Simulation of CO<sub>2</sub> Injection into Fractured Coal Samples
Bibliographic record
Abstract
Abstract Coal bed formation is widely considered a potential reservoir for CO2 geological storage. The injection of CO2 into coalbed formations is beneficial for reducing the CO2 concentration in the atmosphere and enhancing the recovery of coalbed methane, which is a clean fuel. Natural coal mass is a complex system comprising fractures and matrixes. However, fluid transportation in this system is complicated, and its study is challenging. In this study, a dual-permeability model is established to investigate gas transportation and storage in the coal mass during CO2 injection. Furthermore, the fluid transportation in the fracture and the matrix are studied, along with the mass transfer between them. The fully-coupled multiphysics model is solved using the finite element method. Besides, experiments on CO2 injection and storage in the large fractured coal sample are performed using a special apparatus designed by the Taiyuan University of Technology (TUT). Furthermore, the results of the laboratory experiments and those of the numerical simulation are compared, and the model is confirmed to have high reliability. The simulation results demonstrate that the gas transportation in the fracture is much faster than that in the matrix. The pressure difference and the mass transfer between the fracture and the matrix are the primary causes of pressure increase in the matrix. However, there is a time delay between the change in pressure difference and the mass transfer. During the gas injection process, the evolution of permeability is obvious because of a decrease in effective stress. Furthermore, various cases are simulated to explore the influence of matrix permeability on the results. The results show that higher matrix permeability triggers more mass transfer and that it takes less time to complete CO2 storage. The enhancement of coal matrix permeability can promote CO2 flow rate in the coalbed mass and improve CO2 injection efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".