7 Industry Leaders Share Their Insights on the Past, Present, and Future of Artificial Lift
Bibliographic record
Abstract
_ In the past 25 years, the artificial lift industry has seen incredible changes, making hydrocarbon production smarter and more efficient. In this Q&A article, JPT talks with seven industry leaders from this field about its past, present, and future. This conversation showcases the technological progress such as materials, digital tools, and automation and the strategic leadership that has guided the industry to new heights. Join us as we dive into the world of artificial lift and discover the innovation and expertise shaping the future of energy. This roundtable Q&A includes the following participants: - Greg Stephenson, chief production engineer, Occidental Petroleum - Michael Romer, principal artificial lift engineer, ExxonMobil - Laura Labrador, senior production engineer, Ecopetrol, and 2023–2024 chairman of the SPE Artificial Lift and Gas Well Deliquification Technical Section - Shauna Noonan, Oxy Fellow and senior director global supply chain initiatives, Occidental Petroleum - Jose Ernesto Jaua, global product champion, SLB - Kevin Leslie, vice president artificial lift solutions, Weatherford International - Dana Meadows, global portfolio director, artificial lift systems, Baker Hughes JPT: Can you recall an experience, technical paper, or mentor that significantly influenced your early career in artificial lift? How did it shape your approach? _ Stephenson: Numerous individuals impact my early career, the most noteworthy being Herald Winkler, who was in the first class of SPE’s Legends of Artificial Lift awarded in 2014. I traveled to my first ATCE in New Orleans as a first-year petroleum engineering student. I distinctly remember walking the exhibit floor and seeing this little guy get mobbed by people asking him questions. I asked one of my fellow students, ‘Who is that guy? Tom Cruise?’ He told me, ‘No. That’s Wink.’ I then learned that Wink was one of the pioneers of gas-lift technology and wrote the first definitive book on the subject. At that moment, I realized that artificial lift might offer a viable career path for me. Eventually, I got to know him personally, first as a student and then as an artificial lift professional. One of the most impactful conversations I had with him was one in which he told me, ‘I am not a gas-lift expert. You cannot be an expert in gas lift—the field is too complex. I’m still learning things, and I’ve been doing this for over 60 years!’ That encounter taught me to be humble in approaching my craft and never assume I knew everything.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.048 | 0.024 |
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".