Jet Cleaning Processes in the Plug and Abandonment of Oil and Gas Wells: An Experimental Study on Horizontal Miscible Jets
Bibliographic record
Abstract
Abstract The plug and abandonment (P&A) operation is considered as one of the essential stages during life cycle of oil and gas wells. During the P&A operation, each well needs to go through the specific steps; these may include accessing behind the casing (i.e. annulus), cleaning the target area (i.e. inside and outside the casing), and installing the cement plug barriers in the target area. The jetting process is one of the efficient approaches in the cleaning step of P&A operation, where an injection fluid pushes and removes unwanted fluids/materials. To achieve an efficient jet cleaning process, studies on the effects of different parameters (e.g. operational parameters, fluid properties, and geometrical parameters) seem crucial. In this paper, we experimentally study the characteristics of horizontal miscible jets, to develop an understanding about fundamental aspects of the jet cleaning process in P&A operation. We analyze the effects of the injection velocity, the perforation diameter, and the rheological parameters on jet characteristics, such as the laminar length and the mixing index. Based on our results, increasing the injection velocity leads to a decrease in the laminar length. Also, the mixing index before the perforated wall increases by decreasing the perforation diameter. In addition, using a non-Newtonian ambient fluid results in decreasing the mixing index.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".