# Microbial-geochemical interactions in underground reservoirs: Implications for hydrogen storage

Laura Fablet, Arnaud Belcour, Sylvain Stephant, Caroline Michel, Delphine Ropers, Katerina Cerna, Petra Bombach, Jakub Riha, Joachim Tremosa, Biwen Annie An-Stepec, et al.

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Laura Fablet, Arnaud Belcour, Sylvain Stephant, Caroline Michel, Delphine Ropers, et al. Microbial-geochemical interactions in underground reservoirs: Implications for hydrogen storage. 2025. hal-05468099

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# Microbial-geochemical interactions in underground reservoirs: Implications for hydrogen storage

**Laura Fablet**

1.fablet@brgm.fr

BRGM

**Arnaud Belcour**

Univ. Grenoble Alpes

**Sylvain Stephant**

BRGM

**Caroline Michel**

BRGM

**Delphine Ropers**

Univ. Grenoble Alpes

**Katerina Cerna**

Technical University of Liberec, Advanced Technologies and Innovation

**Petra Bombach**

Isodetect GmbH

**Jakub Riha**

Technical University of Liberec, Advanced Technologies and Innovation

**Joachim Tremosa**

Geostock

**Biwen Annie An-Stepec**

NORCE Research AS

**Kristyna Fadrhonc**

Technical University of Liberec, Advanced Technologies and Innovation

**Nicole Dopffel**

NORCE Research AS

**Setareh Rad**

BRGM

## Research Article

**Keywords:** Hydrogen underground storage, subsurface, anaerobic microbiology
**Posted Date:** November 14th, 2025

**DOI:** https://doi.org/10.21203/rs.3.rs-7920847/v1

**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License.

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**Additional Declarations:** No competing interests reported.
# Abstract

# Background

Understanding microbial life in deep geological environments is essential for assessing natural biogeochemical processes and their implications for subsurface activities such as underground hydrogen storage. This study investigates the water chemistry, microbial community composition, and metabolic potential of two contrasting geological settings: porous reservoirs and salt caverns.

# Results

Porous reservoirs, particularly those with carbonate facies, are characterized by carbonate-rich fluids and host anaerobic microbial communities dominated by methanogenic archaea, syntrophic fermenters, and thermophiles. These communities support potential functions such as methanogenesis, hydrogen-producing fermentation, and selenate reduction. In contrast, salt caverns contain salt-saturated brines and are dominated by halophilic and generalist microorganisms adapted to oligotrophic, high-salinity conditions, mainly associated with nitrate reduction and acetate oxidation. Some reservoirs showed atypical microbial profiles influenced by anthropogenic activities or hydrogeological connections, which alter local geochemistry and microbial structures.

# Conclusion

This study demonstrates a clear distinction between salt caverns and porous reservoirs, highlighting the influence of salinity, carbon availability, temperature, and human impact on microbial community structure and functional potential. The presence of hydrogenotrophic and spore-forming microorganisms raises concerns about their potential to consume injected hydrogen and alter gas quality. These findings underscore the need to move beyond detection toward activity-based assessments of microbial hydrogen consumption, in order to anticipate biogeochemical dynamics and develop effective mitigation strategies. A better understanding of the interplay between reservoir chemistry, geology, and microbiology is crucial for ensuring safe and efficient underground hydrogen storage.

# 1. Introduction

The transition to renewable energy sources, essential to meet the challenge of climate change, requires the development of efficient energy storage solutions to overcome their intermittency.¹ Among the available options, hydrogen is particularly attractive for long-term storage. Surplus renewable energy can be converted into hydrogen by electrolysis,²,³ stored for long periods,⁴ and then converted back into energy when demand exceeds supply. Underground Hydrogen Storage (UHS) represents a promising storage option, allowing the temporary storage of large quantities of hydrogen in geological reservoirs.
Depending on the type of geological formation, three main underground storage systems are distinguished: saline aquifers and (depleted-) hydrocarbon reservoirs, which are both porous reservoirs, and salt caverns.⁵ Saline aquifers are deep porous formations saturated with brine, while (depleted-) hydrocarbon reservoirs are former oil or gas fields sealed by impermeable cap rocks. Salt caverns, in contrast, are artificial cavities created within thick salt deposits by solution mining.⁶ Once injected, hydrogen comes into contact with the geological environment, where complex water-rock-gas interactions may occur. These processes control the evolution of fluid chemistry (e.g. concentrations of Na⁺, Cl⁻, SO₄²⁻, Ca²⁺, etc.), porosity changes and mineral phase stability (e.g. halite, calcite, silicates, anhydrite, etc.), resulting in diverse of geochemical signatures.⁷⁻⁹

Besides abiotic reactions, geological reservoirs also host diverse and metabolically active microbial communities, that can impact the fate of stored hydrogen.¹⁰⁻¹² These microorganisms play key roles in the biogeochemical cycles, including carbon, sulfur, nitrogen, iron and trace elements cycles,¹³ thereby influencing mineral stability and fluid composition.¹⁴,¹⁵ Subsurface microbial communities are typically anaerobic and can tolerate extreme conditions of high salinity (up and beyond 250-300 g/L), temperature (up to 121°C), and pressure (up and beyond 100-150 MPa), reflecting the specific physicochemical conditions of the deep surface.¹⁶⁻¹⁸ In the past decade, increasing attention has been given to microbiological processes in the context of hydrogen storage.¹⁹⁻²¹ Despite their importance, our knowledge of these microbial communities remains limited due to the challenges associated with accessing and sampling these environments.²²⁻²⁴ Where studies exist, they frequently reveal a high proportion of unknown or unclassified genera, highlighting the unique and poorly understood biodiversity of subsurface habitats.²⁵⁻²⁷

The composition of microbial community has been shown to correlate with the profiles of available electron donors and acceptors.²⁸ Specifically, sulfate-reducing microbes can reduce sulfate (SO₄²⁻) to the toxic gas H₂S, leading to mineral precipitation (e.g. FeS), and souring of stored hydrogen.¹² Further, methanogenic archaea can consume injected hydrogen and carbon dioxide to produce methane, potentially reducing the efficiency of hydrogen storage.²⁹,³⁰ Microbial growth and activity can also induce corrosion processes, biofilm formation and mineral precipitation, potentially affecting injectivity, reservoir porosity and the long-term integrity of the infrastructure.¹² Understanding the coupled geochemical and microbial dynamics in these environments is therefore essential for predicting and managing the long-term behavior of underground reservoirs subjected to anthropogenic use, while revealing the ecological functions of these normally energy-limited ecosystems.

Despite the recognized importance of coupled geochemical and microbial processes in subsurface environments, most studies have addressed either groundwater geochemistry or microbial community composition separately. While the influence of geochemical parameters, such as pH, salinity, redox conditions, and the availability of electron donors and acceptors on microbial community structure is recognized, their structural and metabolic characterization remains poorly explored. In turn, microbial
processes can have a significant impact on the geochemical environment and hydrogen storage. Understanding how specific chemical signatures shape microbial community structure and how these communities subsequently influence biogeochemical cycles is crucial for assessing the long-term stability and reactivity of underground reservoirs under potential hydrogen storage use. Addressing these knowledge gaps would enable a more predictive understanding of subsurface system behavior, helping to secure the integrity and efficiency of these environments for industrial and climate mitigation purposes.

In this study, the composition and diversity of microbial communities associated with various types of gas storage facilities was determined, exploring in particular their H₂ consumption potential. For this, 19 European underground reservoirs, theoretically suitable for hydrogen storage purposes, were characterized from a chemical and microbial perspective. By combining chemical profiling with microbial community analyses, the interactions between water chemistry and microbial community structures were analyzed to better understand their mutual influences and potential implications for the stability and reactivity of these reservoirs.

