Environmental Conditions and Diffusion-Limited Microbial Transfer Drive Specific Microbial Communities Detected at Different

bioRxiv preprint doi: https://doi.org/10.1101/2020.12.07.415646; this version posted December 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

1 Title: Environmental conditions and diffusion-limited microbial transfer drive specific

2 microbial communities detected at different sections in oil-production reservoir with

3 water-flooding

4 Running title: Microbial communities in oilfield-production facility

6 Peike Gaoa*, Huimei Tianb, Guoqiang Lic, Feng Zhaoa, Wenjie Xiac, Ji-Dong Gud, Jianjun Lee,

7 Ting Mac*

9 a College of Life Sciences, Qufu Normal University, Qufu, Shandong, 273165, The People's

10 Republic of China;

11 b College of Forestry, Shandong Agricultural University, Tai’an, Shandong, 271018, The

12 People's Republic of China;

13 c College of Life Sciences, Nankai University, Tianjin, 300071, The People's Republic of

14 China;

15 d Environmental Engineering, Guangdong Technion - Israel Institute of Technology, 241

16 Daxue Road, Shantou, Guangdong, 515063, The People’s Republic of China;

17 e Exploration and Development Research Institute, PetroChina Daqing Oilfield Limited

18 Company, Daqing, Heilongjiang, 163000, The People’s Republic of China

20 *Corresponding authors: Peike Gao, College of Life Sciences, Qufu Normal University,

21 Qufu, Shandong, P. R. China. Tel: 86-537-4456134. E-mail: [email protected]. Orcid:

22 0000-0002-0320-8187. Ting Ma, College of Life Sciences, Nankai University, Tianjin, P. R.

23 China. Tel: 86-22-23498185. E-mail: [email protected]

24 ABSTRACT This study investigated the distribution of microbial communities in the oilfield

25 production facilities of a water-flooding petroleum reservoir and the roles of environmental

26 variation, microorganisms in injected water, and diffusion-limited microbial transfer in

27 structuring the microbial communities. Similar bacterial communities were observed in

28 surface water-injection facilities dominated by aerobic or facultative anaerobic

29 Betaproteobacteria, Alphaproteobacteria, and Flavobacteria. Distinct bacterial communities

30 were observed in downhole of the water-injection wells dominated by Clostridia,

31 Deltaproteobacteria, Anaerolineae, and Synergistia, and in the oil-production wells dominated

32 by Gammaproteobacteria, Betaproteobacteria, and Epsilonproteobacteria. Methanosaeta,

33 Methanobacterium, and Methanolinea were dominant archaeal taxa in the water-injection

34 facilities, while the oil-production wells were predominated by Methanosaeta,

35 Methanomethylovorans, and Methanocalculus. Energy, nucleotide, translation, and glycan

36 biosynthesis metabolisms were more active in the downhole of the water-injection wells,

37 while bacterial chemotaxis, biofilm formation, two-component system, and xenobiotic

38 biodegradation was associated with the oil-production wells. The number of shared OTUs

39 and its positive correlation with formation permeability revealed differential diffusion-limited

40 microbial transfer in oil-production facilities. The overall results indicate that environmental

41 variation and microorganisms in injected water are the determinants that structure microbial

42 communities in water-injection facilities, and the determinants in oil-bearing strata are

43 environmental variation and diffusion-limited microbial transfer.

44 IMPORTANCE Water-flooding continually inoculates petroleum reservoirs with exogenous

45 microorganisms, nutrients, and oxygen. However, how this process influences the subsurface

46 microbial community of the whole production process remains unclear. In this study, we

47 investigated the spatial distribution of microbial communities in the oilfield production

48 facilities of a water-flooding petroleum reservoir, and comprehensively illustrate the roles of

49 environmental variation, microorganisms in injected water, and diffusion-limited microbial

50 transfer in structuring the microbial communities. The results advance fundamental

51 understanding on petroleum reservoir ecosystems that subjected to anthropogenic

52 perturbations during oil production processes.

53 KEYWORDS Environmental selection · Metabolic profiles · Oilfield production

54 facilities · Petroleum reservoir

55 INTRIDUCTION

56 Petroleum reservoirs contain microorganisms with diverse phylogenetic affiliations and

57 metabolic characteristics, including hydrocarbon-degrading bacteria, fermentative bacteria,

58 sulfate-reducing bacteria, iron-reducing bacteria, acetogens, and methanogens, among others

59 (1, 2). The main microbial processes prevailing in petroleum reservoir ecosystems include

60 hydrocarbon degradation, nitrate reduction, sulfate reduction, fermentation, acetogenesis,

61 methanogenesis, and iron and manganese reduction (3, 4). With the increasing global demand

62 for crude oil, research in the field of petroleum reservoir microbial communities has attracted

63 increasing attention due to its great potential in improving oil production processes, such as

64 microbiologically enhanced oil recovery (5-11) and microbiologically-prevented reservoir

65 souring and equipment corrosion (12-16).

66 In recent decades, a large number of studies have investigated the composition of microbial

67 communities in global petroleum reservoirs (17-21). Given the pronounced differences in

68 inherent conditions among the reservoirs, in particular key microbial growth limiting factors,

69 such as temperature (13, 22), salinity (23, 24), and pH (25), a variety of microbial ecological

70 patterns with high variability in community composition have been identified in these

71 ecosystems. Furthermore, studies are increasingly indicating that microbiologically-improved

72 oil production processes are closely related to changes in the microbial communities of

73 reservoirs, such as altered microbial abundance and composition (5, 26-29). This is of great

74 significance to the study of the microbial communities inhabiting petroleum reservoirs.

75 Petroleum reservoir ecosystems are subject to extreme anthropogenic perturbations during

76 oil exploration and oil production processes, such as drilling, workover, and the application

77 of secondary and tertiary oil recovery techniques, all of which introduce new electron

78 acceptors, donors, and exogenous microbes into reservoir environments. Recently, Vigneron

79 et al. (2017) elucidated the succession of microbial communities that occur over the

80 production lifetime of an offshore petroleum reservoir (19). Their results expanded our

81 current knowledge regarding the shifts of reservoir microbial communities in different stages

82 of oil exploitation. However, little is known about the microbial communities in oilfield

83 production facilities. Research in this field has profound consequences on our understanding

84 of the microbial community distribution in reservoirs and the factors driving changes, and

85 will improve our ability to predict and regulate reservoir microbial communities to

86 microbiologically improve oil production processes.

87 Petroleum and formation water are usually pushed to the surface by pressure naturally

88 found within a reservoir, known as primary recovery. Subsequently, water injection is an

89 efficient and inexpensive secondary recovery process that is widely used to maintain

90 reservoir pressure and achieve a higher oil-production level. The injected water generally

91 consists of recycled water produced from oil-production wells and make-up water consisting

92 of seawater, river water, or underground water, which contains large amounts of inorganic

93 ions (e.g. nitrate and sulfate), dissolved oxygen, and a mass of exogenous microorganisms

94 (17, 19, 22, 30). The water-flooding process results in the continual inoculation of reservoirs

95 with exogenous microorganisms, nutrients, and oxygen, which will likely alter reservoir

96 geochemistry either temporarily or permanently, and significantly influence the subsurface

97 microbial community.

98 In water-flooding oil reservoirs, the injected water from water injection stations flows into

99 water-injection wells, then into oil-bearing strata, and outflows from oil-production wells

100 with crude oil (Fig. 1). Before water flows into oil-bearing strata, the microorganisms in the

101 injected water may be easily transferred from the water-injection station to the wellheads and

102 downhole of the water-injection wells. It is not hard to speculate that the exogenous

103 microorganisms in the injected water and environmental variation within habitats rather than 5

104 diffusion-limited microbial transfer may play a more crucial role in structuring the microbial

105 communities in the pipelines. Once the injected water flows from the downhole of

106 water-injection wells into the oil-bearing strata and then into the oil-production wells,

107 environmental variation within habitats and diffusion-limited microbial transfer may lead to

108 microbial community assembly processes. Several studies have observed considerable

109 uniformity among the microbial communities inhabiting geographic-adjacent or -isolated

110 oil-production wells (17, 18, 31, 32). However, whether the exogenous microorganisms are

111 able to enter oil-bearing strata and reach oil-production wells remains a subject of debate, let

112 alone their effects on subsurface microbial communities. Therefore, a systematic study of the

113 microbial communities is needed to elucidate the roles of environmental variables within

114 habitats, including the microorganisms in injected water and diffusion-limited microbial

115 transfer, in the structuring of the microbial communities in petroleum reservoirs.

