Bibliographic record
Abstract
Technology Focus Welcome to the Offshore Facilities feature in this month’s JPT. It was my pleasure to go through 169 papers submitted to SPE in this field over the past year and select three for inclusion in this issue as well as three for additional reading. I considered making my selections along a theme, but offshore facilities is a broad category spanning vast engineering interests and challenges. The papers I chose are those I enjoyed reading. The result is a rather eclectic mix. The first paper provides a high-level look at the design and construction of the Prelude floating liquefied-natural-gas (FLNG) facilities. This is a world-class gasfield-development project taking on significant technical challenges, and the paper touches on technology selection and development, safety-design aspects, and construction activities. The intent of the developer is for Prelude to be the first of many FLNG projects, using a “design one, build many” strategy to leverage learnings and reduce subsequent development time and cost. The next paper describes the design, construction, and installation of a spar in the Gulf of Mexico, including bulk storage tanks for produced oil, methanol, and diesel and independent tanks for production and subsea chemicals. The design and regulatory requirements are discussed, and lessons learned and recommendations for future projects are supplied. The final paper discusses the development and testing of an active acoustic automatic leak-detection sonar. The technology makes use of the different acoustic impedance of hydrocarbons compared with the surrounding sea water to detect with a sonar head the partial reflection of the transmitted signal by this interface. The paper describes field trials of the technology in the Gulf of Mexico using a substitute target to simulate an oil release and nitrogen to simulate natural gas. I hope you enjoy reading these papers as much as I did. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 172150 The Use of Multirotor Remotely Operated Aerial Vehicles as a Method of Closely Visually Inspecting Live and Difficult-To-Access Assets on Offshore Platforms by Malcolm Connolly, Cyberhawk Innovations OTC 25676 Implementing Constructability in Brownfield Projects: A Case Study by Amir G. Salem, SBM Offshore, et al. OTC 25685 The Risk of Cryogenic-Liquid Release Could Have Been a Show Stopper for the Floating Liquefied-Natural-Gas Market: How Did the Industry Respond? by R. Wade
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".