Digital Twin Provides Virtual Multiphase Flow Metering and Leak Detection to Deepwater Operations for Operational Decision Making on Liwan Field
Bibliographic record
Abstract
Abstract This paper presents lessons learned over eight years of using the first digital twin in the South China Sea by Cenovus Energy and China National Oil Company (CNOOC) on the Liwan Field. The digital twin provides a collaboration platform for integrated operations and is based on dynamic multi-phase flow simulation models for wells, flowlines and slug catchers, coupled to live field measurements. One of the main objectives of the digital twin is to provide continuous real-time virtual instrumentation, as well as aggregated information related to flow assurance and integrity management with a novel multidimensional model-based leak detection workflow. The digital twin workflows enable operators to better prepare and respond to planned and unplanned events in the field and to analyze and optimize performance of the field, in combination with a data analytics tool. Subsea wet gas flow meters (WGFM) in the field initially provided flow rate information to deep water operations in line with the operational strategy. After one year in production, a reduction in WGFM performance was observed. A mitigation strategy was developed to use the digital twin flow rate calculations combined with the remaining WGFMs for improved operational decision making.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".