Analysis of temperature dependency of elastic moduli in heavy oil deposits
Bibliographic record
Abstract
Abstract Seismic monitoring of oil sands during hot or cold production requires a valid rock physics model. The effective elastic properties of heavy oil deposits depend on temperature variations, which consequently alter the P- and S-wave velocities in thermal recovery processes. The authors discuss the relationship between temperature, apparent shear modulus, frequency, and velocity dispersion. We use log data from wells drilled in heated and cold zones of a bitumen reservoir to calculate bulk and shear moduli along the wellbore. We apply various filters to control the unwanted effects of other variables, such as porosity and water saturation. We demonstrate that sonic velocities of steam-saturated sands at reservoir conditions can be lower than the compressional velocity of seismic waves in water. We use modulus-temperature crossplots to verify the existence of the liquidation temperature and apparent shear modulus of the heavy oil. In our study, we observe two rapid-decline events in bulk modulus at around 20°C and 200°C. The first event exhibits the ideas of the viscoelastic model of Maxwell. We attribute the second event to effective replacement of liquid phase with steam. For temperatures between 20°C and 200°C, we use a linear relationship to model bulk modulus decline with temperature. The calculated shear modulus shows a wide range of variations at cold temperatures due to a slight change in the bitumen's API gravity with depth. We attribute the weakening of the shear moduli at temperatures greater than 100°C to thermal expansion of the rock.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".