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Article

Research on Microbial Community Structure in Different Blocks of Alkaline–Surfactant–Polymer Flooding to Confirm Optimal Stage of Indigenous Microbial Flooding

by
Yinsong Liu
1,2,3,
Xiumei Zhang
1,2,
Xiaolin Wu
3,4,
Zhaowei Hou
3,5,
Min Wang
1,2 and
Erlong Yang
2,*
1
Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572024, China
2
Key Laboratory of Enhanced Oil Recovery, Northeast Petroleum University, Ministry of Education, Daqing 163000, China
3
State Key Laboratory of Continental Shale Oil, Daqing 163000, China
4
Daqing Oilfield Co., Ltd., Daqing 163000, China
5
Exploration and Development Research Institute of Daqing Oilfield Co., Ltd., Daqing 163000, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(12), 5243; https://doi.org/10.3390/app14125243
Submission received: 24 May 2024 / Revised: 9 June 2024 / Accepted: 10 June 2024 / Published: 17 June 2024
Browse Figures
Versions Notes

Abstract

:
The microbial communities associated with alkaline–surfactant–polymer (ASP)-flooded reservoirs have rarely been investigated. In this study, high-throughput sequencing was used to analyse the indigenous microbial communities in two different blocks, the water flooding after the alkaline–surfactant–polymer flooding block and the alkaline–surfactant–polymer flooding block, and to ascertain the optimal stage for the implementation of indigenous microbial oil recovery technology. The different displacement blocks had significant effects on the indigenous microbial community at the genus level according to an alpha diversity analysis and community composition. In water flooding after alkaline–surfactant–polymer flooding, the dominant genus of Pseudomonas exceeded 30%, increasing to 52.1% in alkaline–surfactant–polymer flooding, but alpha diversity decreased. Through a co-occurrence network analysis, it was found that the complexity of the water flooding after alkaline–surfactant–polymer flooding was higher than that of alkaline–surfactant–polymer flooding. This means that the water flooding ecosystem after alkaline–surfactant–polymer flooding was more stable and less susceptible to external environmental influences. In addition, there were significant differences in the functional redundancy of microbial communities in different blocks. In summary, the optimal stage for implementing local microbial oil recovery technology may be water flooding after alkaline–surfactant–polymer flooding.