# 2. Material & Methods

## a. Reservoir characteristics

The reservoirs studied are located in six different European countries: Austria, Germany, Italy, Netherlands, Spain and United-Kingdom. A total of nineteen different reservoir samples were collected in salt caverns and porous reservoirs, indicated in **Fig. 1**. Eleven porous reservoirs were sampled, one in Spain (PR2), two in Italy (PR10 and PR11), three in Germany (PR1, PR7 and PR8) and five in Austria (PR3, PR4, PR5, PR6 and PR9). Three salt caverns were sampled in the United-Kingdom (SC2, SC3 and SC4), one in the Netherlands (SC5) and four in Germany (SC1, SC6, SC7 and SC8). The characteristics of each reservoir are indicated in **Table 1**. Six of these reservoirs are knowingly affected by recent anthropogenic activities: (i) PR5 was contaminated by accidental lubricant injection, (ii) SC3 brine contains dissolved ethylene gas, and (iii) an anticorrosive agent (sodium hydroxide) was injected into SC6, SC7 and SC8. Reservoirs PR2, PR7, PR8 and PR9 were sampled at the cavern head (above-ground), while the others were sampled between 317 and 1500 m depth. The salinity of the porous reservoirs reaches up to 176 g/L for the porous reservoirs and 404 g/L for the salt caverns. Groundwater can be divided into several categories according to its salinity, with freshwater having a salinity of less than 3 g/L, brackish water between 3 and 10 g/L, and brine for salinities greater than 10 g/L. In the rest of this text, the general term "groundwater" will be used for simplicity.

**Table 1** Characteristics of samples collected and specific information about reservoir: anthropic impacts, depth (m), salinity (g/L) of groundwaters and ionic balance error (%). SC = Salt cavern and PR = Porous reservoir. A depth of 0 means that the sampling was carried out above the ground (in cavern head).
<table><thead><tr><th>Sample</th><th>Country</th><th>Recent anthropic impacts</th><th>Depth (m)</th><th>Salinity (g/L)</th><th>Ionic balance error (%)</th></tr></thead><tbody><tr><td>PR1</td><td>Germany</td><td>Not known</td><td>623</td><td>20.53</td><td>-10.36</td></tr><tr><td>PR2</td><td>Spain</td><td>Not known</td><td>0</td><td>13.00</td><td>4.68</td></tr><tr><td>PR3</td><td>Austria</td><td>Not known</td><td>1420</td><td>11.71</td><td>3.36</td></tr><tr><td>PR4</td><td>Austria</td><td>Not known</td><td>1495</td><td>16.86</td><td>-3.18</td></tr><tr><td>PR5</td><td>Austria</td><td>Lubricant injection</td><td>1500</td><td>79.94</td><td>3.43</td></tr><tr><td>PR6</td><td>Austria</td><td>Not known</td><td>621</td><td>2.39</td><td>-9.7</td></tr><tr><td>PR7</td><td>Germany</td><td>Close to salt reservoirs</td><td>0</td><td>176.31</td><td>-0.03</td></tr><tr><td>PR8</td><td>Germany</td><td>Not known</td><td>0</td><td>0.06</td><td>-4.62</td></tr><tr><td>PR9</td><td>Austria</td><td>Not known</td><td>0</td><td>10.83</td><td>1.42</td></tr><tr><td>PR10</td><td>Italy</td><td>Not known</td><td>956</td><td>87.00</td><td>-4.99</td></tr><tr><td>PR11</td><td>Italy</td><td>Not known</td><td>965</td><td>85.87</td><td>-4.59</td></tr><tr><td>SC1</td><td>Germany</td><td>Not known</td><td>1310</td><td>331.45</td><td>-0.34</td></tr><tr><td>SC2</td><td>United-Kingdom</td><td>Not known</td><td>409</td><td>307.48</td><td>-1.02</td></tr><tr><td>SC3</td><td>United-Kingdom</td><td>Presence of ethylene</td><td>471</td><td>303.45</td><td>-1.28</td></tr><tr><td>SC4</td><td>United-Kingdom</td><td>Not known</td><td>317</td><td>318.91</td><td>-4.05</td></tr><tr><td>SC5</td><td>Netherlands</td><td>Not known</td><td>1199</td><td>404.41</td><td>-4.59</td></tr><tr><td>SC6</td><td>Germany</td><td>Injection of anticorrosive agent</td><td>894</td><td>308.31</td><td>-8.13</td></tr><tr><td>SC7</td><td>Germany</td><td></td><td>1225</td><td>306.8</td><td>-4.25</td></tr><tr><td>SC8</td><td>Germany</td><td></td><td>956</td><td>302.85</td><td>-3.16</td></tr></tbody></table>

b. Groundwater sampling

All sampling equipment was disinfected with 70% ethanol and/or sterilized at 121°C beforehand. Prior to surface sampling, the water was drained for 5 to 10 minutes to eliminate stagnant water. Water was collected in 1L bottles previously flushed with sterile N₂ gas. A permanent N₂ flush was maintained throughout sampling. The bottles were filled with 80% groundwater, then flushed with N₂ for 2-3 minutes to eliminate any air present. Bottles were sealed with sterile thick caps to prevent oxygen contamination and immediately sent to the lab. During shipping, samples were stored at room temperature in polystyrene boxes to protect them from light. The collected water was then used for chemical analysis. For the determination of microbial communities based on DNA, at least 100 mL of water was filtered
through Millipore Sterivex 0.22 µM filters directly at site to avoid changing community patterns during transport. The Sterivex filters were fixed with 70% isopropanol, closed, and shipped at ambient temperatures to the labs. A detailed groundwater sampling procedure and its standardization are described in Cerna *et al.* (2025).³¹

### c. Physico-chemical analysis

Depending on the laboratory, different methodologies were used, as demonstrated in the study by Cerna *et al.* (2025).³¹ Anions were quantified by ion or liquid chromatography (Cl⁻, SO₄²⁻, NO₃⁻, NO₂⁻) and spectrophotometry (PO₄³⁻). Cation analyses were performed by ICP-OES/AES (Na⁺, Ca²⁺, Mg²⁺, K⁺, Fetotal, Mn²⁺) and spectrophotometry (NO₂, NH₄). Quantification limits vary from 0.1 to 20 mg/L for cations and from 0.01 to 50 mg/L for anions, depending on the element measured. Carbonates (HCO₃ and CO₃) were analyzed by potentiometry (LQ = 10 mg/L). Salinity, conductivity and pH measurements were analyzed using HORIBA LAQUATWIN® probes. To verify the consistency of the analytical results, the ionic balance was calculated with PhreeqC, using the Thermodemm database³² for waters with salinity lower than 30 g/L and Pitzer database³³ for waters with salinity higher than 30 g/L (Table 1). Samples had a relative ion balance error of up to 10%. Considering the high dilutions applied and low concentrations measured in some cases, the sensitivity to analytical errors increased. Therefore, the error margin of up to 10% is acceptable, particularly given the high salinities of the water.

### d. DNA extraction and sequencing

For DNA extraction from Sterivex filters,³⁴,³⁵ DNeasy® Blood & Tissue Kit (Qiagen) was used. The first step of cell lysis took place directly in the filter capsule by adding 1800 µL of ATL buffer and 200 µL of proteinase K. Lysis was carried out at 56°C for around 16h. At the end of this step, the lysed filter contents were transferred to a clean tube with 900 µL ATL buffer and 900 µL absolute ethanol. The mixture was then transferred to a DNAeasy mini-centrifuge and DNA was extracted based on the manufacturers protocol. Samples were then stored at 6°C or -20°C. Extraction kit controls (i.e. clean Sterivex that were not submitted to sample filtration) were systematically included to monitor potential contamination during the extraction process. The samples corresponding to these controls were rated from C1 to C14.

