Quantifying the Occurrence of Shale Oil in Nanoconfined Kerogen Matrices through Integrated NMR Relaxation Theory and Molecular Dynamics Simulations
Bibliographic record
Abstract
Low-field nuclear magnetic resonance (NMR) is a powerful technique for characterizing fluid behavior in shale oil reservoirs. However, the abundant nanopores in shale and the limitations of experimental echo time hinder its further application in characterizing oil occurrence. This study integrates the ratio of longitudinal to transverse relaxation times ( T 1 / T 2 ) with molecular dynamics simulations to determine the distributions of dissolved, adsorbed, and free n -decane in kerogen nanoslits and further develops quantitative models to characterize the occurrence characteristics of n -decane in these confined systems. The results indicate that alkane molecules in the same state exhibit identical T 1 / T 2 values, with the T 1 / T 2 values of dissolved, adsorbed, and free molecules being 25.27, 18.61, and 14.86, respectively. For each occurrence state, both the self-diffusion coefficient of n -decane and its interaction energy with the kerogen nanoslit walls exhibit distinct linear correlations with T 1 / T 2 . Furthermore, three mathematical models are developed to quantify the relationships between T 1 / T 2 and the slit width, the adsorption layer thickness ( H ), and the free-to-adsorbed mass ratio ( m f / m a ). When T 1 / T 2 ranges from 75.21 to 53.25, m f / m a = 0, indicating the absence of a free state. This state corresponds to slit widths of 1–3 nm, within which H varies linearly with T 1 / T 2 . As T 1 / T 2 decreases further from 53.25 to 1.00, m f / m a increases exponentially while H stabilizes at ∼2.86 nm, suggesting three-state coexistence within 3–50 nm slits. Free-state dominance occurs when T 1 / T 2 approaches 1, corresponding to a slit width of approximately 50 nm. Additionally, with increasing kerogen maturity, the kerogen matrix exhibits reduced solubility but enhanced adsorption capacity for n -decane. For kerogen types I-A, II-D, and III-A, T 1 / T 2 shows exponential correlations with both slit width and m f / m a, indicating free-state dominance at slit widths of 45, 50, and 58 nm, respectively. This work is expected to provide novel insights into shale reservoir evaluation and enhanced oil recovery.
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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 | 0.000 | 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 | 0.001 |
| 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 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".