Effect of Different Stimulation Methods on the Activation and Metabolic Performance of Microbial Community to Enhance Heavy Oil Recovery

Abstract

Microbial enhanced oil recovery (MEOR) is an economical and efficient tertiary recovery technology that can be used to increase the recovery of heavy oil reservoirs after steam thermal operation. However, the introduction of high-pressure steam with a temperature as high as 370 °C during thermal recovery can disrupt the indigenous microbial flora of oil reservoirs. Consequently, the effective activation of the functional microbial flora after steam thermal operation is crucial for heavy oil recovery. As such, we investigated the effects of different activation methods on oil viscosity reduction, biogas production, microbial community structure, and microbial metabolic performance. The highest viscosity reduction (61.59%), methane content (25.96%), and asphaltene degradation rates were achieved when low/high content of organic nutrients were alternately added in group L-H. The results of the FT-ICR MS analysis showed that the addition of a high content of organic nutrients promoted the degradation of N₁ classes, and the degree of aromaticity of N₁O₂ class compounds (DBE = 10) was reduced. The analysis of the microbial community showed that function bacteria, such as Firmicutes and Synergistetes, were effectively activated by the alternate addition of nutrients, which could prevent the accumulated fatty acids and accelerate the asphaltene degradation and methane production through the syntrophic relationship between syntrophic bacteria and methanogens. Thus, the alternate addition of nutrients has potential application for enhancing heavy oil recovery by simultaneously reducing heavy oil viscosity and improving methane production.

1. Introduction

Heavy oil resources are abundant worldwide, accounting for approximately 15% of the total crude oil reserves [1,2]. Light crude oil is becoming increasingly scarce with the large-scale development of conventional oil and gas reservoirs. However, the high resin and asphaltene contents in heavy oil increase the viscosity and limit the flow of heavy oil in reservoirs, resulting in high costs for their exploitation. The steam huff and puff method is a conventional approach used to recover heavy oil; however, the effectiveness of oil recovery gradually diminishes as the number of huff and puff cycles increases [2], which leads to uneconomical and expensive oil recovery, even when oil prices are high [3]. Therefore, low-cost and effective methods must be developed for heavy oil reservoir exploitation. The indigenous microbial enhanced oil recovery (IMEOR) method involves the use of indigenous microorganisms and their metabolites to reduce oil viscosity and mobilize heavy oil from its formation to increase heavy oil recovery. IMEOR is a low-carbon, environmentally friendly, low-cost, and sustainable method, providing an alternative method of increasing heavy oil recovery [1].

Microbes can be used to increase oil recovery through their metabolic activities, transforming heavy oil by reducing its average molecular weight [4]. Resin and asphaltene have high molecular weights and are resistant to degradation by microorganisms because of the linkage of aliphatic chains among multiple smaller polycyclic aromatic cores [5] and the hydrogen bonds associated with nitrogen, sulfur, and other heterogeneous elements [6]. In addition, the winding or crossing by long-chain alkanes increases the molecular weight of asphaltene. Hydrocarbon-oxidizing bacteria can transform alkanes and aromatic hydrocarbons into fatty acids, alcohols, aromatic compounds, and other organic matter in the near-well zone. Thus, the biodegradation of internal aliphatic linkages and long-chain alkanes by these microbes, via the surfactants and emulsifiers produced by bacteria, releases smaller molecular units of asphaltene to lower its molecular weight and reduce overall oil viscosity [7]. Fermentative bacteria, such as syntrophic bacteria, use organic matter to produce low-molecular-weight organic acids, H₂, and CO₂, which are then used by methanogens to produce methane. Thus, the synergistic interactions between syntrophic bacteria and methanogens are crucial for the metabolism of methanogenic hydrocarbons [8]. CO₂ and methane can increase oil formation pressure and reduce oil viscosity when dissolved in heavy oil [9]; many functional bacteria used for oil recovery, such as Bacillus [10], Geobacillus [11], and Pseudomonas [6], have both hydrocarbon oxidation and emulsifying functions. Thus, increasing the metabolic activity of these functional bacteria is crucial for degrading and displacing heavy oil.