116 In the present work, we investigated the compositions and metabolic profiles of the

117 bacterial and archaeal communities in water injection stations, wellheads, and downhole of

118 water-injection wells and oil-production wells of a water-flooding petroleum reservoir using

119 16S rRNA gene sequencing, and analyzed the vital influences of environmental variation,

120 microorganisms in injected water, and diffusion-limited microbial transfer in structuring the

121 microbial communities.

122 RESULTS

123 Microbial community compositions through the oilfield production facilities

124 After filtering low quality reads and chimeras, a total of 38,102 and 39,729 of bacterial and

125 archaeal sequences on average were obtained for each sample, respectively. The average

126 OTU numbers of the bacterial and archaeal communities were 470 and 67, respectively. The

127 α-diversity indices of the bacterial and archaeal communities in the soil samples were the

128 highest than those in the petroleum reservoir and the water-injection facilities (Fig. 2a and

129 Fig. S1). While similar α-diversity values of the bacterial and archaeal communities was

130 detected between the water-injection stations and the wellheads of the water-injection wells,

131 the samples in the downhole of the water-injection wells showed higher Sobs (546 vs 791, p

132 < 0.01) and Shannon (3.89 vs. 4.09, p < 0.01) indices for the bacterial communities, and

133 lower values for the archaeal communities (66 vs 72, p < 0.01; 1.92 vs. 2.10). A sharp

134 decrease in the Sobs and Shannon indices were observed for both the bacterial (245, p <

135 0.001; 1.95, p < 0.001) and archaeal communities (53, p < 0.05; 1.61, p < 0.001) in the

136 oil-production wells. The Simpson indices were higher in the oil-production wells than in the

137 wellheads and downhole of water-injection wells for both the bacterial (0.33 vs 0.05, p < 0.01;

138 0.33 vs. 0.11, p < 0.05) and archaeal communities (0.32 vs. 0.21, p < 0.001; 0.32 vs 0.25).

139 Detailed α-diversity indices for the bacterial and archaeal communities are provided in Tables

140 S2 and S3.

141 The bacterial communities in the water-injection stations, wellheads, and downhole of the

142 water-injection and oil-production wells showed a similar community composition as shown

143 in the heatmap (Fig. 3a) and cumulative histogram (Fig. S2a). The distributions of the

144 bacterial communities were further visualized via PCoA based on weighted-Unifrac distance

145 matrices (Fig. 2b). The ordination graph suggested that the samples from the water-injection

146 stations and wellheads of the water-injection wells were clustered together, indicating that

147 these locations shared similar community compositions, as confirmed by permutational

148 multivariate analysis of variance (ADONIS; r2 = 0.083, p = 0.221) and similarity analysis

149 (ANOSIM; r = 0.115, p = 0.223). The microbial communities were dominated by OTUs

150 representing Betaproteobacteria (Comamonadaceae, Alcaligenaceae, and Rhodocyclaceae)

151 and Alphaproteobacteria (Rhodobacteraceae, Sphingomonadaceae, and Burkholderiaceae),

152 followed by Flavobacteria (Flavobacterium) and Gamaproteobacteria (Pseudomonas) (Fig.

153 3b and Fig. S3). The samples collected from the wellheads and downhole of the

154 water-injection wells formed distinct clusters in the PCoA plot (Fig. 2b), and the microbial

155 communities in these locations were statistically significant (ADONIS, r2 = 0.472, p = 0.001;

156 ANOSIM, r = 0.855, p = 0.001). In contrast to the bacterial communities from the wellheads

157 of the water-injection wells, the relative abundances of Clostridia (Clostridiaceae),

158 Deltaproteobacteria (Syntrophaceae, Syntrophorhabdaceae, Desulfobulbaceae, and

159 Desulfovibrionaceae), Anaerolineae (Anaerolineaceae), and Synergistia were significantly

160 higher in the bacterial communities from the downhole of the water-injection wells, where

161 Betaproteobacteria, Alphaproteobacteria, and Flavobacteria were found to diminish markedly

162 (Fig. 3b and Fig. S4). The composition of the bacterial community changed significantly

163 again in the oil-production wells (ADONIS, r2 = 0.333, p = 0.001; ANOSIM, r = 0.695, p =

164 0.001). Gammaproteobacteria, Betaproteobacteria, and Epsilonproteobacteria became the

165 most abundant lineages, and Pseudomonas, Acinetobacter, Thauera, and Arcobacter were the

166 dominant genera (Fig. 3b and Fig. S5).

167 Most of the archaeal populations detected existed persistently throughout the water

168 injection and oil production facilities. Methanosaeta, Methanobacterium,

169 Methanothermobacter, Methanolinea, Methanomethylovorans, Methanocalculus, and

170 Methanoculleus accounted for the majority of the archaeal sequences in each sample (Fig. 4a

171 and Fig. S2b). However, despite this, Methanosaeta, Methanobacterium, and Methanolinea

172 were the dominant genera and species in the water-injection facilities, while the

173 oil-production wells were predominated by Methanosaeta, Methanomethylovorans, and

174 Methanocalculus. In addition, significant changes in the archaeal communities were observed

175 between the samples from the downhole of the water-injection and oil-production wells

176 (ADONIS, r2 = 0.186, p = 0.008; ANOSIM, r = 0.242, p = 0.005). The dominant species

177 Methanobacterium and Methanolinea were higher in the downhole of the water-injection

178 wells than in the oil-production wells, in which Methanomethylovorans (p < 0.01),

179 Methanocalculus (p < 0.01), and Methanosaeta were more abundant (Fig. 4b).

180 Distinct metabolic profiles of the microbial communities

181 The metabolic profiles of the bacterial and archaeal communities in the wellheads and

182 downhole of the water-injection and oil-production wells were inferred from 16S rRNA data

183 using Tax4Fun. For the bacterial communities, the majority of the predicted protein

184 sequences annotated with KEGG pathways were clustered into metabolism (56.42-63.66%),

185 environmental information processing (15.62-22.42%), genetic information processing

186 (9.08-14.25%), and cellular processes (3.97-7.42%). Significant differences were observed in

187 the aforementioned pathways among the wellheads and downhole of the water-injection, and

188 oil-production wells as illustrated in Fig. 5 and Fig. S6-8a. The sequences related to the

189 biosynthesis of other secondary metabolites were found to have the highest abundance in the

190 wellheads of the water-injection wells (Fig. 5a)(p < 0.05). In addition, the relative abundance

191 of the sequences related to amino acid metabolism, xenobiotic biodegradation and

192 metabolism, and lipid metabolism were higher than those of the downhole of the

193 water-injection wells (Fig. 5a) (p < 0.05), while amino acid metabolism, carbohydrate

194 metabolism, translation, nucleotide metabolism, replication and repair, and glycan

195 biosynthesis and metabolism had higher abundances than those of the oil-production wells

196 (Fig. 5a) (p < 0.05). In the downhole of the water-injection wells, the relative abundance of

197 sequences associated with energy metabolism, translation, nucleotide metabolism, and glycan

198 biosynthesis and metabolism were higher than those of the wellheads of the water-injection

199 and oil-production wells (Fig. 5a) (p < 0.05). Compared with the wellheads and downhole of

200 the water-injection wells, the sequences associated with cell motility (bacterial chemotaxis),

201 cellular community (biofilm formation), and signal transduction (two-component system)

202 were more abundant in oil-production wells (Fig. 5a) (p < 0.05). In addition, the sequences 9

203 related to xenobiotic biodegradation and metabolism, lipid metabolism, metabolism of

204 terpenoids and polyketides, amino acid metabolism, and membrane transport were higher

205 than those of the downhole water-injection wells (Fig. 5a) (p < 0.05).

