Bibliographic record
Abstract
Natural Gas Seizing on historic margins in domestic prices, North American oil and gas companies are increasing their efforts to use more natural gas and less diesel fuel to power their field operations. Until now, oil and gas companies have been ordering natural gas-powered rigs to qualify the technology through pilot programs in the field. However, “the inflection point” may have been reached, said Brad Bodwell, senior vice president of corporate and business development at Prometheus Energy. The company is one of the largest suppliers of liquefied natural gas (LNG) to the North American oil and gas industry and is entering a stage of “rapid growth” because of economic forces and major gains in domes-tic gas production. As the technology is evolving, so is its nomenclature. Operators and service companies are using two terms to describe engines that are fueled by a simultaneous injection of natural gas and diesel: “dual fuel” and “bifuel.” Prometheus fueled up its first natural gas-powered generator sets on a drilling rig for operator EnCana in the Haynesville shale in mid-2010. “Since then, we have grown at a rate of about 80% per annum in terms of volume that we ship to our customers,” said Bodwell. In 2013, Prometheus had access to about 500,000 gal/D of LNG and was supplying seven operators in the United States and Canada. In total, North American LNG facilities produced 800,000 gal/D in 2013. New LNG facilities under construction and expansions of older facilities will bring the available supply of LNG up to 1.1 million gal/D by year-end. With several more plants already funded and expected to come online after 2014, the daily supply of LNG is projected to soar to 2.6 million gal/D after next year. Bodwell estimates that natural gas is fueling as much as 7% of North America’s 1,100 horizontal drill rig fleets, but he predicts that share to soar to 50% within the next 2 years. There are environmental drivers as well since natural gas is the cleanest burning of all fossil fuels (Table 1). “You’re getting a fuel that is going to reduce your environmental footprint and, at the same time, it saves enough money that it pays for the cost of conversion,” he said. “So we don’t need to wait to achieve some sort of scale benefit or a government injection of capital into the industry to make it work. It is all stuff we know how to do.”
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".