To compare microbial composition between reservoirs, 16S amplicon sequencing (NGS) was performed. Libraries were prepared by two consecutive PCR reactions were performed: the first with standard primers and the second with barcoded fusion primers. Thermocycling conditions were identical for both PCR setups, except for the number of cycles: an initial denaturation at 95 °C for 3 min; 10 cycles (first PCR) or 35 cycles (second PCR) of denaturation at 98 °C for 20 s, annealing at 50 °C for 15 s, and elongation at 72 °C for 45 s; followed by a final extension at 72 °C for 1 min. Amplification targeted the hypervariable V4 region of the 16S rRNA gene using EliZyme HS HIFI MIX polymerase (Elizabeth Pharmacon, Czech Republic) and universal primers 515F (GTGYCAGCMGCCGCGGTAA) and 816R
(GGACTACNVGRGTWTCTAAT)³⁶. Amplified products were purified (Ampure XP), quantified (Qubit 2.0), and mixed at equimolar concentrations before sequencing on Ion Torrent Genexus System. Raw data were processed using QIIME 2 (2021.8):³⁷ demultiplexing, quality filtering (q2-demux), denoising (DADA2),³⁸ taxonomic assignment via classify-sklearn on the Silva 138 database,³⁹ removing mitochondria and chloroplasts. Classification accuracy was checked on an artificial MOCK community, and results were analyzed using Phyloseq (R).⁴⁰

### e. Prediction of metabolic functions in microbial communities

The taxonomic compositions and abundances were processed using the Tabigecy workflow version 0.1.1⁴¹. Tabigecy relies on EsMeCaTa version 0.6.5⁴² and the EsMeCaTa precomputed database version 1.0.0. EsMeCaTa searches for phylogenically close proteomes associated with the lowest taxonomic affiliation (e.g. genus) provided as input. If no proteomes are available, EsMeCaTa examines higher taxonomic ranks, such as family or class. These proteomes are then searched for metabolic functions in biogeochemical cycles (carbon, sulphur and nitrogen cycles) with bigecyhmm version 0.1.7⁴¹. A sporulation gene was added to the bigecyhmm internal database to account for the extreme conditions (temperature of around 150°C) of some samples.

Polar plots showing the relative abundance of organisms associated with metabolic functions were generated using Plotly version 6.0.1. Heatmaps were generated with seaborn version 0.13.2. Kruskal-Wallis tests were used to assess the differences of functions across the microbial groups. If a Kruskal-Wallis test was significant, Dunn post-hoc tests were used to identify differences between groups, with Bonferroni correction applied to account for multiple comparisons.

Spearman correlations were performed using the hydrochemistry measurements and the relative abundance of organisms associated with hydrogenases predicted by Tabigecy. Since complete hydrochemical measurements were not available for all samples, correlations were performed on samples SC1, PR1, PR2, SC3, PR3, PR4, PR5, PR6, PR9, PR10 and PR11 for acetate, and on samples SC1, PR2, SC2, SC3, PR3, PR4, PR5, PR6, SC4, SC5, PR7, PR8, PR9, PR10 and PR11 for HCO₃.

### f. Statistical analyses

All analyses were performed using R 4.4.2. Details of the statistical analyses performed are presented in **Methods S1**. To assess the reproducibility of biological replicates, Bray-Curtis dissimilarities were computed between all pairs of replicates, and the distribution of intra-sample distances (within replicates) was compared to inter-sample distances (between different samples) using a Wilcoxon rank-sum test. Non-metric multidimensional scaling (NMDS) ordination was then performed on the resulting Bray-Curtis distance matrix to visualize the relationship among replicates. Sampled sites were clustered according to their chemical composition using k-means clustering. The optimal number of clusters was determined by the Elbow method and silhouette method (Fig. S1),⁴³ leading to the identification of four
chemical clusters. To further explore chemical patterns among samples, a Principal Component Analysis (PCA) was performed.<sup>44</sup> Microbial community structures across sites were further analyzed using NMDS on Bray-Curtis dissimilarities, followed by k-means clustering. The number of microbial clusters was determined using the Elbow and silhouette methods (Fig.S2). Differences in microbial community composition between clusters were assessed using ANOSIM. Alpha diversity (species richness, Shannon, Simpson) was calculated for each cluster, and differences were evaluated using Kruskal-Wallis tests, followed by post-hoc comparisons. To characterize the structure of microbial communities within clusters, dominant and indicator genera were identified. Dominant genera were defined as those with the highest relative abundance within each cluster, identified separately for Bacteria and Archaea, based on the ten most abundant genera per cluster. Indicator genera are specific to a given environment and were identified using indicator value analysis based on their preferential association with specific clusters, considering both their frequency and specificity within clusters. The Table S1 regroups the putative ecological characteristics of dominant and indicator archaeal and bacterial genera, based on their trophic and ecophysiological modes (according to literature). Relationships between microbial communities and environmental variables were further explored using Spearman correlations and Canonical Correspondence Analysis (CCA). In order to visualize the correspondences between chemical and microbiological characteristics, a Sankey diagram was drawn.<sup>45</sup>

# 3. Results

## a. Hydrochemical characteristics of formation groundwater

Sample chemical compositions were analysed and subsequently evaluated using a PCA (Fig. 2a). The k-means results groups the sites into four clusters, taking into account physical parameters (pH, Eh, salinity, temperature) in addition to chemical ones ($SO_4^{2-}$, $Cl^-$, $PO_4^{3-}$, $NO_3^-$, $NO_2^-$, $S_2^-$, $Na^+$, $Ca^{2+}$, $Mg^{2+}$, $Mn^{2+}$, $K^+$, $NH_4^+$, $Fe_{total}$, $HCO_3^-$, $CO_3^{2-}$, TIC, DOC), summarized in Table S2. The first two principal components (PC1 and PC2) explain 64.1% of the total variance: PC1 = 40.2% and PC2 = 23.7%. The overall strength of correlations between samples (PCA axis scores and their physicochemical parameters) is summarized in Table S3. $PO_4^{3-}$, $S_2^-$, $NH_4^+$, $HCO_3^-$, $CO_3^{2-}$ and TIC showed a significant positive correlation with PC1, while PC2 had a positive correlation with $SO_4^{2-}$, $Cl^-$, $Na^+$, and salinity, as well as temperature and $Fe_{total}$, but to a lesser extent. Specifically, PC1 isolated the PR5 site, while PC2 separated Clusters 1 (SC6, SC7 and SC8) and 3 (SC1, SC2, SC3, SC4 and SC5) on one side, and Cluster 2 (PR1 to PR 7 and PR9 to PR11) on the other side. Cluster 1 is clearly separated from Cluster 3 due to its higher pH values, which are captured along dimension 3 in the PCA (Fig. S3). The radar chart (Fig. 2a) shows the four clusters characteristics: (i) Cluster 1 is characterized by high concentrations of $Na^+$, $Cl^-$, $SO_4^{2-}$ and $NH_4^+$ with a very high pH, (ii) Cluster 2 is composed of high concentrations of $Fe_{total}$, $Mn^{2+}$, $Ca^{2+}$ with higher temperatures, (iii) Cluster 3 is also defined by high concentrations of $Na^+$, $Cl^-$ and $SO_4^{2-}$, but also Mg with high salinity and redox potential (Eh), and (iv) Cluster 4, made up of a single site, has high concentrations of carbon
(TIC, DOC, CO<sub>3</sub><sup>2-</sup>, HCO<sub>3</sub><sup>-</sup>), PO<sub>4</sub><sup>3-</sup>, S<sub>2</sub><sup>-</sup> and K<sup>+</sup>. The average concentrations of each element for the four clusters are shown in **Fig. S4**.

