Characterization of Shale Pore Size Distribution by NMR Considering the Influence of Shale Skeleton Signals
Bibliographic record
Abstract
As a non-destructive method, the proton nuclear magnetic resonance (1H NMR) technique with low echo time (TE, e.g., 0.07 ms) has been increasingly used for characterizing full pore size distribution (PSD) of shales. However, hydrogen contained in some components of the shale skeleton (e.g., kerogen and structural water) also can be detected by NMR in the case of low TE, resulting in a questionable PSD derived directly from the T2 spectra of oil-saturated shale. In this study, eight shale samples with different organic and mineralogical components from the Jiyang Depression, China were investigated with regular NMR, low-temperature nitrogen adsorption (LTNA), NMR cryoporometry (NMRC), and mercury injection capillary pressure (MICP) techniques to propose a corrected NMR approach for characterizing shale PSD by considering the influence of the shale skeleton signals. The NMR relaxation characteristics (e.g., T2 spectra and T1–T2 map) of as-received shale, solvent-extracted and dried shale (EX), and oil-saturated shale (OS) were discussed to reveal the NMR response from the shale skeleton itself at T2 below 1 ms on the T2 spectra. With the new approach, the NMR T2 spectra of oil occurring in the OS shale were first obtained through inversion of the differentiated T2 decay curves between the T2 decay curves of the EX shale and OS shale and were then converted to PSD by combination of LTNA, NMRC, and MICP results. For pores with T2 less than 1 ms, the PSD obtained from NMR T2 spectra of the oil signals only compared well with the results of LTNA and NMRC, with a relative error of less than 15% in pore volume. In contrast, the relative errors of PSD obtained directly from the NMR T2 spectra of oil-saturated shales were up to 134%. It was found that the higher total organic carbon shale contained, the larger errors in the PSD profiles, pore volume, and porosity that were calculated directly from the oil-saturated shale’s NMR T2 spectra. Compared with the traditional NMR methods, the corrected approach can provide a more accurate PSD for shales, especially for those organic-rich ones.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".