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Article

Microbial Diversity of the Produced Waters from the Oilfields in the Republic of Tatarstan (Russian Federation): Participation in Biocorrosion

by
Elvira E. Ziganshina
1,
Waleed S. Mohammed
1,2 and
Ayrat M. Ziganshin
1,*
1
Department of Microbiology, Institute of Fundamental Medicine and Biology, Kazan (Volga Region) Federal University, 420008 Kazan, Republic of Tatarstan, Russia
2
Department of Biotechnology, Faculty of Agriculture, Al-Azhar University, Cairo 11651, Egypt
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(24), 12984; https://doi.org/10.3390/app132412984
Submission received: 1 November 2023 / Revised: 27 November 2023 / Accepted: 28 November 2023 / Published: 5 December 2023
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Abstract

:
A variety of microorganisms grow in oil reservoirs, where they participate in the biodegradation of oil and lead to changes in oil quality. Today, our knowledge about microbial processes in oil reservoirs is largely limited, and among the urgent tasks for the oil industry, monitoring and controlling microbial activity (including the activity of microbes responsible for biocorrosion) are important. In this work, we aimed to characterize the bacterial community structure of the produced waters from oilfields in the south of the Republic of Tatarstan (Russian Federation) using cultural and molecular methods of analysis. Bacteria from produced water samples from various oil wells were enriched, and 16S rRNA gene amplicon sequencing was used to assess the phylogenetic diversity of the bacterial communities. Thus, analysis of the bacterial communities revealed the presence of various members within the genera Desulfotomaculum, Clostridium, Acetobacterium, Desulfovibrio, Dethiosulfovibrio, Desulfomicrobium, Fusibacter, Dethiosulfatibacter, Thermovirga, Sphaerochaeta, and Desulfoplanes, but in different produced water samples. The role of the bacterial associations in biocorrosion was separately assessed in experiments on monitoring and stimulating biocorrosion. The bacterial community receiving additional nutrition was shown to have a higher corrosion potential, and scanning electron microscopy analysis confirmed changes in the surface of the metal coupons during immersion testing. The practical value of this research lies in identifying the potential contribution of microbial communities to oil pipeline corrosion.

1. Introduction

As the modern world moves towards a more sustainable future, the oil industry, as a leading industry, is undergoing a major transformation. The oil industry is being improved considering economic, social, and environmental factors of development. Since this industry carries impressive environmental risks, it must become more environmentally efficient, reducing pressure on the environment and human health. Special attention is paid to the problems of oil and oil product spills, reducing the impact on the climate, and protecting natural resources [1,2,3,4]. During oil extraction, significant volumes of wastewater are generated, which are characterized as produced waters. For example, to reduce water consumption, oil companies are exploring ways to reuse water and improve the treatment of produced water, including neutralizing aggressive microbial agents. Monitoring microorganisms, both in the subsurface and introduced during oil reservoir operations, is one of the main challenges associated with water management in the oil industry since it is necessary to control and reduce the proportion of microorganisms that produce aggressive products for pipelines (for example, hydrogen sulfide and organic acids). However, these microbial associations may be of broad interest due to their physiological and biochemical characteristics [5,6,7].
Microorganisms associated with oil reservoirs are truly polyextremophiles since they are highly adapted to unfavorable conditions for life (high temperature, salinity, pressure, and toxicity). However, the oil reservoirs are rich in carbon and energy sources necessary for microorganisms. Among them, sulfate-, nitrate-, and iron-reducing, acetogenic, hydrocarbon-, iron-, and sulfur-oxidizing bacteria, as well as hydrogenotrophic and acetoclastic methanogenic archaea, are detected as important microbial groups. Many microorganisms can be associated with the metabolism of hydrocarbons, both individually and in syntrophic relationships [8,9,10,11,12]. Today, the issue of in-depth study of microbial life associated with oil and oilfield waters is becoming increasingly acute. Firstly, these microorganisms are important reservoirs of enzymes, and secondly, their metabolism directly affects the production and quality of oil. Active research in the field of petroleum microbiology is explained by the development of various biotechnologies, including Microbial Enhanced Oil Recovery (MEOR) processes, which use microbes that produce organic acids, surfactants, and gases [13,14,15,16].
To achieve maximum oil recovery, reservoir flooding is often used, and this is one of the main reasons for the activation of microbial processes in oilfield ecosystems. Where surface or seawater is used for injection, microorganisms thrive and new microbial species join native ecosystems, which can have detrimental consequences for the oil industry. Various substrates for microorganisms present in the injected water and the abundance of electron acceptors such as sulfate, in combination with reservoir conditions, create a biotope, including a corrosive-hazardous one. The most common anaerobic microbial processes that can be initiated are fermentation, sulfate reduction, homoacetogenesis, and methanogenesis [8,9,12,17]. It should be mentioned that the water quality and quantity have a direct impact on the microbial diversity in oil reservoirs and produced waters [18]. Thus, the microbial community of oil samples with a high water content is noticeably richer than that of oil samples with a low water content [19]. There are studies indicating a negligible effect of injected water on the native microbial community [20,21], as well as the fact that the microbial life of oilfield ecosystems and formation waters is diverse and unstable [12,22]. For example, Lipus et al. [5] reported that the structure of microbial communities varies with time and environmental conditions and noted the predominance of bacterial taxa of potential operational significance, namely those associated with microbially induced corrosion (MIC; or biocorrosion) and biofouling.
Accurate knowledge of microorganisms’ characteristics of the oilfield itself and microorganisms associated with produced waters is noted by many scientific groups as critical to the control and development of certain technologies, including against MIC. To date, many of the world’s oil reservoirs have been studied to analyze the taxonomic and metabolic diversity of microorganisms, with particular attention paid to sulfate-reducing bacteria (SRB), which are often associated with oil production problems such as souring, pipeline corrosion, and even reservoir plugging [11,18,23,24,25]. However, not only sulfate-reducing microorganisms are responsible for MIC; the activity of other representatives of the microbial world is also responsible for this process. Among the participants in this undesirable process, iron-reducing, iron-oxidizing, and organic acid-producing microorganisms are identified [8,26]. In addition, the syntrophy of corrosive microbes with other representatives can also indirectly enhance corrosion [26,27]. Thus, Vigneron and coauthors [27] noted that representatives of the bacterial and archaeal communities from biofilms recovered from the inside of a corroded steel pipe complement each other and are connected by metabolic processes, such as the reduction of sulfates, iron, and elemental sulfur. MIC can be initiated by microbial metabolites or surface-associated biofilms, causing significant and costly damage to the environment [28]. Corrosion studies often involve experiments using specific species of bacteria cultured under controlled conditions [29]. Although these studies make a major contribution to elucidating the mechanisms of corrosion and protection against it by testing microbial growth inhibitors, the activity of microbes under controlled laboratory conditions is very different from that in real conditions. One reason is that corrosion processes in natural conditions are usually associated with a mixed population of microorganisms [27].
Expanding current efforts to understand the microbial activity in important oilfields is not only of scientific interest but also critical to identifying economic and environmental impacts. Despite a number of studies focusing on microorganisms that inhabit the mysterious underground ecosystems around the world, in particular microbial communities at oil production sites, the true microbiological activity at these sites and its corrosive consequences often remain one-sided and poorly studied. Of particular interest are oilfields with a long history of exploitation that are on the verge of entering the final stage, such as oilfields in the Republic of Tatarstan (Russia).
The objects of this study were bacterial communities associated with oil production sites, in particular, with existing biocorrosion damage. The biodiversity of oilfield waters and deposits on the surfaces of oil pipe fragments was studied using a 16S rRNA gene amplicon sequencing approach. A distinctive feature of the work is a study that directly evaluates the influence of the native microbial communities on biocorrosion patterns under laboratory conditions using real oilfield waters and under field conditions. Since the chemical properties of reservoir products determine their corrosion properties, the main parameters of the studied waters tested in immersion experiments were additionally evaluated.

