Bibliographic record
Abstract
Summary Recent work has shown that flow units characterized by process or delivery speed (the ratio of permeability to porosity) provide a continuum between conventional, tight-gas, shale-gas, tight-oil, and shale-oil reservoirs (Aguilera 2014). The link between the various hydrocarbon fluids is provided by the word “petroleum” in “Total Petroleum System” (TPS), which encompasses liquid and gas hydrocarbons found in conventional, tight, and shale reservoirs. The work also shows that, other things being equal, the smaller pores lead to smaller production rates. There is, however, a positive side to smaller pores that, under favorable conditions, can lead to larger economic benefits from organic-rich shale reservoirs. This occurs in the case of condensate fluids that behave as dry gas in the smaller pores of organic-rich shale reservoirs. Flow of this dry gas diminishes the amount of liquids that are released and lost permanently in a shale reservoir. Conversely, this dry gas can lead to larger recovery of liquids in the surface from a given shale reservoir and consequently more attractive economics. This study shows how the smaller pores and their associated dry gas can be recognized with the use of process speed (flow units) and modified Pickett plots. Data from the Niobrara and Eagle Ford shales are used to demonstrate these crossplots. It is concluded that there is significant practical potential in the use of process speed as part of the flow-unit characterization of shale condensate reservoirs. This, in turn, can help in locating sweet spots for improved liquid production. The main contribution of this work is the association of flow units and different scales of pore apertures for improving recovery of liquids from shale reservoirs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".