Performance of Multiphase Flowmeter and Continuous Water-Cut Monitoring Devices in North Slope, Alaska
Bibliographic record
Abstract
Abstract Alaska's North Slope oil fields offer several different types of production environments that can prove challenging for effective production well testing with conventional gravity type test separators. The Prudhoe Bay field has mature production: high water cuts exceeding 90%, crudes with low 20s API gravity and gas-lifted wells with high gas volume fractions (GVF) >99.9%. The Milne Point field has viscous crude and light oil production and employs electrical submersible pumps (ESP), jet pumps and gas lift. This wide range of production methods and challenging fluid properties create challenges when analysing potential equipment and procedures to provide the critical production data needed to optimize overall production. Over the last several years, BP Exploration (Alaska) Inc. (BPXA), installed over 24 infrared water-cut sensors. Nineteen water-cut devices have been installed in three-phase flow regimes at the wellheads. Five water-cut units have been installed on the liquid legs of two phase test separators. During this period BPXA also installed seven in-line multiphase meters—five in use at production pads for testing wells and two on individual wellheads. This paper will discuss BPXA's experience with these metering devices over two years of operation, and present the verification process used to qualify the devices and their performance data. Additionally, successes, challenges and lessons learned are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".