2. Materials and Methods

2.1. Oilfield Water and Deposit Sampling

Samples of produced water (PW, oil-water mixture) were collected from oil reservoirs in the Republic of Tatarstan, near the two cities of Nurlat (54°26′ N 50°48′ E) (Nurlat oilfield) and Leninogorsk (54°36′ N 52°30′ E) (Romashkino oilfield). The distance between Nurlat and Leninogorsk is about 109 km. Oilfields have been in production for many years and waterflooding is used to increase reservoir pressure and stimulate crude oil production. Water samples were collected directly from production wells in May and June 2023 and included samples from biocide-treated locations. All oil wells are currently being developed by the Oil and Gas Production Departments “Nurlatneft” and “Leninogorskneft” (Republic of Tatarstan, Russia).
Sampling was carried out at ambient temperatures above 18 °C in clear, sunny weather. Each water sample was collected in duplicate in sterile 1-L plastic bottles, after one minute of line flushing. The bottles were immediately sealed with screw caps to avoid contamination and oxygen intrusion and then transported in an ice box to the laboratory within a maximum of 3 h and processed immediately. Five produced water samples from wells near Nurlat were designated as PW_N1, PW_N2, PW_N3, PW_N4, and PW_N5. Two samples of produced water from wells near Leninogorsk were labeled as PW_L1 and PW_L2. The produced water samples were subjected to three processes: (1) chemical characterization; (2) obtaining enriched bacterial cultures; and (3) assessing the ability of microbial communities (from PW_L1 and PW_L2 samples) to initiate biocorrosion of the metal coupons.
Additionally, pipe-sized fragments (20 steel; outer diameter 20 mm, length 30 mm) were placed in oil pipelines (near Leninogorsk) for 2 months to monitor corrosion under in-line conditions. Using sterile instruments, deposits on the surfaces of the pipe fragments were obtained after they were brought to the surface. In total, nine deposit samples were collected from different oil pipelines. Deposit samples were placed in sterile, DNase/RNase-free plastic containers. However, only four samples gave substantial bacterial growth, and, therefore, only they were analyzed further. These samples were designated as D_1, D_2, D_3, and D_4.

2.2. Chemical Analyses of Water

Analyses included the measurement of pH, total solids, and concentrations of main ions. pH was measured with a Starter 5000 pH meter and STMICRO8 electrode (OHAUS Corporation, Shanghai, China). Total solids were analyzed using a drying oven at 105 °C for 16 h. Chloride, nitrite, nitrate, phosphate, and sulfate concentrations were measured using a Dionex ICS-900 Ion Chromatography System (Thermo Fisher Scientific, Wilmington, DE, USA) equipped with an IonPac AG22 (4 × 50 mm) guard column and an IonPac AS22 (4 × 250 mm) analytical column with the protocol described previously but for other samples [30,31]. The ammonium concentration was measured with the Nessler’s reagent (Sigma-Aldrich, St. Louis, MO, USA) as described previously [32]. The capillary electrophoresis system Capel 105M (Lumex, St. Petersburg, Russia) was used to analyze the concentrations of sodium, potassium, calcium, and magnesium ions according to manufacturer protocols. The iron concentration was analyzed using the iron test kit (Hanna Instruments, Nusfalau, Romania). Analyses were conducted in triplicate, and the mean values were presented with standard deviations.

2.3. Enrichment of Microbes

Since oilfield samples were characterized by low microbial content, produced water samples (PW_N1–PW_N5) were inoculated into selective nutrient media to obtain sulfate-reducing enrichment cultures as the primary factor of biocorrosion (n = 5 for each sample). The composition of the modified liquid Postgate’s medium for sulfate-reducing bacteria is as follows (per liter): 0.5 g of Na2SO4; 0.3 g of KH2PO4; 0.5 g of K2HPO4; 0.2 g of (NH4)2SO4; 1.0 g of NH4Cl; 0.06 g of CaCl2 × 6 H2O; 0.1 g of MgSO4 × 7 H2O; 2.0 g of Na lactate; 1.0 g of yeast extract; 0.004 g of FeSO4 × 7 H2O; 0.3 g of Na citrate × 2 H2O with a pH of 7.5. Additionally, 10 mL of sterile 10% Mohr’s salt solution [(NH4)2Fe(SO4)2 × 6 H2O] and 0.05 mL of sterile 1% solution of Na2S × 9 H2O were added to 1 L of the sterile Postgate’s medium. A sterile ascorbic acid solution was also added to achieve a final concentration of 0.1 g L−1 [33].
Thioglycolate broth as a general-purpose medium for the cultivation of anaerobes, microaerophiles, and aerobes was also used for the enrichment of bacteria from deposits on pipe surfaces (n = 5 for each sample). Cultivation was performed according to standard protocols. The composition of the thioglycolate broth is as follows (per liter): 15.0 g of casein enzymatic hydrolysate; 5.0 g of yeast extract; 2.5 g of NaCl; 5.0 g of glucose; 0.5 g of Na thioglycolate; 0.75 g of L-cysteine hydrochloride; 1.0 g of Na2CO3; and 0.75 g of agar.
Culture media were distributed into test and control bottles and sterilized by autoclaving. Samples for the enrichment of sulfate-reducing cultures were added to serum bottles with Postgate’s medium, purged with nitrogen, and clamped with sterile rubbers and aluminum caps. Experimental bottles and bottles with only nutrient media as a negative control were incubated at +32 °C in a thermostat RI 53 Red Line (Binder, Tuttlingen, Germany). A black precipitate formed in the bottles with Postgate’s medium confirmed sulfate reduction and hydrogen sulfide production by microbes, while gas formation and turbidity of the medium were noted as positive results in the thioglycolate growth medium assay.