**b. Microbial diversity: general information**

Replicates per sample were averaged after calculating Bray-Curtis distances between replicates (**Text. S1, Table S4, Fig. S5**). Sequencing revealed the presence of numerous microbial genera in the groundwater samples (n = 1131), and 6.4% of which correspond to unknown genera ("Undetermined" and "*Incertae Sedis*"), referred to in this case by their phylum. The k-means used before NMDS analysis shows a structuring of the microbial communities with three main clusters and a distinct cluster for the kit controls. The results of NMDS analysis are presented in **Fig. 2b**. Cluster A groups PR2, PR3, PR4, PR5, PR6, PR9, PR10 and PR11. Cluster B is made up of samples SC1, PR1, SC2, SC3, SC4, SC5, PR7 and PR8, and represents a second distinct microbiological profile. Cluster C is a small group with three sites: SC6, SC7, and SC8. Finally, the "controls" cluster is relatively isolated from the other clusters, suggesting a different microbial signature, although there is a slight overlap with PR1. To statistically test the separation observed in the NMDS, an ANOSIM test was performed on the same matrix data. When controls were included, ANOSIM test indicated only marginal separation between groups (R = 0.1145, *p* = 0.057). In contrast, when controls were excluded, and only samples were considered, group separation was strong and statistically significant (R = 0.8757, *p* = 0.001), supporting the ecological relevance of the three identified clusters. The mean specific richness of Cluster B was the highest for both Bacteria and Archaea, but was only significantly different from Cluster C for Archaea, and from the Cluster Controls for Bacteria (**Fig. S6a**). Clusters A and B have a Shannon index significantly different from the Controls for Archaea, whereas Cluster C has a Shannon index and a Simpson index significantly different from the Controls for Bacteria (**Fig. S6b, c**). To assess the potential impact of laboratory contaminants on microbial community composition, the decontamination prevalence method was applied using kit controls (Cluster Controls) as negative controls. This analysis identified 11 genera as potential contaminants, with only *Escherichia-Shigella* present in 8 samples among the identified groups (**Table S5**). To evaluate the potential impact of geography on microbial community structure, an NMDS analysis was carried out (**Fig. S7a**). This analysis shows that microbial communities at sites in the same country are more similar to each other (intra-country) than to sites in other countries (inter-country), as confirmed by a significant Wilcoxon test comparing intra- and inter-group difference (Wilcoxon test: *p*-value < 0.001, **Fig. S7b**). Sampling depth was also tested, but was not significant (*p*-value = 0.123).

To better understand the structure of and ecological functioning of subsurface microbial communities, the ten dominant genera, defined as those with the highest relative abundance within communities, and indicator genera, defined as genera specifically associated with particular environmental conditions, were studied for each microbial cluster: Clusters A, B, C and Controls. The most abundant bacterial genera were identified for each microbial cluster (**Fig. 3a**). Each Clusters A, B and C displayed abundant bacterial genera that were poorly represented in the other clusters (**Table S6**). Cluster A was predominantly composed of genera belonging to the Thermotogota, Coprothermobacterota, Bacillota, Bacteroidota, Synergistota and Thermodesulfobacteriota phyla. The ten dominant bacterial genera of
Cluster A represented 65.7% of the total bacterial abundance in this cluster. Genera from the last two phyla (Synergistota and Thermodesulfobacteriota) were also detected in the Controls cluster. Cluster B was primarily composed of genera from the Pseudomonadota, Balneolota and Bacillota phyla, with the ten dominant genera accounting 49.9% of the total bacterial abundance in this cluster. Cluster C included genera from the Pseudomonadota, Balneolota, Bacteroidota, Planctomycetota, and Eremiobacteriota phyla, with its ten dominant genera representing 92.5% of the total bacterial abundance. Finally, the genera identified in the Controls cluster (belonging to Pseudomonadota, Actinomycetota, Bacillota, Bacteroidota and Thermodesulfobacteriota phyla) were also present in the other clusters, but at lower relative abundances (Table S6). The indicator bacterial genera for each cluster were shown in Fig. 3b and Table S8. Three abundant genera (*Acetomicrobium*, *Petrotoga* and *Thermovirga*) in Cluster A were indicator genera, compared with nine (*ML310M-34*, *Whenzhouxiangella*, *Rhodohalobacter*, *Salibacter*, *Glycocaulis*, *Urania-1B-19*, *Arhodomonas*, *Incertae Sedis* and *Owenweeksia*) in Cluster C. The indicator genera in clusters A and C had indicator values close to 1 (Table S7), meaning that these genera were found almost exclusively and frequently in these groups. By contrast, the most abundant bacterial genera in Cluster B and the Controls group were not among the indicator genera. Only two bacterial genera (*Incertae Sedis* of Dependentiae phylum and Chlamydiota phylum) were found to be indicative of Cluster B, with the other genera also present in other clusters. Controls group did not have any perfectly specific genus.

As far as dominant archaeal genera were concerned (Fig. 3c, Table S8), Cluster A and Cluster B were characterised by many abundant genera from Halobacteriota, Methanobacteriota, Thermoplasmatota and Thermoprotera phyla (Cluster A), and Halobacteriota and Nanohaloarchaeota phyla (Cluster B). The ten dominant genera represent 99.9%, for Cluster A, and 88.3%, for Cluster B, of the total archaeal abundance. Cluster C and Controls Cluster displayed fewer than ten archaeal genera. Indeed, cluster C contained just one archaeal genus (Nanoarchaeota phyla), also present in Cluster B and in the Controls group. Cluster C included seven archaeal genera from the Halobacteriota, Thermoproteota, Iainarchaeota, Thermoplasmatota and Methanobacteriota phyla. Among the archaea indicator genera (Fig. 3d, Table S9), the four identified in Cluster A were also among the most abundant (*Methanothrix*, *Methanobacter*, *Methanocalculus* and *Methanobacterium*). Cluster B had eight indicator genera, three of which were also among the most abundant: *Candidatus Haloredivivus*, *Halolamina* and *Halarchaeum*. Several genera exhibited identical indicator value (Table S9) within Cluster B, reflecting their similar occurrence patterns and their exclusive association with this cluster. The only archaeal genus (*Incertae Sedis* from Nanoarchaeota phylum) associated with Cluster C was also indicator genus. In contrast, no indicator genus was identified in the Controls group.

### c. Microbial metabolisms predictions

**Functional predictions in samples containing poorly characterized organisms** Determining the role of microbial communities in biogeochemical cycles and their possible impact on gas storage requires the identification of their metabolic functions. The Tabigecy tool was developed for this purpose⁴², as it can make functional predictions in communities that potentially include poorly characterized
microorganisms (see Section 2e for more details). In short, Tabigecy queries the UniProt database to
find phylogenetically close proteomes associated with the identified taxa. If no proteomes are found at
low taxonomic ranks (such as genus), Tabigecy searches at higher taxonomic ranks (such as family).
Across all samples, functional predictions were mainly inferred at the genus (mean of 37% of the relative
abundance of the sample) and family (mean of 30%) levels (Fig. S8). These ranks were shown to allow
confident functional predictions when compared to metagenomes⁴². For several samples (PR3, PR4,
SC4), higher taxonomic ranks (class or phylum) were selected for around 30% of the community. This
was due to the presence of organisms from poorly characterised phyla such as the Candidate phyla
radiation group (for example, Parcubacteria) or Nanohalarchaeota with small genomes and limited
metabolic capacities. Except from these phyla, these results showed that Tabigecy achieved the
functional predictions at sufficient taxonomic ranks.

