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Article

Shifts in Bacterial and Archaeal Community Composition in Low-Permeability Oil Reservoirs by a Nutrient Stimulation for Enhancing Oil Recovery

by
Kaiqiang Liang
1,2,
Mingming Liu
1,
Quansheng Liang
2,
Hong Yang
2,
Jian Li
2,
Zhenjie Yao
2,
Shanshan Li
1,* and
Wei Yan
1
1
Xi’an Key Laboratory of Solid Waste Recycling and Resource Recovery, Department of Environmental Science & Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
Research Institute of Yanchang Petroleum (Group) Co., Ltd., Xi’an 710065, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(16), 8075; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168075
Submission received: 13 July 2022 / Revised: 8 August 2022 / Accepted: 10 August 2022 / Published: 12 August 2022
(This article belongs to the Section Applied Microbiology)
Browse Figures
Versions Notes

Abstract

:
Indigenous microbial enhanced oil recovery technology by selective nutrient injection is a potential alternative that leads to oil production improvement in low-permeability oil reservoirs. Nutrient flooding in oil reservoirs can shift the balance of microorganisms within a population; an in-depth exploration of this phenomenon can enable us to selectively activate particularly beneficial microbial species for enhancing oil recovery. In this study, high-throughput sequencing was employed to analyse indigenous microorganisms (e.g., archaea and bacteria) in an oil production well (W226), compared to a control well (W202), in the Xingzichuan Oil Recovery Plant (Ansai, Shaanxi, China). According to alpha diversity analysis and community composition, the nutrient injection exhibited a significant impact on indigenous archaea at the genus level. The predominant archaeal genus Methanolobus (more than 66%) in the control well shifted to Methanocalculus (50.8%) and Methanothermococcus (30.6%) genera in the oil production well. Conversely, the activators increased bacterial community richness but reduced its evenness. Bacterial community analysis at the genus level revealed that nutrient injections significantly increased specific populations with the potential to emulsify, lower interfacial tension, and lower oil viscosity, including the genera Arcobacter, Halomonas, and Thalassolituus. At the same time, some microbial species that are harmful for the oil recovery process (e.g., the sulphate-reducing bacteria Desulfovibrus, Desulfocurvus, Desulfocarbo, and Desulfoglaeba), were inhibited. In conclusion, nutrient flooding reduced the abundance of harmful microorganisms and increased beneficial functional microbial populations linked to beneficial functions, contributing to the enhancement of oil recovery in low-permeability oil reservoirs.

