Comprehensive Analysis of Formation Water Microorganisms for Their Biosurfactant Potential in MEOR Applications

Abstract

The study is devoted to the analysis of the physicochemical parameters of formation waters, the metagenomic composition of the microbial community and the characteristics of bacterial isolates from the oil fields of Western Kazakhstan to assess their potential in microbial-enhanced oil recovery (MEOR) technologies. Analyses revealed an adaptation of local microorganisms to extreme conditions of high salinity, temperature and pressure, with the dominant presence of Proteobacteria, including the genus Marinobacter. Screening isolates for biosurfactant synthesis showed a high activity of strains M22-7, M93-8C and M142-2, capable of reducing surface tension to 28.81 ± 0.6 mN/m and forming emulsions. Genetic analysis confirmed the presence of key genes (srfAA, srfp) responsible for surfactin synthesis, but the absence of lchAA and rhlAA indicates that the synthesis of other types of biosurfactants is limited. The results highlight the promise of developing microbial consortia and using biosurfactants in high-salinity environments to enhance oil recovery.

1. Introduction

To contextualize the significance of this study, it is essential to acknowledge the advancements in the microbiological methods of enhanced oil recovery (MEOR) and their growing importance. The fundamental appeal of MEOR lies in its multifaceted advantages over conventional approaches. Beyond its economic viability and reduced environmental footprint, MEOR demonstrates remarkable versatility in application across heterogeneous geological formations, making it suitable for reservoirs with varying characteristics including temperature, salinity and permeability profiles. This adaptability stems from the inherent flexibility of microbial systems, which can be tailored or naturally selected to thrive under specific subsurface conditions [1,2]. Key mechanisms of MEOR include the microbial generation of biosurfactants, biopolymers and organic acids, which significantly enhance oil mobility by reducing interfacial tension, altering wettability and improving reservoir permeability [3,4,5].

Biosurfactants, particularly, represent a cutting-edge focus due to their eco-friendly nature, operational stability under extreme reservoir conditions (high salinity, temperature and pressure) and their ability to emulsify hydrocarbons effectively. Biosurfactants facilitate crude oil extraction through three distinct mechanisms: interfacial tension reduction between oil and aqueous phases, wettability modification and crude oil emulsification. When biosurfactants interact with residual petroleum deposits within reservoir pore spaces, they diminish the interfacial tension at the oil–water interface, thereby enhancing oil mobility by weakening capillary retention forces. The emulsification properties of biosurfactants result in the formation of oil-in-water dispersions, which significantly augment waterflooding efficiency and consequently improve overall hydrocarbon recovery rates [6]. For instance, biosurfactants such as rhamnolipids and sophorolipids have demonstrated significant potential in the laboratory and field-scale applications of MEOR [7]. These biosurfactants not only reduce interfacial tension but also enhance oil recovery, even under extreme conditions of salinity and temperature [8]. The identification of indigenous microorganisms capable of in situ biosurfactant production is particularly advantageous as it eliminates the need for external inoculation, leveraging the natural adaptability of reservoir microorganisms [9]. Additionally, biosurfactants demonstrate minimal toxicity levels and superior biodegradation characteristics relative to artificially manufactured surfactants, positioning them as an ecologically favorable option [10]. Nevertheless, their biodegradable nature may present operational challenges, as it can restrict the duration of their surface-active properties and impose temporal constraints on their effectiveness.

Advancements in metagenomics and gene-based studies have further revolutionized the discovery of biosurfactant-producing microbes. High-throughput sequencing and bioinformatic analyses allow for the precise identification of microbial communities and the key genes responsible for biosurfactant synthesis, enhancing the selection and optimization of microbial strains [11]. Earlier studies demonstrated the efficacy of biosurfactants produced by Bacillus subtilis and Pseudomonas aeruginosa under harsh reservoir conditions, showing notable stability across a wide range of pH, salinity and temperature [12,13].

Field and laboratory experiments have validated the potential of biosurfactants to enhance oil recovery by altering wettability and mobilizing trapped oil in reservoir conditions. A sand-pack column experimental setup demonstrated that B. subtilis strains achieved enhanced oil recovery rates of 19.8% to 35.0%, confirming the effectiveness of in situ biosurfactant production for mobilizing trapped petroleum reserves [14,15]. Additionally, biosurfactant production optimization using agro-industrial waste, such as palm oil mill effluent, has proven to be a cost-effective approach for scaling up MEOR applications [16]. This sustainable feedstock utilization strategy addresses two critical challenges simultaneously: reducing biosurfactant production costs and providing an environmentally responsible disposal solution for agricultural waste streams. Agro-industrial residues characterized by elevated concentrations of carbohydrates, proteins or lipids satisfy the requirements for utilization as feedstock in biosurfactant synthesis. Various agricultural and industrial waste streams have been investigated and documented as appropriate substrates for biosurfactant generation, including molasses, palm oil sludge, cashew apple juice, cassava wastewater, corn steep powder, starch, molasses, soybean oil and sunflower oil [13]. Synergistic effects of biosurfactants and biopolymers have also been observed, further improving oil displacement and recovery efficiency [17].

