A Comprehensive Methodology for Investigating Cocurrent Spontaneous Imbibition in Coal
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Visualizing and quantifying fluid distribution during spontaneous imbibition at the nanomicro scale is vital for understanding microfluid flow and dynamic wettability in coalbed methane (CBM) reservoirs, which could serve as a fundamental basis for optimizing the parameters of the hydraulic fracturing process. In this study, fluid distribution and flow behavior can be acquired by combining nuclear magnetic resonance (NMR) and in situ X-ray microcomputed tomography (μ-CT) technologies. Meanwhile, spontaneous imbibition stages were studied to analyze gas–water exchange efficiency. Additionally, dynamic wettability of gas–water was calculated during the process of spontaneous imbibition based on NMR. The results show that imbibition characteristics can be divided into three categories based on NMR. In type I, changes in imbibition fluid within nanopores are not obvious, while significant changes occur in micro pore-fractures, especially during the early stage of imbibition (0–2 h) for type I, which is almost the opposite of type II. Coal samples of type III exhibit low porosity and permeability, making it difficult for water to flow through the pores in coal due to capillary forces. The dynamic process of spontaneous imbibition can be divided into four stages using μ-CT equipment, with the third stage displaying the highest imbibition efficiency. This stage is characterized by frequent imbibition fluid exchange among various-scale fractures, which is related to the wettability and pore-fracture of the coal sample. A negative correlation between contact angle and T 2g was observed, where hydrophobic samples corresponded to a smaller geometric average of T 2g in the sample. In addition, the wettability of coal samples changed dynamically during the process of imbibition, with the contact angle gradually decreasing as imbibition time increased, which may be related to the formation of water film and hydration reactions.
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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".