Machine Learning Enhanced Upscaling of Anisotropic Shear Strength for Heterogeneous Oil Sands
Bibliographic record
Abstract
Abstract Weak shale beddings are widely distributed in the overburden and reservoir of oil sand deposits and lead to reduced anisotropic shear strength. Understanding the shear strength of the overburden and the reservoir is important in risk assessment of slope stability in open-pit mining and caprock integrity of in-situ thermal recovery of oil sands while optimizing the production of bitumen. Due to the restrictions of computational efficiency, cells used for simulation cannot be smaller enough to capture the details of heterogeneity in the reservoir. Therefore, a robust and efficient upscaling technique is important for modeling the impact of heterogeneity on the deformation and failure of oil sands during mining and in-situ recovery. However, current analytical and numerical upscaling techniques cannot provide computationally efficient geomechanical models that consider the impact of inclined shale beddings on shear strength. Therefore, we propose a machine learning enhanced upscaling (MLEU) technique that leverages the accuracy of local numerical upscaling and the efficiency of machine learning techniques. MLEU generates a fast and accurate machine learning-based proxy model using an artificial neural network (ANN) to predict the anisotropic shear strength of heterogeneous oil sands embedded with shale beddings. The trained model improves accuracy by 12%-76% compared to traditional methods such as response surface methodology (RSM). MLEU provides a reasonable estimate of anisotropic shear strength while considering uncertainties caused by different configurations of shale beddings. With the increasing demand for regional scale modeling of geotechnical problems, the proposed MLEU technique can be extended to other geological settings where weak beddings play a significant role and the impact of heterogeneity on shear strength is important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".