Bibliographic record
Abstract
Development of greenfield deep water oil and gas fields are generally known to be expensive, compared with conventional shallow water fields development. Having marginal fields in deep water only compounds the challenge to development. This presentation aims to discuss the challenges associated with developing marginal gas fields in deep water. It however centers primarily on the subsea portion of the development. Marginal gas fields are typically < 500 bcf, and are best developed when there is an opportunity to combine a number of such marginal fields for a clustered development. However, invariably, each field has different characteristics (volume, pressure, composition) and hence requires careful planning to ensure constant flow to production facilities. The presentation will thus elaborate on the use of software tools such as Maximus for phased field development planning to ensure base load gas production throughout the project lifetime. At FEED stage, the subsea facilities and topsides facilities are typically carried out by separate design contractors, involving an interface at the surface that needs to be managed well to ensure optimum overall system design for smooth economical operation. In the case of developments utilising FLNG the interface issues can become rather complicated when the subsea facilities design team at FEED stage has to interface with multiple FLNG FEED contractors participating in a design competition. The challenges centred round the subsea development include Field Layout planning, FPF interfaces, subsea CAPEX, Flow Assurance, Hardware limitations (qualification), Technology (applications of dual directional subsea wyes, and subsea pigging launcher/receivers). The presentation will also elaborate on the use of other software tools such as ArcGIS for pipeline route layout planning and optimisation, and Star-CCM Plus for sand erosion CFD (Computational Fluid Dynamics) modeling.
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 | 0.001 |
| 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) | 0.010 | 0.002 |
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".