1. Introduction

Since the discovery and use of oil, oil production has undergone significant long-term development. The global demand for oil has continued to increase, without abating. Chemical enhanced oil recovery (EOR) has been recognised as an effective technique for the recovery of bypassed oil, as well as residual oil trapped in the reservoir [1]. Alkaline–surfactant–polymer (ASP) flooding technology has been widely used in the Daqing field, China. ASP flooding technology increases the sweep volume and improves the oil recovery efficiency through the synergistic effects of alkali, polymer, and surfactant [2]. However, almost half of the crude oil still remains in the subsurface after the ASP flooding process. Microbial enhanced oil recovery (MEOR) technology has emerged as one of the most promising methods for oil recovery due to its simple process, low investment, and minimal impact on the environment. MEOR involves the injection of nutrients and suitable bacteria that are able to grow under anaerobic reservoir conditions into a reservoir [3]. MEOR bacteria are carried by injected water and accumulate in porous oil/rock and oil/water interfaces. Microbes can contribute to EOR in two main ways: by growing in the reservoir rock to produce gases, biosurfactants, biopolymers, and other non-toxic biochemicals to recover trapped oil; and by selectively plugging high-permeability channels to increase the sweep efficiency of the recovery process. Selective plugging approaches use microbial cell mass and/or biopolymers to plug high-permeability zones and redirect the flow [4,5]. Other processes produce biosurfactants in situ, leading to an enhanced mobilization of residual oil and a reduced oil viscosity [6,7,8]. Most importantly for the petroleum industry, these microbial products should induce a number of highly desirable changes in the physicochemical properties of the crude oil and significantly improve or almost completely restore the reservoir rock lithological properties [9]. The prolonged interaction of the metabolites with the oil in the reservoir changes the properties of the oil in such a way that the immobile, non-recoverable oil is converted into mobile oil that can flow to the production wells and increase oil production accordingly [10]. Successful in situ MEOR operations depend on the development of microbial consortia capable of surviving and producing the desired metabolites in hydrocarbon-bearing and saline reservoirs [11]. Research activities are continuously focusing on anaerobic extremophiles, including halophiles, barophiles, and thermophiles, for better adaptation to reservoir conditions [12]. Currently, with the use of molecular and culture-based methods in petroleum microbiological analyses, more studies have shown microbial community diversity in different reservoir environments [13]. This is strongly in support of the hypothesis that certain microorganisms have a wide distribution in oil reservoirs. The different types of microorganisms in the reservoir environment interact through material and energy metabolism, forming a stable community structure. This structure is the material foundation of microbial oil recovery technology.
However, there are significant differences in the structures of microbial communities due to the different types of oil reservoir environments and different exploitation methods. Bian, Z. et al. compared the microbial community structures of oil and water phases in a low-permeability reservoir after water flooding [14]. Wei, L. et al. investigated the effect of polymer flooding technology in the Daqing oil field on the microbial community in the oil reservoir system [15]. Furthermore, studies on the microbial diversity and community composition in reservoirs flooded with ASP are limited. This will have a significant impact on the application of microbial enhanced oil recovery technology to analyse indigenous microbial communities in different types of oil reservoir environments, to elucidate the characteristics of the microbial communities in these environments, to identify the key blocks of microbial oil recovery, and to understand the effective role of microorganisms in different types of oil reservoirs.
In this study, a comparative analysis was conducted to reveal the changes in the indigenous microbial community composition after ASP flooding in reservoirs. High-throughput sequencing methods, based on 16S rRNA, were used to identify the microbial composition and potential dominant functional microbes in ASP-flooded reservoirs, combined with the results of a functional microbial analysis. We also conducted a correlation analysis between the genus level and environmental factors in the microbial community to determine effective activation strategies. Successful MEOR typically requires the efficient degradation of complex hydrocarbon components and the in situ production of surfactants, processes which often involve active collaboration among different microbial populations. This in-depth comparative analysis of the microbial communities in two blocks of ASP flooding is crucial for determining the future microbial flooding technology optimal block for enhancing oil recovery.

2. Materials and Methods

2.1. Sample Collection

In total, 43 production wells from a certain oil field were used as test samples, including 28 sets of samples collected during the ASP-flooded block (ASP) and 15 sets of samples collected during the water flooding after the ASP-flooded block (WF), respectively. The average effective permeability of the oil reservoirs in this study was 554MD in the ASP and WF blocks. All of the samples from the production wells were taken from the wellhead. Ten-litre plastic was used to collect the produced fluid samples to ensure that the samples were not contaminated during the sampling process. The fluid in the pipeline had to be completely drained prior to the sample collection. All samples were transported to the laboratory as soon as possible after sampling and the collection of microbial samples and extraction of DNA were carried out as soon as possible after the separation of oil and water. Approximately 1 L of water samples was centrifuged several times at 12,000 rpm for 30 min in order to collect microbial cells [16,17]. Genomic DNA was extracted from the microbial cells using a bacterial genomic extraction kit (TianGen, China). Amplification with the primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GACTACHVGGTATCTAATCC-3′) of the V3–V4 region of the bacterial 16S rRNA gene and high-throughput sequencing were entrusted to Majorbiogroup in Shanghai, China.
The basic physico-chemical parameters (environmental factors) were provided by the Oil Production Plant. This study may have limitations such as the potential for sampling bias.

