Interpreting the Effective Permeability of Pore Network Models Using the Diffuse Source Methodology
Bibliographic record
Abstract
The pore network model obtained from a micro-CT scan of a carbonate outcrop sample has been previously analyzed by former students of the research group for the effective permeability. Analysis techniques included steady state (face), well test derivative and depth of investigation methods. There existed a significant variation in the results from the different methodologies for the same pore network. For example, the results of the carbonate model in the Z-direction ranged from 1,219 md to 36,200 md. The focus of this research work is to find, apply and evaluate alternative methods to explain the large variation seen in the prior methods. Pulse decay and diffuse source approaches were evaluated, where the diffuse source method was eventually chosen due to its ability to capture the range of transient effective transmissibility with respect to time. This method is used in upscaling and modifications are made for its application to the lattice grid. The method is based on a pseudo steady state approach and utilizes the concept of drainage volume. Drainage volume increase with time and the geometry of the increase is based on the diffusive time of flight of each pore within the pore network. The method was applied to both a sandstone and a carbonate pore network. A homogeneous synthetic pore network was created to illustrate the expected differences between the lattice and analytical calculations of the diffuse source method. The comparison of the lattice and analytical solutions for each pore network can indicate the level of heterogeneity within the pore network. As expected, the sandstone model is relatively homogeneous compared to the carbonate model. The variation of permeability values previously calculated is explained as a transient effect. On top of describing the internal heterogeneity, the method can also indicate the level of anisotropy due to the direction of flow. Finally, we are able to visualize the drainage pattern and the sub volumes that contribute to the transient transmissibility calculation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".