Integration of rock physics and reservoir simulation for the interpretation of time-lapse seismic data at Weyburn Field, Saskatchewan
Bibliographic record
Abstract
This thesis research integrates reservoir simulation with time-lapse (4D) seismic monitoring of reservoir processes, through rock and fluid physics modeling.During the COz injection program at Weyburn Field, changes in reservoir fluid pressure, fluid composition and saturation are expected.Fluid models are developed for the acoustic properties of brine, oil, and oil -COz mixtures.An anisotropic rock physics model is used to calculate the sensitivity of the seismic properties of the reservoir to fluid and stress changes.Reservoir simulation of the enhanced oil recovery operations provides estimates of the changes in pore pressure, saturation, and fluid composition.The reservoir simulation output is combined with the rock and fluid physics models to estimate the change in seismic properties of the reservoir.These predicted changes are compared to the time-lapse difference anomalies in the P-wave seismic data.The fluid physics models are based on existing empirical relations, laboratory measurements and equation of state modeling.The pressure-and porosity-depend anisotropic model for the reservoir zones is built from ultrasonic measurements on core samples, analysis of geophysical logs, and effective medium modeling for saturated, fractured rocks.This model can be used to calculate the density and elastic stiffness matrix of a transversely isotropic rock with a horizontal (HTI) symmetry axis.Synthetic seismic modeling shows that changes in the upper and lower (Marly and Vuggy) reservoir zones are not independently resolved in the seismic data.The magnitude of the expected changes in P-wave reflection amplitude due to COz injection is 15% to 20%, and should be detected in the time-lapse seismic data.Through interpretation of P-wave seismic data volumes, areas effectively contacted by CO2 are identified.The observed time-lapse anomalies correlate strongly with the modeled C 0 2 movement and P-impedance decrease.The differences in the seismic data include more spread out anomalies, differences in location of anomalies, and evidence for COz fingering along fractures.This thesis research provides the forward model for calculating changes in seismic properties from reservoir processes.It can be used in future research in integrated reservoir inversion to refine the reservoir model and the reservoir simulation process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".