Storage and Output Flow From Shale and Tight Gas Reservoirs
Bibliographic record
Abstract
Abstract Crossplots of porosity vs. permeability from various North American basins show that there is a continuum between conventional, tight and shale gas reservoirs. This is significant as some of the key issues, particularly in shale and tight gas reservoirs, are having good estimates of storage and flow capacity. The crossplots include data from the Fayettville, Barnett, Ohio and Marcellus shales in the United States; Horn River and soft shales in Canada, tight gas Nikanassin formation in Canada and several conventional North American gas reservoirs. The data used in the crossplots have been obtained from plugs, crushed samples and drill cuttings. The results permit integration of the storage and potential gas deliverability for determining flow units and other important characteristics such as brittleness and/or ductility, hydraulic fracturing alternatives, effect of water saturation and mud filtrate; and differentiation between viscous and diffusion dominated flow. Examples of simulation at the pore throat level, from which it is possible to estimate petrophysical, rock-fluid interaction and rock mechanics properties, are presented. The storage and flow capacity in the case of stacked layers, or lateral variations of conventional, tight and shale gas formations, are discussed in detail. The data suggest that permeability determinations from crushed shale samples might be pessimistic as they do not take into account the possible presence of microfractures and pores in organic matter within shale matrix. It is concluded that crossplots of porosity vs. permeability are very powerful for distinguishing and evaluating storage and flow capacities of conventional, tight and shale gas reservoirs. The concept of flow units in shales and tight gas, and its differentiation from conventional formations, should prove powerful in future simulation work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".