The diversity and the metabolic capacity of microbes can be increased with the introduction of exogenous microbes during the waterflooding process. Drilling and fracturing are widely used in the thermal recovery of heavy oil and microbial oil recovery [12,13,14]. Exogenous microbes can be introduced by drilling and fracturing fluid. However, the microbes in heavy oil reservoirs are negatively affected by the injected steam temperature, which is as high as 370 °C; thus, the activation and performance of functional microbes are crucial for heavy oil recovery. As such, a variety of nutrient activators have been developed to stimulate the indigenous microbes that contain organic nutrients, such as peptone, urea [15], molasses [16], and corn steep liquor [17]. The recovery of heavy oil in the field is substantially increased via the activation of microorganisms [18,19]. Notably, the addition of organic nutrients (yeast extract) strengthens the activity of the microbial community and increases methane production [20]. Chen [21] found that the combined application of rice bran, K₂HPO₄, and urea considerably increased biogas, acid, and emulsifier production. Although functional bacteria can be activated by these organic nutrients to rapidly increase the oil recovery rate, some indigenous microbes, such as syntrophic bacteria, might be inhibited or not effectively activated, and the synergistic effects with methanogens might be weakened, resulting in the accumulation of fatty acids and reductions in methane yield, which inhibit the metabolism of other functional bacteria. To address this issue, hydrocarbon-degrading bacteria are typically screened from inorganic nutrient environments in which hydrocarbons or crude oil serve as the sole carbon source [22,23,24]. Thus, a low-content organic nutrient environment is more conducive to the enrichment of hydrocarbon-degrading bacteria and, thus, to the degradation of hydrocarbons and heavy components. Therefore, activation methods should be adapted for heavy oil exploitation to simultaneously reduce oil viscosity, degrade hydrocarbons, and produce methane. For example, injecting different activators may maintain the metabolic activity of functional bacteria. However, to the best of our knowledge, few studies on microbial community structure and metabolic functional analysis have used different activation methods to extract heavy oil.

In this study, two activators, low-content organic nutrition (LON) and high-content organic nutrition (HON), were used alternately to stimulate the indigenous microbes in the formation water. The resulting changes in oil viscosity, oil components, biogas, and volatile fatty acid (VFA) yield were investigated. Microbial community structure and metabolomics analyses were also employed to determine the mechanism through which viscosity is reduced, and methane is produced.

2. Materials and Methods

2.1. Chemicals and Materials

Yeast powder and peptone were purchased from Beijing Aoboxing Biotechnology Co. Ltd., China, (Beijing, China). Ethyl acetate, glucose, urea, CH₂Cl₂, K₂HPO₄, NaCl, and other reagents were purchased from Sinopharm Group Chemical Reagent Co. Ltd, (Shanghai, China). All reagents used for the analyses were commercially available, and the activators used were as follows. LON: urea 0.1%, K₂HPO₄ 0.27%, NaCl 0.5%. HON: glucose 0.3%, peptone 0.3%, yeast powder 0.2%, K₂HPO₄ 0.27%, and NaCl 0.5%.

Heavy oil and formation water were collected from a heavy oil production well in the Shengli Oilfield, China (37°54′ N, 118°330′ E). The formation temperature was 65 °C, and the viscosity of the degassed crude oil was 1229.97 mPa·s. The injection and production waters were mixed at 1:1 (volume ratio) to simulate the formation water. The parameters of well and formation water are shown in Table S1.

2.2. Experiment Setting and Method

A nutrient activator was added to the formation water and placed in 500 mL sterilized anaerobic bottles containing 5 g/L heavy oil. The anaerobic bottles were sealed with a butyl rubber plug and filled with nitrogen for 5 min to remove the air. Then, all bottles were incubated at 65 °C in an incubator and shaken twice per day. The biogas yield in the bottle was collected and measured using a syringe in an anaerobic glove box every 7 days. A total of 50 mL of the culture solution for each sample was replaced with an equal volume of fresh formation water containing different nutrients. The total experimental period was 35 days; the experimental settings are presented in

Table 1. Experimental setting.