2.4. Experimental Setup for Biocorrosion

To assess the potential participation of the microorganisms associated with oilfield waters in biocorrosion failures, the ability of the native microbial communities to cause steel corrosion under static conditions for 45 days was investigated. Experiments were simulated to determine the influence of microbes in two produced waters from wells near Leninogorsk (PW_L1 and PW_L2) on the degree of biological corrosion of metal coupons under static conditions. The metal coupons (LLC “Stroysnabservice”, Samara, Russia) meet the requirements of some of the industry’s most relevant test methods for assessing corrosion resistance.
The coupons were subjected to immersion testing in standard serum bottles containing PW_L1 and PW_L2 water samples under the additional provision of electron donors and growth factors (2.0 g L−1 of Na lactate and 1.0 g L−1 of yeast extract; (PW_L1_stimul and PW_L2_stimul)) as well as in their absence (PW_L1_unstimul and PW_L2_unstimul). Thus, before the start of the experiments, the surface of the metal coupons (20 steel; L × W × H = 15 × 10 × 2 mm) was polished, cleaned with 70% ethanol, dried, sterilized with UV radiation, weighed, and then coupons were hung into the serum bottles using a fishing line. Then the bottles with tested produced waters were purged with N2 and tightly closed with rubber caps. All bottles were incubated in a thermostat at +32 °C.
On day 45, water samples were taken for analysis, the coupons were examined to analyze total weight loss, the surface, and corrosion products. Additionally, the diversity of bacterial communities involved in corrosion based on the 16S rRNA gene amplicon sequencing approach was studied. The degree of corrosion was determined by standard methods based on the total weight loss of metal coupons and corrosion rate, as well as using scanning electron microscopy [34]. Three independent experiments were performed to test reproducibility. The Tukey method and 95% confidence were used to compare differences (Minitab software version 20.2.0, State College, PA, USA).

2.5. Bacterial 16S rRNA Gene Amplicon Sequencing and Bioinformatic Analysis

The composition of bacterial communities of samples was investigated based on the partial 16S rRNA gene amplicon sequencing approach. Initially, all replicates of each enrichment culture (within a sample) were pooled, and analysis was then processed. DNA was extracted with a FastDNA spin kit for soil (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer’s recommendations. The 16S rRNA gene was amplified by PCR using the primers Bakt_341F (5′-CCT ACG GGN GGC WGC AG-3′) and Bakt_805R (5′-GAC TAC HVG GGT ATC TAA TCC-3′). The cycle parameters for the first round were as follows: an initial denaturation at 98 °C for 30 s, 30 cycles of 10 s at 98 °C, 20 s at 55 °C, 40 s at 72 °C, and a final elongation at 72 °C for 5 min. Q5 high-fidelity DNA polymerase was used for PCR (New England Biolabs, Ipswich, MA, USA). Negative controls confirmed the absence of contamination during DNA extraction and PCR. High throughput 16S rRNA gene amplicon sequencing was performed on the Illumina MiSeq platform (Illumina, San Diego, CA, USA), and data analysis was performed using the QIIME software pipeline [35]. The bacterial 16S rRNA forward and reverse reads were joined and demultiplexed. The chimeric sequences were identified and filtered using the ChimeraSlayer method. The filtered sequences (>0.01%) were clustered and assigned to operational taxonomy units (OTUs) using the SILVA database [36]. Within-sample diversity values were estimated using OTU numbers, Shannon, Simpson, PD_whole_tree, Chao 1, and Fisher’s indices.

2.6. Scanning Electron Microscopy (SEM) and Elemental Analysis

To visualize the degree of corrosion of metal coupons after immersion testing, they were removed from the serum bottles at the end of the experimental period (day 45). Visible black powder was carefully removed from the surface, and the coupons were observed using a MERLIN Field Emission Scanning Electron Microscope (Carl Zeiss, Wetzlar, Germany). High-resolution images were acquired using an accelerating voltage of 5.0 kV at a working distance of 7–11 mm. Elemental analysis of metal surfaces in SEM was performed using energy-dispersive X-ray spectroscopy (EDS; X-max 80 (Oxford Instruments, Oxford, UK)) at 20 kV and probe current 1 nA. SEM was performed at the Interdisciplinary Center for Analytical Microscopy of Kazan Federal University.

3. Results and Discussion

Produced water, containing various organic and inorganic chemical compounds, is the primary wastewater stream generated during the exploration and production of crude oil. It is known that the physicochemical properties of produced waters depend on the geographic location and geological age of the field, as well as the depth and geochemistry of the hydrocarbon layer. The composition of the produced water also depends on the composition of chemical reagents added during well treatment or enhanced recovery and the microorganisms inhabiting it. Since oilfield waters vary widely, analyses of the quality and microbial community of produced water in each specific region are necessary to develop effective reuse methods and to assess the possible risks to both the environment and reservoir management, such as the development of pipeline corrosion [37,38,39,40].

