Deep Learning-Based Geomechanical Upscaling Technique for Reservoir Models Considering Lithological Heterogeneities and Discontinuities
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".