Group	Sample Composition
N	Formation water
L	Formation water + LON
H	Formation water + HON
L-H	Formation water + alternate addition of LON and HON

. Group N was supplemented with only formation water and was established as the control group; group L was supplemented with formation water and LON; group H was supplemented with formation water and HON; and group L-H was alternately supplemented with LON and HON in formation water. The crude oil after incubation was collected and dried at 65 °C for 3 d to remove any water. The viscosity of crude oil at 65 °C was determined using a HAACK rheometer (HAAKE MARS, Thermo, Germany). The viscosity reduction rate of heavy oil after degradation was calculated as follows:

$$\mathrm{v}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{r}\mathrm{e}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}={\displaystyle \frac{{\mu }_{initial}-{\mu }_{degraded}}{{\mu }_{intial}}}\times 100\%$$

where ${\mu }_{initial}$ is the initial viscosity of heavy oil, and ${\mu }_{degraded}$ is the viscosity of heavy oil after degradation.

2.3. Analysis of Biogas and VFAs

The biogas produced by the bacteria was determined using gas chromatography (Shimadzu GC-2010 Plus, Suzhou, China). A thermal conductivity detector was used to determine the methane, H₂, CO₂, and N₂ contents at an injection temperature of 150 °C; a detector temperature of 175 °C; an initial column temperature of 50 °C, maintained for 11 min; and temperatures increasing to 190 °C at a rate of 8 °C/min. A flame ionization detector was used to identify VFA contents. The injection and detector temperatures were 230 °C and 250 °C, respectively; the initial column temperature was 35 °C for 15 min, which was then increased to 230 °C at a rate of 10 °C/min and maintained for 10 min.

2.4. Analysis of Saturated Hydrocarbons and Heterocyclic Compounds

The saturated hydrocarbons and nonhydrocarbons in crude oil were separated and analyzed according to methods previously reported in the literature [25]. The crude oil was divided into four components via column chromatography: saturated hydrocarbons, aromatics, resins, and asphaltenes (SARAs). The composition of n-alkanes in the crude oil was determined via gas chromatography–mass spectrometry (GC–MS) (Agilent 6890 N GC coupled with an Agilent 5973 N mass spectrometer). An HP-5MS fused silica column (60 m × 0.25 mm × 0.25 mm) was used for GC–MS analysis. The chromatographic conditions were as follows: the carrier gas was helium (99.99% purity), and the inlet temperature was 300 °C. The heating procedure involved an initial temperature of 80 °C; after 1 min at constant temperature, the carrier gas flow was 1 mL/min, and the injection volume was 1 μL. The heterocyclic compounds in the crude oil were analyzed via Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS, Dresden, Germany). An appropriate amount of crude oil was weighed and dissolved in toluene/methanol (volume ratio 1:3), diluted to a concentration of 0.1 mg/mL, and gently shaken to ensure uniform mixing. Then, 15 μL/mL ammonia was added in the negative-ion mode of the ESI ionization source to increase the ionization efficiency of the sample. The mass spectrometer used was a PEX Ultra type FT-ICR MS (Bruker Corporation, Karlsruhe, Germany), and the magnetic field strength of the superconducting magnet was 9.4 T. The injection speed was 180 L/h, the nozzle voltage was 3.5 kV, and the capillary inlet and outlet voltages were 4.5 kV and −320 V, respectively. The four-stage rod Q1 optimized the transmission parameters at m/z200; the ion and reservoir radio frequencies were 5 MHz and 400 Vpp, respectively; the collision cell argon flow rate was 0. 3 L /h; the collision energy was 1.5 eV; storage time was 1 s; the iontophoresis analysis pool time was 1.2 ms; and the mass range was 150–1000 Da. Spectral patterns were superimposed 128 times to increase the signal-to-noise ratio. All mass spectrometry peaks with a signal-to-noise ratio greater than six were imported into an Excel table after mass calibration of the alkyl carbazole compound series in the actual sample before and after sample collection. The corresponding compound molecular composition was calculated using data processing software developed in our laboratory.

