Statistical Upscaling of Transport Parameters Considering Heterogeneous Porosity and Facies Distribution: An Application for Warm Solvent Injection Processes Modeling
Bibliographic record
Abstract
Summary This paper presents a statistical upscaling workflow for warm solvent injection (WSI) processes, a more environmentally friendly alternative to traditional thermal-based heavy oil extraction methods. The complexity of the heat and mass mechanisms involved in WSI makes flow simulation and optimization challenging. A two-step flow-based upscaling workflow is presented for handling static (facies proportions, porosity, and permeability) and dynamic properties (longitudinal and transverse dispersivity). The first step involves quantifying the effect of numerical dispersivity for a homogeneous model, while the second step incorporates the scaleup of uncertainty in heterogeneity. The method is flexible for handling anisotropic dispersivity upscaling for 3D models. Several novel aspects include (1) considering facies distributions (e.g., sand vs. shale layers), (2) extending the method to 3D, and (3) implementing a cloud transform to sample from the conditional probability distributions of longitudinal and transverse dispersivity considering porosity and net-to-gross (NTG) ratio. An ensemble of coarse-scale models is simulated, demonstrating the proposed workflow’s effectiveness in capturing spatial heterogeneity and improving WSI simulation accuracy in heterogeneous reservoirs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".