2.2. Statistical Analysis

The sequences were analysed with the QIIME2 (Quantitative Insights Into Microbial Ecology) pipeline, version 2024.2 (https://docs.qiime2.org accessed on 20 April 2024). Sequences of low quality (<20) and sequences of <200 bp were removed. Sequences with at least 99% similarity were clustered and used as ASVs. The bacteria genus with a relative abundance greater than 1% of the total genera was defined as the dominant genus [18]. The ‘vegan’ package in the R program (https://www.r-project.org accessed on 24 April 2024) was used to calculate the diversity index. On the one hand, ANOVA and Tukey HSD tests were used to compare the alpha diversity of the microbial communities in different blocks. Species richness is represented by the Chao1 index, while species diversity is represented by the Shannon index. On the other hand, a principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) based on Bray–Curtis distance were used to analyse the differences in the microbial communities between different blocks. A redundancy analysis (RDA) was used to investigate the correlation between the physico-chemical parameters and the dominant bacterial genus. The positive and negative correlations between the physico-chemical parameters of the reservoirs and the dominant bacterial genus were also evaluated using heat maps. In order to analyse the effect of the two blocks on the microbial co-occurrence patterns, we used the ‘hmisc’ and ‘igraph’ packages in R to construct a co-occurrence network and set the Spearman correlation coefficient |r| > 0.6 and p < 0.05 as an effective relationship. To describe the topological characteristics of the network, the Gephi software (https://gephi.org accessed on 15 April 2024) was used to calculate a set of data (such as total nodes, total edges, average degree, average clustering coefficient, average path length, network density, and modularity index). The visualisation was performed using the Fruchterman–Reingold layout and was colour coded according to the modules. Finally, the FAPROTAX database [19], which links species classification and functional annotation, was used to predict potential microbial ecological functions in the study samples. The ASVs classification table was compared with the FAPROTAX 1.2.6 database using the R program, and the predicted functional annotation results of the microbial communities were output.

3. Results

3.1. Composition of Microbial Communities in Different Displacement Blocks

The bioinformatic analysis of the high-throughput data provided information on the microbial community structures of the different displacement blocks, which can be discussed separately at the phylum level and at the genus level. At the phylum level (Figure 1a), the dominant bacteria in the different block samples belonged to Proteobacteria, which was the main group of the reservoir microorganisms with a relative abundance of 69.80% and 64.85%, respectively. This was followed by Caldatribacteriota, with relative frequencies of 12.30% and 18.58%. We also observed that Firmicutes had a higher proportion in the ASP blocks than in the WF blocks. At the genus level (Figure 1b), there were a large number of unclassified bacterial genera in the produced fluids of different block oil reservoirs. Based on the information of the classified bacterial genus, there were significant differences in the community structures of the different block oil reservoirs. The dominant genera in WF were Pseudomonas sp., Thauera sp., Acinetobacter sp., Tepidiphilus sp., and Thermosyntropha sp. Compared with the WF block oil reservoirs, the dominant genera in ASP block oil reservoirs significantly decreased, but their advantages significantly increased. Among them, Pseudomonas sp. accounted for more than 50% (52.1%) of the total community. Thauera sp., Acinetobacter sp., Tepidiphilus sp., and Thermosyntropha sp. decreased, and a new dominant genus Halomonas sp. emerged.

3.2. Diversity of Microbial Communities in Different Displacement Blocks

The alpha diversity of the ASP and WF blocks contains the Chao1 index and the Shannon Index, etc. The Chao1 index is commonly used in ecology to estimate the total number of species, with a higher value indicating a higher total number of species. The Shannon index takes into account both the richness and the evenness of the community. The higher the value of the Shannon index, the higher the diversity of the community. In this study, it was observed (Figure 2) that the Chao1 index and Shannon index of the ASP were lower than those of the WF block, and there was a significant difference (with different letters) between the ASP and the WF group. The above results indicate that different displacement methods can lead to significant changes in the richness and diversity of microbial communities.
In order to further explore the correlation between the microbial communities in different blocks, an NMDS analysis (Figure 3) related to the ASV abundance was conducted based on Bray–Curtis distance. The NMDS 1 and NMDS 2 axes can explain 41.41% and 19.47% of the results, respectively. In the samples from the ASP and WF blocks, samples from each region were clustered together, which means that the bacterial communities in the samples from both blocks were partially shared and did not form different clusters. From the perspective of community structure, Pseudomonas sp. and some genera were dominant genera shared by the two blocks, with differences only in abundance.

