Modeling and mapping the effects of heat and pressure outside a SAGD steam chamber using time-lapse multicomponent seismic data, Athabasca oil sands, Alberta
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Bibliographic record
Abstract
The field of study is a bitumen producing reservoir within the McMurray Formation.The deposit is a part of the Athabasca oil sands trend in Northeastern Alberta, Canada.This field contains 16 well pads that are, combined, producing more than 41,000 BOPD.Bitumen reservoirs are unique as a result of their high viscosity, low API gravity oil.This oil in this field has been produced by means of a method called Steam Assisted Gravity Drainage (SAGD), since 2007.In this method, two vertically stacked, horizontal wells are drilled.The upper well injects high temperature, high pressure steam and as the viscosity of the bitumen decreases it will begin to flow, via gravity, down to the lower producing well.Reservoir monitoring in this field is very important for multiple reasons, including the shallow depth and the large velocity changes that result from SAGD production.In order to map these changes, time-lapse multicomponent data were incorporated with rock physics modeling in order to map and interpret changes in Vp/Vs with production.When fluid substitution results and pressure estimations are combined, the resulting velocities are consistent with the core sample modeling done by Kato et al. (2008).These results were then compared with the seismic data in order to identify areas affected by steam, heat, and pressure within the reservoir through time-lapse Vp/Vs.PP time-lapse results show the location of the steam chamber within the reservoir, however these data do not give any information about the effects of pressure or heat.Converted-wave (PS) data can be used to image pressure and viscosity changes in the reservoir.When these data are combined into a Vp/Vs volume, the effects of steam, heat and pressure can be identified.Vp/Vs areas of little to no difference indicate steamed zones while the surrounding areas with large differences indicate heated and pressured zones.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it