Comparison of Various Particle-Size Distribution-Measurement Methods
Bibliographic record
Abstract
Summary Sieve analysis, sedimentation, and laser diffraction (LD) have been the methods of choice in determining particle-size distribution (PSD) for sand control design. However, these methods do not provide any information regarding the particle shape. In this study, we introduce the application of dynamic image analysis (DIA) to characterize particle sizes and shape descriptors of sand-bearing formations. DIA, which is an advanced method of particle size and shape characterization, along with other PSD measurement methods, including sieving, sedimentation, and LD, were used to study size and shape variations of 372 unconsolidated formation sand samples from North America, Latin America, and the Middle East. Different methods were compared in the estimation of PSD and fines content, which are the primary factors important in sand-control design. Through minimizing the sampling and measurement errors, the deviation between different PSD measurement techniques was attributed solely to the shape of the particles and the amount of fine fraction. For fines-content measurement, the values obtained through Feret min parameter values (the minimum size of a particle along all directions) calculated by DIA and sieving measurement are comparable within a 5% confidence band. The deviation between the results of different methods becomes more significant by increasing fines content. Moreover, this deviation increases for less isodiametric grains. The fines and clay content show higher values when measured by any wet analysis. LD also tends to overestimate the fines fraction and underestimate silt/sand fraction compared with other dry techniques. By comparing the deviation of the DIA and sieving at standard mesh sizes, an algorithm has been developed that chooses the equivalent sphere sizes of DIA with minimum deviation from sieving. This study performs several measurements on formation sands to illustrate the real advantage of the new methods over traditional measurement techniques. Furthermore, particle-shape descriptors were used to explain the deviation between the results of different PSD measurement methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".