Bibliographic record
Abstract
The SPE Artificial Lift Conference and Exhibition-Americas will be held 25-27 August 2020 in The Woodlands, Texas. The event, an opportunity for energy professionals to gain insights into current trends, grow their networks and connect with leaders, understand field experiences, and explore innovative solutions, will feature a special Legends of Artificial Lift Luncheon. This year, the two individuals who have made outstanding contributions to the field of artificial lift technology are John C. Patterson and Orvel. L. Rowlan. John C. Patterson John C. Patterson is hailed throughout the energy industry as a foremost authority on artificial lift, having authored nine technical papers for SPE and holding six patents on artificial lift and facility concepts. Since joining SPE in 1974, Patterson has primarily been focused on the Production and Operations and Completions disciplines. He served on the 2013 SPE Artificial Lift Forum program committee and both the ALCE-NA ESP and Sucker Rod Pump subcommittees in 2014. Patterson has been active with the SPE Electrical Submersible Pump (ESP) workshop, in the capacity of chairman for the workshop in 2013 and for 10 years as continuing education director. He has also taught attendees about ESP dismantle, inspection, and failure analysis. Orvel L. Rowlan With more than 39 years of service to the oil and gas industry, Orvel L. Rowlan has long been recognized for his many advancements in the area of artificial lift. Rowlan has authored 14 SPE technical papers and more than 80 research papers for the Southwestern Petroleum Short Course, the Solution Mining Research Institute, the Artificial Lift Research and Development Council, Russian Oil & Gas Technologies magazine, the Canadian Petroleum Society, and SPE. He holds a US patent on plunger lift analysis and coauthored Gas Well Deliquification, a book for gas well production optimization.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".