Experimental Study and Intelligent Modeling to Evaluate the Impact of Particle-Size Distribution on the Rheological Properties of Class G Oilwell Cement
Bibliographic record
Abstract
Summary Understanding the complexity of the behavior of cement powder and cement slurry is essential for minimizing operational challenges as well as the maintenance costs in oilwell drilling. In this research, we analyzed 62 cement slurry samples to explore the relationship between the particle-size distribution (PSD) of Class G oilwell cement, three chemical compositions [sulfur trioxide (SO₃), tricalcium aluminate (C₃A), and tetracalcium aluminoferrite (C₄AF)], two common cement slurry additives [antifoam (polypropylene glycols) and dispersant (calcium lignosulfonate)], and cement rheological properties, which play a vital role in cementing operations during drilling. Using artificial intelligence, two predictive models were developed based on the experimental results to model the relationship between mentioned parameters and cement rheology. These models provide a cost-effective alternative to extensive laboratory testing and enhance the assessment of cement quality for well-cementing operations. Besides, the differentiating attribute of this study is that sensitivity analysis further examined the relationship between rheological parameters and these factors simultaneously, confirming that as cement particles become coarser, plastic viscosity (PV), yield point (YP), and both 10-second and 10-minute gel strengths would decrease. In this respect, the 10-minute gel strength decreases to a lesser extent due to increased hydration. In contrast, finer particles, indicated by a lower percentage on the 45-µm sieve, negatively affect rheology, reducing slurry fluidity and highlighting the importance of this sieve size in controlling cement particle distribution. Cement particle size significantly affects slurry rheology, with coarser particles reducing hydration and improving pumpability. Additionally, chemical parameters such as C₃A and C₄AF negatively impact rheology by increasing PV, YP, and gel strength, while SO₃ enhances pumpability by controlling aluminate hydration. Additives such as antifoam and dispersant further improve rheology by lowering rheological parameters, enhancing slurry performance in well cementing operations. Given the critical role of cement performance in blowout prevention, cost reduction, and drilling efficiency, integrating intelligent modeling with experimental methods for analyzing oilwell cement rheology can significantly enhance operational efficiency by reducing dependence on costly and time-intensive laboratory tests.
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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".