Enhancement of Plastering Effect on Strengthening Wellbore by Optimizing Particle Size Distribution of Wellbore Strengthening Materials
Bibliographic record
Abstract
Wellbore strengthening materials (WSMs) have been widely used to strengthen the wellbore stability and integrity, especially those lost circulation materials (LCMs) used for mud loss impairment. To enhance the wellbore strengthening effect rather than a loss impairment, plastering effect can be used to increase the fracture gradient of the wall and minimize the probability of inducing new fractures. This is done by smearing the mudcake and pores and forming an internal cake inside the rock matrix using WSMs (or LCMs). Until now, the particle size distribution (PSD) of LCMs have been widely studied for the minimization on the mud loss (e.g., Abran’s rule, ideal packing theory, D90 rule, Halliburton D50 rule, etc.). However, there are few empirical rules focused on the maximum wellbore strengthening effect. This study attempts to find the desired PSD of plastering materials to enhance wellbore stability. In this research, the Brazilian test was used to quantify tensile strength. Meanwhile, the filtration characteristics of WSMs through the rock matrix were observed using a scanning electron microscope (SEM) and an energy-dispersive system (EDS). Finally, this paper adopts D50 of WSMs to be the mean pore throat size for a maximum improvement on the rock tensile strength. We have observed that the closer the D50 of WSMs in the WSMs to the mean pore throat size, the stronger the saturated rock matrix.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".