206 For the archaeal communities, the sequences clustered into metabolism, environmental

207 information processing, genetic information processing, and cellular processes accounted for

208 62.97-63.43%, 8.05-13.13%, 19.18-20.51%, and 1.38-2.70%, respectively. Significant

209 differences were observed in the pathways between the water-injection and oil-production

210 wells (Fig. 5b and Figs. S6-8b). In the wellheads and downhole of water-injection wells, the

211 relative abundance of the sequences related to energy metabolism, translation, metabolism of

212 cofactors and vitamins, glycan biosynthesis and metabolism, and replication and repair were

213 higher than those of the oil-production wells (Fig. 5b) (p < 0.05). However, the sequences

214 associated with membrane transport (bacterial secretion system), lipid mechanism, and

215 xenobiotic biodegradation and metabolism were found to have higher abundances in the

216 oil-production wells (Fig. 5b) (p < 0.05).

217 Distinct network patterns of the bacterial communities

218 Co-occurrence network analysis was used to assess the interactions of microbial populations

219 within the microbial communities inhabiting the water-injection and oil-production wells.

220 The complexities of the networks were compared based on the number of nodes, edges,

221 average degrees, clustering coefficient, scale-free, and modularity (Fig. 6). The phylogenetic

222 molecular ecological networks were constructed with similarity thresholds of 0.81, 0.98, and

223 0.81 for the wellheads and downhole of the water-injection and oil-production wells,

224 respectively. However, a greater number of nodes and links were observed in the downhole

225 network of the water-injection wells, followed by the wellheads of the water-injection wells,

226 followed by the oil-production wells. Compared with the wellhead network (R2 = 0.463) and

227 the network of the oil-production wells (R2 = 0.197), the downhole network was closely fitted

228 with the power-law model (R2 = 0.863), representing a scale-free network, in which few

229 nodes in the network have a large number of neighbors and most nodes have few neighbors.

230 There was a higher average degree and average clustering coefficient and a lower

231 centralization of degree and betweenness of the nodes in the wellhead network. The

232 modularity and number of modules were higher in the downhole and oil-production well

233 networks than in the wellhead network. In addition, there were more positive correlations in

234 the networks of the wellheads and downhole of the water-injection wells, while more

235 negative correlations were observed in the oil-production wells.

236 The potential keystone taxa in each network were screened (Table S4), including those that

237 act as connectors, module hubs, network hubs, and those with low betweenness and/or high

238 degree (33-36). More nodes were assigned to Betaproteobacteria, Alphaproteobacteria,

239 Flavobacteriia, and Sphingobacteriia in the wellhead network. The bacterial keystone taxa

240 were mainly from Flavobacteriaceae, Commamonadaceae, Lentimicrobiaceae, Syntrophaceae,

241 Pseudomonadaceae, Sphingomonadaceae, Rhodobacteraceae, and Alcaligenaceae, including

242 the dominant genera Flavobacterium, Novosphingobium, Gemmobacter, Roseovarius,

243 Smithella, and Pseudomonas. There were more nodes from Clostridia, Deltaproteobacteria,

244 Anaerolineae, Nitrospira, Betaproteobacteria, and Synergistia in the downhole network. The

245 keystone taxa mainly belonged to Clostridiaceae, Anaerolineaceae, Syntrophaceae,

246 Rhodocyclaceae, Desulfovibrionaceae, and Desulfobulbaceae, including the dominant genera

247 Clostridium, Nitrospira, Syntrophus, Smithella, Desulfovibrio, and Desulfobulbus. In the

248 network of the oil-production wells, the nodes were mainly from Gammaproteobacteria,

249 Betaproteobacteria, Epsilonproteobacteria, and Deltaproteobacteria, and the OTUs that were

250 assigned to Rhodocyclaceae and Syntrophaceae were the main keystone taxa, including

251 dominant genera Thauera, Azoarcus, Dechloromonas, and Smithella. This is also reflected in

252 the differences observed in the overall community compositions among oilfield production

253 facilities.

254 Environmental selection on the microbial communities

255 To observe changes in the environmental variables of water-injection facilities and

256 oil-production wells, NMDS and ADONIS analyses were performed using the contents of

- 2- 257 acetate, NO3 , SO4 , total nitrogen, and total phosphorus. As shown in the NMDS plot, the

258 samples collected from similar locations were clustered together (Fig. S9). No significant

259 differences in the environmental factors were observed between the water-injection stations

260 and the wellheads of the water-injection wells. However, significant differences were

261 observed among the wellheads and downhole of the water-injection and oil-production wells

- 262 (Table S5). The wellheads of the water-injection wells had a higher NO3 concentration (8.33

263 mg/L), which was found to decrease significantly in the downhole of the water-injection

264 wells (2.66 mg/L, ADONIS, R2 = 0.41, p ≤ 0.01) and the oil-production wells (0.90 mg/L,

2 2- 265 ADONIS, R = 0.41, p ≤ 0.01). The concentration of SO4 (32.10 mg/L) in the downhole

266 of the water-injection wells was over 2.5-fold that in the wellheads of the water-injection

267 wells (12.01 mg/L) and 6-fold that in the oil-production wells (5.18 mg/L). The concentration

268 of phosphorus (29.64 mg/L) in the downhole of the water-injection wells was over 10-fold

269 that in the wellheads of the water-injection wells (2.83 mg/L) and the oil-production wells

270 (2.88 mg/L). There were no significant changes in the acetate concentrations, which averaged

271 11.24-11.69–mg/L in the water-injection and oil-production wells (Table S5).

272 The effects of environmental variations on the spatial distributions of the bacterial and

273 archeal communities were further analyzed via the Mantel test, ADONIS, and CCA analysis.

274 The Mantel test showed significant correlations between the environmental variables and

275 microbial community compositions through the water-injection facilities and oil-production

276 wells (Table S6). Significant correlations were observed between the bacterial communities

- 277 and the contents of acetate and NO3 (Table S6; Mantel, p = 0.011 and 0.007, respectively),

- 278 and between the archaeal communities and NO3 content (Table S6; Mantel, p = 0.008) in the

279 water-injection pipelines consisting of combined stations, water-injection stations, and the

280 wellheads of water-injection wells. For the wellheads and downhole of the water-injection

281 and oil-production wells, significant correlations were observed between the bacterial

- 2- 282 communities and the total phosphorus, NO3 , and SO4 contents (Table S6; Mantel, p =

283 0.001), and between the archaeal communities and the total nitrogen content (Table S6;

2- 284 Mantel, p = 0.045). ADONIS showed that total phosphorus and SO4 can effectively explain

285 the changes in the compositions of the bacterial community in the wellheads and downhole of

286 the water-injection wells, and in the downhole of the water-injection and oil-production wells

287 (Table S7). For the archaeal communities, significant correlations between community

2- 288 composition and total phosphorus and SO4 were observed for the downhole of the

289 water-injection and oil-production wells (Table S6). Despite the high correlation, CCA

290 analysis indicated that the environmental variables could only explain 14.8% of the bacterial

291 community changes, and 24.8% of the archaeal community changed through the

292 injection-production facilities (Fig. 2c and Table S8).

293 Diffusion-limited microbial transfer in the water-injection pipelines and oil-bearing

294 strata

295 The persistent OTUs were analyzed to elucidate a potential transfer of microorganisms in the

296 water-injection pipelines and oil-production wells. As shown in Venn diagrams, a large

297 number of persistent OTUs were detected through the water-injection facilities and

298 oil-production wells (Fig. S10). Correlation analysis indicated that the water-injection

299 stations and the wellheads of the water-injection wells harbored a large number of shared

300 OTUs with similar relative abundances (Fig. 7a; Pearson correlation coefficient (r) = 0.78, p 13

301 < 0.001 for bacteria and r = 0.97, p < 0.001 for archaea). A large number of shared OTUs

302 with different relative abundances were detected in the wellheads and downhole of

303 water-injection wells (Fig. 7a, r = 0.78, p < 0.001 for bacteria and r = 0.97, p < 0.001 for

304 archaea). There were also substantial OTUs with different relative abundances in the

305 downhole of the water-injection and oil-production wells (Fig. 7a, r = 0.78, p < 0.001 for

306 bacteria and r = 0.97, p < 0.001 for archaea). As shown in Fig. 7b, 51.1-74.9% of the OTUs

307 detected in the downhole of the water-injection wells were detected in the wellheads of the

308 water-injection wells, and 8.2-43.9% of the OTUs from the oil-production wells appeared in

309 the downhole of the water-injection wells. In addition, the proportions of the shared OTUs in

310 the oil-production wells showed strong correlations with the formation permeability of the

311 oil-production wells. In fact, in blocks d, f, and i, the correlation coefficient reached 0.961

312 (Fig. 7b; p < 0.01). The correlation of the shared OTUs with the formation permeability of

313 the oil-production wells was not clear in blocks e and l (Fig. 7b).

