Performance analysis and mechanistic insights of penetrating crystalline self-healing cement (PCSHC) in dynamically sealing borehole cracks

The extraction efficiency of coalbed methane ( CBM ) poses a pressing concern for coal mining enterprises, necessitating urgent attention. This study delves into the performance analysis and underlying mechanisms of Penetrating Crystalline Self-Healing Cement ( PCSHC ), formulated with ultra-absorbent, alkali-stimulated materials, hydration retardants, and auxiliary self-healing components. Through meticulous single-factor and multi-factor analyses focusing on flow properties, setting characteristics, water absorption, and permeability coefficients, the material composition of PCSHC was optimized. Advanced techniques, including SEM, XRD, and FTIR, were employed to unravel the sealing mechanisms of PCSHC . The findings reveal a notable 28% reduction in the permeability coefficient of the optimized PCSHC , accompanied by a substantial decrease in internal air leakage channels. After 28 days of curing, a substantial amount of C–S–H crystals formed, with a decrease in hydration-participating components like C 2 S and C 3 S , indicating ongoing hydration reactions post-setting. This study demonstrates the potential for achieving dynamic sealing of borehole cracks, thereby enhancing CBM extraction efficiency.

Authors

Institutions

Publication Details

Journal
Journal of Materials Research and Technology
Published
2025-02-01
DOI
https://doi.org/10.1016/j.jmrt.2025.01.236
Citations
5
Primary Topic
Drilling and Well Engineering
Type
article
Field-Weighted Citation Impact
5.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Performance analysis and mechanistic insights of penetrating crystalline self-healing cement (PCSHC) in dynamically sealing borehole cracks

Dezhi Ran, Jianwei Cheng, Zhiyuan Ma, Qiang Fu et al.
5 citations
Journal of Materials Research and Technology
Drilling and Well Engineering
5.73
article

Performance analysis and mechanistic insights of penetrating crystalline self-healing cement (PCSHC) in dynamically sealing borehole cracks

Dezhi Ran, Jianwei Cheng, Zhiyuan Ma, Qiang Fu, Tingxiang Chu
article en
5 citations

Abstract

The extraction efficiency of coalbed methane ( CBM ) poses a pressing concern for coal mining enterprises, necessitating urgent attention. This study delves into the performance analysis and underlying mechanisms of Penetrating Crystalline Self-Healing Cement ( PCSHC ), formulated with ultra-absorbent, alkali-stimulated materials, hydration retardants, and auxiliary self-healing components. Through meticulous single-factor and multi-factor analyses focusing on flow properties, setting characteristics, water absorption, and permeability coefficients, the material composition of PCSHC was optimized. Advanced techniques, including SEM, XRD, and FTIR, were employed to unravel the sealing mechanisms of PCSHC . The findings reveal a notable 28% reduction in the permeability coefficient of the optimized PCSHC , accompanied by a substantial decrease in internal air leakage channels. After 28 days of curing, a substantial amount of C–S–H crystals formed, with a decrease in hydration-participating components like C 2 S and C 3 S , indicating ongoing hydration reactions post-setting. This study demonstrates the potential for achieving dynamic sealing of borehole cracks, thereby enhancing CBM extraction efficiency.

Journal of Materials Research and TechnologyVol. 35
North China Institute of Science and Technology (CN), China University of Mining and Technology (CN)
Openalex Percentile: Top 5%
Drilling and Well Engineering
5.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.