Ancient Microbial Gas in the Upper Cretaceous Milk River Formation, Alberta and Saskatchewan<subtitle>A Large Continuous Accumulation in Fine-grained Rocks</subtitle>
Bibliographic record
Abstract
In the early 1970s, most exploration geologists in the United States considered subeconomic or marginally economic petroleum resources such as coalbed methane, shale gas, and tight-gas sands as unconventional resources (Law and Curtis, 2002). Tax incentives and federally funded research beginning in the late 1970s helped make these resources economically viable in the last two decades of the 20th century. Economics aside, two important geologic attributes characterize most unconventional petroleum resources (Law and Curtis, 2002). Conventional petroleum systems are buoyancy-driven accumulations found in structural or stratigraphic traps, whereas most unconventional systems exist independent of a water column and are generally not found in structural or stratigraphic traps.Shale reservoirs are not new. The first commercial hydrocarbon production in the United States was from a well drilled in 1821 in a shale gas reservoir. By 2000, more than 28,000 wells had been drilled in shale gas reservoirs. Rising gas prices and technological advancements in horizontal drilling and hydraulic fracturing associated with the development of the Barnett Shale led to a boom in shale gas development in the early years of the 21st century. Now the exploitation of shale reservoirs is turning to natural gas liquids, condensate, and oil. Far from being isotropic and homogeneous, as once naively envisioned, shale reservoirs are complexly layered accumulations of fine-grained sediment. Geologic variation on scales ranging from that of stratal architecture to that of lamination within individual beds must be understood in order to locate and exploid areas of higher production within shale reservoirs. Shale reservoirs remain largely geologic plays - notmerely lease plays or strictly engineering plays made possible by improvements in drilling and completion technology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".