3.3. Analysis of Co-Occurrence Networks and Functional Prediction in Different Displacement Blocks

The modularization index (MD) is used to measure the tightness of a community. The higher the MD value, the better the community is divided and the closer the community is. In this study, the modularization index was 0.606 and 0.647 in the networks of ASP-flooded (ASP) blocks and water flooding after ASP-flooded (WF) blocks, respectively, where MD > 0.4 suggests that the network had a modular structure, indicating that the network structure was non-random [20]. In addition, the average clustering coefficient of WF was 0.363, while the average clustering coefficient of ASP was only 0.258, which is 0.105 lower than WF. The difference in the network density (Table 1) between the two blocks was 0.19. These results indicate that the ASP flooding technique reduced the tightness between the reservoir microorganisms. The complexity of the network structure can be identified by the co-occurrence of nodes and the average degree in the network [21]. The network of the ASP block consisted of 220 nodes connected by 944 edges, and the network of the WF block consisted of 239 nodes with 1252 edges. The average degree of WF was 11.382, while that of ASP was only 7.9. This study shows that the network complexity of WF was higher than that of ASP. The higher the network complexity (Figure 4), the more complex and diverse the microbial community structure [22]. Therefore, the microbial community structure in the WF block was more complex and diverse than the ASP block, which is consistent with the results of the alpha analysis in the previous text.
Furthermore, potential functional predictions (Figure 5) about the microbial communities of both blocks were made. In several sets of samples from the WF and ASP blocks fermentation, hydrocarbon degradation, atomic degradation, nitrate respiration, nitrogen respiration, nitrate reduction, sulphide respiration, chemotherapy, and acute chemotherapy were the main functions of the microorganisms, and there were significant differences in the microbial functions between the two blocks in different samples. For functions related to nitrogen metabolism, including nitrate respiration, nitrogen respiration, and nitrate reduction, the degree of functional redundancy was higher in the WF block samples. The term ‘functional redundancy’ refers to the presence of several different taxa or genomes capable of performing the same essential biochemical functions [23,24]. The functional redundancy of sulphide respiration, chemotherapy, and antibacterial chemotherapy in the ASP samples was higher than that in the WF samples (Figure S1). Both of these block samples had potential functions such as fertilization and hydrocarbon degradation, but exhibited higher redundancy in the WF samples.

3.4. Correlation Analysis between Microbial and Environmental Factor of Oil Reservoirs in Different Displacement Blocks

The produced fluid from the production well was analysed for the presence of ionic composition and total salinity (Table S1). The results showed that the produced fluid from the production well mainly contained HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, and CO32−. The total salinity of different production wells ranged from 1963.41 to 19599.92 mg/L. In order to better implement microbial enhanced oil recovery technology, we performed a redundancy analysis (RDA) on the physicochemical parameters and microbial dominant genus. Physicochemical parameters were used as explanatory variables and dominant genus populations as response variables (Figure 6a). Among them, salinity (r2 = 0.71, p = 0.001) was a significant factor influencing the microbial community structure of oil reservoirs. In addition, Na+ (r2 = 0.72, p = 0.001), HCO3− (r2 = 0.66, p = 0.001), Cl− (r2 = 0.61, p = 0.001), and SO42− (r2 = 0.59, p = 0.001) were also important factors influencing the microbial community structure of oil reservoirs. To further explore the positive and negative correlations between these environmental factors and dominant genus populations, a correlation heatmap between dominant genus and environmental factors was generated (Figure 6b). This showed that Ralstonia sp. and Halomonas sp. had a significant positive correlation with Cl−, khd, SO42−, Na+, and CO32−, whereas Desulfomicrobium sp., Hydrogenophaga sp., and Desulforhabdus sp. had a significant positive correlation with HCO3−, Mg2+, and Ca2+.