314 Moreover, diffusion-limited microbial transfer in oil-bearing porous medium was tested in

315 oil-bearing cores using strain SG-rfp marked by a red fluorescent protein-encoding gene (37).

316 For the cores with a permeability of 1.405 μm2, a bright fluorescent signal was observed in

317 the effluent when 1 pore volume (PV) of the displacing fluid containing RFP-labeled

318 Pseudomonas aeruginosa was injected. The maximum fluorescence was observed when 2 PV

319 displacing fluid was injected (Fig. S11). For the cores with a permeability of 0.203 μm2, a

320 fluorescent signal was clearly observed in the effluent when 2 PV of displacing fluid was

321 injected. The maximum fluorescence was observed until 15 PV displacing fluid was injected

322 (Fig. S11). These results indicate that microorganisms in injected water can migrate through

323 oil-bearing porous medium with the water flow, and that the formation permeability imposed

324 significant limitations on microbial migration.

325 DISCUSSION 14

326 Microbial-enhanced oil recovery in production is well known for the involvement of

327 microorganisms and their metabolites under a wide spectrum of oil reservoir types (5, 8, 9).

328 To substantiate the process of microbiologically improving oil production is more effective, a

329 clear understanding of the distribution of microbial communities in oilfield production

330 facilities is necessary. As a result, this study systematically investigated the composition and

331 metabolic characteristics of microbial communities in water-injection pipelines and

332 oil-production wells of a water-flooding petroleum reservoir, and revealed the roles of

333 environmental variation, microorganisms in injected water, and diffusion-limited microbial

334 transfer in determining the structure of the microbial communities.

335 The compositions of the microbial community in oilfield production facilities show spatial

336 specificity. Microbial community α-diversity reflects the number of species in a local

337 homogeneous habitat. In the oilfield production facilities, we found that the α-diversity of

338 both the bacterial and archaeal communities was significantly lower than that found in the

339 ground surface soil. The downhole of the water-injection wells had a higher community

340 α-diversity, while a lower community α-diversity was observed in the oil-production wells. In

341 petroleum reservoirs, both the bacterial and archeal community α-diversity is influenced by

342 extreme environmental conditions and oil-production processes, such as high temperature,

343 hypersalinity, and oil recovery methods (25). This phenomenon may be explained by the

344 adaptability and metabolic types of the microorganisms (see analysis of metabolic profiles).

345 The water-injection stations and the wellheads of the water-injection wells were found to

346 contain similar bacterial communities, dominated by Betaproteobacteria (Comamonadaceae,

347 Alcaligenaceae, and Rhodocyclaceae), Alphaproteobacteria (Rhodobacteraceae,

348 Sphingomonadaceae, and Burkholderiaceae), Flavobacteria (Flavobacteriaceae), including

349 dominant genera Tepidimonas, Extensimonas, Hydrogenophaga, Flavobacterium, Thauera,

350 Smithella, Novosphingobium, Gemmobacter, Roseovarius, Azoarcus, Azovibrio, and

351 Rhodobacter. Most of these populations were comprised of aerobic or facultative anaerobic.

352 The species of the genus Tepidimonas (Comamonadaceae) are generally strictly aerobic and

353 chemolithoheterotrophic (38). Hydrogenophaga (Comamonadaceae) species are

354 hydrogen-oxidizing bacteria that are able to ferment organic acids (39). Thauera

355 (Rhodocyclaceae) has been described as isopropanol, acetone, and aromatic hydrocarbons,

356 and is the main contributor to the mitigation of biological souring in oil reservoirs (12, 40).

357 Some species from Smithella have been described as anaerobic propionate-degrading

358 syntrophs (41). Some species of Azoarcus (Rhodocyclaceae) are able to fix nitrogen (42).

359 Rhodobacter species (Rhodobacteraceae) show a wide range of metabolic capabilities,

360 including photosynthesis, lithotrophy, aerobic and anaerobic respiration, nitrogen fixation,

361 and the synthesis of tetrapyrroles, chlorophylls, heme, and vitamin B12 (43). The downhole

362 of the water injection wells was dominated by Clostridia (Clostridiaceae),

363 Deltaproteobacteria (Syntrophaceae, Syntrophorhabdaceae, Desulfobulbaceae, and

364 Desulfovibrionaceae), Anaerolineae (Anaerolineaceae), Synergistia, and Nitrospira, including

365 dominant genera Clostridium, Syntrophus, Smithella, Desulfovibrio, Desulfobulbus,

366 Longilinea, Aminiphilus, Thermovirga, and Nitrospira. Clostridium is a genus of a group of

367 strictly anaerobic Gram-positive bacteria, and has been widely used for the production of

368 organic acids, organic solvents, and enzymes, such as acetone, butanol, 1,3-propanediol,

369 ethanol, butanol, acetic acid, and biohydrogen (44). Syntrophus species were reported to be

370 able to degrade benzoate into acetate and H2, which were subsequently converted to methane

371 by Methanosarcina and Methanoculleus, and the direct interspecies electron transfer of

372 Desulfovibrio and Methanosarcina (45). Desulfovibrio and Desulfobulbus are commonly

373 detected sulfate-reducers that reduce sulfates to hydrogen sulfide in petroleum reservoirs (46).

374 Anaerobic amino-acid-degrading Thermovirga has been previously isolated from a North Sea

375 oil well (47). The oil-production wells were dominated by Gammaproteobacteria

376 (Pseudomonadaceae), Betaproteobacteria (Rhodocyclaceae, Hydrogenophilaceae),

377 Epsilonproteobacteria (Campylobacteraceae), and Deltaproteobacteria (Geobacteraceae),

378 including facultative anaerobic dominant genera Pseudomonas, Thauera, Hydrogenophaga,

379 Acinetobacter, Atribacteria, Arcobacter, Acetobacterium, and Geobacter. Many strains of

380 Pseudomonas and Acinetobacter are capable of utilizing hydrocarbons and producing

381 biosurfactants (6, 48, 49). Geobacter plays an important role in electron exchange by direct

382 interspecies electron transfer (1). Most of the detected archaeal populations persistently

383 existed throughout the oilfield production facilities. It is worth noting that Methanosaeta,

384 Methanobacterium, and Methanolinea predominated in the water-injection facilities, while

385 the oil-production wells were dominated by Methanosaeta, Methanomethylovorans, and

386 Methanocalculus. Methanosaeta is an acetoclastic methanogen that uses only acetate in

387 methane production, while Methanobacterium, Methanolinea, and Methanocalculus are

388 methylotrophic and hydrogenotrophic methanogens (50). Methanomethylovorans is a

389 methylotrophic methanogen that is able to grow on dimethyl sulfide and methanethiol (51). It

390 is not difficult to see that microorganisms inhabiting petroleum reservoirs maintain a close

391 association with each other and show a high metabolic potential for hydrocarbon degradation,

392 sulfate reduction, nitrate/nitrite reduction, and methanogenesis.

393 Associating community compositions with functional predictions enabled us to decipher the

394 potential ecological traits of the microbial communities found throughout oilfield production

395 facilities. Significant differences in metabolism, environmental information processing,

396 genetic information processing, and cellular processes pathway were observed among the

397 wellheads and downhole of the water-injection and oil-production wells. Pathways associated

398 with the biosynthesis of other secondary metabolites, amino acid metabolism, xenobiotic

399 biodegradation and metabolism, and lipid metabolism likely played more important roles in

400 the wellheads of the water-injection wells. By contrast, the downhole of the water-injection

401 wells showed more activity in pathways associated with energy metabolism, translation,

402 nucleotide metabolism, and glycan biosynthesis and metabolism. These findings suggest that

403 the growth and metabolism of microorganisms in the downhole of the water-injection wells

404 were more active. In the oil-production wells, cell motility (bacterial chemotaxis), cellular

405 community (biofilm formation), and signal transduction (two-component system) were found

406 to be more active. In addition, xenobiotic biodegradation and metabolism, metabolism of

407 terpenoids and polyketides, amino acid metabolism, and membrane transport also played

408 important roles. Bacteria usually use chemotaxis to position themselves within the optimal

409 portion of their habitats to approach specific chemical attractants and avoid repellent ligands

410 (52). Both bacteria and archaea are capable of forming biofilms, which often benefit the

411 survival of microorganisms in the presence of environmental stresses, such as low or high pH

412 and toxic chemicals, and facilitate horizontal gene transfer and syntrophy with other

413 microorganisms (53). Due to the extreme environment in oil reservoirs, microbial populations

414 most likely tend to metabolize substrates in the form of synergetic metabolism and mutualism.

