Operational Experience of Wet Gas Metering in Malaysia
Bibliographic record
Abstract
Abstract Wet gas metering technology was chosen by ExxonMobil for continuous well testing and advanced production measurement at two satellite platforms located in the South China Sea offshore Malaysia. The satellite facilities are equipped with permanently installed SmartVent wet gas venturi based meters at each well location. Special wet gas flow calculation and monitoring software have been developed and installed on a separate flow computer installation interfaced to the host platform DCS system. Prior to installation the wet gas meters have been subjected to full scale testing at a representative high pressure gas/liquid flow test facility. This has resulted in valuable measurement experience and a unique set of experimental wet gas data. After start up of the field on-site verification/calibration will be performed using the tracer technology method. As of today valuable results have been obtained from the system. The present application is a demonstration that wet gas flow measurement is increasingly gaining acceptance in replacing expensive well test separators and related infrastructure in gas/condensate field developments. Besides the significant cost savings, the availability of continuous readings of each well's production rates allows for enhanced reservoir management and production optimisation. At the same time experience has shown that successful implementation of wet gas flow measurement requires adequate attention to every aspect of the metering process.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".