Bibliographic record
Abstract
One sign that things are getting better for US onshore exploration is the revival of talk about looming shortages and bottlenecks. By mid-year, sand to prop open fractures and trucks to pump those jobs are expected to be in short supply. Oilfield hands are already a scarce commodity. Those are solvable problems, but at a price some companies may find uncomfortably high. Surveys by branches of the US Federal Reserve Bank done in the fourth quarter of 2016 said that the break-even oil price for companies in Texas and surrounding states varies widely, and is generally greater than USD 50/bbl. The survey by the bank’s Dallas branch found that nearly 60% of the 141 companies surveyed said that it would take a price from USD 55/bbl to USD 65/bbl to “substantially increase” US crude oil drilling. And that was before service costs began rising. The smallest bar on the chart was for companies that can make money drilling when oil is USD 49/bbl or less. Some analysts publish estimates of average oil prices needed to profitably produce oil, but those averages mask a wide range of break-even levels. There are also differences among analysts on what percentage of the cost reductions made in the industry since 2014 will vaporize in the face of the double-digit service price increases that are expected this year in the unconventional services sector. “We hear every ratio possible,” said Jackson Sandeen, a senior research analyst for Wood Mackenzie, who said the savings are 40% from more efficient operations and 60% due to service sec-tor price concessions, which will shrink. Bain & Company clients put it at 60% of sustainable efficiencies and 40% from price concessions, said Jorge Leis, a Bain partner that leads its Americas Oil & Gas Practice. Rystad Energy said the average price needed to profitably produce oil in the US nonconventional sector has dropped by 50% since the downturn hit in late 2014, but a lot of that is based on supplier discounts. “Lower unit prices of service companies are a major reason for the drop,” said Jon Duesund, senior project manager for Rystad, during a recent briefing in Houston.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".