415 The abundance of two-component systems suggests that the microorganisms in the

416 oil-production wells are likely to have suffered more extreme environmental stresses,

417 particularly nutritional deficiency and oxygen limitation. The two-component systems also

418 regulate a variety of physiological behaviors of microorganisms, such as motility, chemotaxis

419 (54), spore formation (55), and biofilm formation (56).

420 The microbial community composition throughout oil-production facilities was closely

421 correlated with to the nutrients available. Mantel test, ADONIS, and CCA analysis revealed

- 2- 422 significant correlations between the total phosphorus, NO3 , and SO4 contents and the

423 compositions of the microbial communities throughout the oilfield production facilities. It is

2- 424 worth noting that the phosphorus and SO4 contents in the downhole of the water-injection

425 wells were far greater than those in the wellheads of the water-injection and oil-production

426 wells. This is consistent with higher activity in the growth and metabolism of microorganisms

2- 427 in the downhole of the water-injection wells. With the increase in the SO4 content, the

428 abundance of sulfate-reducers, such as Desulfovibrio and Desulforhabdus, also significantly

429 increased. The accumulation of sulfate and other nutrients (especially phosphate) in the

430 downhole of the water-injection wells may be interpreted as chemical deposition under the

431 action of formation brines and the interception role because of the sieve effect of oil-bearing

432 strata. Despite demonstrating a high correlation, the nutrient distribution only partially

433 explained the changes in the bacterial and archeal communities throughout the

434 injection-production facilities. Oxygen levels, temperature (13, 22, 57), salinity (23, 24, 58),

435 and pH (25, 59) are other major determinants of microbial community composition. As the

436 results of the ANOSIM and ADONIS analyses reveal, strong relationships were observed

437 between sampling sites and microbial community compositions. Combined with the

438 distribution of dominant microbial populations throughout the injection-production facilities,

439 oxygen levels are likely to play a crucial role in determining community composition.

440 The water-flooding process seems to continually inoculate reservoirs with exogenous

441 microorganisms. However, whether the microorganisms in injected water can pass through

442 oil-bearing strata and reach oil-production wells, and the influence of these microorganisms

443 on subsurface microbial communities, remains unclear. The low permeability of oil-bearing

444 strata inevitably exerts a significant influence on microbial diffusion in oil reservoirs. Lenchi

445 et al. reported that the bacteria associated with water injected into oil reservoirs were not

446 retrieved from oil-production waters (60). Further research has found that a large number of

447 shared OTUs were detected in water-injection wells and adjacent oil-production wells, with

448 aerobic populations often appearing in oil-production wells (17, 18, 31, 32). It seems that

449 microorganisms on the ground may migrate or be brought into oil reservoirs during the oil

450 production process. Ren et al. recently suggested that the transportation of injected bacteria in

451 oil-bearing strata was impacted by the varied permeability from water-injection wells to

452 adjacent oil-production wells (30). In the present study, a large number of OTUs with similar

453 relative abundances were observed simultaneously in the water-injection stations and the

454 wellheads of the water-injection wells, and greater rations of shared OTUs were observed

455 between the wellheads and downhole of the water-injection wells than those between the

456 downhole of the water-injection wells and the oil-production wells. These findings imply that

457 microorganisms may migrate in water flowing in water-injection pipelines and oil-bearing

458 strata. Furthermore, the rations of shared OTUs detected in the downhole of the

459 water-injection and oil-production wells showed strong correlations with the corresponding

460 formation permeability, highlighting the influence of geographic isolation on microbial

461 transfer in oil-bearing strata. This phenomenon was further demonstrated using the

462 core-flooding test. Although microorganisms can be brought into oil-bearing strata even in

463 oil-production wells, their influence on the subsurface microbial communities is closely

464 related to their adaption and growth in new environments.

465 In this study, we revealed the spatial distribution of microbial communities in the

466 oil-production facilities of a water-flooding petroleum reservoir. Our results indicate that

467 environmental variation and microorganisms in injected water are the determinants of the

468 structure of microbial communities in water-injection facilities, while the determinants in

469 oil-bearing strata are environmental variation and diffusion-limited microbial transfer. These

470 findings provide further insights into the distribution of microbial communities in

471 oil-production facilities and could have profound consequences on the use of reservoir

472 microorganisms for improving the oil production process. However, future studies will need

473 to quantify the effects of these factors on structuring the microbial communities in

474 oil-production facilities.

475 MATERIALS AND METHODS 20

476 Sampling sites and samples collection

477 A water-flooding petroleum reservoir of the Daqing Oilfield in the northeast China was

478 chosen for this study. The temperature of the reservoir was approximately 45°C. Samples

479 were collected from combined stations a, g, and j, corresponding to oil-water separation and

480 water treatment before water re-injection into the well, water-injection stations b, c, h, and k,

481 for transferring water to wellheads and downhole of water injection wells, and oil-production

482 wells of blocks d, e, f, i, and l (Fig. 1). The water of water-injection stations b and c was from

483 combined station a. The injected water flowed from b into the water-injection wells of blocks

484 d and e, and flowed from c into the water-injection wells of block f. The injected water at

485 injection station h was from combined station g and flowed into the water-injection wells of

486 block i. The injected water of injection station k was from combined station j and flowed into

487 the water-injection wells of block l. The average permeability of block d is 0.3 µm2, ranging

488 from 0.197 µm2 to 0.5 µm2. The average permeability of block f is 0.289 µm2, ranging from

489 0.125 µm2 to 0.811 µm2. The average permeability of block e is 0.069 µm2, ranging from

490 0.07 µm2 to 0.128 µm2. The average permeability of block i is 0.099 µm2, ranging from 0.071

491 µm2 to 0.121 µm2. The average permeability of the block l is 0.204 µm2, ranging from 0.028

492 µm2 to 0.245 µm2.

493 The samples from the downhole of the water-injection wells were obtained by water

494 backflow; that is, injected water flowed upward through the well-hole under pressure. The

495 other samples were taken through the reserved sampling valves of the water-injection and

496 oil-production facilities. The samples collected filled 10-L sterilized plastic buckets, which

497 were tightly sealed with screw caps, and immediately transported to the laboratory for DNA

498 extraction and chemical analysis. The samples were numbered with the sampling sites, such

499 as d1 and d2, which represent the samples collected from the wellheads and downhole of the

500 water-injection well d1 in block d. In addition, three soil samples labeled as T1, T2, and T3 21

501 were collected from the ground soil (at a depth of 5 cm) of the water injection wells d1, d3,

502 and d5, respectively.

503 Chemical analysis and DNA extraction

- 2- 504 The concentrations of acetate, NO3 , and SO4 in the water samples were determined using an

505 ion chromatograph (DIONEX ICS-1000) equipped with a Shim-pack IC-C3 column. Total

506 nitrogen and phosphorus were analyzed according to “HJ 636-2012 Water quality -

507 Determination of total nitrogen - Alkaline potassium persulfate digestion UV

508 spectrophotometric method” and “GB 11893-1989 Water quality - Determination of total

509 phosphorus - Ammonium molybdate spectrophotometric method”, respectively. Detailed data

510 are listed in Table S1. Microbial cells were collected from to 2-3 L of water samples by

511 centrifugation at 12,000 × g and 4°C for 20 min in a high-speed centrifuge (Beckman, USA).