1. Introduction

The oil recovery process is complex and expensive. Particularly for low permeability reservoirs, with permeability less than 50 mD, it is difficult to effectively increase oil production using conventional oil recovery techniques because of small pores, large filtration resistance, and low water absorption capacities [1,2]. Microbial enhanced oil recovery (MEOR) is an effective alternative to tertiary oil recovery techniques; it involves the use of indigenous or exogenous microorganisms and their metabolic products, including biogas, biosurfactants, biomass, and acids, to retrieve residual crude oil from reservoirs [3,4]. Since it is difficult for exogenous microbes to migrate into the reservoir strata, injecting exogenous functional microorganisms does not significantly improve oil production [5,6]. To overcome the limitations of exogenous microbial flooding, indigenous microbial enhanced oil recovery (IMEOR) technology has been developed to stimulate the metabolism of beneficial microbes on-site by introducing nutrients into the recovery reservoir [7]. Primarily, however, nutrient flooding focuses on medium- and high-permeability reservoirs. Reports on the selective activation of microbial metabolism in low-permeability oil reservoirs are limited. Xiao et al. evaluated the stimulation of indigenous microbes by introducing an organic nutrient activator and found that activated microbes after the injection of nutrients shift the wettability of rock surfaces from oil-wet to water-wet, thus contributing to enhanced oil recovery [8]. Similarly, Cui et al. found that Fe(III)-reducing microbes and their metabolites could improve reservoir permeability by reducing Fe(III) content and inhibiting clay swelling, thereby enhancing oil recovery in low-permeability oil reservoirs [9]. However, there are only few studies on the changes in microbial community structure and diversity in oil reservoirs after in situ indigenous microbial flooding.
Many indigenous microorganisms in oil reservoirs play a crucial role in improving oil production, including Pseudomonas spp. [10], Arcobacter spp. [11], Bacillus spp. [12] Acinetobacter spp., Rhodococcus spp. [13,14], and Arthrobacter spp. [15]. Most of those populations can survive in the harsh environment of oil reservoirs and are capable of hydrocarbon degradation, biosurfactant synthesis and emulsification. These species belong to groups with diverse functions, such as hydrocarbon oxidizing bacteria (HOB), nitrate reducing bacteria (NRB), methane producing bacteria (MPB), and ammonium oxidizing bacteria (AOB) [16]. During the MEOR process, injected nutrients selectively activate beneficial microorganisms in oil reservoirs and prevent the stimulation of unfavourable microorganisms, such as sulphate reducing bacteria (SRB). During their growth, SRB produce the metabolite H2S, which reduces oil quality and corrodes metal pipelines and oil storage tanks in the subsequent production process. Thus, it is necessary to investigate shifts in microbial community structure corresponding to the injection of nutrients during IMEOR.
Recently, with the emergence of high-throughput next generation sequencing technology, researchers can accurately and quickly obtain the microbial community structure from production water samples of oil reservoirs. Compared to traditional culture-dependent methods, this culture-independent approach is superior in estimating the overall composition of microbial communities in oil reservoirs, including both cultivable and uncultivable microbes. Increased accuracy and coverage have greatly advanced the comprehensive and in-depth analysis of the microbial community structure in the complex oil reservoir environment. In addition, high-throughput sequencing approaches based on the 16S rRNA gene of bacteria or archaea could help us identify the microbes that belong to different domains. Gao et al. revealed the bacterial community structure after intermittent nutrient injection in a post-polymer flooded reservoir of the Daqing Oil Field by high-throughput sequencing of 16S rRNA genes. The positive correlation of oil production with the population of functional bacteria, such as Pseudomonas spp. and Acinetobacter spp., was illustrated [14].
In this study, a comparative analysis was conducted to reveal the shifts in the indigenous microbial (both bacterial and archaeal) community composition after IMEOR in low permeability reservoirs. High-throughput sequencing methods, based on both bacterial and archaeal 16S rRNA, were used to identify the microbial composition and the potential dominant functional microbes in the flooding reservoirs, combined with the results of functional microbial analysis. The in-depth comparative analysis of microbial community before and after nutrient injection can help us to selectively activate particularly beneficial microbial species for enhancing oil recovery.

2. Materials and Methods

2.1. Overview of the Field Test

The test area is located in the northern Shaanxi slope belt of the Ordos Basin, which is an ultra-low-permeability litho-logic oil reservoir. The oil reservoirs in this study have a permeability of less than 10 × 10−3 μm2. They have low formation pressure, gas-oil ratio (38 m3/t), porosity, permeability, and production. The P226 well group was selected as the experimental well group. The development horizon is the Chang 21 oil layer, the effective thickness of the reservoir is 6–13 m, the reservoir porosity is 4.5%, and the permeability is 9.76 mD.
The water injection well group P226 corresponds to six first-line beneficial production wells, in which the W226 well is located at the southeastern edge of the development block and is not affected by other injection wells and oil wells (Figure 1). This well was, therefore, selected as the sampling well for microbial indigenous flooding, and the W202 well, which was closest to the development block, was selected as the control well.
In 2020, the first round of activator (98 tons) was injected in two injections into the P226 well group. After 3 months, the oil and fluid production of the W226 well had increased significantly (Figure 1). The output of the control well W202 was stable, and there was no noticeable change.

2.2. Sample Collection

The produced water from the production well W226 and control well W202 was collected and stored in a 5 L sterile plastic bucket, sealed, and sent to the laboratory at 4 °C. After filtration with 0.45 μm filter membranes, the membranes were shipped on dry ice to Personal Biotechnology Co., Ltd. (Shanghai, China) for microbial diversity analysis via high-throughput sequencing. The filtered water was used for ion analysis. The concentration of Ca2+, Na+, and K+ was analysed using an ICPE-9000 plasma optical emission spectrometry (Shimadzu, Japan) [17]. The basic physicochemical parameters, i.e., total nitrogen (TN) and total phosphorus (TP) content, were determined according to standard methods described elsewhere [18].

