Experimental Investigation of How Supercritical CO2 Changes the Chemistry and Microstructure of Cements
Bibliographic record
Abstract
Abstract Carbon capture and storage (CCS) is a crucial technology for mitigating greenhouse gas emissions and combating climate change. However, the long-term integrity of well systems in CCS applications is a critical concern, as the supercritical CO2 (SC-CO2) can dissolve in water forming carbonic acid, which can chemically alter the cement. This study investigates the effects of SC-CO2 exposure on the integrity of cement blends used in well systems for CCS applications. Three cement blends were examined: neat class G based cement (G), neat class G based cement with fly ash (GF), and preliminary testing on a further optimized low Portland cement based system containing permeability reducing polymers (GFP). Samples were exposed to SC-CO2 for up to 56 days under 4,060 psi, 70°C, and the complex changes in their properties evaluated. A review of the various non-standard analytical techniques is described using the three cement systems and their associated changes as a case study for the unique insight and associated limitations that each of these analysis techniques can provide. The results demonstrate that SC-CO2 exposure leads to dehydration, carbonation, and alteration of cement, affecting its pore structure, permeability, and mechanical properties. Blends GF and GFP exhibited improved resistance to SC-CO2-induced alteration compared to blend G, attributed to reduced starting portlandite (Ca(OH)2) content and less alteration of the pore size during exposure. Permeability tests using Nitrogen (N2) and water revealed that although SC-CO2 exposure increased permeability due to cement alteration and dehydration, the permeability remained low, in the tens of μD range. Post-CO2 exposure uniaxial compressive strength tests are difficult to interpret due to the non-uniform nature of the structure but short-term exposure to SC-CO2 enhanced the mechanical properties of cement due to CaCO3 precipitation, while prolonged exposure led to the carbonation of the outermost layer, creating a more complex failure mode. X-ray diffraction (XRD) analysis revealed that Calcium Silicate Hydrate (C-S-H) is more stable than the portlandite under SC-CO2 exposure, confirming the previously described results that minimizing portlandite content is an effective strategy for enhancing cement formulations for CCS applications. The findings provide valuable insights for the development of robust well integrity systems in CCS applications. Future research should focus on optimizing cement blend compositions, exploring mitigation strategies, and establishing guidelines for the design and operation of well systems to ensure the safe and effective implementation of CCS technology.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".