2.5. Microbial Community Analysis

Bacterial cells after incubation were obtained via centrifugation and stored at −80 °C. The genomic DNA of the flora was extracted using an E.Z.N.A.^® soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) following the manufacturer’s instructions. After extraction, the V3-V4 region of bacterial 16S rRNA was amplified via PCR. The forward and reverse primers used were 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R(5′-GGACTACHVGGGTWTCTAAT-3′) [26], respectively. The PCR reaction system (25 μL) included 0.5 μL each of the forward and reverse primers, 1 μL of DNA template, and PCR premix. The amplified product was purified and sequenced using the Illumina MiSeq platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The 16S rRNA gene fragments were obtained from each sample to calculate the microbial α diversity. The OTU clustering pipeline UPARSE 7.1 was used to select the OTUs with a 97% similarity. The alpha diversity indices ChaO₁, ACE, and Shannon were calculated with Mothur v1.30.1. The ChaO₁ and ACE indices can reflect the richness of the microbial community, while the Shannon index reflects the diversity of microbial community [27]. The OTU composition among different groups was determined via principal component analysis (PCA) [28]. The linear discriminant analysis (LDA) effect size (LEfSe) was used to identify species that were significantly different among the different groups (LDA score > 2, p < 0.05) [29].

2.6. Metabolomic Analysis

The supernatant was collected for nontargeted metabolomic analysis via LC-MS/MS after centrifugation of the formation water of the different samples, with four replicates in each group of samples. We used a high-resolution mass spectrometer Q Exactive (Thermo Fisher Scientific, Waltham, MA, USA) to increase the coverage of metabolites. Compound Discover 3.1 (Thermo Fisher Scientific, USA) software was used for peak extraction, peak alignment, and compound identification. The R software (v 4.3.1) package metaX [30] and metabolomic information analysis were used for data preprocessing, metabolite classification, and functional annotation. Fold changes and t-test results were obtained via univariate analysis to screen for differential metabolites.

3. Results

3.1. Biogas and Oil Viscosity

The cumulative biogas production of the four groups is shown in Figure 1A. A total of 1 and 5 mL of gases were generated in groups N and L, respectively, indicating that biogas could not be produced effectively with no nutrients or low organic nutrient content. High amounts of organic nutrients were added to group H, and the alternating addition of LON and HON was performed in the L-H group. The biogas yields in groups H and L-H were 267 and 165 mL, respectively, indicating that the addition of HON effectively promoted the production of biogas. The biogases produced in groups H and L-H were analyzed using gas chromatography and were mainly CO₂ and CH₄ in both groups, as shown in Table S3. The relative abundances of CO₂ and CH₄ in group H and group L-H were 45.01% and 15.08%, and 25.1% and 25.96%, respectively. The CO₂ content in group H was higher than that in group L-H, whereas the CH₄ content in group L-H was higher than that in group H. Moreover, the H₂ content in group H (0.02%) was lower than that in group L-H (1.03%). The composition and content of VFAs are shown in Figure 1B. The content of seven volatile fatty acids (acetate, propionic acid, butyric acid, isobutyric acid, valeric acid, isovaleric acid, and caproic acid) in group L-H was generally lower than that in group H, suggesting that adding HON alone can increase the production of VFAs. and the pH value of group L-H (pH = 7.42) was higher than that of group H (Ph = 6.28) (Figure 1C). This indicates that the high content of VFAs leads to a decrease in the pH value in group H. Thus, using different methods to add nutrients can affect the yield and composition of biogas and VFAs, and the alternating addition of LON/HON was able to increase the H₂ and CH₄ yield.

The change in the viscosity of the crude oil is shown in Figure 1D. After incubation, the viscosity of heavy oil at 65 °C in group N decreased from 1229.97 mPa·s to 1227.73 mPa·s, showing that the viscosity of crude oil could not decrease under conditions where no nutrients were added. The viscosity of crude oil decreased to 1042.37 mPa·s at a reduction rate of 15.07% in group L, and the oil viscosity in groups H and L-H reduced to 572.57 and 472.38 mPa·s with reduction rates of 53.45% and 61.59%, respectively, indicating that oil viscosity decreased after the nutrients added, and a remarkable reduction was achieved after adding HON. The oil viscosity was reduced the most via the alternating addition of LON/HON.