4. Discussion

This study compared the microbial communities of production wells in ASP and WF, and investigated the structure and characteristics of the microbial community compositions in oil reservoirs of different development blocks. In terms of the microbial community composition, most of these bacteria had the function of denitrification and hydrocarbon degradation [25,26], as well as the ability to reduce hydrocarbon viscosity. Among them, Pseudomonas sp. is a commonly used bacterium for oil recovery, and most species in this genus have the ability to produce biosurfactants such as lipopeptide and glycolipid biosurfactants. In addition, the rhamnolipid produced by Pseudomonas sp. could reduce the interfacial tension of the oil–water phase for EOR [25,27]. Thauera sp. is capable of denitrification using a variety of carbon sources, including aromatic compounds [28]. Acinetobacter sp. has the ability to degrade saturated and aromatic hydrocarbons [29]. From the above results, it can also be seen that, when comparing the significant indicators observed in the microbial communities, the Shannon index and Chaol1 index of the ASP block were both lower than those of the WF block. These results indicate that the microbial diversity of the ASP block of produced wells was relatively low. This is consistent with the previous literature on microbial communities in different reservoirs: the diversity of bacteria and archaea is significantly influenced by extreme reservoir environments such as steam soaking, high temperatures, high pressures, and a high salinity [30]. A highly alkaline environment exceeds the survival limits of most microbial populations. Alkali-tolerant populations of Halomonas sp. dominated the ASP block, but not the bacterial communities of the WF block. These phenomena indicated that some microbial populations inhabiting the WF block disappeared in the ASP block due to the harsh selection pressure of the extreme environment, and fewer microbial populations that were well adapted or tolerant to the extreme environment survived. During the WF block, there was a lack of external selective pressure. It seemed that the WF block was more suitable for the subsequent implementation of microbial flooding.
Most previous studies on microbial community biodiversity have focused on species numbers and abundances rather than on species interactions. However, the impact of species interactions on ecosystem function may be more important than species richness and abundance, especially in complex ecosystems [31]. Recent analyses have shown that the ecological network of ecosystems is highly structured [32], yet the interactions between the network structure and its components are ignored, hindering further assessment of biodiversity and its dynamics. In this study, we applied a correlation-based network analysis to investigate the co-occurrence models of the bacterial communities in the WF and ASP blocks. Previous studies have shown that microbial interactions can maintain ecosystem function and stability [33,34]. Our results indicated that, compared to the WF blocks, fewer connections, lower clustering coefficients, and network density were observed in the co-occurrence network of the ASP blocks. This means that the network complexity of the WF blocks was higher than that of the ASP blocks. This led to a higher diversity of the microbial community structure in the WF block. The more complex and diverse the microbial community structure, the more stable the ecosystem will be. When the external environment changes, a stable ecosystem can play a buffering role [35].
Based on the results obtained in the previous text, we can know that the WF block contained more microorganisms with functions such as fermentation, hydrocarbon degradation, aromatic degradation, nitrate respiration, nitrogen respiration, and nitrate reduction than the ASP block. Fermentation refers to the process of directly transferring the electrons released from the oxidation of organic matter under anaerobic conditions to some intermediate product of the substrate itself that is not fully oxidized, while releasing energy and producing various metabolic processes. During the fermentation process, large-molecule fatty acids, amino acids, and other substances produced during the aerobic stage can be further degraded to produce small-molecule short-chain fatty acids, H2, CO2, and other substances. Hydrocarbon metabolism is the main metabolic activity of reservoir microorganisms in the formation. Zhu, W. et al. found, in their research under high temperature and pressure conditions, that indigenous microorganisms in reservoirs have certain activity, which can significantly reduce the residual oil amount of remaining oil [36]. By analysing past community structures, the diversity of microbial composition, and the influence of environmental factors on microbial communities, it is clear that microbial communities typically exhibit remarkable taxonomic diversity [19]. This raises questions about the mechanisms of species coexistence and the importance of this diversity for community function. Large numbers of coexisting but classified microorganisms may encode the same energy-producing metabolic function, and the population encoding each function may undergo significant changes in space or time with a minimal impact on function. Such differences in classification are often attributed to ecological drift between equivalent organisms [37,38,39]. An assessment of the microbial diversity involved in different metabolic functions also suggests that communities generally exhibit a high degree of ‘functional redundancy’ across multiple functional aspects. This means that each metabolic function can be performed by several coexisting organisms with different taxonomic classifications [40,41]. In summary, we found that the WF block had the necessary oil recovery function and high redundancy for the implementation of microbial oil recovery, which is a good basis for the implementation of microbial flooding.
Chemical composition also influences the distribution and composition of microbial communities inhabiting subsurface oil reservoirs. Differences between microbial communities from different oil wells could even be explained by the different environmental conditions [42]. A canonical correspondence analysis (CCA) reported that the taxonomic composition of Qinghai oil wells had a positive correlation with aliphatic and aromatic hydrocarbons and a negative correlation with polar fractions containing nitrogen, sulphur, and oxygen heteroatoms from crude oil [43]. Bacterial groups of Bacteroidetes, Alpha-Proteobacteria, and Actinobacteria showed positive correlation with NO3− concentration, Gamma-Proteobacteria, and Chloroflexi with SO42− and PO43− concentration. A positive correlation with acetate concentration was found for members of Firmicutes and the genus Methanothermobacter [44]. The bacterial composition in the produced water of Algerian oil fields was significantly correlated with salinity, Cl−, and K+ [45]. A high-throughput sequencing analysis revealed new biochemical capabilities in reservoir systems. As a basis for the implementation of future microbial enhanced oil recovery, we aim to investigate the relationship between reservoir microorganisms and environmental factors to improve our understanding of how to stimulate the rapid growth and proliferation of endogenous microorganisms. In this study, the main factor that hindered the large-scale proliferation of indigenous microorganisms during their formation was the lack of sufficient nutrients. The activation of microorganisms in crude oil involves injecting a nutrient solution into the oil. This enables the rapid multiplication and growth of microorganisms. By studying the optimal environmental factor for microbial growth and development and analysing the relationship between environmental factors and changes in the distribution of the indigenous microbial community structure, the goal of targeted activation can be achieved.

