Pore-scale numerical modeling of transport kinetics for CCUS-EOR and carbon cycling in offshore–onshore integrated energy system
Bibliographic record
Abstract
• A GAN-assisted pore-scale model was developed under the OOIES framework to reveal CCUS-EOR and carbon cycling mechanisms. • Bidirectional gas–oil diffusion and dynamic interfacial tension were incorporated to simulate impure CO 2 flooding. • Molecular and thermal diffusion effects on fluid compositions were comprehensively accounted for. Driven by decarbonization initiatives, Offshore-Onshore Integrated Energy Systems (OOIES) demonstrate significant emission reduction potential by achieving carbon cycle closure through the integration of onshore carbon source replenishment, offshore resource synergy, and associated gas reinjection. Therefore, this study constructs a fully coupled 3D pore-scale model for OOIES framework, integrating multiphase flow, multicomponent transport, convection, and diffusion. The model tracks multiphase interfaces while accounting for bidirectional mass transfer and displacement-sequestration behaviors between gas and liquid phases. It investigates the behavioral characteristics of impure CO 2 and the Carbon Dioxide Capture, Enhanced Oil Recovery (EOR)-Utilization and Storage (CCUS-EOR) mechanisms under varying reinjection gas compositions, temperature gradients, and wettability conditions. The results indicate that increasing the concentration of impurity gases simultaneously intensifies viscous fingering and increases interfacial tension, thereby reducing oil displacement efficiency and CO 2 dissolution trapping effectiveness. Temperature gradients exhibit a dual effect: while thermal diffusion enhances oil recovery in narrow pore channels, interfacial accumulation of heavy hydrocarbons inhibits displacement in larger pores, ultimately weakening the overall sequestration performance. Conversely, the large contact surface area formed in strongly oil-wet environments provides favorable conditions for CO 2 dissolution trapping. This work reveals the core transport mechanisms of the OOIES at the microscale, providing scientific insights for offshore-onshore energy hubs.
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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".