3.2. Crude Oil Composition Analysis

3.2.1. GC–MS Analysis

The amount of SARAs produced via the different nutrient addition methods is shown in Table S2. The resin contents in groups N, L, H, and L-H were 13.55%, 12.35%, 19.11%, and 15.75%, respectively, and the asphaltene contents were 23.89%, 23.53%, 22.92%, and 21.52%, respectively. The asphaltene contents in groups H and L-H were lower than those in groups N and L; in contrast, their resin contents were higher than those in groups N and L. This indicated that the addition of HON increased the biodegradation of asphaltene. The results of GC–MS analysis showed that the degradation rate of long-chain n-alkanes (C14–C35) in groups H and L-H was higher than that in groups N and L, with the highest rate found for group L-H (Figure 2). These results suggest that long-chain n-alkanes are preferentially degraded under the alternating addition of nutrients.

3.2.2. FT-ICR MS Analysis of Heterocyclic Compounds

Heterocyclic compounds are important components of asphaltenes, which increase oil viscosity and are difficult to degrade, have a higher molecular weight in heavy oil. FT-ICR MS analysis was performed to investigate the distribution of the heterocyclic compounds in the resin and asphaltene. During the degrading process, some of the intermediate products of petroleum hydrocarbon are heterocyclic compounds containing N and O atoms [31]. As shown in Figure 3, the heterocyclic compounds detected in the negative-ion ESI mass spectra included the N₁, N₁O₁, N₁O₂, O₁, and O₂ classes. The relative abundance of the N₁ classes was the highest, followed by that of the O₂ classes, in all groups. After incubation, the relative abundance of the N₁ classes was 47.4%, 45.8%, 36.1%, and 38% in groups N, L, H, and L-H, respectively. The abundances of the N₁O₁ and O₁ classes were lower in groups H and L-H than in group N, whereas the relative abundances of the N₁O₂ and O₂ classes were higher, indicating that the addition of HON facilitated the transformation of N₁ classes, increasing the abundances of the N₁O₂ and O₂ classes.

The distribution of O₁ classes is shown in Figure S1. The O₁ classes in group N were dominated by DBE = 4–6 with 15 to 38 carbons and DBE = 16–18 with 18 to 42 carbons, corresponding to phenols and alkylphenols, respectively [32]. After adding LON in group L, the abundance of O₁ classes (DBE= 4–6, carbon number = 15–38) decreased remarkably, and the abundance of the O₁ classes (DBE = 17, 20, carbon number= 28–31) increased, suggesting that the abundance of phenols decreased, and the abundance of some alkylphenols with high carbon number increased. Compared with group L, the abundance of compounds (DBE = 4–6) decreased in the H and L-H groups, suggesting that phenolic compounds were effectively degraded; the abundance of compounds (DBE = 18, carbon number = 25–28) increased, suggesting that alkylphenols with low carbon numbers were produced after adding HON, while the alkylphenols with high carbons (30–40) were degraded.

The DBE value versus the number of carbons for the O₂ classes is shown in Figure S2. The O₂ species were dominated by DBE values of 1–4 with 15 to 35 carbons in group N (Figure S2A), which are acyclic fatty acids (DBE = 1) and naphthenic acids (DBE = 2~4) [33]. In groups L and L-H, the abundance of naphthenic acids (DBE = 2, carbon number = 22–30) was generally lower, whereas the distribution of naphthenic acids (DBE = 3) shifted toward regions with lower carbon numbers, indicating that the alkyl chain might be degraded.

The distribution of N₁O₁ classes is shown in Figure S3. The N₁O₁ classes were dominated by DBE values of 13–18 with 20–30 carbons in group N. After degradation, the relative abundance of compounds (DBE = 13, 14, and 17) with carbons of 20–25 in group L was lower than those in group N, indicating that microorganisms have a certain ability to degrade N₁O₁ compounds under high-content organic nutrition level. The relative abundance of compounds (DBE = 15, 18, carbon number = 20–30) in group L-H was lower than that in other groups, suggesting that those compounds were degraded effectively after alliteratively adding LON and HON.

As shown in Figure S4, the relative abundance of N₁O₂ classes (DBE = 9) was lowest after incubation in group L-H, and the relative abundance of N₁O₂ classes (DBE = 10, carbon number = 15–20) in group L-H was higher than those in the other groups, indicating that the aromaticity of the N₁O₂ classes decreased after biodegradation with the alternating addition of LON and HON.

