Nanosilica functionalized to switch from dormant to active for gas migration mitigation in <scp>Portland</scp> cement
Bibliographic record
Abstract
Abstract We describe the development of a nanosilica‐based oil and gas well cement additive which reduces the risk of casing‐casing annulus (CCA) and sustained casing pressure (SCP) through gas migration mitigation. Nanosilicas added to oil and gas well cement have been shown to accelerate cement hydration and reduce the cement porosity and permeability. While these qualities can potentially reduce the risk of zonal isolation loss, there are known rheological effects associated with adding nanosilicas to cements. It is known that cements loaded with nanosilicas produce gels prematurely, which has the deleterious effect of leading to air entrainment in cement and can also effect the pumpability of the cement slurry. This property can also interfere with gas migration mitigation because it is the formation of the gel that reduces the hydrostatic pressure on the formation. This can, in‐turn, allow for fluid from the formation to invade the cement prior to building enough mechanical strength to resist the fluid influx. The nanosilica product described in this article has been developed to display no gelation effect in the cement and a rapid hydration onset. These performance attributes are due to a specialized functionalization or coating on the nanosilica particle. At temperatures equal to or below 120°F (49°C), this functionalization renders the nanosilica inert from the cement until its timed degradation and thus does not interact with the cement phases responsible for the gelation behaviour observed with other commercially available nanosilicas in the process of cement placement.
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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 | 0.000 |
| 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".