Reservoir Modeling by Constraining Stochastic Simulation to Deterministically Interpreted Three-dimensional Geobodies
Bibliographic record
Abstract
Abstract Tidally influenced meandering river deposits of the Cretaceous middle McMurray Formation are characterized by rapid vertical and lateral lithological and associated reservoir property changes. Within the reservoir, water may occur below, above, and in the middle of the bitumen column, and there may be multiple gas intervals. Although conceptual understanding about the depositional environment and its control on distribution of different fluids (bitumen, water, and gas) is documented in literature, integration of these concepts into reservoir models and history matching through flow simulation is lacking. Thus, even in areas with closely spaced wells (as much as several hundred meters apart), geostatistical modeling approaches show high degrees of randomness. This chapter closes the gap between the conceptual mapping and numerical modeling approaches. Specifically, the workflow for creating a deterministic three-dimensional (3-D), object-based (geobody) model, which integrates data from closely spaced wells, high-quality 3-D seismic data, and sound geologic concepts is shown. The geobodies are typically large-scale depositional elements comprising meandering river deposits. Geobodies include channel lag breccia (tens to hundreds of meters wide and as much as several meters thick), lower and upper point-bar deposits (from several hundreds of meters to as much as 5 km [3 mi] wide and as much as 40m [131 ft] thick), and mud plug deposits (as much as 500m [1640 ft] wide and as much as 40m [131 ft] thick). Because of the potential impact on reservoir development economics, top water, top gas, and low-bitumen, high-water saturated zones are mapped as distinct geobodies. Based on their reservoir development potential, geobodies can then be classified as reservoir flow unit types 1 and 2, reservoir flow barriers, and reservoir flow impairments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".