Efficient Use Of High-Frequency Data Through Production Data Management System Implementation
Bibliographic record
Abstract
Abstract The McCully Field (New Brunswick, Canada) is highly instrumented and generates a massive quantity of high-frequency data stored in a data historian. Data time range and frequency of interest had to be manually retrieved through spreadsheet macros for analysis, plotting, and gas allocation. As the production history grew, the amount of data generated was overwhelming and unconsolidated, making the task of manual data handling and visualization both difficult and time consuming. The implementation of a production data management system resulted in a fully automated, end-to-end workflow that acquires five-minute data from the Supervisory Control and Data Acquisition (SCADA) system into the operating database; performs accurate allocation of gas, condensate and water volumes; as well as creates and distributes daily production summaries to a custom email list in a matter of minutes. The implementation of an automated approach was driven by five main objectives: automate production data acquisitionoptimize field production through real-time well surveillanceincrease data processing speed (reduce time spent on data handling, preparation, cleansing and reporting)take advantage of existing high-frequency databaseimprove communications regarding well performance between office and field operations After a successful implementation, data acquisition time has been reduced from a manual 30 minutes to an automated 5 minutes each day. Daily production reports, instead of only being accessible through the server, are now automatically emailed to a distribution list within the company. Real-time well surveillance is now possible from the main office and not only through the SCADA system in the field, which provides the engineering group with a better understanding of individual well performance and also allows any production disruptions to be identified early and resolved efficiently. Finally, the consolidated database is now integrated with engineering analysis tools for further use of information, which ultimately increases the effectiveness of the technical team.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".