2.3. Microbial Diversity Analysis via High-Throughput Sequencing of 16S rRNA

With regard to DNA extraction and pyrosequencing, the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany) was used for total DNA extraction from the samples. After purification and quantification, the DNA was used as a template for the amplification of the partial 16S rRNA gene. The PCR reaction was conducted using Q5 high-fidelity DNA polymerase (NEB, Ipswich, MA, USA), with primers that targeted the variable regions V3-V4 and V4-V5 of the 16S rRNA gene of bacteria and archaea, respectively (Table 1). After purification and quantification by the AxyPrepTM DNA Gel Extraction kit (AXYGEN, San Francisco, CA, USA) and Quant-iTTM PicoGreenTM dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA), respectively, the recovered PCR products were sequenced on an Illumina MiSeq 2500 sequencer (Personal Bio, Shanghai, China).

2.4. Operational Taxonomic Unit (OTU) Cluster and Alpha Diversity Analysis

The sequences were analysed using QIIME (Quantitative Insights Into Microbial Ecology) pipeline, version 1.9.1 [21]. Low-quality sequences (<25) and sequences < 200 bp were removed. Sequences that were similar at or above 97% were clustered and served as an OTU. At the same time, to improve the efficiency of analysis of flora data, rare OTUs (abundance value < 0.001%) were removed.
The diversity of each sample community, namely alpha diversity, was calculated through the obtained OTU abundance matrix. Chao1, ACE, Shannon, and Simpson indices were analysed using QIIME with the flattening method.

2.5. Taxonomy Composition and Metabolic Function Analysis

Representative sequences from each OTU were phylogenetically assigned with taxonomic classifications obtained from the Ribosomal Database Project (RDP) classifier [22]. Then, the statistical algorithm Metastats in Mothur software was used to analyse the differences in taxonomic composition between the two samples [23]. Metabolic function analysis and functional cluster analysis of microbial flora are important because they help in understanding the composition, structure, and function of the flora. Using PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), the microbial community metabolic function prediction tool, gene sequencing data were compared with the metabolic function microbial databases (KEGG, COG, and Rfam) [24]. The annotation information of the spectral database and the abundance matrix of the functional groups were predicted. In addition, to ensure the accuracy of the results of the comparative analyses, the abundance of species in the original data was adjusted and corrected according to the difference in the copy number of 16S rRNA gene of different species.

2.6. Functional Microorganism Analysis

The total number of microbes in each sample was determined by flow cytometry, as described previously [25]. Briefly, 1 mL of each sample was prepared for cell counting using the Guava® easyCyteTM 11 flow cytometer (Luminex, Austin, TX, USA). Dilutions were carried out with sterile filtered water when necessary. The DNA of each sample was stained with SYBR Green I (Takara, Shanghai, China) before analysis.
The numbers of different functional microorganisms, including HOB, NRB, and SRB, were determined using the most probable number (MPN) technique in triplicate [26,27]. Briefly, decimal dilutions were made. Five tubes per dilution, including selective liquid media, were used to enumerate three physiological groups of microbes. HOBs were counted using basal mineral salt medium containing 2% liquid wax [16]. The nitrate-reducing medium was used to count NRB numbers. The medium containing sulphate and Fe2+ was used for SRB [28].

2.7. Statistical Analysis

Data were analysed with GraphPad Prism 9 (GraphPad Software, San Diego, CA, USA) based on triplicate results from three independent experiments. One-way ANOVA was performed to analyse alpha diversity among three independent treatments, and significant differences were determined using Duncan’s multiple com-parisons test at the 95% confidence level. To reveal the differences of microbial community in detail, comparative analysis between bacterial and archaeal community of the production and control well at the genus level were performed. The genera with the relative abundance of more than 1% were retained for the analysis. Differences between the control and treated groups were analysed using Student’s t-test. p values less than 0.05 were considered significant.

3. Results

3.1. Chemical Characteristics of Water Sample from Control and Production Wells

The produced water from the production well W226 and control well W202 was analysed for the presence of typical cations, TN and TP (Table 2). A notable increase in content of Na+ (from 10,300 to 40,900 mg·L−1) and K+ (from 81.9 to 164.0 mg·L−1) in the production well W226 was observed after nutrient injection. Moreover, the TN and TP content increased from 20 and 0.50 mg·L−1 to 26 and 0.56 mg·L−1, respectively. These results suggested that the injected nutrients reached and were partially produced in the W226 well at the time of sampling.

