Microbial Abundance and Community Composition Influence

Apr 14, 2014 - Microorganisms live in petroleum reservoirs in the form of microbial communities.(6) The community composition and abundance will chang...
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Microbial Abundance and Community Composition Influence Production Performance in a Low-Temperature Petroleum Reservoir Guoqiang Li,†,‡,⊥ Peike Gao,†,⊥ Yunqiang Wu,§ Huimei Tian,† Xuecheng Dai,§ Yansen Wang,† Qingfeng Cui,∥ Hongzuo Zhang,† Xiaoxuan Pan,† Hanping Dong,∥ and Ting Ma*,†,‡ †

Key Laboratory of Molecular Microbiology and Technology, Ministry of Education, Tianjin 300071, P.R. China College of Life Sciences, Nankai University, Tianjin 300071, P.R. China § Experiment and Test Institute, Xinjiang Oilfield Company, Karamay 834000, P.R. China ∥ Research Institute of Petroleum Exploration and Development, Langfang branch, CNPC, Langfang 065000, P.R. China ‡

S Supporting Information *

ABSTRACT: Enhanced oil recovery using indigenous microorganisms has been successfully applied in the petroleum industry, but the role of microorganisms remains poorly understood. Here, we investigated the relationship between microbial population dynamics and oil production performance during a water flooding process coupled with nutrient injection in a low-temperature petroleum reservoir. Samples were collected monthly over a two-year period. The microbial composition of samples was determined using 16S rRNA gene pyrosequencing and real-time quantitative polymerase chain reaction analyses. Our results indicated that the microbial community structure in each production well microhabitat was dramatically altered during flooding with eutrophic water. As well as an increase in the density of microorganisms, biosurfactant producers, such as Pseudomonas, Alcaligenes, Rhodococcus, and Rhizobium, were detected in abundance. Furthermore, the density of these microorganisms was closely related to the incremental oil production. Oil emulsification and changes in the fluid-production profile were also observed. In addition, we found that microbial community structure was strongly correlated with environmental factors, such as water content and total nitrogen. These results suggest that injected nutrients increase the abundance of microorganisms, particularly biosurfactant producers. These bacteria and their metabolic products subsequently emulsify oil and alter fluid-production profiles to enhance oil recovery.

1. INTRODUCTION Water flooding is an efficient oil recovery process and is employed worldwide, particularly in China over the past four decades.1 High-yield petroleum reservoirs have been extensively water flooded, and most of these have reached a high water content stage (>90%).1−3 Therefore, it is essential to develop alternative enhanced oil recovery techniques. Since Ivanov et al.4 successfully established their method based on the stimulation of indigenous microorganisms by introducing oxygen and salts through water-based injections to enhance oil recovery, indigenous microbial enhanced oil recovery (IMEOR) has been widely used in oil exploitation to successfully increase oil production.5,6 Although indigenous microbial flooding is believed to be one of the most promising MEOR techniques, the role of microorganisms is poorly understood. Microorganisms live in petroleum reservoirs in the form of microbial communities.6 The community composition and abundance will change with the injection of nutrient solutions and the water−air mixture. These changes will result in a successful IMEOR process if the dominant microbes are able to © 2014 American Chemical Society

produce metabolites, such as organic acids, biosurfactants and gas, and improve the properties of crude oil.6,7 Hence, determining the relationship between microbial population dynamics and oil production performance is important for clarifying the mechanism of enhanced oil recovery by microbial flooding. Over the past few decades, molecular- and cultivationbased methodologies have revealed that a diverse range of microorganisms inhabit petroleum reservoirs.1,2,8−10 However, because these methods are associated with low-throughput that renders many taxa undetectable, it is difficult to compare microbial communities in detail. Fortunately, pyrosequencing has provided the opportunity to examine microbial community dynamics with unprecedented levels of coverage and detail.11 It is well-known that microbial community structure is determined by environmental parameters, such as the abundance and quality of carbon, nitrate and phosphate.12−14 Received: Revised: Accepted: Published: 5336

January 15, 2014 April 14, 2014 April 14, 2014 April 14, 2014 dx.doi.org/10.1021/es500239w | Environ. Sci. Technol. 2014, 48, 5336−5344

Environmental Science & Technology

Article

To date, only a few studies have investigated the response of microbial communities in bioreactors and field trials.3,15 However, how the subsurface microbial community succession and the stimulated microorganisms enhance oil recovery during the pumping of the nutrients and air into the oil stratum has rarely been studied. Our knowledge of the structure and dynamics of microbial communities as a function of the injected nutrients and production performance remains very limited. Answers to these questions have the potential to reveal important structure−function relationships during the microbial flooding process. To determine the relationship between microbial population dynamics and oil production performance during the flooding process, we tracked dynamic changes in bacteria and oil production during an IMEOR field trial in the low-temperature petroleum reservoir of the Karamay oil field, China.

