Consortium for Petroleum & Natural Gas Stripper Wells PART 3 OF 3
Bibliographic record
Abstract
The United States has more oil and gas wells than any other country. As of December 31, 2004, there were more than half a million producing oil wells in the United States. That is more than three times the combined total for the next three leaders: China, Canada, and Russia. The Stripper Well Consortium (SWC) is a partnership that includes domestic oil and gas producers, service and supply companies, trade associations, academia, the Department of Energy’s Strategic Center for Natural Gas and Oil (SCNGO) at the National Energy Technology Laboratory (NETL), and the New York State Energy Research and Development Authority (NYSERDA). The Consortium was established in 2000. This report serves as a final technical report for the SWC activities conducted over the May 1, 2004 to December 1, 2011 timeframe. During this timeframe, the SWC worked with 173 members in 29 states and three international countries, to focus on the development of new technologies to benefit the U.S. stripper well industry. SWC worked with NETL to develop a nationwide request-for-proposal (RFP) process to solicit proposals from the U.S. stripper well industry to develop and/or deploy new technologies that would assist small producers in improving the production performance of their stripper well operations. SWC conducted eight rounds of funding. A total of 132 proposals were received. The proposals were compiled and distributed to an industrydriven SWC executive council and program sponsors for review. Applicants were required to make a formal technical presentation to the SWC membership, executive council, and program sponsors. After reviewing the proposals and listening to the presentations, the executive council made their funding recommendations to program sponsors. A total of 64 projects were selected for funding, of which 59 were fully completed. Penn State then worked with grant awardees to issue a subcontract for their approved work. SWC organized and hosted a total of 14 meetings dedicated to technology transfer to showcase and review SWC-funded technology. The workshops were open to the stripper well industry.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".