A Physics-Informed Transformer Framework for Spatio-Temporal Modeling and Energy-Carbon Tradeoffs in CCUS-EOR
Bibliographic record
Abstract
Abstract Carbon Capture, Utilization, and Storage for Enhanced Oil Recovery (CCUS-EOR) plays a vital role in achieving carbon neutrality while ensuring energy and economic development. Although process modeling and scheme optimization are essential for CCUS-EOR, existing methods often have difficulty in simultaneously describing the complex spatiotemporal behavior of reservoirs and performing effective optimization design. Therefore, they cannot fully meet the task requirements of energy-carbon integrated coupled prediction and optimization. To address the challenge, we propose an intelligent decision-making framework that integrates a Spatio-Temporal Physics-Informed Transformer (STPIformer) with an Adaptive Operator-Controlled NSGA-II (AOC-NSGA-II) algorithm. By embedding CO2–oil–water three-phase flow partial differential equations (PDEs) into a Swin Transformer backbone and incorporating WAG-aware temporal encoding module and spatio-temporal attention mechanisms, STPIformer enables high-fidelity prediction of reservoir dynamics, including oil migration and CO2 plume evolution. The AOC-NSGA-II algorithm further enhances multi-objective optimization by adaptively balancing energy-carbon trade-offs among cumulative oil production, CO2 geological storage, net present value (NPV), and carbon emissions. Field-scale 3D model validation on the Tuha oilfield demonstrates that the proposed method significantly enhances spatio-temporal prediction accuracy and generalization performance, while delivering optimization schemes that satisfy underlying physical constraints and balance environmental and economic objectives for task-specific deployment. This study provides a robust modeling and optimization framework to support the large-scale deployment of CCUS-EOR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 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".