Inflow Analysis and Optimization of Slotted Liners
Bibliographic record
Abstract
Abstract Slotted liners have been used for many years to provide sand control in oil industry applications. They are relatively inexpensive to manufacture and tolerant of installation loads, but historically they have not been able to offer the very small opening sizes of wire-wrap screens for controlling production of fine sands. However, recent advances in slot manufacturing methods provide slot openings that match and surpass the size and tolerance of wire-wrap screens. Furthermore, slotted liners offer an advantage in providing a variable slot density that can be used to optimize inflow or outflow distributions. In the development of its South Bolney reservoir, Marathon Oil Canada and Noetic Engineering Inc. performed an analytical evaluation of inflow characteristics for a new generation of commercially available slots. Several interesting conclusions were reached, the most significant being that inflow resistance depends much more strongly on slot density than on open area. The inflow characterization was also used to develop an optimized slot density distribution to promote more uniform production over the entire well. The slotting design was incorporated into a new well that was designed to control thermally induced loads, thereby avoiding the sand control characteristics of slotted liner being compromised.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".