Empirical modeling of the saturated shear modulus in oil sands
Bibliographic record
Abstract
ABSTRACT Seismic reservoir characterization and monitoring require the knowledge of seismic wave velocities and their dependencies on reservoir properties and production-induced changes. In heavy-oil saturated rocks at cold temperatures, due to the nonzero shear rigidity of the fluid, the saturated shear modulus is higher than the dry shear modulus and, consequently, the observed P- and S-wave velocities are higher than Gassmann’s predicted velocities. Appropriate modeling of the saturated shear modulus can greatly enhance the accuracy of quantitative interpretation of spatial fluid saturation and temperature distribution within a reservoir undergoing thermal production. Using a well-log data set of an Athabasca heavy-oil play and measured oil viscosities from core samples, we estimate fluid viscosity, shear modulus, and the American Petroleum Institute (API) gravity logs by training a neural network (NNT) with available well logs. We also estimate the dry shear modulus of heavy-oil saturated rocks using an NNT approach after modeling the pressure variations within the reservoir. Our empirical model uses the apparent shear modulus of the oil, its saturation, porosity, and dry shear modulus to estimate the saturated shear modulus of the rock. We calibrate the model to ultrasonic lab measurements. Available literature data support the validity of the model and show the improved performance compared to the Ciz and Shapiro model. The range of applicability of the model is defined mathematically, and the behavior of the model with respect to the input parameters is examined through sensitivity analyses.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".