Evolutionary Mechanisms of Deep Coal Rock Chemical Structures Under Various Pre-Fracturing Fluids
Bibliographic record
Abstract
Hydraulic fracturing is an effective method for enhancing coalbed methane (CBM) recovery. The injected fluids affect the chemical and physical structures of coal, resulting in diverse stimulation effects. While most current research primarily focuses on alterations in pore-fracture structures, few studies have examined or compared the changes in chemical structures during the fracturing process. This study presents a comparative analysis of the effects of five types of pre-fracturing fluids—slick water, acid solutions, and oxidant solutions—on coal, with the aim of identifying the similarities and differences in how these fluids modify the chemical structure of coal. The results indicate that, after being treated by five pre-fracturing fluids, the aromaticity index (I) increased, and the degree of aromatic condensation polymerization (DOC) decreased. The length of the aliphatic chain (L) increased after being treated by PAM but decreased after being treated by acids and oxidizers. Additionally, the graphitization (g) of all coal samples increased. Among the treatments, the combined acid system of hydrochloric acid (HCl) and hydrofluoric acid (HF) demonstrated a more pronounced effect on enhancing aromaticity and graphitization of the microcrystalline structure compared to HCl alone. Sodium hypochlorite (NaClO) had the most significant impact on the ordering of the macromolecular structure, while hydrogen peroxide (H2O2) exerted the most pronounced effect on the graphitization of the microcrystalline structure. These findings contribute to a deeper understanding of the interaction mechanisms between fracturing fluids and coal, providing theoretical support for the efficient development of CBM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".