Distribution Characteristics of C-N-S Microorganism Genes in Different Hydraulic Zones of High-Rank Coal Reservoirs in Southern Qinshui Basin

Wei Shi; Shuheng Tang; Wenhui Huang; Songhang Zhang; Zhongcheng Li

doi:10.1021/acsomega.1c02169

Distribution Characteristics of C-N-S Microorganism Genes in Different Hydraulic Zones of High-Rank Coal Reservoirs in Southern Qinshui Basin

ACS Omega. 2021 Aug 12;6(33):21395-21409. doi: 10.1021/acsomega.1c02169. eCollection 2021 Aug 24.

Authors

Wei Shi^{1

2

3}, Shuheng Tang^{1

2

3}, Wenhui Huang^{1

2

3}, Songhang Zhang^{1

2

3}, Zhongcheng Li⁴

Affiliations

¹ MOE Key Lab of Marine Reservoir Evolution and Hydrocarbon Enrichment Mechanism, China University of Geosciences, Beijing 100083, China.
² MOLR Key Lab of Shale Gas Resources Survey and Strategic Evaluation, China University of Geosciences, Beijing 100083, China.
³ School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China.
⁴ China United Coalbed Methane Corporation Ltd., Beijing 100011, China.

Abstract

Microbial decomposition of carbon and biogenic methane in coal is one of the most important issues in CBM exploration. Using metagenomic technologies, the microbial C-N-S functional genes in different hydraulic zones of high-rank coal reservoirs were systematically studied, demonstrating the high sensitivity of this ecosystem to hydrodynamic conditions. The results show that the hydrodynamic strength of coal reservoir #3 in the Shizhuangnan block gradually weakened from east to west, forming a transitional feature from a runoff area to a stagnant area. Compared with runoff areas, stagnant areas have higher reservoir pressure, gas content, and ion concentrations. The relative abundance of genes associated with C, N, and S cycling increased from the runoff area to the stagnant area, including cellulose-degrading genes (e.g., cellulose 1,4-beta-cellobiosidase), methane metabolism genes (e.g., mcr, fwd, mtd, mer, and mtr), N-cycling genes (e.g., nifDKH, amoB, narGHI, napAB, nirK, norC, and nosZ), and S-cycling genes (e.g., dsrAB, sir, cysN, sat, aprAB, and PAPSS). This indicates that the stagnant zone had a more active microbial C-N-S cycle. The machine learning model shows that these significantly different genes could be used as effective indices to distinguish runoff and stagnant areas. Carbon and hydrogen isotopes indicate that methane in the study area was thermally generated. Methanogens compete with anaerobic heterotrophic bacteria to metabolize limited substrates, resulting in a low abundance of methanogens. In addition, the existence of methane-oxidizing bacteria suggests that biogenic methane was consumed by methanotrophic bacteria, which is the main reason why biogenic methane in the study area was not effectively preserved. In addition, weakened hydrodynamic conditions increased genes involved in nutrient cycling, including organic matter decomposition, methanogenesis, denitrification, and sulfate reduction, which contributed to the increase in CO₂ and consumption of sulfate and nitrate from runoff areas to stagnant areas.