Uncovering potential of seismic for reservoir characterization in Canadian oil sands
Bibliographic record
Abstract
Abstract Despite of high cost, seismic data have routinely been collected for oil sands development. While these data can be extremely valuable for the whole array of applications, including reservoir characterization, they are still, for the most part, largely underutilized. The main reason for the limited use of seismic in oil sands is the subtle sandstone-shale elastic differences and the lack of practical methods and techniques that make efficient use of the seismic information and mimic geophysical interpretation. In this paper, we present two novel approaches to deal with this challenge. The first approach works with 3D post-stack inverted seismic acoustic impedance data to derive facies trend models based on the local analysis of impedance geobodies. Too much impedance overlap between different facies that is observed globally and prevents efficient facies differentiation is resolved by extracting and analyzing objects from the 3D seismic volume that have local impedance contrasts. The second approach presents an optimization of empirical differential compaction calculations for the use in probabilistic 2D mapping of continuous/SAGD-able pay and its quality characteristics. Both approaches are shown to be straightforward and easy to implement into any software of choice. They are proven to lead to significant improvements in oil sands reservoir characterization based on a study of the Christina Lake and Kirby East leases of Cenovus Energy Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".