Optimizing the Treatment of Low Flow Pipelines Using a Time-Released Product with the Use of Residence Time Distribution Models
Bibliographic record
Abstract
Abstract Many shallow gas systems in North America experience a large decline in production with time. Flow rates are often low and liquid residence times are long in such systems. A new time-released, encapsulated product has been developed for such systems. Residence time distribution functions are often used to understand reactant conversion in non-ideal reactors and it is believed that their use in understanding the transport of chemicals can be applicable in corrosion control of slow moving systems. There are many parameters that affect the deliverability, effectiveness and control of the time-release of products in an oilfield system. One factor is the diffusion of inhibitor within the polymer matrix (i.e. pellet). This can be controlled by particle size. Other factors are related to the mass transfer to an external fluid phase and the intrinsic residence time of fluids within the system. Some factors can be controlled by the design of the product while others are controlled by the system conditions. In many pipeline-gathering systems for sour gas, the residence time of fluids is relatively long. In this presentation, the factors controlling time release of the product are discussed. Laboratory results on product release are best fit to an appropriate diffusion-mass transfer model of the product. A residence-time distribution model for an existing field in Canada is developed based on the best fit of an earlier field trial. The residence-time distribution with a model of time release of a newly developed product has been used to predict the time release in a field trial. The predictions and the actual experimental results of the field trial will be compared in different systems in terms of long-term inhibitor release profile.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".