A New Permeability Anisotropy Characterization Approach for Heterogeneous Reservoir
Bibliographic record
Abstract
Abstract A new absolute permeability upscaling method based on geological hierarchical models that affect different scales reservoir heterogeneities is presented. Reservoir anisotropy is evaluated via horizontal permeability (Kh) and vertical permeability (Kv). The new approach based on the geologic viewpoint that various geologic hierarchical-elements set result in relevant permeability display of different reservoir scales. For reservoirs, from micro-scale to macro-scale, influencing factors of permeability become abstruse. In conventional scenario, the calculation method based on single-phase numerical simulation test, core analysis and data statistics integrates all these factors as much as possible to upscale permeability. Considering reservoir anisotropy, horizontal permeability (Kh) and vertical permeability (Kv) are studied to show how anisotropy changes according to different reservoir heterogeneities. In the case study of Mackay River Oil Sand Block, Alberta, Canada, database includes regional depositional setting, core, and logging data for more than 20 wells. Generally, reservoir sedimentary setting poses a direct effect on permeability. Local rock bedding affects permeability anisotropy greatly, as well. There is no obvious linear parity between horizontal permeability (Kh) and vertical permeability (Kv) in core-plug. Vertical and lateral grain size variance also alters permeability. The mm-cm scale mud drapes have a worse effect on vertical permeability (Kv) than on horizontal permeability (Kh). Besides, bioturbations in the transitional facies could be favor of permeability. The three factors have non-linear relationship on effecting permeability. The new upscaling model synthesizes all these factors to upscale the permeability for nearly all scales of reservoirs, from the scale of core to lithofacies or even to the entire reservoir. Comparisons study is also conducted between this model and current upscaling algorithms such as arithmetic average, harmonic average, etc. The results showed that the upscaling model of this paper is more reasonable. Meanwhile, reservoir characterization hierarchical models can also be applied to explicate heterogeneity effect on the attribute of reservoir fluid qualitatively. The novelty of this approach lies in solving reservoir fluids' attributes quantitatively through exact heterogeneities analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".