512 Total genomic DNA was extracted using AxyPrepTM Genomic DNA Miniprep Kit (Axygen,

513 USA) combined with bead shaker treatment, as previously described (5).

514 16S rRNA sequencing and bioinformatics analysis

515 The bacterial and archaeal 16S rRNA genes were amplified using the universal prokaryotic

516 primers 515f (5’-GTG CCA GCM GCC GCG GTA A-3’), 907r (5’-CCG TCA ATT CMT

517 TTR AGT TT-3’), 524f (5’-TGY CAG CCG CCG CGG TAA-3’), and 958r (5’-YCC GGC

518 GTT GAV TCC AAT T-3’), respectively. PCR amplicons were paired-end sequenced (2× 250

519 bp) on an Illumina MiSeq platform, according to the standard protocol (Majorbio Bio-Pharm

520 Technology Co., Ltd, Shanghai, China). Raw fastq files were demultiplexed and

521 quality-filtered using QIIME2 (61). The sequences were assigned to operational taxonomic

522 units (OTUs) at a 97% sequence similarity level using the UPARSE pipeline (62). The

523 representative sequence sets were aligned and given a taxonomic classification by RDP (63)

524 against the SILVA Small Subunit rRNA database at an 80% confidence threshold. The

525 α-diversity, including observed OTUs, Chao1, Shannon, and Simpson indices, was calculated

526 based on the OTUs. The community β-diversity was estimated based on weighted-UniFrac

527 dissimilarity between samples.

528 Tax4Fun (64) was used to predict the functional profiles of the microbial communities based

529 on the 16S rRNA data obtained on Majorbio Cloud Platform (www.majorbio.com). Tax4Fun

530 transforms the SILVA-based OTU classification into a taxonomic profile of identical or

531 closely-related genomes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

532 These taxonomic profiles were converted into artificial metagenomes/metatranscriptomes by

533 incorporating the functional data calculated from the genomes of each KEGG organism. The

534 statistical significance of differentially abundant functional categories was tested using

535 Statistical Analysis of Metagenomic Profiles (STAMP) software (65). To explore potential

536 interactions among the microbial populations, co-occurrence network analyses were carried

537 out based on the Pearson correlation between OTUs using Molecular Ecological Network

538 Analyses Pipeline (MENAP) (33). Poorly represented OTUs (i.e. those existing in fewer than

539 50% of the samples and had less than 0.05% average relative abundance in each group) were

540 removed from the network analyses. To describe the topology of the resulting network,

541 average node connectivity, average path length, diameter, cumulative degree distribution,

542 clustering coefficient, and modularity were calculated. The constructed networks were

543 visualized using Cytoscape version 3.7.2 (66).

544 Statistical analysis

545 Changes in the community α-diversity of the water-injection and oil-production facilities

546 were analyzed using analysis of variance (ANOVA). To visualize the relationships of the

547 microbial communities, principal coordinates analysis (PCoA) was performed based on

548 weighted-UniFrac dissimilarity matrices. To determine the significant differences in the

549 microbial β-diversity of the water-injection and oil-production facilities, permutational

550 multivariate analysis of variance (ADONIS) and similarity analysis (ANOSIM), depending

551 on the weighted-UniFrac distance matrices, were carried out using the “adonis” and “anosim”

552 function of the “vegan” package in R. Wilcoxon rank-sum test was used to identify the

553 microbial populations with statistically differential abundances among the wellheads and

554 downhole of the water-injection and oil-production wells. Correlations between the relative

555 abundance of bacterial or archaeal OTUs and the shared OTU proportions with permeability

556 of oil-bearing strata were analyzed using the “ggplot2” package in R. Mantel test and

557 ADONIS analysis were used to detect correlations between environmental variables and

558 microbial community compositions using the “vegan” package in R. Based on the long length

559 of the first axis of detrended correspondence analysis (DCA), the unimodal ordination

560 method, canonical correspondence analysis (CCA) was performed to elucidate the

561 relationship between environmental factors and OTU-level microbial communities using the

562 “vegan” package in R.

563 Sequences accessibility

564 The raw reads obtained by Illumina MiSeq sequencing were deposited in the Sequence Read

565 Archive (SRA) at the National Center for Biotechnology Information

566 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA489604).

567 ACKNOWLEDGMENTS

568 This work was funded by the National Natural Science Foundation of China (Grant No.

569 31500414 and 41773080), Shandong Provincial Agricultural Fine Species Project

570 (2019LZGC020), National Key Research and Development Project (Grant No.

571 2018YFA0902101), Jining key research and development project (Grant No.

572 2019ZDGH019), and National High Technology Research and Development Program of

573 China (Grant No. 2013AA064402). We thank Prof. Ying Zhang, Institute of Applied Ecology,

574 Chinese Academy of Sciences, Shenyang, China, and Dr. Jianlong Xiu, Research Institute of

575 Petroleum Exploration and Development (Langfang), Langfang, China, who provided

576 technical support in core-flooding test. We also thank Fang Liu and Xiaobo Liu, No. 2 Oil

577 Production Plant, PetroChina Daqing Oilfield Limited Company, Daqing, China, for their

578 assistance in sample processing.

579 Conflict of Interest

580 The authors declare no competing financial interest.

581

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764

765 Figure captions

766 Fig. 1 Diagrammatic sketch of the location of the sampled petroleum reservoir block (a), the

767 water-injection facilities and oil-production wells (b and c). The injected water flows from

768 combined injection station (a, g, and j) to water injection station (b, c, h, and k), and then to

769 water-injection wells and oil production wells of reservoir block (d, e, f, i, and l)

770 Fig. 2 The alpha diversity (a) and beta diversity (b) of the bacterial and archaeal communities

771 from the water-injection facilities and oil-production wells, and (c) the relationships between

772 the community compositions with the environmental variables. PCoA was performed based

773 on Weighted-UniFrac dissimilarity matrixes. Canonical correspondence analysis (CCA) and

774 Monte Carlo permutation test were performed to reveal the correlations between

775 environmental variables and community compositions.

776 Fig. 3 Heatmaps (a) and Wilcoxon rank-sum test (b) showing the distinct distribution of

777 dominant bacterial populations through the water-injection facilities and oil-production wells

778 Fig. 4 Heatmaps (a) and Wilcoxon rank-sum test (b) showing the distinct distribution of

779 dominant archaeal populations through the water-injection facilities and oil-production wells

780 Fig. 5 Distinct metabolic profiles and the statistically significant differences among the

781 bacterial (a) and archaeal (b) communities through wellheads and downhole of the

782 water-injection wells and the oil-production wells. The ordination graph showed that the

783 samples with similar metabolic profiles were clustered together, otherwise, formed distinct

784 clusters. The bar charts show the differences between the proportions of sequences in each

785 group with a confidence interval of 95%

786 Fig. 6 Co-occurrence networks and topological properties of the bacterial and archaeal

787 communities through wellheads (a) and downhole (b) of the water-injection wells and the

788 oil-production wells (c). Nodes are colored according to microbial class, and the nodes with a

789 larger size show the potential keystone OTUs. The taxonomic information for the numbered

790 nodes and the potential keystone OTUs is listed in Table S4. Edges indicate correlations

791 among nodes, and the red and green edges represent positive and negative correlations,

792 respectively

793 Fig. 7 Correlations of the bacterial (a) and archaeal (b) communities inhabiting in the

794 water-injection facilities and oil-production wells, (c) the distribution of shared bacterial

795 OTUs between wellheads and downhole of the injection wells, and downhole of the injection

796 wells and oil-production wells, and (d) the correlations with stratal permeability of the oil

797 production wells.

798

32 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.07.415646; this version posted December 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

a b Water-injection station Combined Station Reservoir block d Oil-Water 1113m Sampling Sampling

Reservoir block1710m f Treated Water Reservoir block e Sampling Sampling

Sampling 5681m

5646m Water-injection well Oil-production well

Reservoir block i 9953m c d (d1, d2, d3, d4, d5, d6, d7, d8, d9, d10, d11) b e1 a e ( , e2, e3, e4, e5, e6, e7) 5926m c f (f1, f2, f3, f4, f5, f6, f7)

g h i (i1, i2, i3, i4, i5, i6, i7) Wellhead of water-injection well

Downhole of water-injection well

j k l (l1, l2, l3, l4, l5, l6, l7) Oil-production well Combined Water-injection Reservoir Reservoir block l Station station block bioRxiv preprint doi: https://doi.org/10.1101/2020.12.07.415646; this version posted December 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Bacterial community Archaeal community a ** ** * * *** *** 6 ** * * *** *** 5 **

