Geologically-based permeability anisotropy estimates for tidally-influenced reservoirs using quantitative shale data
Bibliographic record
Abstract
The effect of the vertical to horizontal permeability ratio ( k v / k h ) on many displacement properties is significant, making it an important parameter to estimate for reservoir models. Simple ‘streamline’ models have been developed which relate k v / k h at the reservoir scale to shale geometry, fraction and vertical frequency. A limitation of these models, especially for tidally-influenced reservoirs, is the lack of quantitisative geological inputs. To address this lack of data, detailed shale characteristics were measured, using Lidar point clouds, from four different tidally-influenced reservoir analogues: estuarine point bar (McMurray Formation, Alberta, Canada), tidal sand ridge (Tocito Sandstone, New Mexico), as well as both unconfined and confined tidal bars (Sego Sandstone, Utah). Estuarine point bars have long ( x̄ = 67.8 m) shales that are thick and frequent relative to the other units. Tidal sand ridges have short shales ( x̄ = 8.6 m dip orientation) that are thin and frequent. Confined tidal bars contain shales that are thin, infrequent and anisotropic (x– = 16.3 m dip orientation). Unconfined tidal bars contain nearly equidimensional shales of intermediate length ( x̄ = 18.6 m dip orientation) with moderate thicknesses and vertical frequency. The unique shale character of each unit results in a different distribution of estimated k v / k h values. Estuarine point bars have lower average k v / k h values ( x̄ = 8.2 ×10 −4 ) than any other setting because of the long shales they contain. Tidal sand ridges have short, but frequent shales, which results in moderate k v / k h estimates ( x̄ = .011). Estimates of k v / k h are typically highest in confined tidal bars ( x̄ = .038), which contain anisotropic and infrequent shales. Unconfined tidal bars have moderate lengths and frequency resulting in k v / k h estimates averaging 0.004. The results of this study highlight the link between heterogeneity, reservoir architecture and flow parameters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".