Cold Heavy Oil Reservoir Characterization by Time-lapse Seismic Inversion, a Case Study
Bibliographic record
Abstract
Abstract Cold heavy oil production with Sand (CHOPS) is one of the major techniques which is applied in heavy oil production fields, especially in Canada. In this technique, high viscosity heavy oil is extracted with sand by using cavity pumps. In addition to causing fluid changes in the reservoir during the period of production, the CHOPS technique causes changes in the mass of the reservoir rock during the production time due to the simultaneous extraction of sand and oil. Therefore, it is important to model the reservoir changes in CHOPS fields in order to determine how to increase the recovery of the heavy oil. Time-lapse seismic analysis has proven to be one of the best methods which is capable of monitoring production-related changes beyond the wells. Time-lapse seismic analysis involves the use of repeated seismic surveys which are acquired in the same area. By analyzing the differences between these repeated surveys we are able to infer changes due to production. To start, rock physics modeling was done on the available well log data from wells in the area to model how fluid substitution in the reservoir would change elastic properties such as P-wave velocity, S-wave velocity and density within the reservoir. This modeling can help us understand how elastic properties extracted from time-lapse seismic data can be interpreted. Rock physics modeling was done by using the Gassmann equation and applying the Batzle and Wang relationships for fluids. Next, 4D calibration steps were applied to the time-lapse seismic data to reduce non-reservoir related anomalies and allow us to focus only on production-related changes in the reservoir. Model-based post-stack and pre-stack seismic inversion was then applied to the time-lapse seismic data to extract the elastic properties at both horizontal and vertical scales, over one of the CHOPS fields in Saskatchewan, Canada. Different elastic volumes such as time-lapse acoustic impedance, shear impedance and density were output from the seismic inversion process. Other time-lapse seismic attributes such as Lambda-Rho (P-impedance squared minus two times S-impedance squared) and Mu-Rho (S-impedance squared) were also computed after the performing time-lapse seismic inversion. The changes observed from the time-lapse seismic inversion and other time-lapse attributes helped us to recognize high production zones and to delineate the areas which could be targeted during the next recovery steps in the reservoir production. Combining rock-physics modeling and time-lapse seismic analysis provided us with a useful tool to monitor the production processes in the reservoir between wells. The seismic results are in good agreement with observed production changes and reservoir simulation results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".