5. Conclusions

In this study, the dominant bacteria in the indigenous microbial community in the two reservoir blocks belonged to the phylum Proteobacteria, which is the major group of reservoir microorganisms. At the genus level, the common main genus of bacteria was Pseudomonas, which was the most widespread and adaptable oil recovery functional bacteria in the reservoir. The microbial community diversity in the WF block was higher, but there were no obvious dominant bacteria; after alkaline surfactant polymer injection, there was a significant change in the microbial community of the reservoir, with a decrease in diversity. However, the microbial dominance of some specialised alkali-tolerant bacteria gradually increased, becoming the dominant bacteria in the reservoir.
A microbial co-occurrence network analysis showed that alkaline–surfactant–polymer flooding block reduced the tightness of microbial interactions and the complexity of microbial networks, making the microbial ecosystem of oil reservoirs unstable and more susceptible to external environmental changes. There were significant differences in the functional redundancy of the microbial communities in different blocks. By studying the optimal environmental factor for microbial growth and development and analysing the relationship between environmental factors and changes in the distribution of the indigenous microbial community structure, the goal of targeted activation can be achieved. In the subsequent implementation of microbial displacement technology, different activation strategies need to be developed based on their unique microbial community characteristics in order to achieve the on-site application of microbial oil recovery.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app14125243/s1, Figure S1: Comparison of functional redundancy between ASP and WF blocks; Table S1: Environmental factors for ASP and WF blocks.

Author Contributions

Conceptualization, Y.L. and X.Z.; software, X.Z.; validation, X.W. and M.W.; resources, Z.H.; writing—original draft preparation, X.Z.; writing—review and editing, E.Y.; visualization, X.Z.; project administration, Y.L.; funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City, Grant No: 2021CXLH0028; the Hainan Province Science and Technology Special Fund, Grant No: ZDYF2022SHFZ107; the Local Efficient Reform and Development Funds for Personnel Training Projects supported by the Central Government, Study on nanosystem displacement method of tight reservoir in Daqing Oilfield.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The libraries of 16S rRNA gene fragment sequences of produced fluids from production well were deposited in NCBI SRA, BioProject PRJNA1109445.

