Development of a Foam Monitor for High Pressure Separators
Bibliographic record
Abstract
Abstract Many deepwater platforms installed in the Gulf of Mexico produce large amounts of both gas and oil. Due to the limited space aboard these vessels, some separation equipment may become undersized particularly when record setting production rates are attempted. Additionally, there is increased activity in adding new subsea production to platforms that are within specified production limits. When the increased throughput arrives, separation equipment can be overloaded. In other instances, the addition of a new well (or wells) to existing production can greatly change the foaming characteristics of the overall composite. If the change is towards more severe foaming, an immediate problem can arise. The high pressure separator (HPS) is where gas/oil separation begins. It is imperative that efficient separation occurs in the HPS, otherwise performance in downstream vessels will likewise diminish. With enough foaming, the platform can be forced to take an unwanted, and sometimes unexpected, shutdown. Foaming can often be viewed as a two-fold problem. While foam (or liquid carry-over) takes place through the overhead outlet of the HPS, there is generally simultaneous gas carry-under through the bottom outlet of the separator. This "double problem" can often be observed by watching process gauges (e.g. pressure, flow rate, and level monitors). While these monitors are often useful for detecting foam, they are located either after the HPS or often do not respond quickly to an impending foam situation. To develop an immediate response to separator foaming, a probe was developed to monitor the conditions directly within the HPS. Lab development and field evaluation of a probe capable of handling the high pressure and flow rates of a HPS will be reviewed. Examples of using the probe to assist in selection and optimization of antifoams will also be presented.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".