Upscaling of Plastic Geomechanical Properties to Reproduce Anisotropic Failure in Heterogeneous Continua
Bibliographic record
Abstract
Summary The importance of geomechanical simulation is well documented for projects associated with significant pressure and temperature changes. Deformations and failure zones in reservoirs will impact fluid flow, caprock integrity, and well integrity. However, geological modelling cell sizes are typically at the centimetre scale in order to incorporate geological features resulting in models with millions of cells which are computationally expensive for current reservoir-geomechanical simulations. One option to overcome these computational challenges is to properly upscale geomechanical properties and simulate at a larger scale with fewer gridblocks. While many current upscaling techniques normally assume failure criteria for each upscaled cell, anisotropic failure response caused by sub-grid heterogeneity are significant complicating factors for heterogeneous continua. A local numerical upscaling technique is proposed to obtain the anisotropic failure criteria for heterogeneous continua. The implemetation of it in a highly heterogeneous IHS system shows a large difference in M-C failure envelopes in different directions caused by different failure modes. With the optimum loading rate selected for the local triaxial tests based on a sensitivity analysis, the proposed techinique can be efficiently applied in large-scale models and determine anisotropic strength parameters which can reproduce the change of shear strength at different stress state.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".