Reinforcement Learning‐Based Injection Schedules for CO <sub>2</sub> Geological Storage Under Operation Constraints
Bibliographic record
Abstract
This study develops an advanced deep reinforcement learning framework utilizing the Advantage Actor–Critic (A2C) algorithm to optimize periodic CO 2 injection scheduling with a focus on both containment and injectivity. The A2C algorithm identifies optimal injection strategies that maximize the CO 2 injection volume while adhering to fault‐pressure constraints, thereby reducing the risk of fault activation and leakage. Through interactions with a dynamic 3D geological model, the algorithm selects actions from a continuous space and evaluates them using a reward system that balances injection efficiency with operational safety. The proposed reinforcement learning approach outperforms constant‐rate strategies, achieving 22.3% greater CO 2 injection volumes over a 16‐year period while maintaining fault stability at a given activation pressure, even without incorporating geomechanical modeling. The framework effectively accounts for subsurface uncertainties, demonstrating robustness and adaptability across various fault locations. The proposed method is expected to serve as a valuable tool for optimizing CO 2 geological storage that can be applied in complex subsurface operations under uncertain conditions.
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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) | 0.000 | 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) | 0.002 | 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".