Geostatistical modeling and upscaling permeability for reservoir scale modeling in bioturbated, heterogeneous tight reservoir rock: Viking Fm, Provost Field, Alberta
Bibliographic record
Abstract
While burrow-affected permeability must be considered for characterizing reservoir flow, the marked variability generated at the bed/bedset scale makes bioturbated media difficult to model. Study of 28 cored wells of the Lower Cretaceous Viking Formation in the Provost Field, Alberta, Canada integrated sedimentologic and ichnologic features to define recurring hydrofacies possessing distinct permeability grades. Transition probability analysis was employed to model spatial variations in biogenically enhanced permeability at the bed/bedset scale. Results suggest that variations in permeability are strongly related to variations in hydrofacies rather than grain size. The variability in permeability at the bed/bedset scale was simplified by calculating an equivalent permeability that represents the thickness-weighted sum of permeability at the bed/bedset scale using expressions for layered media. Numerical block models were then generated for both the bed/bedset hydrofacies and the upscaled hydrofacies. Vertical and horizontal flows were simulated at both scales, and the volumetric flows in each direction were compared to verify the representativeness of the equivalent permeability. Vertical and horizontal flows simulated for bed/bedset scale and composite hydrofacies differ by less than +/-5%, suggesting that permeabilities at the bed/bedset scale can be simplified through upscaling. Reservoir-scale groundwater flow was simulated along a hydrogeological cross section comprised of the composite hydrofacies. The resulting flow regime was consistent with those simulated using permeability estimates from tight reservoir units of the Viking Formation. This approach may lead to improved reserve calculations, estimates of resource deliverability, and understanding of reservoir responses during recovery.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".