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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".