The Role of Geomechanical Observation in Continuous Updating of Thermal-Recovery Simulations With the Ensemble Kalman Filter
Bibliographic record
Abstract
Summary In-situ thermal methods such as steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) are widely used in oil-sand reservoirs. The physics of such thermal processes is generally well-understood, and it has been shown that rock properties are highly influenced by the geomechanical behavior of the reservoir during these recovery processes. Geomechanics improves the process dynamically, and its response can depict the progress of production within a reservoir. However, the potential of geomechanical monitoring is not usually practiced. With increased implementation of highly instrumented wells and communication technologies providing real-time monitoring data from different sources, combining available data into reservoir geomechanical simulations can improve updating numerical models and the prediction process. This research explores effective uses of geomechanical observation data for history matching and types of geomechanical observation sources adequate for thermal recovery. The ensemble Kalman filter (EnKF), combined with an iterative geomechanical coupled simulator, has been chosen as the data-assimilation algorithm to update the model continuously on the basis of geomechanical observations and production data. The results show that considering geomechanical modeling and observation improves history matching when geomechanical behavior plays a role in the process.
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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 | 0.000 | 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 | 0.000 |
| 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".