Aplicability of an Innovative and Light Seismic Approach to Monitor SAGD Operations in Surmont: A Blind Test
Bibliographic record
Abstract
Abstract Surmont is a heavy oil field located in northeast Alberta which is currently being developed by a joint venture between ConocoPhillips and Total using Steam Assisted Gravity Drainage (SAGD). To monitor the enhanced oil recovery process and caprock integrity, highly repeatable 4D seismic surveys using dynamite have been completed over the years. In order to maximize the value of information while controlling costs, a novel light seismic monitoring approach has been "blind-tested" on existing 4D data. The concept requires the use of only one source and one receiver couple, optimally placed in the field to monitor one or several subsurface spots, using time redundancy to detect 4D changes in these zones of interest. Three spot locations have been defined by the client on a well pad for which the history was not provided. For each of these spots, specific series of seismic processing steps have enabled the identification of the optimum source/receiver locations. Then, these optimum raw seismic traces extracted from different 4D campaigns have been analysed to detect potential time shift changes in the selected horizon induced by the growth of the steam chamber. Time-shift changes were plotted for all 3 spots. An increase was observed for one of the spots (Spot 3) from the first 4D monitor in 2010 up to the last monitor in 2015. An increase was also plotted between March 2013 and September 2013 for another spot (Spot 1), changes attributed to the dynamics of the steam chamber. On the contrary, spot 1 did not see any effect of the steam. These time-shift changes were then successfully cross-checked with temperature data from observation wells, confirming the qualitative variations attributed to the effects of the steam chamber evolution. It demonstrated the viability of this innovative seismic and focused monitoring approach to monitor the evolution of the steam chamber in Surmont. This also paves the way for a simpler and yet reliable and cost-effective way of monitoring the evolution of the steam chamber to further optimize production and increase rentability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".