Bibliographic record
Abstract
Technology Update Almost a century after its first oil discovery, Argentina is making headlines again through reports of the potential of the Vaca Muerta Shale in the Neuquén Basin—the most prolific basin in Argentina (Fig. 1). The existence of Vaca Muerta, which means “dead cow,” has been known for years, but the formation received little attention until the development of shale plays in North America. Vaca Muerta is virtually a virgin play with only a few operators involved. Yet with Argentina’s need to increase production and reduce dependence on foreign oil and gas, change is clearly in the air. Expanding Reserves and Production YPF has reported reserves and resources in the Vaca Muerta formation to be an estimated 22.8 billion BOE (YPF 2012). Encouraged by the estimate, YPF and several other Argentinean operators are continuing exploration activity to define the extent and productivity of the Vaca Muerta, particularly to identify the oil, wet gas, and dry gas areas. Early results suggest that Argentina has an opportunity to replicate the unconventional resource revolution of the United States and eventually meet domestic oil and gas demand through internal production. Argentina has the potential to increase oil and gas production by 50% in 10 years, which would reverse the negative trend that turned the country into a net importer in recent years. Yet this potential does not come easily, as preliminary estimates indicate the need for more than 1,000 wells to be drilled as a first step and 40 additional rigs to accomplish the goal. Besides the investment challenge that the Vaca Muerta now faces, there is an immediate challenge that needs to be dealt with: What is the best way to produce Vaca Muerta? Maximizing Production in Shale, Other Tight Plays In North America, the production of oil and gas from shale has increased greatly since 2007, with the development of several key plays such as the Bakken in North Dakota, the Barnett and Eagle Ford in Texas, and the Marcellus in the eastern United States, which have helped to reverse the declining production of the past few decades. The US and Canadian oil and gas industries were prepared for shale development. While these plays were not new to them, the use of new, unconventional exploitation technologies has been critical to success. In particular, as several studies have proved and is widely known in the industry, the challenge in these shale plays is connecting productive channels and creating fractures that will allow proper fluid movement and endure through the production cycles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".