Analyzing the Impacts of Meshing and Grid Alignment in Dual-Porosity Dual-Permeability Upscaling
Bibliographic record
Abstract
Summary The discrete fracture network (DFN) model is widely used to simulate and represent the complex fractures occurring over multiple length scales. However, computational constraints often necessitate that these DFN models be upscaled into a dual-porosity dual-permeability (DPDK) model and discretized over a corner-point grid system, which is still commonly implemented in many commercial simulation packages. Many analytical upscaling techniques are applicable, provided that the fracture density is high, but this condition generally does not hold in most unconventional reservoir settings. A particular undesirable outcome is that connectivity between neighboring fracture cells could be erroneously removed if the fracture plane connecting the two cells is not aligned along the meshing direction. In this work, we propose a novel scheme to detect such misalignments and to adjust the DPDK fracture parameters locally, such that the proper fracture connectivity can be restored. A search subroutine is implemented to identify any diagonally adjacent cells of which the connectivity has been erroneously removed during the upscaling step. A correction scheme is implemented to facilitate a local adjustment to the shape factors in the vicinity of these two cells while ensuring the local fracture intensity remains unaffected. The results are assessed in terms of the stimulated reservoir volume calculations, and the sensitivity to fracture intensity is analyzed. The method is tested on a set of tight oil models constructed based on the Bakken Formation. Simulation results of the corrected, upscaled models are closer to those of DFN simulations. There is a noticeable improvement in the production after restoring the connectivity between those previously disconnected cells. The difference is most significant in cases with medium DFN density, where more fracture cells become disconnected after upscaling (this is also when most analytical upscaling techniques are no longer valid); in some 2D cases, up to a 22% difference in cumulative production is recorded. Ignoring the impacts of mesh discretization could result in an unintended reduction in the simulated fracture connectivity and a considerable underestimation of the cumulative production.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".