High-Throughput Analysis of Offshore Well Drill-Cuttings via SEM-Automated Mineralogy Using Single-Step Trans-Vertical Moulds
Bibliographic record
Abstract
Single-step trans-vertical moulds used to prepare grain mounts for SEM-automated-mineralogy based instruments have been shown to lead to more representative sample statistics. For samples that contain a variety of mineral phases, denser phases can settle to the bottom of an epoxy mould during the curing phase. Slicing and polishing a cross section through density-stratified moulds of the sample material can compensate for this. Aliquots from offshore drill well cuttings were prepared using three different methods: (1) traditional epoxy mounting, (2) two-step trans-vertical moulds, and (3) single-step trans-vertical moulds. All were analysed with a FEI Quanta 650 FEG instrument using MLA software with similar acquisition parameters. The results indicate that the single-step trans-vertical moulds are reproducible, lead to more accurate statistics, and yield MLA calculated elemental assays that closely match with data from standard analytical methods. In addition, these moulds can be made in half the time of the two-step trans-vertical moulds, and because of the unique geometry of the custom-made SEM holders, the analytical throughput is doubled. Depending on the size fraction of the well cuttings, the throughput can be further increased by slicing off the remaining epoxy to allow for more samples in the 26x TV holder. A unique cell holder was created to accommodate these trans-vertical samples, allowing them to fit into a LA-ICP-MS instrument such that detailed follow-up microanalyses can be conducted on specific minerals. For instance geochronology can be conducted on grains of interest (e.g., zircons), which had been mapped by the SEM-MLA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".