3.2. Microbial Diversity of the Samples from Production and Control Wells

Using high-throughput sequencing technology based on the Illumina platform, 42,074 bacterial sequences and 66,428 archaeal sequences from the production well W226 and 40,795 bacterial sequences and 29,302 archaeal sequences from the control well W202 were clustered to 711, 499, 445, and 262 different OTUs, respectively (Table 3). In each sample, the number of bacterial OTUs was greater than that of the archaeal OTUs. Figure 2a,c show the rarefaction curve with a dissimilarity cut-off of 3% to indicate microbial diversity within each sample. As the number of sequences increases, the number of OTUs obtained gradually increases. The number of bacterial OTUs in the production well W226 and the control well W202 was higher than the number of archaeal OTUs, indicating that the diversity of bacteria in both samples was higher than that of archaea. For bacteria, when the number of sequences from samples W226 and W202 was greater than 25,000, the number of OTUs did not differ between the samples; for archaea, when the number of sequences from sample W202 was greater than 10,000, the increase in OTUs exhibited a flat pattern, and when the number of sequences in the sample was greater than 20,000, the number of OTUs in W226 exhibited a gradual plateau pattern. Overall, the OTUs did not increase as the number of sequences increased, which indicated that the number of the obtained sequences in our study was sufficiently large to reflect the diversity of microorganisms (bacteria and archaea) contained in each sample, and the analysis results obtained based on these data reflected the actual composition of the microorganisms in each sample. All the OTUs of both bacteria and archaea could be taxonomically assigned to the phylum level. On the contrary, only 81.7% and 78.6% of bacterial OTUs were assigned to the genus level from the W226 and W202 samples, respectively. Furthermore, 89.2% and 78.2% of bacterial OTUs were assigned to the genus level from the W226 and W202 samples, respectively (Figure 2b).
To reduce the impact of the number of obtained sequences of different samples on diversity analysis, an alpha diversity index was introduced to reflect OTU abundance at a consistent sequencing depth, which included the Chao1, ACE, Simpson, and Shannon indices (Table 3). The Chao1 and ACE indices focus on reflecting the richness of the community, whereas the Simpson and Shannon indices take the evenness of the community into account [29]. For bacteria, the Chao1 and ACE indices of the W226 sample were higher than those of the corresponding W202 sample, indicating that the richness of the bacterial community of the W226 sample was significantly higher than that of the W202 sample. However, both the Simpson and Shannon indices of bacteria in the production W226 sample were lower than those in the control W202 sample, which suggests that the community evenness of the W226 sample was lower than that of the W202 sample. One of the potential interpretations of this phenomenon is that the injected nutrients increased the community richness, but reduced the community evenness of bacteria in the production well W226. Therefore, several specific bacterial populations in the production well W226 were enriched significantly when compared to that in the control well W202. The Chao1, ACE, Shannon, and Simpson indices of the archaeal sequences from the W226 sample were all higher than those of the W202 sample. This result indicated that both the archaeal community richness and evenness were increased after flooding.