3.3. Microbial Community Analysis

We performed 16S rRNA gene sequencing analysis for groups N, L, H, and L-H after 35 d of incubation. Alpha diversities were calculated, including ChaO₁, ACE, and Shannon, with the results shown in

Table 2. Alpha diversity analysis.

Group	Chao	ACE	Shannon	Coverage
N	135.98	151.49	3.01	0.999572
L	99.84	99.68	1.35	0.999635
H	30.50	33.78	1.14	0.999865
L-H	46.83	48.54	0.35	0.999858

. The ChaO₁ and ACE indices reflect the richness of a microbial community. The ChaO₁ and ACE indices of groups N and L were higher than those of groups H and L-H, indicating that the bacterial community richness in groups N and L was higher than in groups H and L-H. The addition of a high content of organic nutrients lowered bacterial community richness. The Shannon indices are used to comprehensively evaluate community evenness. The Shannon indices in groups H and L-H were lower than those in groups N and L, indicating that the microbial community was less even after the addition of organic nutrients. The bacterial community evenness of oil reservoirs always decreases after injecting nutrients [34]. However, although the diversity of the microbial community in group L-H was the lowest, its Chao 1 index was higher than that of group H, indicating that the abundance of some species increased after the alternating addition of nutrients. The PCA results (Figure S5) revealed the differences among the different stimulation methods. We found a notable separation among the four groups, indicating large differences in the microbial communities among the groups due to the microbial stimulation method.

The species composition of each group at the phylum level is shown in Figure 4A. Firmicutes, Thermotogae, Thermodesulfobacteri, and Atribacteria were the dominant phyla in group N. These phyla are usually identified as the main bacterial groups in oilfield reservoirs [35].

After incubation, the bacterial community structure markedly changed. The relative abundance of Firmicutes in groups N, L, H, and L-H was 43.74%, 81.89%, 99.9%, and 4.23%, respectively. Interestingly, the relative abundance of Firmicutes increased and had the highest abundance in both groups L and H, where only one type of nutrient was added, which is consistent with the findings of Liang [34] as well as Shahi [36]. However, the abundance of Firmicutes was significantly decreased in group L-H, where HON and LON were added alternately.

The relative abundance of Thermotogae in groups N, L, H, and L-H was 31.39%, 2.54%, 0.0008%, and 1.49%, respectively. Thermodesulfobacteri and Atribacteria were not detected in groups L and H. The relative abundance of Thermodesulfobacteri was 4.6% and 0.016% in groups N and L-H, respectively, and the relative abundance of Atribacteria was 14% and 0.15% in groups N and L-H, respectively, indicating that the addition of neither LON nor HON promoted the growth of Thermotogae, Thermodesulfobacteri, and Atribacteria.

Proteobacteria was always the predominant phylum and could be enriched effectively [34,37]. However, the abundance of Proteobacteria remained low in all four groups, indicating that neither LON nor HON can activate the Proteobacteria.

The abundance of Synergistetes in groups N, L, H, and L-H was 0.19%, 13.65%, 0.005%, and 94%, respectively, indicating that the addition of LON could effectively activate Synergistetes. The alternate addition of nutrients could achieve the highest abundance of Synergistetes.

The genus-level species composition is shown in Figure 4B. Species with abundances below 0.05% and those that were not annotated were merged into others. The abundances of Pseudothermotoga (22.88%), Geobacillus (10.57%), Pelotomaculum (9.9%), and Thermotoga (7.5%) were relatively higher in group N. Group L was dominated by Geobacillus (41.58%), Coprothermobacter (38.94%), Anaerobaculum (13.65%), and Fervidobacterium (2.06%). Group H was dominated by Thermoanaerobacter (62.61%) and Gelria (30.11%). For the L-H group, Anaerobaculum (93.98%), Caldanaerobacter (3.26%), and Pseudothermotoga (1.36%) were the dominant species. Anaerobaculum can transform organic acids, carbohydrates, and amino acids into acetate, hydrogen, and carbon dioxide [38,39]. Pseudothermotoga produces acetate, propionic acid, and other VFAs [40]. In addition, Pseudothermotoga elfii can ferment crude oil into H₂ in an anaerobic environment, and the production of H₂ can be increased via the addition of biosurfactants [41]. Caldanaerobacter and hydrogenotrophic methanogens are involved in syntrophic interactions for methane production [42].

