Squeezing Every Last Drop Out of a Coarse 3D Oil Sands Dataset for Refining 3D Survey Design - A Case History*
Bibliographic record
Abstract
The Alberta oil sands form one of the largest bituminous hydrocarbon reserves in the world (ERCB 2011). Most of the oil sands properties are too deep for traditional open mining techniques and require the application of in-situ thermal recovery processes such as Steam Assisted Gravity Drainage (SAGD). In SAGD, 8 to 10 pairs of horizontal wells, 100m apart and a kilometer long, are drilled parallel to each other. The well on the top of the pair is used to inject steam, which melts the bitumen by heating. The bitumen seeps down towards the bottom producing well, which pumps out the bitumen to the surface. As bitumen is produced, a steam chamber grows around the well pair. The growth of the steam chamber is non-uniform and mainly controlled by the stratigraphy of the reservoir (mud bodies acting as baffles or barriers), the preexisting structural framework (faults, joints, etc.), the respective fluid saturations and mobility, and the local in-situ stress field. To understand the geological setting, the burial history, and to characterize the reservoir and monitor the growth of the steam chambers, 3D (and 4D) seismic surveying is the method of choice. Numerous papers have been published regarding the extraction of the petrophysical properties of the oil sands reservoirs from seismic data (Xu and Chopra 2009, Gray 2011, Roy et. al., 2008, Solano and Schmitt 2004), the very near surface effects (De Meersman, 2011), or the geomechanical effects of the thermal recovery processes (Kendall and Wikel, 2012). However, limited material has been published regarding the design of 3D and 4D seismic surveys in the oil sands. In this case history, we demonstrate the cyclical process of seismic survey design, data acquisition, data processing and quantitative interpretation, followed by a survey design refinement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".