3.3. Microbial Community Profile of the Samples from Production and Control Wells

The microbial community profiles from the production and control wells were compared for the following two taxonomies: phylum and class. For bacteria (Figure 3), Proteobacteria was the predominant phylum, accounting for more than 80% of all bacterial sequences retrieved from both samples. After nutrient treatment, the proportion of Firmicutes in the W226 sample increased to 12% compared to the 1.5% in W202, which indicated that bacteria from the Firmicutes phylum were enriched after flooding. At the class level, ε-Proteobacteria increased from 71.0% in the W226 sample to 60.4% in the W202 sample. In the control well W202, δ-proteobacteria was the second dominant class, with an abundance of 21.6%, followed by Spirochaetes (4.2%) and α-proteobacteria (4.0%). For the production well W226, the abundance of δ-proteobacteria dropped sharply from 21.6% to 3.9% in W202, whereas another class of Proteobacteria–γ-proteobacteria rose from 1.3% to 7.0%. Moreover, Clostridia, which belongs to the Firmicutes phylum, increased to 10.6% and became the second most abundant class. The above results suggested that bacteria from three classes, ε-Proteobacteria, Clostridia, and γ-proteobacteria, were fully enriched through flooding, since the proportion of these three classes was more than 88% of all the sequences retrieved from well W226.
The archaeal compositions of the production well W226 and control well W202 were similar at the phylum level (Figure 4). The Euryarchaeota phylum was the most abundant group, representing more than 95% of the retrieved sequences in both samples; most of these were methanogenic archaea. Some differences in the composition were also observed. For example, the abundance of the Thaumarchaeota phylum, which contains most of the ammonia-oxidizing archaea, increased from 0.13% in the W202 sample to 3.85% in the W226 sample. However, the archaeal composition in the production well W226 and control well W202 showed larger differences at the class level. The most extreme difference in the sample was the composition of Methanomicrobia, which accounted for 93.8% of the retrieved sequences in W202, but only 57.9% in W226. On the contrary, there was a significant increase in the abundance of Methanococci from 0.6% in the control well to 30.9% in the production well. Furthermore, some classes from the W202 sample, such as Thermococci, Archaeoglobi, Thaumarchaeota Incertae Sedis, and Aenigmarchaeota Incertae Sedis, could not be detected in the W226 samples. These results indicate that the richness and evenness of the archaeal community in W226 was higher than that of W202, which is consistent with the alpha diversity analysis.
To further explore microbial diversification after flooding, a comparative community analysis of the production and control well was conducted at the genus level (Figure 5). The bacterial community composition of the two samples was similar at the genus level but the proportion of the dominant bacteria was different. In the control well W202, the hydrocarbon-degrading bacterium Arcobacter was the most abundant genus, representing 60.1% of the retrieved sequences; this was followed by Desulfovibrio, with a proportion of 14.7%. After treatment with nutrients, the abundance of Arcobacter increased to 71.0% in the W226 well, whereas the abundance of Desulfovibrio dropped sharply to 0.96%. The populations of other SRBs, including Desulfocurvus, Desulfocarbo, and Desulfoglaeba, decreased in the W226 well, from 3.4% to 2.5%, 0.87% to 0.15%, and 0.64% to 0.02%, respectively. Nevertheless, the abundance of hydrocarbon-utilizing Thalassolituus spp. increased to 3.7% at low temperatures. The community analysis at the genus level indicated that the bacteria had been enriched in fewer genera. However, the community composition of the archaea at the genus level was very different between the two samples. In the control well W202, Methanolobus was the most abundant genus, accounting for more than 66%; it was followed by Methanolacinia (9.7%), Methanocalculus (7.3%), and Methanosaeta (5.9%). In the W226 sample, Methanolobus spp. decreased significantly to 0.3%, and the dominant Methanocalculus (50.8%) and Methanothermococcus (30.6%) genera accounted for more than 80% of the retrieved sequences. The injection of indigenous activators has a significant impact on archaea at the genus level.

3.4. Variation in the Composition of Functional Microorganisms after Flooding

Table 4 shows the variation in the composition of microorganisms that belong to different physiological groups in the control and production wells. The compositions of various functional microorganisms in W226 and W202 were similar prior to nutrient injection. At day 30 of nutrient injection, the counts of total bacteria and indigenous functional microorganisms HOB and NRB began to increase in production well W226, compared to those of W202. By day 75, the counts of TB, HOB, and NRB in W226 increased by more than 47, 53, and 147 times, respectively. Furthermore, the number of functional microbes (TB, HOB, and NRB) increased by more than 50 times in W226 than those in W202 after 75 days of injection. Conversely, the counts of SRB were reduced by 87% in the production well W226 at day 75 compared to that of the control well W202. These results were consistent with the analysis of the microbial communities; the nutrient injection enhanced the count of beneficial functional microorganisms (such as HOB) and reduced the counts of harmful SRB.