**Metabolic differences between reservoir types** We analyzed if the microbial clusters identified in Section 2b, which vary in terms of microbial diversity, have also different metabolic functions (Fig. 4). This would show that the communities have adapted their metabolism to the environment. Indeed, most of the porous reservoirs (except PR1, PR7 and PR8) were grouped with microbial cluster A, while salt caverns (along with the three previously mentioned porous reservoirs) were grouped with microbial clusters B and C. In order to verify this, the abundance associated with the predicted metabolic functions of each sample was computed by summing the abundance of organisms predicted to possess the functions in the corresponding sample. Then, for each cluster, functional predictions of their associated samples were averaged and are shown in Fig 6. All the organisms in each cluster have the same fermentation and organic carbon oxidation functions. The microbial clusters associated with salt caverns do not show any statistical differences in the relative abundance of metabolic functions (B against C). This means that the differences found at the taxonomic level are not reflected at the metabolic level, probably because the microbial communities in clusters B and C share similar salt environments. Interestingly, statistical differences were found between microbial cluster A (porous reservoirs) and microbial clusters B and C (salt caverns). The microbial communities in cluster A possess the capacity for methanogenesis (9% of the microorganisms, on average), selenate reduction (31%), and hydrogen generation (73%), while communities in clusters B and C are more specialized in acetate oxidation (66% in B and 86% in C) and nitric oxide reduction (14% in B and 55% in C). A comparison between clusters A and B reveals that cluster A has a significantly higher number of microbial taxa associated with acetogenesis than cluster B. In contrast, cluster B has a greater number of microbial taxa associated with iron reduction than cluster A. In summary, microbial communities have adapted to the type of reservoir: organisms performing methanogenesis were more abundant in porous reservoirs, whereas organisms performing acetate oxidation, nitric oxide reduction and iron reduction were more present in salt caverns.

**Hydrogenotrophic functions** Since porous reservoirs and salt caverns can be used for storing hydrogen, we wanted to find out whether the microbial communities in these reservoirs could consume the stored gas. The presence of hydrogenases in the communities was screened to identify hydrogenotrophic potential among the metabolic functions previously identified. This was achieved by analyzing the relative abundance of microbial organisms that were predicted to possess hydrogenases (Fig. 5). The
enzymes were screened using Tabigecy and the hydrogenase classification from Søndergaard et al. (2016)⁵⁴. Generalist hydrogenase NiFe-1 was found in all samples. More specialized hydrogenases were also present in the samples. We focused on those involved in the metabolic functions of clusters A to C, specifically methanogenesis and acetogenesis. Hydrogenases associated with methanogenesis⁵⁵ (subtypes Fe-only, Ni-Fe 3c and Ni-Fe 4hi) were found in most porous reservoirs (7 out of 11) compared to salt caverns (2 out of 8). This result is consistent with the above finding that methanogenesis is prevalent in porous reservoirs, as well as with the hydrochemical measurements: the abundance of organisms associated with hydrogenases Fe-only, Ni-Fe 3c and Ni-Fe 4hi was found to be positively correlated with carbonate measurements (Spearman's correlation coefficient of 0.72, 0.77, 0.79, respectively, with a p-value < 0.05). Carbonates are used as substrate for hydrogenotrophic methanogenesis. Sample SC1 was found to have an abundant community of methanogenic archaea (*Methanohalobium evestigatum*), but no hydrogenases associated with methanogenesis were detected. This is consistent with experiments showing that this species performs methylotrophic or acetoclastic methanogenesis⁵². These results indicate a prevalence of hydrogenotrophic methanogenesis in porous reservoir sites and a limited one in salt caverns. We then analyzed the hydrogenases associated with acetogenesis, the first step of which is known to involve formate dehydrogenase-linked hydrogenase (FeFe-a4). Fe-Fe hydrogenases were present in most samples except for SC3, SC6, SC7 and SC8. The abundance of microorganisms associated with the FeFe-C 1-2-3 hydrogenases (associated with hydrogen sensing) was positively correlated with acetate measurements (Speaman's correlation coefficient of 0.77, 0.78, 0.73, respectively, p-value < 0.05). The abundance of Fe-Fe hydrogenases in sample PR8 can be related to the high abundance of organisms from the genus *Sporomusa* found in this sample, which includes several species known for their acetogenesis potential.⁴⁶ In summary, most of the sites (excluding SC3, SC7 and SC8) have microbial communities with hydrogenotrophic potential, which are likely to consume hydrogen if conditions are met. There is a particular prevalence of hydrogenotrophic methanogenesis in porous reservoirs.

**Metabolic functions in response to environment and human activities** Several samples showed very distinct function abundances compared to others that could be the result of environmental or human stress. Microbial cluster C (samples SC6, SC7 and SC8) contained organisms associated with both acetogenesis and acetate oxidation but also with methanotrophy and nitric oxide reduction. These functions were predicted from the genus *Halomonas* as this genus was dominant in the associated samples. Indeed, the *Halomonas* genus represented 53%, 66% and 44% of the total abundance in SC6, SC7 and SC8, respectively, while it was present between 0.004% and 0.6% in five other sites (SC2, PR3, PR6, SC5 and PR8), at 14% in one kit control (C7) and absent from the others. This could be explained by the alkalinity (pH > 11) of the brine induced by human activities (injection of NaOH), for which several species of *Halomonas* have shown adaptation.⁵⁰ Inside these clusters there was still variability. PR8 from Cluster B exhibited a high abundance of organisms associated with thiosulfate disproportionation and identified as genus *Sporomusa*. Furthermore, this sample contained a high abundance of spore-forming microorganisms (Fig S9). The stress induced by the high reservoir temperature (150°C) or a temperature variance of this reservoir has very probably put a selective pressure on the community, thus
selecting spore-forming organisms.⁵¹ The two other samples with highest abundance of organisms linked to a sporulation regulation gene were PR2 (linked to a high temperature of 90°C) and PR5 (accidental lubricant injection), which has communities that experienced high stress. SC1 in Cluster B was dominated by microorganisms associated with methanogenesis and identified as *Methanohalobium evestigatum*. This species was found to have genes performing either methylotrophic or acetoclastic methangoenesis.⁵² Other microorganisms in SC1 were also associated with methylotrophy (*Methylobacterium* or *Methylorubrum*).

**d. Chemical-microbial interactions**

The correspondence between the chemical facies, the chemical (determined by PCA) and microbiological (determined by NMDS) clusters, and the origin country are illustrated in **Fig. 6** using a Sankey diagram. Sites belonging to chemical cluster 1, all located in Germany (SC6, SC7 and SC8) were systematically associated with microbial cluster C. Reservoirs classified in chemical cluster 2 were divided into two microbial clusters: Cluster A for sites in Austria – Vienna Basin – (PR3, PR4, PR6 and PR9), Italy – Apennines – (PR10 and PR11) and Spain – Ebro basin – (PR2), and Cluster B for sites in Germany (PR1, PR7 and PR8). Sites associated with chemical cluster 3, located in Germany (SC1), United-Kingdom (SC2, SC3 and SC4) and Netherlands (SC5), all in Permian basin, had microbial communities corresponding to Cluster B. Finally, the single site of chemical cluster 4 (PR5), originating from Austria, harbored a microbial community affiliated with Cluster A.

