Artificial intelligence model for rheological properties of oil well cement slurries incorporating SCMs
Bibliographic record
Abstract
In this study, an artificial intelligence model has been developed to predict the rheological properties of oil well cement (OWC) slurries incorporating supplementary cementitious materials (SCM) such as metakaolin (MK), silica fume (SF), rice husk ash (RHA) or fly ash (FA). An experimental study has been carried out to create the database used for training the model. OWC slurries having a water-to-binder ratio of 0·44 along with a new-generation, polycarboxylate-based, high-range water-reducing admixture (PCH) were prepared. They had 5 to 15% partial replacement of API class-G OWC by MK, SF, RHA or FA. The rheological properties of the slurries were investigated at different temperatures in the range 23 to 60°C using an advanced shear-stress/shear-strain controlled rheometer. Experimental data thus obtained were used to develop a predictive model based on feed-forward back-propagation artificial neural networks. The developed model could effectively predict the effect of key variables such as temperature, dosage of SCM and dosage of PCH on the rheological properties of OWC slurries with an absolute error of less than 7%. The developed model could also effectively predict the rheological properties of new slurries designed within the range of input parameters of the experimental database used in the training process.
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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 | 0.001 | 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) | 0.000 | 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".