Geological screening of onshore saline aquifers for CO<sub>2</sub> storage: Paraná and Espírito Santo basins, Brazil
Bibliographic record
Abstract
Abstract. To meet the global goal of net-zero CO2 emissions by 2050, many governments are turning to Carbon Capture and Storage (CCS) as a critical technology. Brazil is the largest CO2 emitter in South America and the 7th largest globally, yet it is in the early stages of CCS development. Using publicly available geological and infrastructure data, this study evaluates the feasibility and suitability of saline aquifers for CO2 storage in onshore intracratonic basins in southern Brazil. Saline aquifers deemed suitable for CO2 sequestration are those below 800 m deep, with a thickness greater than 20 m, and in areas with regionally extensive caprock at least 10 m thick. Using these criteria, we classify areas within the Rio Bonito Formation of the Paraná Basin and the Mucuri Member in the onshore extent of the Espírito Santo Basin into potential and exclusion areas for CO2 storage. The Rio Bonito Formation is present in the central-southern Paraná Basin and ranges from outcrop exposures to over 3900 m depth. The formation ranges in thickness from 25–350 m, and sandstone intervals within the formation have a cumulative thickness between 13 and 150 m. Caprocks consist mainly of shale and siltstone. Based on the criteria used, CO2 sequestration in the Rio Bonito Formation is optimal in two zones of the Paraná Basin that extend over ca. 27 700 km2 (northern zone) and ca. 38 100 km2 (southern zone). The Mucuri Member is present in almost the entire onshore extent of the Espírito Santo Basin and ranges in depth from 500 to 2650 m. The unit reaches 400 m thick with a cumulative thickness of sandstone layers ranging from 0 to 320 m. Caprocks consist mainly of evaporites, but shale and sandstone intervals are also present. CO2 sequestration in the Mucuri Member is optimal in one zone of the onshore Espírito Santo Basin, which extends over ca. 630 km2. Together, these zones have the capacity to store > 6.1 Gt CO2 providing Brazil with suitable capacity for reducing its CO2 emissions over the short- to medium-term.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".