Bibliographic record
Abstract
Like other segments of the oilfield services industry, the US onshore pressure pumping space looks very different compared with a couple of years ago. Decreased demand during the downturn forced some pumpers out of business and spurred consolidation, resulting in high personnel turnover in the field. Meanwhile, exploration and production (E&P) companies are drilling longer laterals faster and expecting similar efficiencies from pressure pumpers. Hoping to get the absolute most out of their wells, operators are intensifying completions by using more stages, water, and frac sand. Now, with oil prices higher, E&Ps more focused on cash flow, and a growing count of wells awaiting completion, demand for pressure pumping is increasing fast, creating a race for horsepower among a leaner group of opportunistic firms hoping to gain market share. This includes the rise of large integrated, pure-play, and regional pressure pumpers and less use of integrated well services that include a full suite of completions work. Market research firm Energent, part of research and consultancy Westwood Global Energy Group, estimates that US onshore pressure pumping horsepower during the first quarter totaled 17 million with a utilization rate of 83%, and it expects an additional 3.5 million hp to come online this year—primarily newbuilds—with many more currently being ordered. For comparison, total horsepower in the first quarter of 2015 was just less than 12 million with 61% utilization, according to Energent data. Much of the unused horsepower that was able to be refurbished has been redeployed. Given expected lead time in delivery of new equipment, a surge of fleets is expected to be deployed by late summer, assuming oil prices hold in the mid-$60/bbl and demand remains strong, said Todd Bush, Energent Group vice president, commercial, North America research. “We know of several companies that are already adding pumps and making larger orders for fluid ends and power ends,” which will most likely be filled in by late summer, he said. Transmissions and fluid ends have had longer back-logs compared with late last year. There also have been bottlenecks for pumps. But Bush expects manufacturers in those areas, especially for pumps, “to be pretty aggressive coming back for all the pressure pumpers to be able meet their E&P demands.” Alex Yang, senior analyst of shale research at consulting service Rystad Energy, has observed that many equipment manufacturers now “are at capacity doing refurbishments rather than newbuilds.” He noted that “a lot of the cold-stacked equipment was cannibalized or not maintained correctly while in the yard” after demand plunged during the downturn.
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 | 0.001 | 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".