Bibliographic record
Abstract
Meet the Legends Artificial lift is a critical technology used to keep wells producing when they are incapable of providing enough energy—in the form of pressure—to produce liquids to surface at economic rates. Most development plays throughout the world would be uneconomic without artificial lift. JPT Features Editor Joel Parshall, in his March 2013 JPT article titled, “Challenges, Opportunities Abound for Artificial Lift,” writes There is no global repository of artificial lift statistics; however, industry observers estimate that 90% to 95% of the world’s producing wells currently use artificial lift, said Bill Lane, vice president of artificial lift systems emerging technologies at Weatherford. “It is trending more toward 95% than 90%, and probably 100% of producing wells would use artificial lift at some point in their lives, except for wells shut in prematurely because of economic factors.” The 2014 SPE Artificial Lift Conference and Exhibition for North America, held in Houston 6–8 October, features a special Legends in Artificial Lift Luncheon on its final day. At the luncheon, five people who have dedicated their careers to artificial lift (AL) will be honored for their outstanding contributions to the field of AL technology. The SPE Legends of Artificial Lift Award recipients are Herald Warren Winkler, James F. Lea Jr., Maurice Patterson, Sam Gavin Gibbs, and Joe Dunn Clegg. Serving on the SPE Board as 2014 SPE Technical Director for Production and Operations, I am proud to host the ceremony along with 2014 SPE President Jeff Spath, who will give an address and present the special recognition awards to the five men whose lifework distinguishes them as AL legends. Each honoree is being recognized for accomplishments in a specific type of artificial lift—or as a champion of all AL techniques. These individuals’ curiosity, keen observation, and dedication to physical truths have, time and time again, trumped conventional wisdom, revealed ineffective techniques and exaggerated product claims, and led to forward-looking insights that have until this day driven many AL advancements in understanding, technology, and techniques. For many production engineers—including me—these five modest men are influential mentors whose work has played a major role in making the AL industry such an exciting and rewarding field to work in. JPT
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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 | 0.001 | 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".