Towards net-zero: assessing the carbon storage potential of onshore saline aquifers in Brazil
Bibliographic record
Abstract
Global warming poses a major challenge that humanity will face during the 21st century, requiring a significant reduction in anthropogenic CO2 emissions to mitigate the escalating global temperature. Several governments worldwide, including Brazil, have committed to achieving net-zero CO2 emissions by 2050, which will be impossible without Carbon Capture, Utilisation and Storage (CCUS) deployment. Ranked 12th globally in CO2 emissions (and 1st in South America), Brazil is in the early stages of studying CCUS. At present, CCUS efforts in the country primarily revolve around enhanced oil recovery, with limited exploration of CO2 injection in alternative geological settings. A total of 31 sedimentary basins span Brazilian territory, encompassing an area of approximately 6.4 million km2, 75% of which is situated onshore. The potential for CO2 storage in saline aquifers is gaining attention globally, proving a successful and effective approach in various sites. In this work we combine the available surface (geological maps, roads, and gas pipelines) and subsurface data (seismic lines and borehole data) to assess the logistics and feasibility of utilizing saline formations in onshore intracratonic basins as CO2 sinks, aiming to enable Brazil to reach net-zero CO2 emissions by 2050. Previous studies indicate that the Parnaíba, São Francisco, Amazonas, and Paraná basins present saline formations with favorable characteristics for CO2 injection, such as adequate depths, porosity, and permeability. Building upon prior research, we introduce the onshore portion of Espírito Santo Basin to the list of potential sinks, where the target saline aquifer is the pre-salt Mucuri Formation. Results show that greenhouse gases emissions from industrial processes are notably higher in the southeast region of Brazil. Within this region, two formations exhibit considerable potential for carbon sequestration in saline aquifers: (i) the Mucuri Formation, located in the onshore Espírito Santo Basin, reaching 350 m of thickness and shallowest depths of about 950 m, and (ii) the Rio Bonito Formation, in the proximities of the São Paulo state, with over 100 m of thickness and shallowest depths of about 650 m. For large-scale projects, CO2 transport in the region can be accomplished using the available infrastructure and the available gas pipelines, while smaller-scale research projects can utilize trucks, rail, and ships. Brazil's untapped potential for CCUS presents a unique funding opportunity from the private sector, marking a crucial step toward sustainable and impactful climate action.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".