Bioturbation influence on reservoir quality: A case study from the Cretaceous Ben Nevis Formation, Jeanne d'Arc Basin, offshore Newfoundland, Canada
Bibliographic record
Abstract
Abstract The delineation Ben Nevis L-55 well, located in the Hebron-Ben Nevis field, offshore Newfoundland, targeted the Cretaceous Ben Nevis Formation in the petroleum-rich Jeanne d'Arc Basin. This case study focuses on the bioturbated net-pay horizons and assesses the importance of animal-sediment interactions in controlling the porosity and permeability of sandstone reservoir intervals. Our data reveal that bioturbation can either reduce permeability and porosity by as much as approximately 33% or enhance it by up to 600%, dependent on burrow type and behavior of the trace-making organism. The net-pay interval in the cored interval of Ben Nevis L-55 is characterized by Ophiomorpha-dominated ichnofabrics. The action of bioturbators can be classified in terms of sediment mixing, sediment cleaning, sediment packing, and pipe-work-building strategies. Bioturbation has the potential to (1) increase isotropy or uniformity of grain size by destroying sedimentary laminae through burrow homogenization, or (2) decrease isotropy by selectively sorting grains into burrow lining and fill by grain size, and through creation of open-burrow systems filled with later sediments of differing character to the host sediment. The petrophysical characteristics of the reservoir facies are highly dependent on trace fossil morphology, presence or absence of burrow linings, nature of burrow fills, burrow size, and bioturbation intensity. Mudstone-rich facies and ichnofabrics containing mudstone-filled and/or lined burrows (e.g., Ophiomorpha and clusters of Chondrites) have the net effect of permeability reduction. In contrast, permeability enhancement is documented from muddy sandstone facies with clean sand-filled burrows (e.g., Thalassinoides) and clean sandstones with burrow-mottled or diffuse to massive textures.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".