3.1. Taxonomic Distribution of Bacterial Communities from Oil Wells near Nurlat

One of the blocks of microbial ecology in oil environments is the study of the composition of complex microbial communities in oil-producing mixtures. Microbes associated with production mixtures have access to both electron donors and electron acceptors [41,42]. Researchers conclude that fermentation, methanogenesis, and sulfate reduction are common to oil reservoirs; however, other microbial groups such as iron-, manganese-, or nitrate-reducing bacteria were also detected in various oil reservoirs [8,9,12,17]. Understanding the microbial nature and processes in oil reservoirs is important for the oil industry, for example, for the development of technologies such as MEOR, mitigation of MIC, and bioremediation of oil-polluted sites [43,44]. It should be noted that many species cannot be cultivated, so methods of molecular biology must be used in such studies.
Five produced water samples from wells near Nurlat (PW_N1, PW_N2, PW_N3, PW_N4, and PW_N5) were characterized by a very high salinity. The pH values of all samples were in the range of 5.8–6.2, and the total solids content (as an indicator of all inorganic and organic compounds) of the water samples was in the range of 17–24%. Such produced waters contain a high level of chemical compounds, and the pH values of various oil reservoirs range from 3 to 7 [18,45,46]. Given that analysis of the water and oil phases revealed huge differences in the composition of their microbial communities [41,43], we did not separate the oil-water mixture to obtain a general understanding of the composition of the microbial community. Total DNA was isolated from enrichments obtained using Postgate’s medium and subjected to PCR with primers targeting the bacterial 16S rRNA gene. High-throughput amplicon sequencing and bioinformatics tools were used to characterize bacterial communities in PW_N samples. A total of 263,332 valid sequences were analyzed and assigned to 47–79 bacterial OTUs. Alpha-diversity (Table 1) and the Venn diagram (Figure 1) showed some differences between the investigated samples. In general, the bacterial community in sample PW_N4 was more diverse. OTUs common among all samples were limited (Figure 1), which can be explained by the difference in the chemical properties of the water samples. The microbial compositions of PW_N5 demonstrated greater individuality among all studied samples (38% of unique OTUs were characteristic of this sample).
The taxonomic composition of the enrichment cultures was determined by amplicon sequencing of bacterial 16S rRNA genes. The results showed that the bacteria in the produced water samples could be divided into several phyla, including the important phylum Firmicutes for samples PW_N1, PW_N2, and PW_N5. Proteobacteria was the dominant phylum for samples PW_N3 and PW_N4, while members of Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, and Synergistetes were detected in enrichments of PW_N4 and PW_N5 (Figure 2A). Figure 2B illustrates the class-level classification of bacterial communities. Clostridia was the important class of the phylum Firmicutes in samples PW_N1, PW_N2, and PW_N5 (with relative abundances of 99%, 99%, and 64%, respectively), whereas it was not major in samples PW_N3 (6%) and PW_N4 (10%). Bacteria of the class Gammaproteobacteria (phylum Proteobacteria) were enriched in sample PW_N3 (78%) and were also found in samples PW_N4 and PW_N5, but not with high relative abundance values. Representatives of the Deltaproteobacteria were detected in samples PW_N3, PW_N4, and PW_N5. Actinobacteria (12–20%), Bacteroidia (4–13%), and Synergistia (0.6–21%) were detected in samples PW_N4 and PW_N5. The classes Anaerolineae, WS6 (Dojkabacteria), Spirochaetia, and Mollicutes were noted as minor groups.
Members of Clostridiales were detected in all samples, with the exception of sample PW_N3, in which the order Enterobacteriales acted as the major group. Micrococcales, Bacteroidales, Desulfovibrionales, Pseudomonadales, and Synergistales were common orders in samples PW_N4 and PW_N5. Thus, samples PW_N1, PW_N2, and PW_N3 were poor, and PW_N4 and PW_N5 were richer in bacterial composition. On the family level, the bacterial community in sample PW_N1 was represented by Peptococcaceae, Lachnospiraceae, Clostridiaceae 1, and Peptostreptococcaceae. Clostridiaceae 1 and Peptococcaceae were major in sample PW_N2, while members of the families Enterobacteriaceae and Moraxellaceae were major in sample PW_N3. The bacterial community detected in sample PW_N4 was more diverse. The main bacterial families were Desulfovibrionaceae, Synergistaceae, Intrasporangiaceae, Dysgonomonadaceae, and Clostridiales_Family XII. In sample PW_N5, the main bacterial families were Eubacteriaceae and Micrococcaceae.
On the genus level, differences between the investigated bacterial communities were very clear. Figure 3 presents the classification of bacterial communities on the genus level. In sample PW_N1, bacteria affiliated with the genus Desulfotomaculum were enriched (46%), followed by bacteria of Clostridium sensu stricto 3 (18%), unknown Lachnospiraceae (17%), and Terrisporobacter (12%). Clostridium sensu stricto 3 and Desulfotomaculum genera were also typical for sample PW_N2, accounting for 53% and 26% of the total sequences, respectively. Enterobacter (60%), Acinetobacter (13%), Desulfovibrio (8%), Dethiosulfovibrio (7%), Desulfotomaculum (6%), and Shewanella (3%) were mainly observed in sample PW_N3. For sample PW_N4, Desulfovibrio (27%), Dethiosulfovibrio (15%), Ornithinimicrobium (12%), Petrimonas (9%), Fusibacter (8%), and Thermovirga (6%) were important. For sample PW_N5, Acetobacterium (59%), unclassified Micrococcaceae (20%), and Fusibacter (5%) were specific. It is worth noting that there were many 16S rRNA gene sequences that could not be assigned to any known bacterial genera.
Despite the use of a selective medium for the enrichment of sulfate-reducing bacteria, representatives of other physiological groups of microorganisms were also detected.

3.2. Taxonomic Distribution of Bacterial Communities Enriched from Deposits

Additionally, four deposit samples from pipe fragments (near Leninogorsk) were analyzed. Using sterile instruments, deposits were obtained after bringing pipe fragments to the surface (D_1, D_2, D_3, and D_4). Then, for the enrichment task, thioglycolate broth was used as a universal medium for the cultivation of anaerobes, microaerophiles, and aerobes. Total DNA was isolated from these enrichments and subjected to PCR with primers targeting the bacterial 16S rRNA gene. A total of 112,145 valid sequences were obtained and assigned to 41–74 bacterial OTUs (Table 2). Figure 4 shows the relative abundance of bacterial genera obtained in thioglycolate broth. Despite the substantial level of bacterial OTUs observed in these enrichment cultures, they were completely dominated by members belonging to the genera Staphylococcus (sample D_1) and Bacillus (samples D_2, D_3, and D_4). Bacillus species were also found on the surfaces of metal coupons immersed in produced water samples collected from the Indian crude oil reservoir [47]. In addition, a low relative abundance of Desulfomicrobium and Dethiosulfovibrio was noted in analyzed samples (<1.0%).

