Data-Driven Modeling Approach for Recovery Performance Prediction in SAGD Operations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Quantitative ranking of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for SAGD, the simulation process is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for real-time decision making and forecasting. In this paper, Artificial Neural Network (ANN) is employed as a data-driven modeling alternative to predict SAGD recovery performance in heterogeneous reservoirs, an important application that is lacking in existing literature. In this study, numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and relevant production/injection parameters with the corresponding recovery factor as output. The network is trained using the data set to identify all significant patterns and relationships that exist between these attributes and the output parameters. The model is then tested using a verification data set (cases that have not been used at the training stage). Sensitivity studies on network configurations are also investigated. In addition, new modifications are proposed to identify and reduce extrapolations in predictions, which are often considered as major drawbacks in most data-driven modeling approaches. The approach described in this paper can be integrated directly into most existing reservoir management routines. In addition, the technique can be used as a viable tool for analyzing large amount of competitor data efficiently. Given that robust forecasting and optimization of heavy oil recovery processes is a major challenge faced by the industry, the proposed research has great potential to be applied in other recovery projects such as solvent-additive steam injection.
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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 it