2. MATERIALS AND METHODS 2.1. Sampling Sites and Reservoir Information. The field study was conducted in the L-field block reservoir area of the Karamay oil field, China, which belongs to Xinjiang Oil Field Co. Ltd., PetroChina. All field experiments complied with PetroChina production safety regulations and Chinese national guidelines. This field block has seven production wells and four injection wells in a relatively closed site in a 0.2 km2 area (Supporting Information (SI) Figure S1). The injection wells have a direct influence on seven neighboring producers, with interwell distances of 100−150 m. The conglomerate reservoir bed has been exploited for about 30 years by long-term water flooding with an average water content of 86.8%, and the injection water was recycled after the oil−water separation of the production liquid from the production wells. The average depth of the petroleum horizons is 480 m, with a formation temperature of 22.6 °C and a pressure of 7.2 MPa. The viscous oil recovered from the production wells in this block has a viscosity of approximately 80 mPa·s−1 in the formation, containing 32% resins and asphalts. The average permeability is 0.251 μm2, and the effective thickness of the stratum is 22.92 m, which is divided into three oil-bearing strata, S73−1, S73−2 and S73−3. 2.2. Field Trial Design and Nutrient Injection. According to culture-dependent experiments and physical simulations of oil displacement (SI Table S1) carried out in the laboratory, an optimal nutrient medium for stimulating microorganisms to enhance oil recovery was determined. One liter of the medium contained: 7 g NaNO3, 3 g Na2HPO4· 12H2O, 3 g NH4Cl and 14 g molasses. From September 3, 2010 to May 22, 2011, a total of 17 881 m3 (0.046 PV) of nutrient medium prepared by formation brine and 117 260 m3 air (about 0.0042 PV at reservoir pressure underground) was injected into four targeted injection wells. The injected air was used to stimulate oil degrading bacteria or biosurfactants producing bacteria by supplying electron acceptors. The nutrients were prepared in the preparation tank, with the fresh water coming from the injection pipeline, and then flowed into each injection well through a pump (Figure 1). The daily injection rate was 20−30 m3 (average 22 m3) per well. A moderate injection volume was determined by the short interwell distances (100−150 m) and the conglomerate because a larger injection rate might lead to water channeling. The air was injected into the four targeted injection wells with the nutrients at a ratio of 1/6.56 (liquid/air, at 1 atm). This was divided into four equal-sized small plugs and distributed

Figure 1. Injection system used in the indigenous microbial flooding field trial.

alternately in accordance with their respective weight into the strata through an injection pump and an air compressor. After a nutrient solution plug was injected, the injection pump was turned off, the air compressor was opened, an air plug was injected, and so on. 2.3. Sample Collection and Preprocessing. Production performance of the tested block was constantly monitored from July 2010 to September 2012. Sampling of the production wells was conducted on a monthly basis. Oil−water mixture samples with three replicates were collected directly from each wellhead of the production well by field personnel of PetroChina. Samples completely filled 15 L sterilized plastic bottles, which were immediately sealed with screw caps to avoid contamination and oxygen intrusion. The bottles were then transported to the laboratory as soon as possible for further analysis. Microbial cells were collected from 5 L of each water sample by centrifugation at 4 °C for 15 min at 10 000g in a high-speed centrifuge (Beckman, Pasadena, CA) within 24 h of collection. The cell deposits and a 100 mL cell-free water sample were stored at −80 and 4 °C for DNA extraction and chemical analysis, respectively. 2.4. Chemical Analysis. All cell-free water samples obtained from the same well were mixed together for ion analysis. The concentrations of cations and anions in the injection and production waters were analyzed using an ion chromatograph (DIONEX ICS-1000) with a Shim-pack IC-A3 column for cation analysis and a Shim-pack IC-C3 column for anion analysis. Total sugar was determined using the phenol− sulfuric acid method.16 Detailed ion compositions of the production brines obtained during the field trial are listed in Table 1. 2.5. DNA Extraction and Pyrosequencing of Partial 16S rRNA Genes. Total genomic DNA was extracted from the cell deposits using methods previously described in Zhao et al.17 Collected cells were resuspended with TE buffer (Tris 80 mM, EDTA 40 mM, pH 8.0), and then lysed using a mini beadbeater (BioSpec, Bartlesville, OK) at 4 °C, 200 rpm for 1 min at room temperature with 0.1 mm glass beads. DNA was extracted from the suspension solution using an AxyPrep Bacterial Genomic DNA Miniprep Kit (Axygen Biosciences, Union City, 5337

