Effect of Supplementary Cementitious Materials on Rheology of Oil Well Cement Slurries
Bibliographic record
Abstract
Abstract This study explores the effects of supplementary cementitious materials (SCMs) on the rheological properties of oil well cement slurries. Four different mineral admixtures including metakaolin (MK), silica fume (SF), rice husk ash (RHA), and class F fly ash (FA) were used as partial replacement for API class G oil well cement. A new generation polycarboxylate-based high-range water reducing admixture was used to improve the fluidity of slurries. A series of flow tests was performed using an advanced shear-stress/shear strain controlled rheometer at three different temperatures, namely 23, 45, and 60°C. Rheological properties of cement slurries were calculated from the resulting flow curves using the Bingham plastic model and the Herschel–Bulkley’s model. Changes in shear stress–shear rate relationships, yield stress, plastic viscosity, and shear thinning/thickening behavior were found to be related to temperature and the type and dosage of supplementary cementitious material. Among the four different mineral admixtures tested, low calcium fly ash was found to achieve most suitable yield stress and plastic viscosity values.
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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 | 0.000 | 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".