In Situ Analysis of Methanogenic Pathways and Biogeochemical Features of CBM Co-produced Water from the Shizhuangnan Block in the Southern Qinshui Basin, China

Li, Yang; Tang, Shuheng; Zhang, Songhang; Xi, Zhaodong

doi:10.1021/acs.energyfuels.9b04351

Cited by 22 publications

(25 citation statements)

References 74 publications

(168 reference statements)

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“…The block is generally a monocline structure plunging towards the north-west, and is with a series of axial near-east-west folds in the south-east as arranged in arcuate form therein. The strata include Ordovician, upper Carboniferous Benxi Formation, Carboniferous-Permian Taiyuan Formation, Lower Permian Shanxi Formation, Middle Permian Shihezi Formation, Upper Permian Shiqianfeng Formation, and Quaternary from bottom to top [52,53]. The No.…”

Section: Geological Settingmentioning

confidence: 99%

Classification of Coal Structure Combinations and Their Influence on Hydraulic Fracturing: A Case Study from the Qinshui Basin, China

Liu

Wang

et al. 2020

Energies

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Coal structure directly correlates to permeability and hydraulic fracturing effects. Underground coal mining indicates that a single coal section generally contains multiple coal structures in superposition, making how to recognise the coal structure combination and predict its influence on coal permeability a challenging problem. Based on well-drilling sampled cores, the geological strength index (GSI), and well-logging data, the DEN, GR, CALX, and CALY were selected to establish a model to predict GSI by multiple regression to identify coal structure from 100 coalbed methane wells. Based on fitting GSI and corresponding permeability test values, injection fall-off (IFO) testing, and hydraulic fracturing results, permeability prediction models for pre- and post-fracturing behaviour were established, respectively. The fracturing effect was evaluated by the difference in permeability. The results show that a reservoir can be classified into one of nine types by different coal structure thickness proportion (and combinations thereof) and the fracturing curves can be classified into four categories (and eight sub-categories) by the pressure curve. Up-down type I and type II reservoirs (proportion of hard coal >60%) and intervening interval type I reservoir (proportion of hard coal >70%) are prone to form stable and descending fracturing curves and the fracturing effects are optimal. Intervening interval type II (hard coal:soft coal:hard coal or soft coal:hard coal:soft coal ≈1:1:1) and up-down type III (hard coal:soft coal =1:1) form descending type II, rising type I and fluctuating type I fracturing curves and fracturing effect ranks second; up-down type IV and V (proportion of hard coal <40%), interval type III (proportion of hard coal <30%), and multi-layer superposition-type reservoirs readily form fluctuating and rising fracturing curves and fracturing effects therein are poor. The research results provide guidance for the targeted stimulation measured under different coal structure combinations.

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Section: Geological Settingmentioning

confidence: 99%

Classification of Coal Structure Combinations and Their Influence on Hydraulic Fracturing: A Case Study from the Qinshui Basin, China

Liu

Wang

et al. 2020

Energies

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“…The resulting product formed generates methane under the action of methanogens. However, these studies mainly monitored the biomarkers in coals, and the interaction between these microorganisms has received extensive attention , merely in recent years. In addition, whether such an interaction between microorganisms can affect the methanogenic potential of coals remains unknown.…”

Section: Introductionmentioning

confidence: 99%

Keystone Microorganisms Regulate the Methanogenic Potential in Coals with Different Coal Ranks

Liu¹,

Chen

2022

ACS Omega

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Microorganisms are the core drivers of coal biogeochemistry and are closely related to the formation of coalbed methane. However, it remains poorly understood about the network relationship and stability of microbial communities in coals with different ranks. In this study, a high-throughput sequencing data set was analyzed to understand the microbial co-occurrence network in coals with different ranks including anthracite, medium-volatile bituminous, and high-volatile bituminous. The results showed similar topological properties for the microbial networks among coals with different ranks, but a great difference was found in the microbial composition in different large modules among coals with different ranks, and these three networks had three, four, and four large modules with seven, nine, and nine phyla, respectively. Among these networks, a total of 46 keystone taxa were identified in large modules, and these keystone taxa were different in coals with different ranks. Bacteria dominated the keystone taxa in the microbial network, and these bacterial keystone taxa mainly belonged to phyla Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. Besides, the removal of the key microbial data could reduce the community stability of microbial communities in bituminous coals. A partial least-squares path model further showed that these bacterial keystone taxa indirectly affected methanogenic potential by maintaining the microbial community stability and bacterial diversity. In summary, these results showed that keystone taxa played an important role in determining the community diversity, maintaining the microbial community stability, and controlling the methanogenic potential, which is of great significance for understanding the microbial ecology and the geochemical cycle of coal seams.

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“…The resulting products could further generate methane under the action of methanogens. However, these studies mainly monitored the biomarkers in coals, but the important factors influencing the coal biodegradation and the interaction between microorganisms have received extensive attention , merely in recent years.…”

Section: Introductionmentioning

confidence: 99%

Carbon–Nitrogen–Sulfur-Related Microbial Taxa and Genes Maintained the Stability of Microbial Communities in Coals

Liu

Chen

et al. 2022

ACS Omega

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Coal microbes are the predominant form of life in the subsurface ecosystem, which play a vital role in biogeochemical cycles. However, the systematic information about carbon–nitrogen–sulfur (C–N–S)-related microbial communities in coal seams is limited. In this study, 16S rRNA gene data from a total of 93 microbial communities in coals were collected for meta-analysis. The results showed that 718 functional genera were related to the C–N–S cycle, wherein N 2 fixation, denitrification, and C degradation groups dominated in relative abundance, Chao1 richness, Shannon diversity, and niche width. Genus Pseudomonas having the most C–N–S-related functions showed the highest relative abundance, and genus Herbaspirillum with a higher abundance participated in C degradation, CH 4 oxidation, N 2 fixation, ammoxidation, and denitrification. Such Herbaspirillum was a core genus in the co-occurrence network of microbial prokaryotes and showed higher levels in weight degree, betweenness centrality, and eigenvector centrality. In addition, most of the methanogens could fix N 2 and dominated in the N 2 fixation groups. Among them, genera Methanoculleus and Methanosaeta showed higher levels in the betweenness centrality index. In addition, the genus Clostridium was linked to the methanogenesis co-occurrence network module. In parallel, the S reduction gene was present in the highest total relative abundance of genes, followed by the C degradation and the denitrification genes, and S genes (especially cys genes) were the main genes linked to the co-occurrence network of the C–N–S-related genes. In summary, this study strengthened our knowledge regarding the C–N–S-related coal microbial communities, which is of great significance in understanding the microbial ecology and geochemical cycle of coals.

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In Situ Analysis of Methanogenic Pathways and Biogeochemical Features of CBM Co-produced Water from the Shizhuangnan Block in the Southern Qinshui Basin, China

Cited by 22 publications

References 74 publications

Classification of Coal Structure Combinations and Their Influence on Hydraulic Fracturing: A Case Study from the Qinshui Basin, China

Classification of Coal Structure Combinations and Their Influence on Hydraulic Fracturing: A Case Study from the Qinshui Basin, China

Keystone Microorganisms Regulate the Methanogenic Potential in Coals with Different Coal Ranks

Carbon–Nitrogen–Sulfur-Related Microbial Taxa and Genes Maintained the Stability of Microbial Communities in Coals

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