4. Discussion

MEOR is regarded as a low-cost, simple and environment-friendly technique for improving oil recovery in low permeability reservoirs. After mixing with appropriate nutrients, the production water is injected into the formation. Water-soluble nutrients can enter the cracks of the rock and activate indigenous microorganisms, and their metabolites drive the oil to the bottom of the production well to improve the recovery yield of several materials, including biosurfactants, organic acids, and gases [16,30]. The oil reservoirs studied in this research are located at Xingzichuan oil field, which is an ultra-low permeability oil reservoir. After two rounds of activator injection in the W226 well using the slug alternate injection method, the oil production of W226 increased to a rate 3.55 times more than that of the control well W202. To reveal the potential mechanism of MEOR by nutrient injection in the W226 well, a comparison of the microbial community and functional microorganisms was carried out between the production water samples from the production well W226 and the control well W202.
The addition of activators increased the richness but reduced the evenness of the bacterial community, whereas it increased both the richness and evenness of the archaeal community, thus indicating the significant impact of activators on the archaeal community, especially at the genus level (Figure 5b). The hydrogenotrophic methanogens of the mesophilic Methanocalculus and thermophilic Methanothermococcus genus (from phylum Euryarchaeota) were predominant in production well W226, which has been detected in most oil reservoirs as the dominant archaea [31]. The thermophilic methanogen Methanothermococcus spp. appeared to conduct nitrate and methane metabolism in the production water samples, where they were predominant [32]. Consequently, the increased diversity of archaea, especially halophilic archaea in the W226 well, indicates that the archaeal community is more adaptable in high salinity conditions after adding the activator.
Conversely, the nutrient injection increased bacterial community richness but reduced the evenness of production well W226, which indicates that several specific bacterial populations, especially functional bacteria, were significantly enriched in well W226. According to an analysis of community profiles at the genus level, Arcobacter, Halomonas, and Thalassolituus, which are associated with emulsification, lowering of interfacial tension, and lowering of oil viscosity, showed high increases in relative abundance. These capabilities may contribute to enhanced oil recovery. Among them, the abundance of Arcobacter, a genus involved in polycyclic aromatic hydrocarbons (PAH) degradation, nitrate reduction, and biosurfactant production, increased to more than 70% after nutrient stimulation. Previous studies have shown that this genus is one of the three ‘core’ bacterial genera found in the water produced at eight oil reservoirs throughout northern China [14,33,34]. Furthermore, Arcobacter and Pseudomonas were also dominant in both the production wells in Daqing Oil Field, after flooding with polymer or alkaline-surfactant-polymer solutions [35]. Several studies have indicated that some species of Arcobacter are capable of degrading oil and producing lipopeptide surfactants [36,37]. In combination, these results suggest that Arcobacter spp. may contribute to enhanced oil recovery. The halophilic bacterium Halomonas is involved in hydrocarbon and polycyclic aromatic hydrocarbon degradation and accounts for a large number of the bacterial communities that inhabit many oil reservoirs, including the alkaline-surfactant-polymer (ASP)-flooded blocks [35,38,39]. Moreover, the type species of Thalassolituus spp.—T. oleivorans could produce biosurfactants and emulsify crude oil when cultivated with n-alkanes [40,41]. In addition, the proportion of Pseudomonas spp. after nutrient flooding also increased by 1.14 times in this study. Pseudomonas is currently one of the most frequently detected genera in oil reservoirs worldwide [42,43,44]. Pseudomonas populations have been applied widely in the reduction in oil/water tension and in enhancing oil recovery because of their ability to degrade alkanes of different chain lengths and to produce various lipopeptides and glycolipid surfactants. Cui et al. investigated the microbial populations in low permeability reservoirs with different water cuts in Changqing Oil Field. The results showed that Acinetobacter and Pseudomonas spp. were the dominant functional bacterial genera for crude oil degradation. Furthermore, Pseudomonas and Halomonas spp. were the top two dominant groups in the reservoirs with the water cut of more than 80% [10]. Therefore, nutrient injection enriched functional bacteria associated with hydrocarbon degradation- and emulsification-capabilities in the oil well W226. This phenomenon is consistent with the finding that functional bacterial populations, such as HOB and NRB, flourished after nutrient flooding. The enrichment of these functional bacteria may improve the emulsification and viscosity of oil, and contribute to the improvement of oil recovery. This conclusion could also be confirmed by the variation in predicted functional genes in the production well W226 and control W202 (Figure 6). Compared with the functional gene profile of the W202 sample, some categories of metabolism, including energy metabolism and carbohydrate metabolism of both bacterial and archaea, were more abundant in the W226 sample. Furthermore, bacterial membrane transport of the environmental information processing category displayed higher percentages in W226. These increases in the abundance of microbial functional genes indicated the improvement of metabolic functions, such as biosurfactant production and hydrocarbon degradation, which may help enhance oil recovery.
It is worth noting that the nutrient injection significantly reduced the population of SRB. According to the population analysis of functional bacteria, SRB decreased by 2 orders of magnitude in W226, compared to that in W202 at day 30. Moreover, Desulfovibrus and Desulfocurvus were predominant in control well W202, except for Arcobacter, as its number decreased sharply after nutrient injection. A similar trend was also observed in the case of the other SRB, such as Desulfocarbo and Desulfoglaeba. During the process of growth and metabolism, SRB populations usually utilize sulphate ions as a terminal electron acceptor to generate acidic H2S gas, causing biosouring in the system. Furthermore, the H2S produced can have an adverse impact on subsequent processes, such as reduce the oil quality and cause the corrosion of metal pipelines and oil storage tanks used in the exploitation and transportation of crude oil [25,45]. One of the potential approaches to reduce the adverse effect of SRB is an increase in the growth of NRB [46,47]. Heterotrophic NRB can compete with SRB for electron donors in the system, thus inhibiting the growth of SRB. According to an analysis of functional bacteria, NRB shows an increase in W226 compared to that in W202, which is in accordance with the hypothesis that NRB compete for electron donors with SRB and inhibit the growth of SRB in oil reservoirs. Therefore, in this study, the injection of nutrients effectively inhibited the growth of SRBs, which contributed to the improvement of oil recovery.
The results of this study have provided several interesting insights into MEOR by nutrient stimulation. First, the components in the injected nutrients can be detected in the production water of well 226, indicating that the nutrient has entered the target block. However, due to the severe horizontal and vertical heterogeneity of the tested oil reservoirs and the low viscosity of the nutrient mixture, the contact time of the nutrients with the target microorganisms may be limited, which may restrict the growth of functional microbes. Second, on day 30 of nutrient flooding, the indigenous functional bacteria were activated. The abundance of functional bacteria, HOB and NRB, exhibited a significant increase at day 75, revealing successful selective stimulation. One of the main mechanisms of nutrient flooding is to activate microorganisms that are capable of biosurfactant production, which can also be used as indigenous nutrients for the metabolism and reproduction of microorganisms in situ [16]. Therefore, the combined technology of nutrient and biosurfactants should be studied to strengthen the activation effect of indigenous functional microorganisms and improve the properties of the oil/water/rock interface to improve and prolong the effectiveness and effective period of MEOR.

