Long Gas Tiebacks – Pseudo Dry Gas Systems
Bibliographic record
Abstract
Abstract Due to the laws of physics and multiphase flow, subsea tie back systems are generally limited to approximately 110km as a single pipeline or 150km as dual pipelines after which the production plateaus are shortened and increasing amounts of reserves remain in the ground. This paper presents an overview of an innovative new technology which demonstrates that gas tie-backs can be achieved without the need of compression. The premise of the technology is to achieve pseudo-dry gas conditions through intermittent in-line separation with segregated transport of the associated liquid phase. Achieving near dry gas conditions in the main production conduit removes hydraulic constraints on line size and turndown, leading to improved recovery for long distance tieback opportunities. The paper demonstrates this innovative technology and its value proposition by means of a ‘bench-marked’ study of a 200km long gas tie-back in 1,800m (5,900ft) of water. The Computational Fluid Dynamics (CFD) work has demonstrated high separation efficiencies at significant superficial gas velocities, while the required hardware fits within the installation envelope of an ‘In-line’ pipeline tee. This has been coupled to the flow assurance work showing improvements in recoverable reserves, while leading to capital expenditure reductions of upwards of 50% due to the removal of offshore structures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".