Biogenic Textural Heterogeneity, Fluid Flow and Hydrocarbon Production
Bibliographic record
Abstract
Abstract Recent research focuses on the characterization of fluid flow through a burrow-mottled sandstone from the upper reservoir target within the Ben Nevis Formation in the Hibernia Field, offshore Newfoundland. Understanding effective permeability distributions, which are a function of relative saturations and the nature of the reservoir is key for enhanced hydrocarbon recovery strategies. In bioturbated reservoirs, the burrows are key to both parameters, thus trace fossils should not be overlooked or ignored. Understanding the nature of bioturbation gives great insight into how petroleum will flow through the reservoir. In the case of the bioturbated facies of the upper Ben Nevis Formation, mud-filled burrows represent a rather intricate, relatively impenetrable three-dimensional network of obstacles or baffles to fluid flow. As seen in the oil migration scenario, the resulting trajectory of petroleum migration is highly sinuous and tortuous as burrows induce dispersion (macroscopic mixing) caused by uneven co-current laminar flow A very important tool is introduced in this study – detailed, controlled probe permeametry combined with invasion-percolation modeling software. Textural and reservoir engineering data are then inputted into MPath, numerical modeling software that uses a modified percolation-invasion technique to simulate secondary petroleum migration through bioturbated media. The methodology outlined holds great potential for resolving reservoir heterogeneities and predicting their effect on hydrocarbon production. In the case of the Ben Nevis/Avalon, the technique utilized here can also be expanded to other bioturbated facies and upscaled for use toward reservoir recovery strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 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".