A novel method to evaluate cleaning quality of oil in shale using pyrolysis pyrogram
Bibliographic record
Abstract
Abstract Complete and thorough core cleaning is a critical prerequisite for the precise measurements of most rock's petrophysical parameters. In shale, the oil cleaning process, aimed to remove the volatile hydrocarbons, is often complicated by the requirement for intact solid organic. Evaluation of shale's cleaning methods needs to take structural integrity of organic matrix into account but neglected in the existing researches. Here, we develop a novel evaluation method using a modified ESH (extended slow heating) pyrolysis cycle, which starts at a lower initial temperature of 150°C for 10 minutes and then slowly increases to 650°C by 10°C/min. Hydrocarbons on the ESH pyrogram were divided into light free hydrocarbon (SA), FHR (fluid‐like hydrocarbon, SB), and solid organic matter (SC). We propose a set of quantitative evaluation criterions comparing the results of pyrograms, for different types of the hydrocarbons, at different cleaning conditions. We showed that a modified pyrogram achieves complete cleaning with SA and SB removed while SC remains almost intact. The modified pyrogram achieves complete removal of FHR in the second stage of pyrogram, while earlier researches often report residual FHR. The introduced method improves the accuracy in the identification of production potential in kerogen‐rich shale reservoirs up to about 3% of the total pore volume. Further, the new approach allows a quantitative assessment for the cleaning quality without altering the sample's organic matrix. Future studies on the petrophysical properties of the hydrocarbon‐bearing reservoir rocks may benefit from the thorough hydrocarbon removal achieved through the modified pyrogram methods proposed in this study.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".