dx.doi.org/10.1021/es500239w | Environ. Sci. Technol. 2014, 48, 5336−5344

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Table 1. Physical and Chemical Properties of the Production Wells during the Microbial Flooding Process well no.

production stagesa

daily production (m3)b

oil content (tons)b

water content (%)b

incremental oil (tons)b

residual sugar (mg L−1)c

total nitrate (mg L−1)c

HCO3− content (mg L−1)c

acetate content (mg L−1)c

T73

A B C D

16.08 15.41 8.29 9.12

2.72 1.64 4.71 4.83

82.97 88.11 45.50 45.13

0 8.75 43.00 49.40

5.41 26.51 37.19 1.46

6.06 46.94 37.96 7.50

1924.99 2006.20 2256.33 2218.01

4.86 15.86 25.30 0

T80

A B C D

6.20 10.21 3.50 3.07

0.91 1.92 0.57 0.35

85.32 80.64 83.85 88.44

0 24.75 0 0

19.41 61.18 75.67 2.08

7.79 111.06 45.96 1.71

2016.40 3202.98 3474.09 2886.19

3.43 134.06 111.10 37.30

T89

A B C D

8.63 11.23 6.12 5.97

1.17 3.83 2.75 1.80

86.16 65.00 56.27 67.65

0 86.90 54.30 26.10

4.71 90.93 64.54 2.84

2.60 290.13 160.59 35.01

1894.70 4559.02 3016.96 3076.16

0.97 158.70 179.53 29.63

a

A represents the period May, 2010 to August 2010; B represents the period September 2010 to May, 2011; C represents the period June 2011 to September 2010; D represents the period October 2011 to September 2012. bDaily production, oil content, water ration and incremental oil are the average value of each production stage. cResidual sugar, total nitrate, HCO3− and acetate content are the average value of the mixed water samples obtained during each production stage. The oil−water mixture samples with three replicates were collected on a monthly basis.

bacteria, respectively. Reactions were performed using the FastStart Universal SYBR Green Master PCR mix (Roche Applied Science, Germany) in a Bio-Rad iQ5 Sequence detection system (Applied Biosystems, Carlsbad, CA). Bacterial 16S rRNA genes were amplified with the primer set 8F (5′AGA GTT TGA T(CT)(AC) TGG CTC-3′)/338R (5′-GCT GCC TCC CGT AGG AGT-3′).22 Gene copy numbers in unknown samples were determined based on standard curves constructed from 10-fold serial dilutions of the standard plasmids. Amplification efficiencies were calculated from the slope of standard curves.23 The specificity of the PCR amplification was determined by the melting curve and gel electrophoresis. The standard curves were constructed according to the method described in the SI. Because of the high diversity of alkBs catalyzing the first step of the hydrocarbon-degradation process, a novel universal primer-multiplex quantitative PCR (UPM-qPCR) method was established to reduce nonspecific amplification, expand the testing coverage and enhance quantitative accuracy. The alkBs were quantified by qPCR with a universal oligonucleotide modified specific primer (SI Table S2). Quantitative analysis was performed by a two-step procedure with a Bio-Rad iQ5 Sequence detection system. The primer design and the twostep PCR is described in detail in SI.