5 * 4 * 4 3 3 2 2 Shannon diversity Shannon diversity 1 1

0 0 IS WW DW PW Soil IS WW DW PW Soil

0.6 0.25 b T2 R²=0.449, P=0.001 R²=0.630, P=0.001 0.5 0.2 f5 0.4 l7 d9 e7 i6i7i5 l6 0.15 0.3 d8 T4 f7d10e5d11 0.1 0.2 T3 k e5 i5e6e7i7 d9 d11 f6 0.1 i3 0.05 f5l4i6d10d5 l5 l7d6d7 d1 f6 l4 k h f3 l1 d1 d2 d7 h e6 l6f1 d4 0 l3l2 T4T2 d4 0 bd3e4 i2 l3 T3 d2 a f2 l1 f4 i4e1 PCoA2 (14.86%) PCo’A2 (23.69%) i2 l2 f3 -0.1 e4 e2 c a f2 -0.05 g c j f7 d8 0.1 i1 i1 0.08 R²=0.245, P=0.016 e5 d6 i4 e7 d3 e2 i5 -0.2 e3 0.06 g i3 f4 e6 b 0.04 d10 f1 f5l4 e3e1 -0.1 l5l7 i7 j 0.02 j e3 f7 l6 d7d5 d5 i3 b f1 d6 g f6k i6 d9 -0.3 0 i1 ci4ad3 d1 f2 f4 -0.02 e2 f3e1 e4 h

PCoA2 (8.49%) l3 l5 -0.04 l2 d11 -0.15 d8 i2 d2 -0.4 -0.06 l1 d4 -0.08 -0.1

-0.5 -0.2 -0.3 -0.2 -0.1 0 0.1 0.2 PCoA1 (69.81%) -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 PCoA1 (30.57%) PCoA1 (67.62%) c 2 6 i6i7 i5 1.5 l6 d9f7f5 e5 d8e7 5 e2 Total phosphorus l7d11 e4 1 d10 f6 jgl4 4 e1 0.5 e6 Acetate a l1 c l5 i2 3 l2 0 k f3 e4 e2 l3 Total 2 l2 NO - d1 f4 d2f2 3 -0.5 Total nitrogen d2 phosphorus l5 f2 d4 d4 d5bf1 1 l1i4 e3j h SO 2- AC -1 e1 4 d6d3af3gi2i3 d7d3d6 h c i1 0 d5d1bf1 f6d7k d8l3 SO 2- -1.5 f5d10 4 d11d9f7l7e6i6l4 l6 e3 i4 -1 i7e7e5i5

CCA2 (3.71%) -2 i3 CCA2 (8.30%) -2.5 - -2 NO3 -3 -3 Total nitrogen -3.5 -4 f4 -4 i1 -5 -4.5 -6

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 -3 -2 -1 0 1 2 3 4 5 6 7 8 CCA1 (4.81%) CCA1 (11.10%) CS IS WW DW PW Combined station Water-injection station Wellhead of water-injection well Downhole of water-injection well Oil-production well bioRxiv preprint doi: https://doi.org/10.1101/2020.12.07.415646; this version posted December 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a

Sample group Clostridium_sensu_stricto_1 Acidobacteria DW PW Soil Soehngenia Selenomonadales WW IS CS Arcobacter Proteiniphilum Azonexus Taxon group Geobacter Spirochaetaceae Clostridia Rhodocyclaceae Dechloromonas Epsilonproteobacteria Tepidiphilus 4 Deltaproteobacteria Acetobacterium Longilinea Synergistia Thermovirga Norank_Atribacteria Aminivibrio 3 Mesotoga Norank Smithella Sphingobacteriia Anaerolineaceae Acidobacteria Gaiellales 2 Peptococcaceae Bacteroidia Atribacteria TTA-B15 Spirochaetes Thiofaba Norank 1 Thermotogae Syntrophobacter Caldisericia Syntrophorhabdus Norank_WS1 Norank_WS1 Nitrospira 0 Flavobacteriia Saprospiraceae NS11-12_marine_group Negativicutes Flavobacterium Comamonadaceae Betaproteobacteria Lentimicrobiaceae Anaerolineae Draconibacteriaceae Extensimonas Actinobacteria Gemmobacter Gammaproteobacteria Roseomonas Roseovarius Nitrospira Novosphingobium Alphaproteobacteria GKS98_freshwater_group Alcaligenaceae Hydrogenophaga Tepidimonas Azoarcus Combined station (CS) Water-injection station (IS) Pseudomonas Thauera Wellhead of water-injection well (WW) Rhodobacteraceae Acinetobacter Downhole of water-injection well (DW) Pannonibacter Oil-production well (PW) j c l4 f5 l6 i6 l7 b i3 h l5 l1 f4 l2 d9 e5 T3 T4 d7 d5 e1 d1 d2 e2 d10

k f7 i5 i7 f6 i1 f1 f3 l3 g i2 f2 i4 a e7 d8 T2 d3 d6 e3 e6 d4 e4 b d11 Betaproteobacteria Betaproteobacteria *** Gammaproteobacteria ** Sphingobacteriia Clostridia *** Betaproteobacteria * Alphaproteobacteria Deltaproteobacteria ** Clostridia ** Flavobacteriia Alphaproteobacteria *** Deltaproteobacteria *** Deltaproteobacteria Sphingobacteriia ** Alphaproteobacteria Clostridia * Flavobacteriia *** Anaerolineae ** Bacteroidia * Anaerolineae ** Synergistia ** Gammaproteobacteria Synergistia ** Sphingobacteriia ** Spirochaetes Gammaproteobacteria Caldisericia * Caldisericia Caldisericia * Epsilonproteobacteria Synergistia Actinobacteria Actinobacteria *** Anaerolineae Planctomycetacia Thermotogae ** Actinobacteria Bacteroidia Spirochaetes Planctomycetacia Thermotogae *** Bacteroidia * Mollicutes * Spirochaetes ** Nitrospira *** Cytophagia Nitrospira ** Deferribacteres Thermotogae Ignavibacteria Planctomycetacia *** Erysipelotrichia Cytophagia ** Latescibacteria *** Ignavibacteria Chlorobia Ignavibacteria *** Chlorobia Epsilonproteobacteria Chloroflexi ***

IS -25 -20 -15 -10 -5 0 5 10 15 20 WW -40 -30 -20 -10 0 10 20 30 40 DW -30 -20 -10 0 10 20 30 40 50 60 WW Difference between proportions (%) DW Difference between proportions (%) PW Difference between proportions (%) bioRxiv preprint doi: https://doi.org/10.1101/2020.12.07.415646; this version posted December 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

a Sample group Norank_Soil_Crenarchaeotic_Group_SCG Unclassified_Soil_Crenarchaeotic_Group_SCG Soil PW DW Candidatus_Nitrososphaera Norank CS WW IS Methanosarcina Methanolobus Taxon group Norank_Marine_Benthic_Group_D_and_DHVEG-1 4 Unclassified_Methanomicrobia Soil_Crenarchaeotic_Group_SCG Norank_Methanomicrobiaceae Thermoplasmata Norank_WCHA1-57 Methanobacteria Methanocalculus 2 Norank Unclassified_Euryarchaeota WCHA1-57 Methanosaeta Candidate_division_YNPFFA Methanobacterium 0 Methanomethylovorans Methanomicrobia Unclassified_Methanobacteriaceae Unclassified_Euryarchaeota Methanolinea Methanothermobacter Combined station (CS) Water-injection station (IS) Methanoculleus Wellhead of water-injection well (WW) Candidate_division_YNPFFA

i3 i1 i7 i5 l4 l6 l3 h l2 i4 Downhole of water-injection well (DW) T2 T4 f4 d8 d9 d1 d5 e7 f5 f2 d4 e4 f1 e1 f3 j l5 l7 i6 k a l1 i2 b g c T3 e3 e2 d6 e6 e5 f6 d7 d2 f7 d3 d11 d10 Oil-production well (PW) b Methanosaeta Methanosaeta Methanobacterium Methanobacterium ** Methanolinea Methanolinea Methanothermobacter Methanothermobacter Methanomethylovorans * Methanoculleus * Methanoculleus Methanomethylovorans * Methanomicrobia f__Methanobacteriaceae * Methanocalculus c__Methanomicrobia f__Methanobacteriaceae *** Methanocalculus * Candidatus_Iainarchaeum Candidatus_Nitrosoarchaeum p__Altiarchaeales * f__Methanomicrobiaceae Methanospirillum p__Euryarchaeota ** f__Methanomicrobiaceae Candidatus_Iainarchaeum Methanofollis Methanofollis ** Methanolobus Methanospirillum **