5. Conclusions

Although there are many studies on indigenous microbial activation technology to improve oil recovery, the function of activators on the microbial community is still unclear. This study explored the changes in the microbial community structure and detected the shifts in the abundance of functional bacteria after on-site nutrient flooding in Xingzichuan oil field.
Comprehensive analysis of the experimental data shows that the nutrient injection increased bacterial community richness and reduced community evenness. Thus, several specific bacterial populations of HOB and NRB, including Arcobacter, Halomonas and Thalassolituus spp., were significantly enriched. Conversely, both archaeal community richness and evenness were increased after flooding. A decline in the number of harmful SRBs is conducive to improving oil recovery and the nutrient injection reduced SRB populations significantly, which contributed to the improvement of oil recovery. Therefore, the implementation of nutrient flooding technology in Xingzichuan oil field will help increase the abundance of advantageous functional microbial communities and enhance oil recovery. Future research should focus on the synergy of indigenous microorganisms, deep profile control technology to seal fractures and high-permeability layers, and improving the contact of nutrients with functional microorganisms in oil reservoirs to enhance oil recovery.

Author Contributions

Conceptualization, S.L.; resources, J.L.; methodology, K.L., J.L. and Z.Y.; data curation, K.L.; writing—original draft preparation, K.L.; writing—review and editing, Q.L. and S.L.; visualization, M.L.; supervision, W.Y.; project administration, H.Y.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China, grant number 31670512; Natural Science Basic Research Plan in Shaanxi Province of China, grant number 2018JM3039.

Data Availability Statement

The raw data of 16S rRNA gene-sequencing were deposited in Sequence Read Archive (SRA) at the National Center for Biotechnology Information (BioProject: PRJNA807855).

Conflicts of Interest

The authors declare no conflict of interest.

