Bibliographic record
Abstract
Technology Focus Artificial-lift technology has been around for many years, yet the concept of artificial-lift selection and design has been more of an art than a science. Throughout the last decade, both operating companies and manufacturers have made considerable effort toward understanding the science behind artificial-lift systems and their performance. For example, research on beam-pump slippage and gas lift valve performance has greatly influenced the way in which the industry now designs those lift systems. In 2007, SPE published a revised Petroleum Engineering Handbook; Volume IV includes many of these advancements and understandings of lift performance for common forms of artificial lift. This publication is recommended highly for anyone involved in production engineering and artificial lift. Artificial-lift systems continue to evolve, and their operational envelopes expand. The 2009 ATCE Technical Program Committee received a significant number of artificial-lift related abstracts, which resulted in two sessions showcasing great artificial-lift research, field trials, and case studies. When you come to New Orleans this October, you will learn about the most recent advancements and lessons learned on all types of artificial lift. Three examples of such advancements are featured in this publication. Each of these technologies enhances a lift system or methodology that has been available for years, putting a new spin (pun intended) on something "old." While the field trials of these systems have been specific to a given region (i.e., Alaska, Cuba, and Russia), their application potential is global. The papers selected for additional reading below, reflect other areas in the artificial-lift community that are "hot" topics—finding and understanding better gaswell-deliquification techniques, solutions to high-volume thermal applications, and effective artificial-lift optimization. Artificial Lift additional reading available at OnePetro: www.onepetro.org SPE 116659 - "Artificial Lift Optimization in the Orito Field" by Sandy Williams, SPE, ALP Ltd., et al. SPE 115849 - "Pushing the Limit: High-Rate Artificial-Lift Evaluation for a Sour, Heavy-Oil, Thermal EOR Project in Oman" by G.H. Lanier, SPE, Petroleum Development Oman, et al. SPE 115934 - "Development of a New Plunger-Lift Model Using Smart Plunger Data" by G.K. Chava, SPE, Texas A&M University, et al.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".