Fast Modeling of Local Capillary Trapping during CO2 Injection into a Saline Aquifer
Bibliographic record
Abstract
Abstract When CO2 migrates upwards under buoyancy in the subsurface saline aquifer and encounters local capillary barriers (regions of rock with large capillary entry pressure), CO2 would accumulate beneath these small barriers, and these accumulations are called local capillary trapping (LCT). LCT benefits storage because locally trapped CO2 has a much larger saturation than residual gas, and such trapped gases cannot escape from the formation even if leakage conduits (fractures or fault) in the seal develop during the long-term storage of CO2. Thus predicting and maximizing LCT is valuable in design and risk assessment of geologic storage projects. Modeling LCT is computationally expensive and may even be intractable by using a conventional reservoir simulator. In this work, we decouple the problem into two parts: permeability-based flow simulation and capillary entry pressure-based local capillary trapping phenomenon. The connectivity analysis originally developed for characterizing well-to-reservoir connectivity is adapted to the flow simulation by means of a newly defined edge weight property between neighboring grid blocks, which accounts for the multiphase flow properties, injection rate, and buoyancy effect. Then the connectivity was estimated from shortest path algorithm to predict the CO2 migration behavior and plume shape during injection. A geologic criteria algorithm is developed to estimate the potential LCT only from the entry capillary pressure field. The latter is correlated to a geostatistical realization of permeability field. The extended connectivity analysis shows a good match of CO2 plume computed by the full-physics simulation. We then incorporate it into the geologic algorithm to quantify the amount of LCT structures identified within the entry capillary pressure field that can be filled during CO2 injection. Several simulations were conducted in the reservoirs with different level of heterogeneity (measured by the Dykstra-Parsons coefficient) under various injection scenarios. We demonstrate the reservoir heterogeneity affects the optimal injection rate in maximizing the LCT during injection. Both the geologic algorithm and connectivity analysis are very fast; therefore, the integrated methodology can be used as a quick tool to estimate LCT. It can also be used as a potential complement to the full-physics simulation to evaluate the total safe storage capacity.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".