3.3. Corrosive Potential of Microbial Communities

Before conducting immersion corrosion tests of metal coupons in produced waters, the initial composition of the water samples was studied. Two samples of produced water from wells near Leninogorsk (PW_L1 and PW_L2) were characterized by substantially lower salinity compared to samples obtained from wells located in the Nurlat region. The pH values of all samples were in the range of 7.3–7.5, and the total dry matter content of water samples was comparable and ranged from 2.4% to 2.5%. Sample PW_L1 contained: Na+—8.65 ± 0.03 g L−1; Mg2+—0.38 ± 0.01 g L−1; Ca2+—0.90 ± 0.01 g L−1; Cl−—10.4 ± 0.3 g L−1; SO42–—3.45 ± 0.04 g L−1; NH4+—16.1 ± 0.91 mg L−1; PO43–—2.35 ± 0.3 mg L−1. Sample PW_L2 contained: Na+—7.32 ± 0.02 g L−1; Mg2+—0.33 ± 0.02 g L−1; Ca2+—0.69 ± 0.01 g L−1; Cl−—10.7 ± 0.2 g L−1; SO42–—6.35 ± 0.04 g L−1; NH4+—17.2 ± 1.21 mg L−1; PO43–—12.4 ± 0.2 mg L−1. Fe2+, Fe3+, NO2−, and NO3− were not detected in both samples.
Characterizing the microbial communities associated with oilfield waters is fundamental to understanding, predicting, and preventing MIC, as well as improving the sustainability of the oil industry. Therefore, a separate task in this work was to assess the degree of corrosion of steel coupons by microbial communities in the produced waters collected from wells near Leninogorsk (PW_L1 and PW_L2). The corrosion rate based on the weight loss of steel coupons after 45 days of immersion in the production water is shown in Figure 5.
A study of mass loss in systems with studied produced water samples showed that metal coupons in systems with the additional provision of electron donors and growth factors (sodium lactate and yeast extract) are subject to stronger corrosion effects compared to systems with their absence. Both samples with additional nutrients (PW_L1_stimul and PW_L2_stimul) showed higher corrosion rates (from 0.101 to 0.225 mm year−1). In unstimulated systems, the corrosion rate varied from 0.0098 to 0.010 mm year−1, which is considered low corrosiveness. Probably, the maintenance of microbial activity as well as the accumulation of their metabolic products contributed to the accelerated biodegradation of coupons in systems with additives of organic compounds. The destruction of metal surfaces by bacteria occurs due to the initial formation and subsequent adhesion of biofilms, which, because of physical and chemical reactions, modify this interface [48].
SEM images of coupons of selected points with signs of corrosion are presented in Figure 6. These images of coupons immersed in both produced waters indicate localized corrosion, probably in the form of pitting. However, images of steel immersed in the produced waters with the addition of nutrients showed stronger localized corrosion compared to experiments without them. After microbial consortia formed biofilms on the metal surface, they began to produce high levels of sulfide and other compounds that potentially stimulated the corrosion process. Within the various microorganisms associated with MIC, sulfate-reducing bacteria have a significant effect on the corrosion of metals and usually contribute to pitting corrosion. This is a more serious type of corrosion than uniform corrosion because of the rapid penetration into the material structure and its further deep destruction [48].
Figure 6 also proves that the corrosion products of coupons submerged in PW_L1 were very different from those submerged in PW_L2, which can be explained by the different chemical composition of the waters and their microbial diversity. Thus, SEM-EDS analysis of the corrosion products formed on the surface of the steel coupons after 45 days was conducted. The EDS analysis of the original coupon showed the presence of the chemical elements: Fe, C, Mn, and Si. The EDS analysis of the coupons immersed in PW_L1 (without stimulation) showed that the main corrosion products included the following elements: Fe, O, S, C, Mn, Ca, and Si. The EDS analysis of the coupons immersed in PW_L1 (with stimulation) showed that the main corrosion products consisted of the elements: Fe, O, C, S, Mn, Ca, Si, Cl, and Al. The EDS analysis of the coupons submerged in PW_L2 (without stimulation) showed that the main corrosion products included the elements: Fe, S, O, C, Mn, Si, Ca, and Al. The EDS analysis of the coupons immersed in PW_L2 (with stimulation) showed that the main corrosion products consisted of the elements: Fe, S, O, C, Ca, Cu, Mn, Cl, Al, Si, Mg, and P. It should be noted that corrosion products under stimulated conditions contained increased contents of sulfur and oxygen. It can be assumed that the main corrosion products were iron sulfides and oxides.
Oliveira et al. [34] investigated the corrosion of X80 steel coupons after immersion in biotic and abiotic static systems. The products that formed on the coupons immersed in the biotic systems were Fe(OH)3, Fe(OH2), FeOOH, Fe3O4, BaSO4, CaCO3, FeS2, and FeCl3. The products formed on the coupons immersed in abiotic systems were the following: BaSO4, SrSO4, CaCO3, NaCl, CaCl2, and cubic body-centered α-Fe. The authors concluded that the planktonic microorganisms present in the produced water collected from a Brazilian oil production plant increased the corrosion rate of X80 steel. The results of the study conducted by Elumalai et al. [47] showed that in systems with enriched produced water collected from an Indian crude oil reservoir, coupons were subject to biofouling and greater corrosive effects compared to the abiotic system. The results of SEM confirmed the pitting type of corrosion, while X-ray diffraction analysis indicated the formation of Fe oxides/oxide-hydroxides. Liu et al. [49] studied the corrosion rate of coupons in produced water from a Tuha oilfield under static or dynamic conditions. The results indicate that the main corrosion product in the water injection pipe was FeOOH, whereas the corrosion scales were mainly composed of CaCO3 and Mg6Al2CO3⋅(OH)16⋅4H2O. Produced water usually has a high concentration of dissolved salts, which leads to increased pipe corrosion. It should also be noted that the biogeochemical behavior of iron and the formation of its (oxyhydr)oxides are the subject of not only studies related to both electrochemical and microbial corrosion but also to bioremediation since they act as agents influencing the fate of pollutants [50,51,52].

