Effect of Nanoparticle Adsorption on the Pore Structure of a Coalbed Methane Reservoir: A Laboratory Experimental Study
Bibliographic record
Abstract
Abstract Coalbed methane (CBM) is an important unconventional energy resource, and its micropore structure has a vital impact on its exploitation. Based on the nuclear magnetic resonance (NMR) experiment, the low-temperature liquid nitrogen adsorption experiment, and the contact angle experiment, in this paper, we investigated the influence of nanofluids on the micropore structure of a CBM reservoir from many aspects. The influence of different adsorption mechanisms of TiO2 nanoparticles on the surface wettability of rock samples was analyzed. The influence of nanoparticle adsorption on the drainage and distribution of liquid in the rock sample was discussed in depth. In addition, the effects of nanofluid treatment on the micropore structure were investigated by comparing the data of low-temperature liquid nitrogen adsorption experiments, including the pore diameter, pore volume, and specific surface area (SSA). The experimental results show that the treatment of nanofluids helps to open the micropores and greatly increases the SSA, pore diameter, and pore volume of the sample. The maximum increase percentages of SSA, pore volume, and pore diameter are 228.12, 80.65, and 18.89%, respectively. It is found that the adsorption of particles is conducive to enhancing the water wettability of the pore throat surface and reducing the damage to water locks.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".