Control of Contained-Annulus Fluid Pressure Buildup
Bibliographic record
Abstract
Abstract Fluid trapped in the annulus of subsea wells can cause casing strings to fail. This condition occurs when casing annuli attain a closed-volume circumstance (when a well is cased, cemented, and head seals are set). During production, the heat transfer of the produced fluids to the casing strings causes the trapped fluid to increase in pressure. This condition is magnified in deepwater because annular fluids are cooler due to the cold deepwater environment. Laboratory testing indicates that thermal expansion of these fluids can cause trapped water- or oil-based fluids to increase in pressure above casing-collapse pressure, resulting in annular pressure buildup (APB). This paper outlines a simple laboratory procedure and resulting data to determine the resulting trapped-volume pressure. Data from eight fluid combinations are presented. The temperature change during testing is an increase from 80°F to 230°F. The testing relates to the conditions commonly found in deepwater Gulf of Mexico. The laboratory data obtained from this testing was used to design a spacer system for Walker Ridge 285 #1, a deepwater, subsea well located in the Gulf of Mexico. This paper also presents the job design and related procedure for the executed spacer system.
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 | 0.000 | 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 | 0.000 | 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".