Impact of sub seismic heterogeneity on CO2 injectivity
Bibliographic record
Abstract
The geometry of the depositional facies and the sandbody continuity in turbidite and fluvial reservoirs controls the stratigraphic heterogeneity, and therefore controls permeability structure. This has implications for CO 2 injectivity from localized pressure build up around injection wells, and migration pathways due to dispersive flow, which results in CO 2 contacting more of the rock volume than would be the case in a homogenous system. This reservoir simulation study is an investigation of the impact of geological heterogeneity in channelized sandstone formations on pressure buildup during CO 2 injection. Four geological models of fluvial and turbidite depositional systems were constructed, typical of those which occur in the Southern North Sea and the Central North Sea regions. Model grid cells were reduced to less than 10 m in places to properly represent the individual channel structures and 2 m near wellbores. This presented a challenge for simulation to capture the impact of injectivity accurately with high resolution for a basin-scale model. Sensitivity studies were carried out in two groups with different net to gross (NTG) ratios and mean permeabilities. The simulation results showed that connectivity to sand-body volumes, through the individual fluvial channel interconnections, may be poor, and so CO 2 does not readily access the entire volume. Furthermore, if the mean permeability is less than 10 mD, only NTG, or the volume fraction of high permeability channels, affects the injectivity; the facies type, i.e. fluvial or turbidite, does not affect strongly the minimum injectivity for all models with 80% sand.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".