3.4. Taxonomic Distribution of Bacterial Communities from Oil Wells near Leninogorsk

After the immersion test, the diversity of bacterial communities was studied. Total DNA was isolated from enrichments (PW_L1_stimul and PW_L2_stimul) and subjected to PCR with primers targeting the bacterial 16S rRNA gene. A total of 163,591 valid sequences were analyzed and assigned to 108–125 bacterial OTUs. Alpha-diversity analysis (Table 3) and the Venn diagram (Figure 7) showed several differences between the samples investigated. The indices also indicated that bacterial communities associated with samples PW_L1 and PW_L2 were more diverse compared to samples PW_N1–PW_N5. Differences may also be due to the use of different cultural media. Shared OTUs were high in both samples, although each oil well also contained unique bacterial OTUs (Figure 7). The bacterial compositions of PW_L2 demonstrated greater individuality among both analyzed samples (25% of unique OTUs were characteristic of this sample).
The taxonomic composition of the enriched bacterial communities in samples PW_L1 and PW_L2 was then investigated. The results demonstrated that the bacteria in the produced water samples could be classified into several phyla (Figure 8A). Thus, sample PW_L1 contained representatives of Firmicutes (23%), Bacteroidetes (22%), Proteobacteria (19%), Spirochaetes (16%), and Synergistetes (11%), as well as members of many other minor phyla. Sample PW_L2 contained representatives of Synergistetes (31%), Firmicutes (24%), Spirochaetes (13%), Proteobacteria (10%), and Patescibacteria (9%) as well as members of many other minor phyla. Figure 8B shows the classification of bacterial communities on the class level. Thus, sample PW_L1 was mainly characterized by Bacteroidia, Clostridia, Deltaproteobacteria, Leptospirae, and Synergistia, while sample PW_L2 was mainly characterized by Synergistia, Clostridia, Spirochaetia, Deltaproteobacteria, Parcubacteria, and Coriobacteriia. Campylobacteria, Thermotogae, Mollicutes, Anaerolineae, Bacilli, and Actinobacteria were noted as minor classes.
On the family level, the bacterial community in sample PW_L1 was predominantly represented by Desulfomicrobiaceae, Leptospiraceae, Synergistaceae, Dysgonomonadaceae, Eubacteriaceae, Lachnospiraceae, Clostridiales_Family XII, and Petrotogaceae, as well as many other families. The bacterial community in sample PW_L2 was mainly represented by Synergistaceae, Spirochaetaceae, uncultured Moranbacteria, Defluviitaleaceae, uncultured Coriobacteriia, Desulfomicrobiaceae, Lachnospiraceae, Clostridiaceae, Desulfobacteraceae, as well as many other families. On the genus level (Figure 9), the bacterial community in sample PW_L1 was predominantly represented by uncultured Leptospiraceae (13%), Fermentimonas (10%), Desulfomicrobium (10%), uncultured Marinilabiliaceae (8%), Thermovirga (8%), Desulfoplanes (7%), Dethiosulfatibacter (5%), Acetobacterium (5%), Fusibacter (4%), and uncultured Lachnospiraceae (4%), as well as many other minor genera and unknown bacteria. The bacterial community in sample PW_L2 was mainly represented by Dethiosulfovibrio (29%), Sphaerochaeta (13%), uncultured Moranbacteria (9%), Defluviitaleaceae UCG-011 (7%), uncultured Coriobacteriia (6%), Desulfomicrobium (5%), uncultured Lachnospiraceae (5%), as well as many other genera and unknown bacteria. It should be noted that many 16S rRNA gene sequences could not be assigned to any known bacterial genus. Interestingly, thiosulfate-reducing bacteria (e.g., Dethiosulfovibrio) were found in higher concentrations in the produced water PW_L2, which positively correlates with increased rates of corrosion in a given water.

