Bibliographic record
Abstract
Interview with 2020 SPE President Shauna Noonan Shauna Noonan will take office as 2020 SPE President during the SPE Annual Technical Conference and Exhibition 30 September–2 October in Calgary. She is the director of artificial lift engineering for Occidental Petroleum Corporation, based in Houston, where she directs the company’s efforts in artificial lift system performance globally. Prior to joining Oxy in late 2015, she worked worldwide on artificial lift projects and technology development at ConocoPhillips and Chevron for more than 22 years. She has authored or coauthored more than 25 technical publications on the subject of artificial lift and is a frequent speaker at industry events. She has been an avid SPE volunteer in many roles since first serving as a student section officer. She served on the SPE International Board of Directors as the Technical Director for Production and Operations from 2012–2015. She has driven the development of industry standards and recommended practices for artificial lift systems while serving as chair for ISO and API committees, and has received industry awards for her contributions to the discipline. Noonan holds a BS degree in petroleum engineering from the University of Alberta. What key issues will you emphasize during your year as president? I want to focus on strengthening five areas for SPE to remain a trusted, unbiased source of technical information, be the membership of choice for professionals in oil and gas, and to be respected both within and outside of our industry. When people see that SPE logo, it should be synonymous with excellence in terms of all our offerings to our membership. The theme of my presidency is “SPE strong,” strengthening the core of our society. The five areas are: - Strengthening the technical quality of our publications, presentations, and events - Strengthening the tools and methods by which we disseminate knowledge to our members - Strengthening our core functions, with an additional focus on automation and digital science - Strengthening knowledge transfer in unconventional resources - Strengthening the feedstock of incoming talent into the industry. How will you go about strengthening these areas? For the past year in my role as incoming president, I have been working with my fellow board members and SPE staff to develop the various initiatives around my five focus areas. I will share those details in each of my monthly JPT columns along with updates on our progress. In the latest SPE Strategic Plan, there is an emphasis on instilling professional pride. What should be the focus? I am proud to be a petroleum engineer but I, like many of our members, have not been adept at explaining what exactly our industry does. We need to learn how to have an effective conversation with those outside of our industry on how we lift the world out of poverty and improve the quality of life by providing affordable, sustainable energy while targeting carbon neutrality. An effective conversation is an open dialogue, not a PowerPoint presentation or a factual document.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".