To better understand the links between reservoir chemistry and microbiology, the dominant and indicator genera of Archaea and Bacteria within each cluster were categorized according to their ecological characteristics, as described in Material and Methods part (Table S2). In the case of Archaea, genera felt into five categories: Non-methanogenic halophilic, non-halophilic methanogens, halophilic methanogens, specialized methanogens, and other extremophiles. **Fig. 7a** shows the distribution of indicator (marked with a red star) and dominant archaeal genera, each colored according to the associated microbial cluster. Spearman correlation tests were performed between the relative abundance of each ecological group and the physicochemical variables to identify potential associations. Significant correlations (*p* < 0.05) are shown in **Fig. 7a** for Archaea. Cluster A (red) includes halophilic, non-halophilic and specialized methanogens, as well as another extremophile (thermophiles, acidophiles). The genera belonging to cluster B (yellow) were all grouped as non-methanogenic halophiles with the exception of *Methanohalobium*, which is a halophilic methanogen. A single archaeal was dominant and indicative of Cluster C and is part of the “other extremophiles” category. Finally, one genus in the Cluster Controls is a non-methanogenic halophile and the others were classified as other extremophiles. Non-methanogenic halophiles showed positive correlations with major ions (Mg²⁺, K⁺, SO₄²⁻, Cl⁻, Na⁺, Ca²⁺) and salinity, and negative correlations with NH₄⁺, pH, Mn²⁺, Feₜotal, DOC, TIC, S₂⁻ and temperature. Halophilic methanogens were positively correlated with DOC. Non-halophilic methanogens showed positive correlations with Feₜotal, DOC, S₂⁻ and DIC, and negative correlations with SO₄²⁻, Mn²⁺, Cl⁻, Na⁺, NO₃⁻, and salinity. Specialised methanogens were positively associated with Ca, S₂⁻, and DIC, and negatively with Cl⁻, Na⁺,
$Ca^{2+}$, and salinity. Finally, other extremophiles exhibited positive correlations with $S_2$, DIC, and pH, and negative correlations with $Ca^{2+}$.

Bacterial genera were most numerous and could be divided into seven categories: generalists, sulfate-reducing bacteria, human-associated bacteria, methanogenic syntrophs, thermophiles, planctomycetes and halophiles (Table S2). The distribution of indicator and dominant genera according to microbial clusters is shown in Fig. 7b. The bacterial genera in Cluster A were mainly thermophiles. This cluster also contained methanogenic syntrophs, sulfate-reducing bacteria, as well as planctomycetes and generalist genera. Cluster B housed mainly generalist genera but also included one halophilic genus and one human-associated genus. Cluster C contained halophiles, planctomycetes, human-associated bacteria, but also many generalists. Finally, the Cluster Controls is mainly composed of human-associated bacteria and generalist genera, with also the presence of sulfate-reducing bacteria. As with archaea, bacterial groups were analyzed using Spearman correlation tests with physicochemical parameters. Significant results (*p* < 0.05) are presented in Fig. 7b (full results in Table S11). Generalist bacteria showed positive correlation with Cl and Mn, and negative correlations with $PO_4^{3-}$, $Mg^{2+}$, $NO_2^-$, $S_2^-$ and DIC. Sulfate-reducing bacteria were positively correlated with Mg and Ca. Human-associated bacteria showed positive correlations with Ca, and negative correlations with $NO_2^-$, $S_2^-$, DIC, pH, and $SO_4^{2-}$. Syntrophic methanogens were positively correlated with $S_2^-$ and $Fe_{total}$, and negatively with Cl, $Na^+$ and salinity. Thermophilic bacteria showed positive correlations with $S_2^-$, DIC, $NH_4^+$, K, DOC, temperature and $Fe_{total}$, and negative correlations with Cl, $Mn^{2+}$, $SO_4^{2-}$, $Na^{2+}$ and salinity. Finally, halophilic bacteria had positive correlations with pH and $SO_4^{2-}$, and negative correlations with $Fe_{total}$.

Canonical Correspondence Analysis (CCA) were performed by differentiating Archaea from Bacteria and indicators from dominant genera to visualize the association between genera, sites and physicochemical variables. The results are presented in Table S12 for Archaea and Table S13 for Bacteria. Three CCA were carried out based on the physicochemical data available for each site. Detailed results are presented in Text S1. Overall, archaeal communities were structured by major ions such as $SO_4^{2-}$, $Na^+$, Cl and carbon (TIC and DOC). Temperature and pH played a secondary role in this structuring. Indicator genera were more impacted by physicochemical variables than dominant genera, possessing higher F-values. Indicator bacterial genera were also structured by $SO_4^{2-}$, Cl and carbon, but less intensely than archaea. In contrast, dominant bacterial genera were weakly impacted by the physicochemical variables studied.

# 4. Discussion

Underground reservoirs, traditionally assessed from geological and geochemical perspectives, host microbial communities that may interact with injected hydrogen. In this study, we provide a combined geochemical and microbiological characterization of 19 European reservoirs to evaluate factors potentially influencing hydrogen storage.
a. Influence of water chemical and physical parameters on microbial communities in porous reservoirs and salt caverns

The sampled underground gas reservoirs comprise two distinct geological settings: salt caverns (SC) and porous reservoirs (PR), each exhibiting distinct water chemistries and microbial communities. Salt caverns are characterized by high salinity and elevated $SO_4^{2-}$ concentrations, whereas carbonate porous reservoirs display higher $Fe_{total}$, $Ca^{2+}$ and carbonate contents. This geochemical contrast is mirrored in the microbial assemblages inhabiting these environments. Carbonate-rich porous (PR2 to PR6 and PR9 to PR11) harbor communities dominated by methanogenic archaea, methanogenic syntrophic bacteria, sulfate-reducing bacteria, thermophilic microorganisms, and generalist taxa, microorganisms typically associated with carbonate environments.<sup>48-50</sup> Metabolically, these microbial communities are oriented toward hydrogenotrophic methanogenesis, hydrogen-producing fermentation, and selenate reduction. The chemical conditions prevailing in these reservoirs promote the generation of hydrogen and small organic compounds, which in turn support hydrogenotrophic methanogenesis.<sup>51-53</sup> The co-occurrence of methanogens and syntrophic bacteria reflects well-established anaerobic degradation pathways in carbonate-rich reducing subsurface environments, often under elevated temperature conditions.<sup>54,55</sup> Organic carbon availability, a typically critical limiting factor in deep anaerobic ecosystems,<sup>56-58</sup> is relatively high in these porous reservoirs, stimulating fermentation, and consequently, the activity of methanogens and sulfate reducers. Temperature also influences microbial community composition in porous reservoirs: thermophilic conditions observed in PR2 (90°C) and PR8 (150°C) are associated with the presence of sporulating genera, persisting mainly as dormant spores. Indeed, at 150°C, it is unlikely that any microbial activity is maintained, and the communities detected likely represent dormant forms adapted to survive these harsh conditions.<sup>59,60</sup>

In contrast, microbial communities in salt caverns (SC1 to SC8) and certain porous reservoirs (PR1, PR7 and PR8) are dominated by halophilic and generalist taxa not involved in methanogenesis. Saline environments are well known to favor halophilic microorganisms.<sup>61</sup> Their predicted metabolic potentials are mainly focused on nitrate reduction and acetate oxidation, more consistent with oligotrophic and high-salinity conditions.<sup>62</sup> These environments, rich in sodium and chloride, exert a high osmotic pressure that selects for microorganisms capable of specific adaptations to maintain their osmotic balance.<sup>63,64</sup> As a result, energy-intensive metabolic pathways like methanogenesis are less likely under these conditions. Interestingly, PR7, classified as a high-salinity porous reservoir, shares both microbial and chemical profiles with salt caverns.<sup>65</sup> Even more puzzling are the microbial communities in PR1 and PR8, which resemble those of salt caverns despite their low salinity and low $SO_4^{2-}$ concentrations, typical of other porous reservoirs. Two hypotheses may explain this observation: (i) these reservoirs could have inherited ancient brines, allowing halophiles to persist in an oligotrophic environment due to low competition, or (ii) local saline micro-niches not detected by general chemical analyses could allow the persistence of these halophiles.<sup>65,66</sup>
Beyond water chemistry, geographic origin appears to influence microbial community composition. For instance, reservoirs from Austria, Italy and Spain, which are typical of carbonate-rich waters, are largely dominated by methanogenic taxa. On the other hand, reservoirs from the United-Kingdom, Netherlands and Germany exhibited higher relative abundances of halophilic taxa, both in salt caverns and porous reservoir samples. These trends suggest that geological factors significantly shape the structure of deep subsurface biospheres. This aligns with prior studies showing that substrate mineralogy, porosity and water-rock interactions are critical determinants of microbial community structure in the deep subsurface.⁶⁷⁻⁷⁰