3.5. Function of Microbial Communities

Reuse and purification of produced water, including reinjecting it back to maintain reservoir pressure and increase crude oil production, are strategies to improve the sustainability of the oil industry. Therefore, great attention must be paid to the quality of injected water [53]. Further in-depth research will contribute both to the development of corrosion mitigation technologies and to improving the stability of the oil industry through, for example, the development of MEOR technologies.
Some studies have reported relationships between the microbial composition/presence of functional genes in individual water bodies and physical properties (e.g., temperature as a major factor) and chemical properties (e.g., salinity concentration), as well as the availability of essential nutrients [12,27,43]. It should be noted that water extracted with crude oil does not reflect the true microbial composition of the oilfield ecosystems studied, as some microbes develop by attachment to the rock matrix or as biofilms at the oil-water interface (as an adaptation of microbes against external environmental factors in oil reservoirs).
Thus, in this work, we studied the planktonic microbial communities of an oil–water mixture. Based on the results of the work, individual taxa were identified as important microorganisms with rich functional potential, which opens new opportunities for their implementation in the industrial application of MEOR and bioremediation of oil-contaminated sites [43], as well as biocorrosion monitoring. Despite the impossibility of establishing the origin of microorganisms found in the studied oil reservoirs, we note their excellent adaptation to the physical and chemical conditions of their habitat, which is interesting both from the point of view of their possible indigenous nature and from the point of view of their occurrence as a result of anthropogenic activity. It should be added that despite the use of sterile bottles and sterile instruments, we do not exclude the possibility that a number of bacteria not associated with the oil microbiota could have come from the environment during the sampling stage.
The results obtained in this work are consistent with the results of studies in which microorganisms in the oil and water phases of oil production mixtures around the world were analyzed using culture-dependent and culture-independent methods [12,54,55]. Representatives of Proteobacteria are considered ubiquitous in oil reservoirs around the world in all temperature ranges [12]. However, they were not widely distributed in all the produced water samples examined. Interestingly, the bacteria within the phyla Firmicutes and Proteobacteria are most actively involved in syntrophic interactions with methanogenic archaea in oil reservoirs [12]. Regarding such an important group as sulfate-reducing microorganisms, it is worth noting that this phylogenetically diverse group includes bacteria of the phyla Proteobacteria, Firmicutes, Nitrospira, and Thermodesulfobacteria, as well as the archaea belonging to the phyla Crenarchaeota and Euryarchaeota [56].
Representatives of the class Clostridia form spores and are the main bacteria in anaerobic systems [57,58]. Additionally, they are adapted to survive in the harsh conditions of oil reservoirs and are often found in samples from various oil wells [12,59]. In this regard, the presence of representatives of this class among the bacterial communities of the studied samples was expected. Gammaproteobacteria and Deltaproteobacteria were identified as important microbial groups in samples obtained from the Enermark oilfield in Canada [54] and from the Qinghai oilfield in China [43]. Members of the classes Synergistia, Deltaproteobacteria, Gammaproteobacteria, and Desulfovibrionia, as well as many others, were also observed in the Uzen high-temperature oilfield in Kazakhstan [45]. When analyzing produced water obtained from onshore oilfields in Brazil, representatives of Enterobacteriales, Desulfovibrionales, and Pseudomonadales were noted [25].
Bacteria of the obligately anaerobic genus Desulfotomaculum (observed at high levels in samples PW_N1 and PW_N2), as sulfate-reducing bacteria, can grow with sulfate as electron acceptor or ferment in combination with methanogens if sulfate is not available [60]. They are considered important bacterial agents of hydrocarbon degradation, including under relatively high salinity conditions [27] and during steel corrosion [61,62]. Members of the bacterial genera Enterobacter and Acinetobacter made up the microbial community in sample PW_N3. Previous research work confirmed that Enterobacteriaceae were involved in the biocorrosion of metallic materials under marine conditions [63]. Metal-reducing bacteria of the genus Shewanella are reported to be involved in biocorrosion [64,65,66] and were detected only in sample PW_N3. On the other hand, the facultative anaerobic bacterium Shewanella xiamenensis CQ-Y1, isolated from the wastewater of the Changqing oilfield in China, was proposed by researchers for application in the field of biodegradation and bioremediation. Studies show that Shewanella isolates from different habitats with different physiological and biochemical characteristics, including resistance to high temperatures and high salinity, are relevant for biotechnology [67,68]. Desulfovibrio, Dethiosulfovibrio, Ornithinimicrobium, Petrimonas, Fusibacter, and Thermovirga were important bacterial genera in PW_N4, whereas members of the bacterial genus Acetobacterium and unknown Micrococcaceae were the major representatives of the microbial community in sample PW_N5.
In addition to the already mentioned genus Desulfotomaculum, our samples contained strictly anaerobic sulfate-reducing bacteria of the genus Desulfovibrio [69] and bacteria of the genus Dethiosulfovibrio, capable of reducing thiosulfate and elemental sulfur to hydrogen sulfide [70,71]. They are described as important microbial agents associated with steel corrosion [72], as well as important representatives of sulfide-rich habitats [73]. Dutra et al. [74] investigated the composition of the produced water and enrichment cultures of oil pipelines to study the issue of MIC. Analysis of the produced water demonstrated a high phylogenetic diversity of bacteria and archaea, while enrichment cultures were dominated by bacteria of the dominant genus Desulfovibrio as associated with MIC. Interestingly, synergistic interactions between the mentioned genera Desulfovibrio and acid-producing Petrimonas were reported in a study investigating sulfate-reduction coupling polycyclic aromatic hydrocarbon degradation as a remediation process for contaminated sediments [75]. Petrimonas members as strictly anaerobic bacteria with a fermentative type of metabolism were also detected in anaerobic digesters [57,58], and their ability to produce organic acids could explain their coexistence with representatives of sulfate-reducing bacteria in some investigated samples. A mesophilic, non-spore-forming, anaerobic, fermentative Petrimonas sulfuriphila capable of using elemental sulfur and nitrate as electron acceptors was first described by Grabowski et al. [76] as a novel organism isolated from a producing well of a biodegraded oil reservoir in Canada. Bacteria of the genus Fusibacter belong to the class Clostridia and are anaerobic bacteria that can perform thiosulfate reduction. Fusibacter paucivorans was originally isolated from reservoir water from an oil-producing well in the Congo [77]. Other bacteria involved in the sulfur cycle, such as Desulfomicrobium, Desulfoplanes, Dethiosulfatibacter, and members of Thermovirga, were important bacterial genera in PW_L1, while Dethiosulfovibrio, Sphaerochaeta, and Defluviitaleaceae UCG-011 were important bacterial genera in PW_L2. Desulfomicrobium members of the order Desulfovibrionales were found by many researchers in various oil reservoirs as putative agents of microbial corrosion [5,78]. Thermovirga members are on the list of fermentative bacteria isolated from various oilfield environments [78,79].
Although the produced waters have been studied as enrichments of sulfate-reducing bacteria as the primary factor of MIC, many other anaerobic species have been identified as important taxa for corrosion monitoring, which is consistent with the conclusions of researchers that the oil environments are niches with broad microbial ecology [5,12]. Finally, taxonomic analysis of produced waters from various oil wells and deposits as the objects of this study allowed the identification of several microbial taxa putatively involved in biocorrosion, namely metal-reducing, acid-producing bacteria, and sulfidogenic taxa. The concentration of sulfates in analyzed samples confirms the possibility of the biological formation of sulfides. In addition, the produced waters were “rich” in aggressive salts, which also contributed to corrosion.

4. Conclusions

This study examined the microbial characteristics of the produced waters of oilfields in the Republic of Tatarstan (Russia) as well as deposits on the surfaces of the pipe fragments. Diverse bacterial groups within Firmicutes, Proteobacteria, Bacteroidetes, Spirochaetes, and Synergistetes were identified in the investigated samples. Among the detected taxa, the important genera were Desulfotomaculum, Clostridium, Acetobacterium, Desulfovibrio, Dethiosulfovibrio, Desulfomicrobium, Fusibacter, Dethiosulfatibacter, Thermovirga, Sphaerochaeta, and Desulfoplanes, but in different produced water samples. It is worth noting that the analysis of bacterial diversity confirms that many microorganisms are unknown; however, they could also play an important role in biocorrosion. In addition, the impact of bacterial communities on biocorrosion under static conditions was studied. The bacterial community provided with additional nutrition was shown to have a higher corrosion potential, and SEM analysis confirmed surface changes during immersion testing. The data obtained will expand the base on the diversity of microbial communities in the petroleum industry and will contribute to the mitigation and prediction of biocorrosion and the development of biocides targeting specific groups of microbes.