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Applsci 12 08075 g001 550
Figure 1. The location of the P226 test block and production changes in the production well W226 before and after nutrients injection.
Figure 1. The location of the P226 test block and production changes in the production well W226 before and after nutrients injection.
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Figure 2. Community diversity of each sample. Rarefaction curves of observed bacterial (a) and archaeal (c) OTUs for the control and sampling wells. Distribution of bacterial (b) and archaeal (d) OTUs at different classification levels.
Figure 2. Community diversity of each sample. Rarefaction curves of observed bacterial (a) and archaeal (c) OTUs for the control and sampling wells. Distribution of bacterial (b) and archaeal (d) OTUs at different classification levels.
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Figure 3. Community compositions of bacterial populations from the production and control well at the phylum and class level.
Figure 3. Community compositions of bacterial populations from the production and control well at the phylum and class level.
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Figure 4. Community compositions of archaeal populations from the production and control well at the phylum and class level.
Figure 4. Community compositions of archaeal populations from the production and control well at the phylum and class level.
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Figure 5. Comparative bacterial (a) and archaeal (b) community analysis of the production and control well at the genus level. The analysis was conducted among the genera with the abundance of more than 1%. Data were analysed using the results from triplicate independent experiments by t-test. p values less than 0.05 were considered significant.
Figure 5. Comparative bacterial (a) and archaeal (b) community analysis of the production and control well at the genus level. The analysis was conducted among the genera with the abundance of more than 1%. Data were analysed using the results from triplicate independent experiments by t-test. p values less than 0.05 were considered significant.
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Figure 6. Variation in predicted genes from bacteria (a) and archaea (b) in production and control well at the KEGG level 2 categories by PICRUSt analysis.
Figure 6. Variation in predicted genes from bacteria (a) and archaea (b) in production and control well at the KEGG level 2 categories by PICRUSt analysis.
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Table
Table 1. The primers for PCR.
Table 1. The primers for PCR.
MicroorganismPrimersSequences (5′-3′)Product SizeReference
Bacteria338FACTCCTACGGGAGGCAGCA420 bp[19]
806RGGACTACHVGGGTWTCTAAT
ArchaeaArch519FTGYCAGCCGCCGCGGTAA440 bp[20]
Arch915YCCGGCGTTGAVTCCAATT
Table
Table 2. Dissolved ion content in the water samples.
Table 2. Dissolved ion content in the water samples.
Well No.Ion Concentrations/(mg·L−1)
Ca2+Na+K+TNTP
W20211,60010,30081.9200.50
W226307040,900164.0260.56
Table
Table 3. The observed OTUs and alpha diversity indices of the samples.
Table 3. The observed OTUs and alpha diversity indices of the samples.
MicroorganismWell No.Qualified SequencesObserved OTUsChao1ACEShannonSimpsonGood’s Average
BacteriaW22642,074 711711.02712.523.300.580.98
W20240,795 499500.00500.004.140.760.97
ArchaeaW22666,428445473.66498.154.880.920.97
W20229,302262268.00268.003.150.620.97
Table
Table 4. Enumeration of functional microorganisms in oil reservoirs.
Table 4. Enumeration of functional microorganisms in oil reservoirs.
Well No.Functional Bacteria0 d
(cells·L−1)		30 d
(cells·L−1)		75 d
(cells·L−1)
W226Total bacteria2.54 ± 0.42 ×1074.32 ± 0.78 × 1071.22 ± 0.24 × 109
HOB9.78 ± 1.15 × 1061.16 ± 0.10 × 1075.34 ± 0.62 × 108
NRB6.54 ± 0.22 × 1051.14 ± 0.24 × 1069.73 ± 1.23 × 107
SRB7.54 ± 0.39 × 1064.71 ± 0.63 × 1068.76 ± 0.92 × 105
W202Total bacteria1.94 ± 0.35 × 1079.16 ± 0.85 × 1062.31 ± 0.42 × 107
HOB8.14 ± 0.92 × 1063.38 ± 0.41 × 1064.54 ± 0.68 × 106
NRB4.98 ± 0.39 × 1056.58 ± 0.54 × 1044.92 ± 0.72 × 104
SRB5.93 ± 0.42 × 1062.74 ± 0.63 × 1066.78 ± 0.65 × 106
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Liang, K.; Liu, M.; Liang, Q.; Yang, H.; Li, J.; Yao, Z.; Li, S.; Yan, W. Shifts in Bacterial and Archaeal Community Composition in Low-Permeability Oil Reservoirs by a Nutrient Stimulation for Enhancing Oil Recovery. Appl. Sci. 2022, 12, 8075. https://0-doi-org.brum.beds.ac.uk/10.3390/app12168075

AMA Style

Liang K, Liu M, Liang Q, Yang H, Li J, Yao Z, Li S, Yan W. Shifts in Bacterial and Archaeal Community Composition in Low-Permeability Oil Reservoirs by a Nutrient Stimulation for Enhancing Oil Recovery. Applied Sciences. 2022; 12(16):8075. https://0-doi-org.brum.beds.ac.uk/10.3390/app12168075

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Liang, Kaiqiang, Mingming Liu, Quansheng Liang, Hong Yang, Jian Li, Zhenjie Yao, Shanshan Li, and Wei Yan. 2022. "Shifts in Bacterial and Archaeal Community Composition in Low-Permeability Oil Reservoirs by a Nutrient Stimulation for Enhancing Oil Recovery" Applied Sciences 12, no. 16: 8075. https://0-doi-org.brum.beds.ac.uk/10.3390/app12168075

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