An Automatic Production Monitoring System - Design and Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Periodic field measurements and surveys often result in an abundance of data that needs to be analyzed to assist in optimizing the field production. Without a proper approach to managing and interpreting the data, valuable information that may be realized from the data can easily be overlooked. This paper presents the design and application of an automatic production monitoring system that can be set up on a spreadsheet utilizing the spreadsheet's data operation and graphical capabilities. The program can be used as the ‘first pass’ screening tool to evaluate the production performance. Based on the historical production data incorporating the user-specified criteria, the current performance of each well is categorized as ‘normal', ‘damaged’ or ‘under-performed'. The potential production increases that may be realized by working over candidates in a typical oil field can also be estimated. With the innovative multi-scale plotting and field-wide mapping techniques, the program can provide the reservoir or production engineers with a gross scoping tool for a high-level overview of both individual well behavior and field-scale performance. In this paper, the design considerations of the program, the advantages and disadvantages of its features, and field examples illustrating its applications are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it