Heavy oil sands measurement and rock-physics modeling
Bibliographic record
Abstract
ABSTRACT Heavy oil reservoirs are important alternative energy resources to conventional oil and gas reservoirs. However, due to the high viscosity of heavy oil, much production of heavy oil reservoirs involves injecting steam, and determining the temperature distribution is significant for production. To do this, time-lapse inversion is commonly used to derive the change of the oil sand properties during steam injection, and rock-physics models are used to link the properties and temperature. Many people have done research on simulating variations of the oil sand properties with temperature; however, the previous models fail to adequately represent our experimental data, and they overestimate their values. The errors between previous models’ predictions and measurements are quite large, especially at low temperatures. To study the oil sand properties, we first measured eight oil sand samples including five presteam samples and three poststeam samples, and we experimentally quantified the pressure sensitivity of velocity, the temperature sensitivity of velocity, and the corresponding VP/VS ratios. Then we developed a new model, introducing a frame damage parameter and a solid oil proportion parameter. This model integrates the solid oil into the sand frame, and it incorporates the temperature-dependent frame damage to characterize the frame moduli variations with increasing temperature. The solid-Gassmann equation was then applied to saturate the sands’ frame with heavy oil. Our simulation results determined that the errors at low temperature and high temperature were both compensated, and the new model fitted better than previous models over the whole measurement temperature range. The modeling was also extended to the thermal production temperature range, and the phase transition of water was considered, which provided a useful indicator of the steam.
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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 | 0.001 | 0.001 |
| 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".