New Method to Determine the Strength of Wax Deposits in Field Pipelines
Bibliographic record
Abstract
Mechanical pigs are generally used to remove the wax deposit in oil pipelines. A better understanding of the deposit strength is beneficial to make a suitable pigging schedule, preventing the pig from blocking. The previous studies mainly examined the effect of the operating conditions on the thickness and wax content of wax deposit. However, there was little work on the deposit strength, especially the field-deposit strength. This study focuses on a new method to determine the strength of wax deposit in field pipeline. First, the structure of wax deposit obtained from field pipelines and wax deposit formed in laboratory was observed. The results showed that the structure of the field wax deposits is much looser than that of wax deposit formed in lab. The looser structure could result in lower strength. Second, according to analyze, three basic factors contributing to the deposit strength are solid wax content, deposit structure and morphology of wax crystals. Based on above analyzation, a method by preparing model wax-oil gels in the lab instead of field deposit was proposed to measure the field deposit strength indirectly. The model gels were prepared by using the oil obtained from pipeline and a wax. As the structure of field deposit is looser than that of the model gels, a wax was chose to form the smaller size of wax crystals in model gels than that of wax crystals in field deposit for approximately the same strength between field deposit and model gels at the same solid wax content. The strength was measured by using the vane, and the solid wax content was determined by using differential scanning calorimetry (DSC). Third, the accuracy of new method was evaluated. Verification experiments showed that the new method is an effective method for determining the strength of field deposit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".