Acknowledgments

The authors are grateful to Majorbio for high-throughput sequencing of 16S rRNA genes of prokaryotes in two different blocks samples.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. (a) Composition of the bacterial community at the phylum level from the production wells of the ASP and WF blocks and (b) composition of the bacterial community at the genus level from the production wells of the ASP and WF blocks.
Figure 1. (a) Composition of the bacterial community at the phylum level from the production wells of the ASP and WF blocks and (b) composition of the bacterial community at the genus level from the production wells of the ASP and WF blocks.
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Figure 2. (a) The box plot shows the distribution of the Alpha diversity Chaol1 index within two blocks and the inter-group statistics and (b) the box plot shows the distribution of the Alpha diversity Shannon index within two blocks and the inter-group statistics; different letters indicate significant differences between the blocks (p < 0.05, ANOVA, Tukey-HSD test).
Figure 2. (a) The box plot shows the distribution of the Alpha diversity Chaol1 index within two blocks and the inter-group statistics and (b) the box plot shows the distribution of the Alpha diversity Shannon index within two blocks and the inter-group statistics; different letters indicate significant differences between the blocks (p < 0.05, ANOVA, Tukey-HSD test).
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Figure 3. Beta diversity of bacterial communities in ASP and WF blocks.
Figure 3. Beta diversity of bacterial communities in ASP and WF blocks.
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Figure 4. The co-occurrence patterns of ASV revealed by network analysis. Nodes were arranged according to Fruchterman–Reingold and coloured according to different types of modular classes. A connection stands for a strong correlation (Spearman’s |r| > 0.6) and significant (p < 0.05) of bacterial communities in ASP and WF blocks.
Figure 4. The co-occurrence patterns of ASV revealed by network analysis. Nodes were arranged according to Fruchterman–Reingold and coloured according to different types of modular classes. A connection stands for a strong correlation (Spearman’s |r| > 0.6) and significant (p < 0.05) of bacterial communities in ASP and WF blocks.
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Figure 5. Prediction of the functionality of ASP and WF blocks. The percentage of ASV with the same characteristics can reflect the degree of functional redundancy in microbial communities. P11 and P7 are WF block samples, while the rest are ASP block samples.
Figure 5. Prediction of the functionality of ASP and WF blocks. The percentage of ASV with the same characteristics can reflect the degree of functional redundancy in microbial communities. P11 and P7 are WF block samples, while the rest are ASP block samples.
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Figure 6. (a) A spatially constrained, distance-based redundancy analysis (RDA) was performed to assess the quantitative composition of bacterial communities in plot-based samples. The dominant genus is highlighted in a different colour. Arrows indicate the direction of maximum change in variables. (b) Heatmap was performed to assess the positive and negative correlation between environmental factors and dominant genus in two blocks. ‘**’ indicates significant correlation at 0.01 level and ‘*’ indicates significant correlation at 0.05 level; otherwise, there is no significant difference.
Figure 6. (a) A spatially constrained, distance-based redundancy analysis (RDA) was performed to assess the quantitative composition of bacterial communities in plot-based samples. The dominant genus is highlighted in a different colour. Arrows indicate the direction of maximum change in variables. (b) Heatmap was performed to assess the positive and negative correlation between environmental factors and dominant genus in two blocks. ‘**’ indicates significant correlation at 0.01 level and ‘*’ indicates significant correlation at 0.05 level; otherwise, there is no significant difference.
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Table 1. The co-occurrence network topology properties of ASP and WF.
Table 1. The co-occurrence network topology properties of ASP and WF.
Topological PropertiesASPWF
Total nodes220239
Total edges9441252
Average degree7.911.382
Average clustering coefficient0.2580.363
Average path length4.3334.709
Network density0.0330.052
Modularity index0.6060.647
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Liu, Y.; Zhang, X.; Wu, X.; Hou, Z.; Wang, M.; Yang, E. Research on Microbial Community Structure in Different Blocks of Alkaline–Surfactant–Polymer Flooding to Confirm Optimal Stage of Indigenous Microbial Flooding. Appl. Sci. 2024, 14, 5243. https://doi.org/10.3390/app14125243

AMA Style

Liu Y, Zhang X, Wu X, Hou Z, Wang M, Yang E. Research on Microbial Community Structure in Different Blocks of Alkaline–Surfactant–Polymer Flooding to Confirm Optimal Stage of Indigenous Microbial Flooding. Applied Sciences. 2024; 14(12):5243. https://doi.org/10.3390/app14125243

Chicago/Turabian Style

Liu, Yinsong, Xiumei Zhang, Xiaolin Wu, Zhaowei Hou, Min Wang, and Erlong Yang. 2024. "Research on Microbial Community Structure in Different Blocks of Alkaline–Surfactant–Polymer Flooding to Confirm Optimal Stage of Indigenous Microbial Flooding" Applied Sciences 14, no. 12: 5243. https://doi.org/10.3390/app14125243

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