Subsea Cap & Contain Method for a Deepwater Tension Leg Platform
Bibliographic record
Abstract
Abstract A tremendous amount of effort has been placed on subsea cap and containment in order to demonstrate the exploration and production industry's response to a subsea well control event. This paper will focus on the methods and processes planned to contain a subsea blow out beneath a Tension Leg Platform (TLP) in deepwater Gulf of Mexico. Response to a well control event of this type is divided into 3 major categories: 1) TLP Health and Stability Monitoring - Understanding the structure stability is key in planning the response and determining the time allowed to deploy containment assets, 2) Debris Clearing-a path must be cleared into the well pattern horizontally and vertically for the capping stack to be deployed and 3) Stack Deployment - with the TLP still floating above the well pattern the stack must be deployed laterally under the facility and onto the well. Several challenges were encountered during the design and approval of this containment method, leading to the development of alternative capping strategies, purpose built capping stacks, installation of permanent monitoring / response equipment and use of Delmar's Heave Compensated Landing System (HCLS) to accomplish these critical subsea tasks.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".