4D seismic monitoring applied to SAGD operations at Surmont, Alberta, Canada
Bibliographic record
Abstract
Surmont is a heavy oil field located in northeast Alberta which is currently being developed by a joint venture between ConocoPhillips and Total. The estimated oil in place over the Surmont lease is approximately 20 billion barrels of bitumen located approximately 400 meters below the surface. Steam Assisted Gravity Drainage (SAGD) is the in‐situ thermal recovery method being used to develop the field. This method utilizes a pattern of horizontal well pairs that continually inject steam into the reservoir to mobilize the heavy oil so it can be produced to surface (Butler, 1994). The acoustic properties of heavy oil sands exhibit a strong response to temperature changes resulting in a significant velocity decrease through zones in the reservoir which have been thermally altered by the SAGD process. This unique response makes it possible to utilize time lapse seismic methods to monitor the thermal evolution of the steam over time (Pullin et al., 1987, Eastwood et al., 1994, Schmitt, 1999). Highly repeatable 4D seismic surveys have been acquired at Surmont over six month intervals since commercial production began in 2007. The 4D results identified several SAGD well pairs which were underperforming due to poorly developed steam chamber conformance (the fraction of the well affected by steam) along significant portions of the well pair. These poor performing wells can have a negative impact on the project economics due to inefficient use of the steam resulting in a higher operating steam‐oil ratio (SOR). Using the 4D observations, an optimized well operating strategy was implemented to improve conformance and recovery from these well pairs by more effectively managing heel/toe injection and production splits. A volumetric relationship between cumulative oil production and the 4D anomaly volumes was identified which has been used to estimate current recovery factors at discrete intervals along each SAGD well pair. These results are currently being used to monitor individual well pair performance and history match reservoir simulations in order to provide more accurate predictions from the reservoir models.
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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) | 0.001 | 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 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".