-25 -20 -15 -10 -5 0 5 10 15 20 -30 -20 -10 0 10 20 30 WW DW DW Difference between proportions (%) PW Difference between proportions (%) bioRxiv preprint doi: https://doi.org/10.1101/2020.12.07.415646; this version posted December 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

a WW DW PW 0.02

Signal transduction 5.09e-5 0.01 Cell motility 4.99e-5 Amino acid metabolism 4.67e-3 Translation 5.32e-3 Carbohydratemetabolism 4.07e-3 0.00 Replication and repair 3.56e-3 Nucleotide metabolism 4.60e-3

Metabolism of cofactors and vitamins 4.70e-3 value P Cell growth and death 7.94e-6 PC2 (20.1%) Glycan biosynthesis and metabolism 0.015 -0.01 Metabolism of other amino acids 7.99e-3 Cellular community - prokaryotes 0.016 Biosynthesis of other secondary metabolites 0.021 -0.02 0.0 13.1 -3-4 -2 -1 0 1 2 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03

PC1 (52.7%) Translation 6.45e-7 Energy metabolism 1.63e-3 Xenoblotics biodegradation and metabolism 5.19e-9 Amino acid metabolism 1.91e-5 Signal transduction 0.013 Xenoblotics biodegradation and metabolism 6.31e-8 Amino acid metabolism 3.29e-3 Energy metabolism 3.74e-4 Nucleotide metabolism 6.60e-5 Signal transduction 0.015e-3 Carbohydratemetabolism 2.78e-3 Translation 1.75e-3 Membrane transport 0.049 Lipid metabolism 1.03e-6 Replication and repair 2.92e-5 P value P Metabolism of other amino acids 1.11e-6 Lipid metabolism 4.74e-6

Nucleotide metabolism 0.017 value P Cell motility 0.044 Glycan biosynthesis and metabolism 2.21e-3 Glycan biosynthesis and metabolism 3.53e-6 Cell growth and death 5.33e-6 Metabolism of terpenoids and polyketides 0.040 Metabolism of terpenoids and polyketides 0.029 Cellular community - prokaryotes 7.25e-8 Cellular community - prokaryotes 8.09e-5 Metabolism of cofactors and vitamins 0.039 Biosynthesis of other secondary metabolites 1.61e-3 Metabolism of other amino acids 2.52e-5

0.0 13.0 -2-3 -1 0 1 2 3 0.0 13.1 -2.0 -1.0-1.5-0.5 0.0 0.5 1.0 1.5 2.0 Mean proportion (%) Difference in mean proportion (%) b 0.025 Membrane transport 3.91e-3 Energy metabolism 5.68e-3 WW DW PW Translation 3.24e-3 0.020 Metabolism of cofactors and vitamins 0.017 Glycan biosynthesis and metabolism 0.012 Xenoblotics biodegradation and metabolism 4.93e-4 P value P 0.015 Replication and repair 5.13e-4 Lipid metabolism 0.021 Transport and catabolism 5.01e-3 0.010 Signaling molecules and interaction 0.039 0.0 15.1 -1.5 -1.0 -0.5 0.0 0.5 1.0

0.005 Membrane transport 0.010

PC2 (19.5%) Energy metabolism 0.013 Translation 0.049 0.000 Xenoblotics biodegradation and metabolism 234e-3 Replication and repair 1.02e-3

Metabolism of other amino acids 0.014 value P -0.005 Lipid metabolism 2.86e-3 Biosynthesis of other secondary metabolites 3.58e-3 Transport and catabolism 5.20e-3 Signaling molecules and interaction 5.97e-3 -0.01 0.00 0.01 0.02 0.03 0.04 PC1 (79.4%) 0.0 15.2 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 Mean proportion (%) Difference in mean proportion (%) Wellhead of water-injection well (WW) Downhole of water-injection well (DW) Oil-production well (PW) bioRxiv preprint doi: https://doi.org/10.1101/2020.12.07.415646; this version posted December 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

a Wellhead of water-injection well b Downhole of water-injection well c Oil-production well

8 57 51 105 77 36 117 113 24 4 56 40 81 46 72 49 72 10 66 109 4 22 54 75 20 17 29 63 131 34 91 66 58 117 54 24 48 116 42 39 120 106 99138 50 17 121 46 18 32 113 108 31 5 47 71 47 121 42 118 70 30 23 65 69 8 77 62 136 43 135 57 2 64 119 145 45 3 55 7 147 27 28 139 130 82 37 46 37 120 52 35 26 45 109 149127 56 26 155 42 122 43 41 59 19 83 59 10 6 67 17 78 1 102 32 148137 123 53 13 111 107 18 5 88 47134 2 68 36 53 33 40 132 63 11437 31 69 16 74 80 110142 115 94 44 90 98 99 1051 39 16 83 31 87 129 15444 128 101 106133 80 110 112 103 30 34 12 6514 114 25 13 118 90 86 73 9 21 1 7 48 11 61119 10 11 20 32 81 9527 93 1433 45 15 100 61 93 74 87 71 55 1256079 25 153 52 49 35 38 43 35 3 62 18 29 115 104 85 30 126 84 53 111 144 48 50 68 94 13 141 51 76 58 107 96 27 56 60 102 88 22 100 21 151 12 38 4 5 92 38 25 96 49 5041 104 95 6 14 15 143 82 19 34 28 15 64 89 41 36 22 97 73 92 33 8 54 28 9 91 67 146 21 89 103 55 23 78 116 85 3 7 24 98 40 39 108 112 51 86 20 11 12 9 75 19 26 2 29 101 79 52 152 70 23 97140 6

44 Network properties Wellhead of water-injection well Downhole of water-injection well Oil-production well Similarity threshold 0.81 0.98 0.81 R square of power-law 0.463 0.863 0.197 Total nodes 121 155 56 Total links 424 455 168 Positive edges 372 416 109 Negative edges 52 39 59 Average degree 7.008 5.871 6 Average path distance 3.082 3.519 2.501 Average clustering coefficient 0.427 0.392 0.249 Connectedness 1 0.949 1 Centralization of degree 0.102 0.165 0.17 Centralization of betweenness 0.107 0.194 0.153 Modularity 0.355 0.613 0.568 Number of module 3 10 6

Node colore Alphaproteobacteria Gammaproteobacteria Flavobacteriia Clostridia Anaerolineae Deferribacteres Nitrospira Synergistia Spirochaetes Betaproteobacteria Deltaproteobacteria Bacteroidia Bacteroidetes Chlorobia Acidobacteria Phycisphaerae Planctomycetacia Epsilonproteobacteria Sphingobacteriia Dehalococcoidia Mollicutes Cytophagia Erysipelotrichia Fimbriimonadia Actinobacteria Thermotogae Cyanobacteria Ignavibacteria Caldisericia Chthonomonadetes Norank at class level Edge colore Positive correlation Negative correlation bioRxiv preprint doi: https://doi.org/10.1101/2020.12.07.415646; this version posted December 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

a R = 0.78, P < 0.001 R = 0.22, P < 0.001 R = 0.095, P < 0.001 R = 0.23, P < 0.001

9 9 20 20

6 6 PW WW PW WW 10 10 3 3

0 0 0 0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 IS DW DW IS b R = 0.97, P < 0.001 R = 0.8, P < 0.001 R = 0.53 P < 0.001 R = 0.93, P < 0.001 30 40 40

20 20 30 30 PW WW PW WW 20 20 10 10 10 10

0 0 0 0 100 20 30 0 105 15 20 25 0510 15 20 25 100 20 30 IS DW DW IS

c 80 *** d R = 0.32, p = 0.081 R = 0.95 , p < 0.001 • • • • R = 0.96 , p = 0.0022 • • R = 0.91 , p = 0.011 60 block R = 0.4 , p = 0.43 • 60 • d 30 • e • • • f • • i • • • l • 40 40 • 20 • Percent, % Percent, % Percent, Percent, % Percent, • • •• • • • • • dfi 20 • • 20 • 10 • • d • • f • •• • • • • • • •• • • • • i • • •• 0.10 0.15 0.20 0.25 The number of shared OTUs The number of shared OTUs 0.1 0.2 0.3 between WW and DW between DW and PW Formation permeability Formation permeability The number of OTUs in DW The number of OTUs in PW Water-injection station (IS) Wellhead of water-injection well (WW) Downhole of water-injection well (DW) Oil-production well (PW)