LEfSe analysis was used to identify the species or biomarkers with the strongest contribution in each group. As shown in Figure S6, Firmicutes contributed the most to group H (LDA score > 4.8), which included Clostridia, Thermoanaerobacterales, and Gelria. Synergistetes contributed the most to group L-H, which included Synergistales, Synergistaceae, and Anaerobaculum.

3.4. Metabolomic Analysis

As shown in Figure 5, the differential metabolic pathways between groups L-H and H primarily involved metabolic pathways, the biosynthesis of secondary metabolites, the biosynthesis of amino acids, ABC transporters, and purine and 2-oxocarboxylic acid metabolism. In terms of differential metabolites (Table S4), the metabolites upregulated in group L-H included organic heterocyclic compounds, organic acids, benzene and its derivatives, organic nitrogen compounds, amino acids, peptides, and alkaloids, which indicated that the alternating addition of nutrients increased the microbial use of heterocyclic compounds and aromatic compounds in heavy oil. The increases in the contents of amino acids, peptides, and alkaloids indicated that amino acid synthesis and metabolism were higher in group L-H.

4. Discussion

4.1. Effect of Nutrient Addition Method on Reducing Viscosity and Degrading Heavy Oil

Heavy oil is highly viscous due to the high resin and asphaltene contents [43]. The asphaltene contents after incubation in groups H and L-H were lower than those in the other groups. The degradation rates of long-chain n-alkanes (C14–C35) were also higher in groups H and L-H, indicating that adding HON was beneficial for degrading n-alkanes and asphaltenes. N-alkanes are the most degradable compounds in crude oil, so their extent of degradation is higher than that of the other crude oil fractions [25]. The entanglement and intercalation of long-chain n-alkanes and aliphatic chains among the asphaltenes can increase the molecular weight of asphaltene [44]. Thus, we inferred that the degradation of long-chain alkanes and aliphatic chains is beneficial for reducing the molecular weight of asphaltenes. The asphaltene content in group L-H was lower than that in the other groups, at 21.52%, and the degradation rate of the long-chain alkanes was higher than that in the other groups, indicating that the alternating nutrient supplementation effectively facilitated the degradation of both asphaltenes and alkanes, consequently leading to a decrease in oil viscosity.

Resin and asphaltene biodegradation can be characterized using the distribution of polar compounds. O₁ and O₂ classes are intermediates in the crude oil biodegradation pathways [45]. Some O₁ and O₂ classes can be degraded under aerobic and anaerobic conditions [46]. The addition of HON led to the substantial degradation of O₁ class compounds with a DBE of 4–6 in both the H and L-H groups (Figure S1). Moreover, the number of carbons in the O₁ classes with high aromaticity (DBE = 18) was notably reduced. This suggests that the addition of HON promotes the degradation of low-aromaticity phenolic compounds and effectively degrades the alkyl chains of O₁ classes with high DBE values. In contrast, the degradation of O₁ classes in group L, to which only LON was added, was weaker. Additionally, O₁ class compounds with high DBE values of 17 and 20 accumulated. Figure 3 illustrates that the abundance of O₂ classes in groups L, H, and L-H was higher than that in group N. Specifically, in groups H and L-H, the abundance of O₂ species increased with DBE values of 1 and 3 and between 10 and 15 carbons, suggesting that n-alkane and cycloparaffin degradation was enhanced, leading to the accelerated production of acyclic fatty acids and naphthenic acids. In parallel, the abundance of O₂ classes with higher numbers of carbon (23–30) decreased, indicating that the long aliphatic chains within these O₂ classes were effectively degraded following the addition of HON.

