Classification and characterizations of biogenically enhanced permeability
Bibliographic record
Abstract
Abstract Recent research shows that ichnology has significant application to production geology. As such, permeability enhancement in bioturbated media has been recognized in five interrelated scenarios: (1) surface-constrained textural heterogeneities; (2) nonconstrained textural heterogeneities; (3) weakly defined textural heterogeneities; (4) diagenetic textural heterogeneities; and (5) cryptic bioturbation. Our data demonstrate that substrate-controlled ichnofossil assemblages can enhance the permeability and vertical transmissivity of an otherwise relatively impermeable matrix. Permeability enhancement develops when burrows excavated into a firm ground are filled with a contrasting sediment from the overlying strata. Fill contrasting with the encasing firm-ground substrate leads to anisotropic porosity and permeability. The same concept can be applied to carbonate reservoirs, where burrow fills are subjected to different diagenetic phases. This may also lead to anisotropic porosity and permeability that can have dramatic effects on reserve calculations. If the burrow fills have enhanced permeability but burrow effects are not recognized, reserve calculations will be too low. Likewise, if the burrow fills have reduced permeability, the reserve calculations may be too high. Understanding the flow dynamics of the resulting anisotropic permeability provides a potentially powerful reservoir-development tool. The implications are far reaching, particularly pertaining to calculations of reserves and their deliverability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".