abandonment of Oil and Gas Wells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The plug and abandonment (P&A) of oil and gas wells is a crucial process to prevent the migration of the reservoir fluids and avoid the contamination of fresh water resources, soil, and atmosphere. In order to plug and abandon a well, the cement plug placement is conducted via several methods, such as the dump-bailing method, i.e. dumping of the cement slurry into an in-situ fluid in the wellbore, at specific intervals. In this process, an extensive range of Newtonian or non-Newtonian fluids is used to displace and remove the in-situ fluid (drilling fluid or water) in the wellbore. Based on the large number of parameters of the flow, such as the density and viscosity differences between the fluids, the geometry type (pipe, annulus, etc.), the operation conditions (velocity, geometry inclination, dumping height), various kinds of placement and mixing flows can occur, and different flow regimes (e.g. inertial, viscous) can develop. Motivated by the fluids mechanics of this process, we experimentally investigate the placement of a fluid in an inclined closed-end pipe to replace a slightly lighter fluid. In our experiments, the heavy fluid can be Newtonian or viscoplastic, and the light fluid is always Newtonian. The fluids of our interest are miscible. We investigate the effects of the flow parameters, such as the density difference, the indication angle, the viscosity ratio between the fluids, and the rheological parameters on the placement flow patterns, and quantify the different flow regimes versus the appropriate dimensionless groups that describe the flow.
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.
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 it