Deep Learning Based Upscaling of Geomechanical Constitutive Behavior for Lithological Heterogeneities
Bibliographic record
Abstract
Abstract Large-scale coupled reservoir-geomechanical simulation is becoming a necessity for an in-depth assessment of subsurface energy developments such as hydrocarbon recovery and geological carbon storage, while a robust and efficient upscaling technique for the geomechanical constitutive behavior of heterogeneous reservoir is still missing to push forward the application of time-consuming coupled reservoir-geomechanical simulation. Here, we focus on the impact of lithological heterogeneity on the shear strength and stress-strain behavior and propose a deep learning-based upscaling technique that can provide the upscaled shear strength and stress-strain behavior from facies models and geomechanical parameters. The objectives of the proposed upscaling technique lie in the following two aspects: 1) bridge the gap between the fine-scale geological models and computationally efficient reservoir-geomechanical models used for large-scale subsurface energy development; 2) provide the upscaled realizations needed for geomechanical assessments considering geological uncertainties. The first step of the deep learning-based upscaling technique is generating a dataset that contains a sufficient number of data samples. Each sample consists of a randomly generated spatial correlated sand-shale realization (input) and the computed macroscopic shear strength and stress-strain behavior via finite element simulations (outputs). Using the assembled dataset, convolutional neural network (CNN) models are trained to build proxy models as an alternative for numerical upscaling. The trained CNN models can provide the upscaled shear strength (R2 > 0.95) and stress-strain behavior (R2 > 0.93) that highly agree with that from the computationally extensive numerical upscaling method in a much shorter time frame. The proposed deep learning-based upscaling technique can promote the application of large-scale reservoir-geomechanical simulation for geomechanical assessment and quantify the impact of geological uncertainties by conducting coupled simulations on a variety of reservoir realizations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".