Author Contributions

Conceptualization, E.E.Z. and A.M.Z.; methodology, E.E.Z., W.S.M. and A.M.Z.; investigation, E.E.Z. and A.M.Z.; writing—original draft preparation, E.E.Z.; writing—review and editing, W.S.M. and A.M.Z.; visualization, E.E.Z. and A.M.Z.; supervision, A.M.Z.; funding acquisition, A.M.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The reported study was funded by the Russian Science Foundation (Grant No. 22-24-00364).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Acknowledgments

This paper has been supported by the Kazan Federal University Strategic Academic Leadership Program (PRIORITY-2030).

Conflicts of Interest

The authors declare no conflict of interest.

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Applsci 13 12984 g001
Figure 1. The Venn diagram showing the number of unique and shared OTUs of five produced water (PW) samples (1–5) from wells near Nurlat. Overlapped areas depict common OTUs between water samples.
Figure 1. The Venn diagram showing the number of unique and shared OTUs of five produced water (PW) samples (1–5) from wells near Nurlat. Overlapped areas depict common OTUs between water samples.
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Figure 2. Relative abundances of bacterial phyla (A) and classes (B) detected in produced water (PW) samples (1–5) from wells near Nurlat.
Figure 2. Relative abundances of bacterial phyla (A) and classes (B) detected in produced water (PW) samples (1–5) from wells near Nurlat.
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Figure 3. Relative abundance of bacterial genera detected in produced water (PW) samples (1–5) from wells near Nurlat. Only genera comprising at least 1% relative abundance in at least one sample are presented.
Figure 3. Relative abundance of bacterial genera detected in produced water (PW) samples (1–5) from wells near Nurlat. Only genera comprising at least 1% relative abundance in at least one sample are presented.
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Figure 4. Relative abundance of enriched bacterial genera obtained from deposits. Genera with abundances below 1% are summarized as “other”.
Figure 4. Relative abundance of enriched bacterial genera obtained from deposits. Genera with abundances below 1% are summarized as “other”.
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Figure 5. Corrosion rate based on weight loss of steel coupons. Experiments were performed in the absence (unstimul) and presence (stimul) of additional nutrients. Means that do not share a letter are significantly different (ANOVA, Tukey method, α = 0.05).
Figure 5. Corrosion rate based on weight loss of steel coupons. Experiments were performed in the absence (unstimul) and presence (stimul) of additional nutrients. Means that do not share a letter are significantly different (ANOVA, Tukey method, α = 0.05).
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Figure 6. SEM analysis of steel coupons’ surface after 45 days of exposure in unstimulated systems ((A)—PW_L1, (C)—PW_L2) and stimulated systems ((B)—PW_L1, (D)—PW_L2).
Figure 6. SEM analysis of steel coupons’ surface after 45 days of exposure in unstimulated systems ((A)—PW_L1, (C)—PW_L2) and stimulated systems ((B)—PW_L1, (D)—PW_L2).
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Figure 7. The Venn diagram showing the number of unique and shared OTUs of two produced water (PW) samples (1–2) from wells near Leninogorsk. Overlapped areas depict common OTUs between water samples.
Figure 7. The Venn diagram showing the number of unique and shared OTUs of two produced water (PW) samples (1–2) from wells near Leninogorsk. Overlapped areas depict common OTUs between water samples.
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Figure 8. Relative abundance of bacterial phyla (A) and classes (B) detected in produced water (PW) samples (1–2) from wells near Leninogorsk. Phyla and classes with abundances higher than 0.5% are presented.
Figure 8. Relative abundance of bacterial phyla (A) and classes (B) detected in produced water (PW) samples (1–2) from wells near Leninogorsk. Phyla and classes with abundances higher than 0.5% are presented.
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Figure 9. Relative abundance of bacterial genera detected in produced water (PW) samples (1–2) from wells near Leninogorsk. Genera with abundances higher than 0.5% are presented.
Figure 9. Relative abundance of bacterial genera detected in produced water (PW) samples (1–2) from wells near Leninogorsk. Genera with abundances higher than 0.5% are presented.
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Table 1. Alpha diversity of enriched bacterial communities obtained from produced waters from oil wells near Nurlat.
Table 1. Alpha diversity of enriched bacterial communities obtained from produced waters from oil wells near Nurlat.
SampleOTUsChao1ShannonSimpsonPD Whole TreeFisher’s Alpha
PW_N147542.560.733.555.34
PW_N247472.090.643.904.95
PW_N349511.810.394.355.28
PW_N479803.680.877.329.11
PW_N577782.410.627.208.88
Table 2. Alpha diversity of enriched bacterial communities obtained from deposits.
Table 2. Alpha diversity of enriched bacterial communities obtained from deposits.
SampleOTUsChao1ShannonSimpsonPD Whole TreeFisher’s Alpha
D_141530.660.153.144.12
D_272960.760.193.497.75
D_374850.800.193.617.93
D_456861.090.363.725.72
Table 3. Alpha diversity of enriched bacterial communities obtained from produced waters from oil wells near Leninogorsk.
Table 3. Alpha diversity of enriched bacterial communities obtained from produced waters from oil wells near Leninogorsk.
SampleOTUsChao1ShannonSimpsonPD Whole TreeFisher’s Alpha
PW_L11081144.160.877.2812.30
PW_L21251334.020.888.7614.43
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Ziganshina, E.E.; Mohammed, W.S.; Ziganshin, A.M. Microbial Diversity of the Produced Waters from the Oilfields in the Republic of Tatarstan (Russian Federation): Participation in Biocorrosion. Appl. Sci. 2023, 13, 12984. https://doi.org/10.3390/app132412984

AMA Style

Ziganshina EE, Mohammed WS, Ziganshin AM. Microbial Diversity of the Produced Waters from the Oilfields in the Republic of Tatarstan (Russian Federation): Participation in Biocorrosion. Applied Sciences. 2023; 13(24):12984. https://doi.org/10.3390/app132412984

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Ziganshina, Elvira E., Waleed S. Mohammed, and Ayrat M. Ziganshin. 2023. "Microbial Diversity of the Produced Waters from the Oilfields in the Republic of Tatarstan (Russian Federation): Participation in Biocorrosion" Applied Sciences 13, no. 24: 12984. https://doi.org/10.3390/app132412984

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