CA). The extracted DNA was checked by agarose gel electrophoresis. Pyrosequencing using a Roche 454 FLX Titanium platform (Roche) was performed by Majorbio Company (Shanghai, China) following the manufacturer’s instructions. Universal primers 27F (5′-AGA GTT TGA TCC TGG CTC AG-3′) and 533R (5′-TTA CCG CGG CTG CTG GCA C-3′) were used to amplify bacterial 16S rRNA gene sequences. Primer 533R contained the A linker for sequencing and unique Roche multiplex identifiers (MID). The barcode was permuted for each sample and allowed the identification of individual samples in a mixture in a single pyrosequencing run.18 Polymerase chain reaction (PCR) amplification was performed following the protocols described in the SI. PCR amplicons from different water samples were then mixed to achieve equimolar concentrations in the final mixture for sequencing. The raw reads were deposited in the National Center for Biotechnology Information (BioProject ID: PRJNA232606, http://www.ncbi.nlm.nih.gov/bioproject/232606). 2.6. Sequence Processing and Statistical Analyses. The open-source, platform-independent, community-supported software program, Mothur (http://www.mothur.org)19 was used to process and analyze the sequence data. The detailed process is described in the SI. The hierarchical clustering method using average linkage was used to analyze the relationships between samples. The similarity among the microbial communities was determined using UniFrac analysis in which principal coordinate analysis (PCoA) and nonmetric multidimensional scaling (NMDS) were performed. MEGAN 5 was used to visualize the abundance distribution of the taxa as a pie chart and word cloud.20 The plotpv and corrgram21 functions in R (www.R-project.org) were used to analyze the correlations between the relative abundance of genera in each sample. Canonical correspondence analysis (CCA) of community correlation with environmental variables was performed using R. 2.7. Quantification of Community Abundance. Evaluation of community abundance by quantitative PCR was performed using the alkB gene and the 16S rRNA gene as molecular markers for hydrocarbon degraders and total

3. RESULTS 3.1. Production Performance During the IMEOR Process. During the nutrient injection, the residual nutrients were detected in production brines after 10 days later. The production wells showed a positive response to various degrees, 2−8 months after nutrient injection, and a cumulative incremental oil production of 2812 t was obtained by the end of September 2012, with an average incremental oil production of 0.1−5.0 t d−1 in each well (Figure 2). Based on the results of the physical simulations of oil displacement (SI Table S1) and produced metabolites (Table 1), the incremental oil production clearly resulted from the microbial flooding, not from the gas injection. According to the nutrient injection process and the production curves, there are clearly four production performance stages. Stage A is the normal water flooding before 5338

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did not detect biosurfactant concentration in the production brines, water-in-oil and oil-in-water microemulsions were observed during the microbial flooding process (SI Figure S2). Furthermore, the emulsification degree of the production liquid showed a positive response to the incremental oil production in each well. To explore microbial community restructuring during the microbial flooding process, we analyzed over 63,620 bacterial 16S rRNA gene sequences from the three production wells during the nutrient injection. The bacterial sequence libraries of the three production wells ranged in size from 2870 sequences to 6,553 sequences and contained 327−639 OTUs, with library coverage of 87.74−98.27% (more detailed information is listed in SI Table S3). According to the sequence analyses, diverse initial bacterial structures were determined in each production well before the microbial flooding process (Figure 4). Detailed data are described in the SI (Figure S3, S4, and S5). Thus, although the production wells were located in the same reservoir and were flooded by the same injection water, each well had its own unique ecological niche. The phylogenetic variation between these microhabitats was measured via UniFrac distances, revealing a significant difference in initial bacterial communities between wells T73 and T89, and between wells T80 and T89 (P ≤ 0.001). Low correlation coefficients were also observed between the three samples (SI Figure S6). Different physiochemical conditions may account for the discrepancy in microbial communities. During nutrient injection, the proportion of the shared microorganisms in the three microhabitats decreased, revealing a different differentiation in each production well (Figure 4, more detailed information is listed in the SI). The shared microorganisms in each production well also decreased after nutrient injection, indicating that, excluding shared species, some specific microbial species become dominant during the indigenous microbial flooding process (Figure 4 and SI Figures S3−S5). For example, Proteiniphilum (41.25%) and Pseudomonas (9.08%) were dominant before nutrient injection in production well T89 (Stage A); during nutrient injection (stage B), Proteiniphilum decreased to 1.18%, Pseudomonas increased to 18.73%, and Castellaniella increased from 0.80 to 28.08%. The community continued to change after nutrient injection (T89c): Pseudomonas decreased to 16.01%, Arcobacter increased from 0.79% to 14.55%, Castellaniella decreased to 0.02%, and Rhodococcus increased to 20.99%. After one year (T89d), the most abundant genera were Sulf urospirillum (22.54%), Thalassolituus (22.96%), and Arcobacter (12.72%), while Pseudomonas continuously decreased to 6.2% and Rhodococcus disappeared. The bacterial community in production wells T73 and T80 also changed and evolved into a new structure during the field trial (Figure 4 and SI Figures S3−S5). Detailed data are described in the SI. Cluster analysis and multivariate ordination diagrams of microbial communities indicated that the bacterial communities obtained during and after nutrient injection (Stages B, C, and D) were classified as new groups (Figure 5). 3.3. Biosurfactant Producers Closely Related to Incremental Oil Production. Further investigation was conducted to explore the relationship between the microbial structure−function and oil production performance during the field trial (Figure 3). A much higher proportion of abundant potential oil degraders or biosurfactant producers were observed in the incremental oil production stages (T80b; T89b,c; and T73c,d), providing evidence for important

