Geomechanical-Data Acquisition, Monitoring, and Applications in SAGD
Bibliographic record
Abstract
Summary Steam-assisted gravity drainage (SAGD) has proved to be a commercially viable method to extract bitumen from oil-sands reservoirs in western Canada. To understand the influence of steam injection on reservoir and surrounding rocks and potential impacts of surface deformation on the environment, various types of instrumentation and 4D-seismic surveys have been applied in SAGD projects. The effect of geomechanics on SAGD has been well documented. Collecting essential geomechanical data, properly interpreting them, and incorporating them into numerical models are necessary to ensure meaningful history matching and understanding of reservoir performances. This paper outlines geomechanical-data acquisition and field-monitoring methods from a reservoir-engineering perspective, and the applications of geomechanics in SAGD analyses. Minimal-data-acquisition programs are suggested to collect the necessary geomechanical data for different analysis purposes in SAGD projects. Primary instrumentation is briefly overviewed, and recommendations for instrumentation selection are provided. Using generic Canadian-oil-sands reservoir and rock properties, the subsurface and surface changes and deformations are simulated, including permeability changes, reservoir movements, and strains and surface uplifts. Simulations are completed with a widely applied thermal simulator, and its limitations are also discussed. The method to couple the results of geostatistics modelling, reservoir simulation, and geomechanics in SAGD simulation and to link them with a 4D-seismic survey in history matching is provided.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".