Utilizing smear mounts for X-ray diffraction as a fully quantitative approach in rapidly characterizing the mineralogy of shale gas reservoirs
Bibliographic record
Abstract
X-ray diffraction (XRD) sample preparation methods were compared for fine grained reservoir rocks. The viability of using a hand ground, smear mount method was investigated compared to the widely used micronized, cavity mount method of sample preparation for quantitative phase analysis. Micronizing a sample before analyzing by XRD has been used successfully to reduce the average crystallite size to 10 μm. However, because of the fine grained nature of shale gas reservoirs, the average crystallite size is already below 10 μm. Therefore, the sample only requires disaggregation of larger particles which is easily accomplished by hand grinding. Samples were prepared using smear and cavity mount methods to compare the differences in quantitative phase abundances determined by Rietveld refinement. In addition, samples of known composition were prepared to assess the accuracy and precision of the methods. Quantitative analysis on whole rock samples shows excellent precision between the methods of sample preparation with an absolute error of ±2.25 wt.% at the 95% confidence level per individual phase. Quantitative analysis on artificially prepared samples using the smear mount method shows both excellent precision and accuracy with an absolute error of ±0.9 wt.% at the 95% confidence level per individual phase. A hand ground, smear mount method is therefore a quantitative and viable method for quickly assessing the mineralogy of shale gas reservoirs and fine grained rocks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".