Bioturbation changing porosity, permeability, and fracturability in chalk? Insights from an Upper Cretaceous chalk reservoir (Buda Formation, Texas, USA)
Bibliographic record
Abstract
The continuous rise in hydrocarbon demand, the production decline in conventional oilfields, and the remarkable improvement in extraction methods have allowed the hydrocarbon industry and subsequently, geoscientists, to turn to studies on both unconventional and mature fields with untapped potential. In such reservoirs, the application of advanced drilling, completion, and production techniques must be preceded by the identification of permeable stratigraphic intervals that favour commercial exploitation. Accordingly, bioturbated deposits can play a key role in providing permeable pathways for enhanced production. Herein, we analyse the effects of bioturbation on the porosity and permeability of the Buda Formation, a highly bioturbated, tight (low-porosity matrix) Upper Cretaceous chalk reservoir from the Texas Gulf Coast Basin, which has been commercially exploited because of the occurrence of natural fractures. Our results show that (in addition to the natural fractures) burrows (and borings) substantially increased the porosity and permeability of this formation, thus, potentially contributing to enhanced hydrocarbon (or groundwater) storage and production. These biogenic structures might also have favoured the development of natural fractures and stylolites in the Buda Formation. However, further studies are required to prove this hypothesis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".