Physics-Informed Neural Network for CH4/CO2 Adsorption Characterization
Bibliographic record
Abstract
Abstract This study addresses the critical need for accurate characterization of methane (CH4) and carbon dioxide (CO2) adsorption behavior in shale formations, pivotal for optimizing hydrocarbon extraction and advancing carbon neutrality goals. The research introduces a novel approach utilizing Physics-Informed Neural Networks (PINNs) to predict adsorption isotherms across diverse shale cores, integrating Langmuir adsorption theory into a data-driven model. By collecting a limited core dataset and leveraging automatic differentiation techniques, the PINN systematically incorporates physics knowledge into neural networks, compensating for data scarcity and enhancing predictive robustness. The method is validated through statistical analysis, feature selection, and cross-validation, demonstrating its superior performance compared to conventional Machine Learning (ML) models like Random Forest, with a 4.75% improvement in R2 for model performance. Overall, this approach represents a valuable tool for optimizing hydrocarbon recovery, offering insights into competitive adsorption phenomena and paving the way for more efficient and environmentally friendly extraction techniques in complex subsurface environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".