Particle size distribution analysis of mudstone based on digital image processing
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Bibliographic record
Abstract
Abstract Mudstone is becoming increasingly important for unconventional oil and gas development. The morphological characteristics of clastic are not only contributions to the 3D framework of mudstone, but they can clearly affect the physical properties of a mudstone reservoir, including surface area, pore-size distribution, and porosity. However, it is very difficult and time-consuming to measure the particle size distribution (PSD) of mudstone because of its small particle size and strong cementation. However, because of the importance of oil and gas extraction, it is urgent to develop a fast, accurate, and objective analysis method to measure the PSD of mudstone. Based on a comparison of various PSD measurement methods commonly used in the energy industry and geology, the best PSD measurement method for mudstone should be digital image processing. Two imaging methods, trainable Weka segmentation (TWS) and black mudstone particle size measurement, were used to analyze sections of the early Silurian Longmaxi mudstone of China’s Sichuan Basin. The PSD data obtained by manual measurement, TWS, and the contribution method are compared. Image analysis finds that the particle sizes of all samples fall in the range of coarse silt to clay, and the average sizes fall in the range of coarse silt to fine silt. The skewness, kurtosis, standard deviation, and other distribution characteristics parameters find minor errors, and the relative error is less than 15%. The Pearson correlation coefficient of the 10th quantile of TWS, black mudstone particle size measure, and manual measurement were calculated, which found R2 values typically ranging between 0.64 and 0.87. Kolmogorov-Smirnov test results find that the data obtained by the three measurement methods are from the same distribution at the level of 0.05. Analytic results find that the method presented is effective, cost-efficient, and could avoid artificial errors.
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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) | 0.000 | 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 it