## b. Impacts of anthropogenic activities on microbial communities and potential metabolisms

Although most microbial community profiles align with the natural geochemical and geological reservoir characteristics, several of them exhibit atypical features potentially resulting from anthropogenic interventions. Reservoirs subjected to industrial operations show altered subsurface conditions and shifts in microbial community composition. For instance, SC6, SC7, and SC8 present extremely alkaline pH values (>11), which are not typical of natural deep subsurface environments. This alkalinity is attributable to the use of alkaline anticorrosive treatments in the salt caverns. These harsh chemical conditions significantly reduce microbial diversity and select for a narrow range of alkaliphilic taxa, including *Halomonas*, which dominates the microbial profile of these samples. Functions associated with these communities include acetogenesis and methanotrophy. Contaminations with specific chemicals are known to trigger a decrease in overall microbial diversity while increasing specific functional groups, often consisting of microorganisms resistant to altered conditions.⁷¹,⁷² This simplification of microbial networks suggests reduced ecological resilience and a possible shift in metabolic pathways toward generalist, stress-resistant functions, reflecting a specific adaptation to the altered pH. Despite high salinity, the community is dominated by taxa capable of methylotrophic and acetoclastic methanogenesis, illustrating how targeted chemical inputs can reshape metabolic potentials. Similarly, PR5 underwent accidental lubricant injection, introducing a complex anthropogenic source of organic matter while also increasing the pH. This perturbation resulted in altered water geochemistry, with elevated phosphate, sulfide, and potassium concentrations. These anthropogenic changes appear to promote the proliferation of spore-forming *Firmicutes* in several samples, known for their ability to produce resistant endospores under stress conditions.⁷³,⁷⁴ While these organisms may not be metabolically active, their persistence in dormant form contributes to a microbial "seed bank" adapted to environmental disturbances. The detection of such sporulating genera confirms the exposure of PR5 to significant anthropogenic stress. However, although SC3 contains ethylene, the chemistry of the brine and microbial communities does not appear to be affected compared to other saline cavities. Together, these examples underscore that industrial activities can substantially and durably reshape microbial community composition. While the core functional potential of the communities may be maintained, their structure, diversity, and ecological resilience are clearly impacted. These findings highlight the necessity of incorporating operational history when interpreting microbial data from
subsurface environments, especially in the context of environmental monitoring or biotechnological applications. In the same way, any human action on a reservoir has to be carefully considered.

**c. Taxa as indicators**

The composition of microbial communities in the studied subsurface environments reveals specific taxa whose occurrence or relative abundance is strongly associated with particular reservoir types or geochemical conditions. In porous reservoirs, several microbial taxa appear to be characteristic for carbonate-rich anaerobic conditions.⁷⁵ Methanogenic archaea such as *Methanothermobacter*, *Methanobacterium*, *Methanothrix*, and *Methanocalculus*, along with syntrophic bacteria including *Acetomicrobium* and *Syntrophus*, are recurrently enriched in these environments. Their co-occurrence indicates the presence of robust syntrophic networks supporting hydrogenotrophic and acetoclastic methanogenesis, metabolic pathways commonly favored in reducing environments with elevated organic carbon availability.⁷⁶,⁷⁷ Additionally, the frequent detection of thermophilic genera such as *Thermovirga* and *Petrotoga* in high-temperature reservoirs underscores their role as reliable indicators of deep, warm, and thermally active anaerobic niches. In contrast, microbial communities in salt caverns are dominated by halophilic archaea such as *Halarchaeum*, *Haloarcula*, *Halomicrobium*, *Haloplanus*, and *Natromonas*. These taxa form a characteristic consortium that serves as an effective biological signature of hypersaline conditions.⁶¹,⁷⁸ Their recurrent presence across multiple salt cavern samples reinforces their reliability as indicators of saline geochemical regimes. Collectively, these indicator taxa provide a biological readout of both geochemical states and anthropogenic disturbances in subsurface environments and potential hydrogen storage sites. Their presence, relative abundance, or absence can inform reconstructions of reservoir history, evaluations of ecological resilience, and assessments of microbial risks to technical systems, including those related to hydrogen storage integrity and biocorrosion.

**d. Considerations for hydrogen storage: risks of microbial consumption**

The deep subsurface environments investigated in this study provide critical insights into microbial processes that may influence long-term hydrogen storage. While many reservoirs appear to harbor communities with limited metabolic activity due to harsh physical or chemical conditions, several factors suggest that microbial hydrogen consumption represents a potential risk, particularly in porous reservoirs. Carbonate-rich porous reservoirs enriched in methanogenic archaea and hydrogenotrophic syntrophic bacteria, especially those with functional signatures of active hydrogenase genes (involved in methanogenesis or acetogenesis), are of particular concern. These consortia are capable of hydrogenotrophic methanogenesis, wherein $H_2$ is consumed to reduce $CO_2$ to $CH_4$. This metabolic activity could result in both the loss of stored hydrogen and the unintended production of methane. Additionally, the presence of hydrogen-producing fermentation pathways in these communities indicates a microbial network already structured around hydrogen cycling. In the context of underground hydrogen storage, such microbial communities can contribute to significant hydrogen consumption, with methanogenesis,³⁰ sulfate redicton or homoacetogenesis.⁷⁹ While this does not preclude storage
feasibility, it highlights the need for further investigation — particularly through kinetic experiments at various scales — to better quantify microbial impacts and inform operational strategies. Furthermore, predicted metabolic functions such as selenate, sulfate, and nitrate reduction, also reliant on hydrogen as an electron donor, highlight the potential for multiple microbial hydrogen consumption pathways to become active under favorable conditions. In addition, metabolic signatures related to acetogenesis and sulfate reduction, both of which are likely to consume hydrogen, have also been identified in the microbial communities of salt caverns.

It is clear that a limitation of the performed analyses is the primary focus on dominant and indicator taxa in the liquid brine, meaning that potentially active hydrogen-consuming organisms associated with rock surfaces or biofilms may remain undetected. It is well known that microbial communities inhabiting solid matrices can differ substantially from those found in the aqueous phase.<sup>71,80</sup> As such, the functional inferences derived from water-phase microbial data and geochemistry likely capture only a portion of the active processes. It should also be emphasized that the present study focuses on a limited number of reservoirs. Therefore, any generalization to all potential hydrogen storage sites should be made with caution. To validate and refine the trends observed in this study, investigations are underway to expand the dataset by including additional reservoirs.

