Deep Learning-Based Geomechanical Upscaling Technique for Reservoir Models Considering Lithological Heterogeneities and Discontinuities
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT: Understanding the geomechanical response of reservoirs with lithological heterogeneity and natural fracture networks is crucial for assessing their stability and mechanical behavior in subsurface. Complex interactions between fractures, weak beddings, and host materials introduce significant uncertainties, especially under deformation and failures. Traditional numerical models often simplify fracture networks to improve computational efficiency, yet this oversimplification limits their predictive accuracy. To address this challenge, we propose a deep-learning-based upscaling technique to efficiently predict the geomechanical response of heterogeneous rock masses containing weak beddings and discrete fracture networks (DFN). The proposed method leverages convolutional neural networks (CNNs) to learn stress-strain behavior directly from fracture geometry and lithological heterogeneity, enabling rapid and accurate predictions. This approach provides a computationally efficient framework for analyzing complex fractured heterogeneous reservoirs, facilitates the upscaling of coupled flow and geomechanical processes from the microscopic to macroscopic scale. The findings advance geomechanical upscaling methodologies by incorporating both lithological heterogeneity and discontinuites, providing a valuable tool for reservoir management and subsurface engineering applications.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it