Experimental Insights Into Geometrical Parameter Effects on the Jet Cleaning Process in the Plug and Abandonment of Oil and Gas Wells
Bibliographic record
Abstract
Abstract The plug and abandonment (P&A) operation is a critical phase in the lifecycle of oil and gas wells, aimed at permanently sealing wells to prevent fluid migration between hydrocarbon-bearing zones and surrounding formations. The primary objective is to establish secure barriers and prevent crossflow after abandonment. This process involves sequential steps, including accessing the annulus space behind the casing, cleaning the target area, and installing cement plugs. Effective jet cleaning is crucial to ensure proper cement-casing bonding and avoid contamination. The efficiency of jet cleaning depends on various factors, including well geometry (e.g., inclination, nozzle-casing distance, nozzle-perforation diameter ratio) and fluid properties (e.g., rheology). This study experimentally investigates horizontal jets in a miscible medium to understand jet cleaning dynamics during P&A operations. Using a setup that mimics perforated casing structures, fluid is injected horizontally into a tank containing a miscible ambient fluid. Key parameters analyzed include injection velocity, perforation diameter, and ambient fluid rheology. Results show that smaller perforation diameters increase the mixing index, indicating higher mixing levels, while non-Newtonian ambient fluids reduce the mixing index. These findings provide insights for optimizing jet cleaning in P&A contexts.
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.
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".