Quantifying Local Capillary Trapping Storage Capacity Using Geologic Criteria
Bibliographic record
Abstract
Abstract The capillary pressure heterogeneity or local capillary trapping (LCT) determines the final distribution of CO2 in a saline aquifer during geological carbon sequestration. This locally trapped CO2 would not escape from the storage formation even if caprock integrity is compromised. It is, therefore, essential to predict the extent and storage capacity of LCT during the design of GCS projects. This work employs a fast method based on the geologic criteria to estimate the structures of local capillary traps. The method assumes a critical capillary entry pressure (CCEP) and a geostatistical realization of the reservoir entry pressure field as inputs. It then finds the capillary barriers inside the domain and identifies the grid blocks beneath clusters of barriers. These grid blocks are the local capillary traps. The criterion for choosing the CCEP is important, and we suggest a criterion in this work. We verify this algorithm by full-physics simulations in small 2D and 3D domains. We employ a large CO2 injection rate (Ngr~0.1) to fully sweep the storage domain, followed by a long period of buoyant flow to allow for complete charging of those local capillary traps. We test several CCEPs to determine the most physically representative value by comparing the LCT predicted from both methods. We find that a single value of CCEP enables the geologic algorithm to give a very good approximation of LCT distribution as well as LCT volume in uncorrelated and weakly correlated porous media. This means that the concept of CCEP is a reasonable approximation to the physical process by which traps are filled. LCT can be described in terms of percolation theory. The percolation threshold arises from the competition between connected clusters of barriers and connected clusters of local traps. We show that both the percolated CCEP (corresponding to the percolation of LCT) and optimal CCEP (corresponding to the best match between geologic criteria and full-physics simulation) change with each other in a predictable linear way for the uncorrelated capillary entry pressure field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".