N₁ classes detected by FT-ICR MS in the negative-ion ESI mode are alkylated carbazoles, benzocarbazole, and some other pyrrolic nitrogen compounds [47]. N₁O₁ and N₁O₂ classes are intermetabolites in the degradation of N₁ compounds by microbes [24]. The N₁O₁ and N₁O₂ class concentrations were likely increased because of the release of small polycyclic aromatic cores after the degradation of the aliphatic chains of the high-molecular-weight N₁ classes, which is beneficial for lowering the molecular weight of heterocyclic compounds and reducing the viscosity of crude oil. The relative abundance of N₁O₁ classes with DBE values of 13, 15, and 18 was lower in group L-H than in group H. Concurrently, the abundance of N₁O₂ classes at DBE values of 10 and 11 was higher, particularly within the low carbon range of 15 to 25 carbons. A low DBE value indicates the low unsaturation and aromaticity of a compound [48]. Moreover, a marked decrease in the abundance of N₁O₂ classes (DBE = 9) was observed in group L-H. These observations indicated a more pronounced degradation effect of the L-H treatment on the N₁ classes. Thus, alternatively adding LON and HON accelerates the degradation of N₁ classes, thereby promoting the degradation of N₁ compounds in heavy oil.

Asphaltenes have large molecular weight and complex structure, which includes condensed aromatic rings, alkyl side chains, cycloalkane rings, and heteroatoms. Thus, it is difficult to describe their degradation process using precise molecular structures. The putative biodegradation pathway of asphaltenes is shown in Figure S7. The degradation of condensed aromatic rings is based on the mechanisms of polycyclic aromatic hydrocarbons (PAHs), involving ring-opening and degradation under the action of enzymes such as dioxygenase, hydroxylase, and hydrogenase [49]. Nitrogen-containing heterocyclic compounds are also degraded via hydroxylation, ring-opening to form hydroxyalkanoic acids, and conversion to phenolic compounds. The degradation of alkyl side chains follows alkane degradation pathways [50]. The degradation mechanism of asphaltenes still needs further investigation.

4.2. Effect of Nutrients Addition Methods on Bacterial Community and Metabolism

Synergistic metabolism is an important interspecific interaction between microorganisms, which can prevent the accumulation of fatty acids through their anaerobic metabolism to produce acetate and H₂ [51,52]. H₂ and CO₂ are catalyzed into methane with the association of methanogen acetate. Thus, the activation of Synergistetes is crucial for methanogenic hydrocarbon metabolism. Synergistetes becomes one of the dominant species in the microbial community during the later stages of cultivation in the methanogenic transformation of crude oil [53]. As shown in Figure 4A, Synergistetes abundance was high in group L-H at the phyla level, and Anaerobaculum and Pseudothermotoga were the dominant species, which can transform VFA into acetate, H₂, and CO₂. The pH in group L-H was higher than that in group H (Figure 1D). The lower VFA concentration (Figure 1C) indicated that the Synergistetes in group L-H metabolized propionic, butyric, valeric, and caproic acids to prevent the accumulation of fatty acids that would inhibit microbial metabolism. The methane yield was highest in group L-H, suggesting that the enrichment of Synergistetes within this group supplied ample substrate for methanogens to generate CH₄. This process, in turn, accelerated the degradation of heavy oil, especially for the long-chain alkane and asphaltenes, and reduced the viscosity of heavy oil effectively. Thus, the viscosity reduction rate of heavy oil was achieved in group L-H. However, the species composition of the methanogens and the mechanism of methane production should be further studied. Similarly, the effect of alternative activation methods under different reservoir temperatures should also be evaluated.

5. Conclusions

The alternating addition of LON and HON in group L-H effectively reduced the viscosity of crude oil and enhanced methane production. This alternating nutrient addition method effectively stimulated the activity of functional bacteria, including that of Firmicutes and Synergistetes. Synergistetes, as an important phylum in the microbial community, prevented volatile fatty acid accumulation, thereby suggesting that methane yield was increased through the syntrophic metabolism between syntrophic bacteria and methanogens. Furthermore, the degradation of heterocyclic compounds was accelerated. Thus, we concluded that the alternating nutrient addition method effectively reduces the viscosity of heavy oil and increases methane conversion simultaneously. Future research will focus on the methanogenic pathways of microorganisms under alternating nutrient addition. Based on this, the effects of microorganisms on heavy oil degradation and viscosity reduction will be evaluated under different reservoir temperatures.