Figure 2. Production performance and incremental oil production curves during the field trial.

nutrient injection. Stage B is the production increment period during the nutrient injection. Stage C is the production decrement period from two to six months after nutrient injection. Stage D is the production sustained and stable period after stage C for one year. To elucidate the relationship between microbial community and both injected nutrients and production performance, production wells T73 (no significant response to nutrient injection, but abundant incremental oil production), T80 (significant response to nutrient injection, but less incremental oil production) and T89 (significant response to nutrient injection, with abundant incremental oil production) were selected for analysis. A response, including remnants of nutrients, microbial growth and metabolite production, was observed in production wells T80 and T89 (Table 1). In production well T89, the incremental oil production was 1282 t, accounting for 46% of the total incremental oil. Oil production increased rapidly in Stage B during the nutrient injection, and remained at 30−50 t month−1 in stages C and D (Figure 3c I and II). However, only a 198 t incremental oil production (7% of the total) was obtained from production well T80 in the Stage B (Figure 3b I and II). In contrast, no significant response of nutrient residues and metabolite production was observed in production well T73, but the water content decreased and oil production increased after the nutrient injection (Stages C and D) (Figure 3a I and II), with an incremental oil production of 832 t, accounting for 30% of the total incremental oil. 3.2. Microbial Stimulation and Community Restructuring During the IMEOR Process. A typical sample in each stage was selected for microbial community pyrosequencing analysis (Figure 3a II, 3b II, and 3c II). For example, T89b was a typical sample in the incremental oil production stage (Stage B) in production well T89. Assuming that bacteria contained one copy of the metabolic functional genes,24,25 and 3.6 copy numbers of 16S rRNA genes per bacterial cell genome,26 qPCR indicated that the total number of bacteria in production wells T73, T80, and T89 reached 107−108 cells mL−1, compared with the initial 105−106 cells mL−1, whereas hydrocarbon oxidation bacteria increased by 1−2 orders of magnitude (Figure 3 IV). According to the residual sugar, nitrate and metabolites (HCO3− and acetate) observed in each production well (Table 1), it was clear that the injected nutrients enhanced microbial abundance and metabolite production. Although we 5339

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Figure 3. Production performance (I), incremental oil curves (II), and microbial response (III and IV) during the microbial flooding process for production wells. a represents the production well T73; b represents the production well T80; and c represents the production well T89. In part III, only the potential biosurfactants producers with >1% abundance are listed. Part IV represents the abundance of the total bacteria and hydrocarbondegrading bacteria obtained by real-time quantitative polymerase chain reaction analyses; the x axis (e.g., T73a−T73d) corresponds to the sample times shown in Part II.

structure−function relationships. The proportion of some shared biosurfactant producers showed little change, whereas some previously undetected bacteria became dominant during the microbial flooding process. Due to the total number of bacteria and oil degraders increasing by 1−2 orders of magnitude (Figure 3a IV, b IV, and c IV), these shared (Pseudomonas, Marinobacterium, Sulf urimonas, and Planococcus) and previously undetected bacteria (Dietzia, Shewanella, Rhizobium, and Rhodococcus) were clearly the major stimulated biosurfactant producers.

We assumed that if the microbial species were