Towards a Machine Learning Based Dynamic Surrogate Modeling and Optimization of Steam Injection Policy in SAGD
Bibliographic record
Abstract
Abstract This paper presents a methodology for the identification of a dynamic-surrogate model and the optimization of steam injection rates of a multi-well heterogeneous SAGD process. The optimization refers to finding the steam injection rates at every time step (steam injection policy) that will maximize cumulative net present value at the end of the production horizon. The solution methodology consists of identifying one-step prediction non-linear models and then using these models in a recursive scheme to predict the established production horizon. These models are identified offline and then used as a substitute for the reservoir simulation model, considered computationally expensive, in the optimization process. This approach makes use of the reinforcement learning agent-environment interaction: based on the current state St, the agent takes an action At, and the environment transitions into a new state St+1 and offers a scalar reward Rt. Additionally, the well-known genetic algorithm is used for optimization purposes. The approach is applied to a multi-well reservoir simulation model, built using publicly available data that includes data from northern Alberta SAGD operations considering two (2) time step lengths: daily (Case 1) and weekly (Case 2). Furthermore, the performance of the approach is evaluated in terms of: i) Mean Absolute Error (MAE) between the predicted time-series and the true values (effectivity), ii) the effect of randomness of the design of experiments over the MAE (robustness regarding the design of experiments) and iii) changes in the variance of the errors over the prediction time frame (performance as number of time step prediction increases). Results show that for a daily time step (Case 1) the proposed approach was able to predict significantly well the selected output as opposed to Case 2 which exhibit much higher MAE values. Also, there is a small but important effect of the randomness of the design of experiments over the MAE values in both cases. Furthermore, Case 1 showed a significant higher level of robustness over the prediction than Case 2. In particular, the changes in variance of the error in Case 1 was much less that for Case 2.
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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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".