An Integrated Approach to Building History-Matched Geomodels to Understand Complex Long Lake Oil Sands Reservoirs, Part 1: Geomodeling
Bibliographic record
Abstract
Abstract At the Nexen Long Lake in situ steam-assisted gravity drainage (SAGD) oil sands recovery project, the bitumen-saturated reservoir is in the Lower Cretaceous McMurray Formation. The main depositional environment in the reservoir unit is fluvial-estuarine meandering channels. Stacked channel deposition exhibits a high degree of variability both vertically and laterally over short distances and depositional complexity occurs at many scales. Many papers have been written on characterizing oil sands deposition geologically or geostatistically. However, complete characterization cannot be achieved at all scales due to the degree of complexity. Building a history-matched geomodel can be very time consuming and very challenging in complex reservoirs such as in Long Lake, where the Quaternary (Gregoire) Channel, collapse features, top gas and top water, lean zones, as well as shale barriers and baffles, contribute to the complexity. This paper presents a practical geological modeling approach used at Nexen to quantify uncertainties of reservoir properties. This approach has been validated by history matching and prediction. The solution is based on the integration of all available geology, geophysics, petrophysics, reservoir engineering, and production information. Using the proposed solution, the number of modeling iterations and the time required to achieve the desired objectives of history matching and prediction have been significantly reduced.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".