Furthermore, all our findings are based on 16S rRNA gene sequencing and predictive functional analyses. While this approach provides a comprehensive overview of community composition across numerous reservoirs, it does not distinguish between metabolically active and dormant organisms. Given that all DNA is sequenced regardless of metabolic state, the actual activity of the microbial community may be over- or underestimated. The high prevalence of sporulating taxa, particularly in high-temperature environments, further supports the likelihood that dormant or inactive organisms are being detected. To assess the real impact of microbial communities on hydrogen storage, complementary investigations are required. Future studies should include enrichment cultures to identify $H_2$-consuming organisms under reservoir-relevant conditions, as well as metagenome-assembled genomes and metatranscriptomics to directly assess the potential for hydrogen metabolism. In this context, understanding the baseline microbiological potential of each reservoir is essential before considering its use for hydrogen storage. Monitoring key indicator taxa, evaluating metabolic gene content, and tracking community shifts during pilot injection trials can help anticipate microbial risks. Such knowledge can inform mitigation strategies, including reservoir selection, monitoring protocols, and targeted biocide application, ultimately supporting the safe and efficient deployment of underground hydrogen storage technologies.

# 5. Conclusion

This study provides an integrative characterization of the geochemical and microbiological diversity of various deep salt caverns and porous reservoirs in Europe, with a focus on their potential for underground hydrogen storage use. The results show that, although general water characteristics influence microbial community structure, other environmental factors such as salinity, carbon,
temperature, geology, and anthropogenic activities can also modify it. A clear distinction between salt caverns and porous reservoirs was observed. Porous reservoirs, rich in carbonates and sulfides, harbor microbial communities that promote methanogenesis, acetogenesis, and sulfate reduction. Saline reservoirs are dominated by halophilic communities, but also by hydrogen-consuming microorganisms. The presence of spore-forming genera in high-temperature reservoirs as well as in reservoirs with substantial anthropogenic impact highlights the selection of specific taxa under extreme conditions, while also highlighting the limitations of 16S sequencing for assessing active microbial processes in situ, particularly under conditions where microbial activity is unlikely. Geochemical conditions and anthropogenic disturbances play a crucial role in the structure and functional potential of deep subsurface microbial communities. Understanding these factors is essential for anticipating the biogeochemical dynamics that may arise during hydrogen injection and storage, which is key to developing safe and efficient underground storage strategies. This indicates that while reservoir exploitation alters community composition, the functional resilience of these communities in the context of hydrogen storage remains to be understood. Given the prevalence of microbial groups capable of consuming hydrogen in carbonate and saline reservoirs, it is important to go beyond mere detection and assess their actual activity. In order to identify the microbial mitigation or gas treatment strategies necessary for future underground hydrogen storage, it is important to understand the extent to which these communities can consume hydrogen and alter gas quality. Future work should focus on assessing the actual hydrogen consumption potential of these microbial communities. In conclusion, understanding the interaction between reservoir chemistry, geology, and microbiology is essential to ensure better control of hydrogen storage processes in the context of energy transitions.

# Declarations

## Ethics approval and consent to participate

Not applicable.

## Consent for publication

Not applicable.

## Availability of data and material

The data supporting this article have been included as part of the Supplementary Information. Sequencing data have been deposited in the NCBI Sequence Read Archive under BioProject accession number [PRJNA1328997] (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1328997).

## Competing interest

The authors declare that they have no competing interests.

## Funding
This research was funded by CETPartnership, the European Partnership under Joint Call 2022 for research proposals, cofunded by the European Commission [GA No101069750] and with the funding organizations of Norway, France, Czech Republic, and Saxon state parliament of Germany listed on the CETPartnership website. In France, this work was supported by the ANR project HyLife [ANR-23-CETP0002].

## Author's contributions

L.F. and A.B. wrote the first draft, prepared all figures, realized the data interpretation, carried out the statistical analyses and edited the manuscript. S.S. and S.R. are the project administrators. S.S., D.R. K.C., P.B., J.T., B-A.A-S, N.D and S.R contributed to the funding acquisition. K.C., P.B., J.R. and K.F. realized data acquisition. N.D. is the project coordinator. All authors reviewed the manuscript.

## Acknowledgements

We want to thank our industry partners Lanxess, BP, TotalEnergies, SLB, Corronation and OMV for co-funding this project. We want to specifically thank all the storage site operators who provided samples for this study including Gasunie, RAG, Kistos, INEOS Inovyn, OMV, Habour Energy and Eni S.p.A (in particular Ilaria Pietrini and Alessia B. Bastianoni, for their availability/support during sampling).

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## Figures
<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>

Location of sampled reservoirs on geological map of Europe (modified from West, 2012). The positions of samples are approximate and should not be interpreted as exact. The color indicates the type of reservoir: green for salt caverns (SC) and blue for porous reservoirs (PR), the number of circles corresponds to the number of sampled reservoirs and the number in the circles indicates the number of replicates for the sequencing data.
<figure><img src="image_3.png" /><figcaption>Figure 2</figcaption></figure>

Chemical and microbial characterization of groundwater samples. (a) Principal Component Analyses (PCA) biplot of samples based on their chemical composition and regrouped in four clusters: 1, 2, 3 and 4, determined by k-means. The first two principal components, PC1 and PC2, explain 43.4% and 22.7% of the total variance, respectively. Arrows indicate the direction and strength of each chemical variable contribution to the principal components. Radar chart showing the average chemical composition of
samples in each cluster. Cluster 1 is colored in purple, Cluster 2 in green, Cluster 3 in red and Cluster 4 in orange. (b) Non-metric multidimensional scaling (NMDS) plot showing microbial community for each site, with the associated clustering: Cluster A (red), Cluster B (yellow), Cluster C (green) and Controls (grey), determined by k-means test.

<figure><img src="image_4.png" /><figcaption>Figure 3</figcaption></figure>
Heatmaps of most abundant (a) bacterial and (c) archaeal genera for each microbial cluster: Cluster A, B and C, and Controls. Dot plot of (b) bacterial and (d) archaeal indicator genera for each cluster. Connectors indicate abundant genera (high relative abundance) that are also indicator genera (specifically associated with a certain set of environmental conditions).

<figure><img src="image_5.png" /><figcaption>Figure 4</figcaption></figure>
Polar plots indicating for each major metabolic function the relative abundance of microorganisms found in the three microbial clusters (a) Cluster A, (b) Cluster B and (c) Cluster C. In each polar plot, maximal, mean (line in grey) and minimal values are shown according to the cluster samples. For each function, its distribution among the clusters was tested with the Kruskal-Wallis test to assess differences between clusters. If the test was significant (*-value< 0.05), then the difference between groups was examined using a Dunn post-hoc test with a Bonferroni correction for multiple comparisons. Significant Dunn tests are indicated in the figure by an asterisk (*) associated with the group showing a difference (*-value < 0.05).

<figure><img src="image_6.png" /><figcaption>Figure 5</figcaption></figure>

Heatmap showing the abundances of organisms predicted to possess different groups of hydrogenases following classification from Søndergaard *et al.* (2016).<sup>47</sup> The clusters identified in the previous sections (from hydrochemistry or microbiology) are shown in the abscissa.
<figure><img src="image_7.png" /><figcaption>Figure 6</figcaption></figure>

Association between the country, the chemical cluster (determined by PCA) and the microbiology cluster (determined by NMDS) for each site sampled. The color indicates the country of origin with blue for Germany, red for Austria, green for United Kingdom, yellow for Netherland, purple for Italy and grey for Spain.
<figure><img src="image_8.png" /><figcaption>Figure 7</figcaption></figure>

Sankey diagram representing a categorization of dominant (without red star) and indicator (with) a red star) Archaea (a) and Bacteria (b) according to their putative ecological function, associated with Spearman tests performed with physicochemical parameters. Genus-function traits are colored according to the microbiological clusters determined: A in red, B in yellow, C in green and Controls in gray. Function-parameter lines are colored according to the rho value obtained for significant Mantel
tests ($p<0.05$). Red lines represent positive correlations and blue negative correlations. Complete results are indexed in **Table S10** and **Table S11**.

**Supplementary Files**

This is a list of supplementary files associated with this preprint. Click to download.

* ManuscriptSIfabletetal..docx