This study builds on these foundational insights by performing a comprehensive metagenomic and chemical analysis of produced water to explore the potential of indigenous microbial communities for biosurfactant production. By advancing environmentally sustainable and efficient MEOR technologies, this research addresses the dual challenges of declining oil reserves and increasing environmental concerns, paving the way for innovative solutions in the petroleum industry.

2. Materials and Methods

2.1. Sample Collection

Formation water samples totaling nine were obtained from three designated sampling wells in the main oil-producing reservoirs of West Kazakhstan in accordance with GOST 2517-2012 [18]. To minimize the risk of contamination and post-sampling alterations, samples were collected in 500 mL sterile plastic bottles, immediately stored in a temperature-controlled cool box (4–8 °C) and transported to the laboratory for further analysis.

2.2. Chemical Analysis of Formation Water

The chemical composition of the samples was analyzed in accordance with established standards. Bicarbonate ions (HCO₃⁻) and pH were measured using an electrometric method. Suspended solids and salinity were determined gravimetrically following GOST 26449.1-85 [19]. Sulfate (SO₄²⁻), chloride (Cl⁻), calcium (Ca²⁺), magnesium (Mg²⁺) and nitrate (NO₃⁻) ions were quantified using gravimetric, mercurimetric, complexometric and ion chromatography methods, while the total concentration of sodium and potassium (Na⁺ + K⁺) ions was calculated by difference, in compliance with GOST 26449.1-85 and MT No. 13-2020 [19,20]. Iron ions were assessed via complexometric titration according to GOST 23268.11-78 [21]. Iodide and bromide ions were identified using ion chromatography, iodometric titration and general titrimetric methods such as GOST 23268.16-78, GOST 23268.15-78 and MT No. 13-2020 [20,22,23]. Viscosity measurements were performed using a viscometer (Technoglas Laboratoriumapparatuur B.V., Voorhout, Netherlands) according to the national standard ASTM D 445-2011 [24], and density was determined with a hydrometer in accordance with GOST 18995.1-73 [25].

2.3. Next-Generation Sequencing (NGS)

16S metagenomic sequencing was performed using the MiSeq next-generation sequencer (Illumina, San Diego, CA, USA) following the 16S Metagenomic Sequencing Library Preparation Guide. DNA libraries (a collection of fragments of the DNA sample under study) were prepared following the 16S Metagenomic Sequencing Library Preparation Guide (part no. 15044223 rev. A). Variable V3 and V4 regions of the 16S rRNA gene were amplified using universal bacterial primers with Illumina adapters.

The reaction mixture consisted of 2.5 µL of DNA template, 5 µL of each primer at a concentration of 1 µM and 12.5 µL of 2X KAPA HiFi HotStart ReadyMix (KAPA Biosystems, Cape Town, South Africa). PCR amplification was carried out with the following program: 95 °C for 3 min, followed by 25 amplification cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s and a final step of 72 °C for 5 min. The PCR product was purified using the Agencourt AMPure PCR purification kit (Beckman Coulter Inc., Beverly, MA, USA).

Nextera XT Index primers (Illumina Inc., San Diego, CA, USA) were added to each sample through amplification in a reaction mixture containing 12.5 µL of KAPA HiFi HotStart ReadyMix, 5 µL of each index primer, 10 µL of water and 5 µL of PCR product. The amplification program was as follows: 95 °C for 3 min, followed by 8 amplification cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s and a final step of 72 °C for 5 min. The PCR product with added indexes was purified using the Agencourt AMPure PCR purification kit (Beckman Coulter Inc., Beverly, MA, USA).

The pooled samples were loaded into a cartridge from the v3 kit (600 cycles) (Illumina, USA). Sequencing was performed on the MiSeq instrument. Upon the completion of sequencing, secondary data processing was automatically conducted using the MiSeq Reporter software 2.5.36.12.

2.4. Isolation and Screening of Microorganisms

The isolation of bacteria from formation water samples was carried out using nutrient agar (NA) (TM Media Laboratories, Mumbai, India). First, 0.1 mL of the sample was plated on NA plates, spread on the medium surface with sterile plastic sticks and incubated at 40 °C under aerobic and anaerobic conditions. After three days of incubation, individual bacterial colonies of various morphologies were inoculated onto NA plates and, after a day of cultivation at 40 °C, transferred to liquid nutrient broth (NB) (TM Media Laboratories, India) containing glycerol (15% w/v) and finally stored in a freezer at −18 °C. Mineral salt solution (MSS) was used for cultivation and screening of the biosurfactant-producing bacteria (g/L): NH₄NO₃—4; Na₂HPO₄—7.12; KH₂PO₄—4.08; MgSO₄·7H₂O—0.2; glucose—20 [26]. To assess the ability of bacteria to displace oil, the Morikawa method (oil-spreading assay) was used [27]. In this method, 50 mL of distilled water was poured into a Petri dish, and 20 µL of crude oil was carefully spread on the surface to create a hydrophobic layer. To assess biosurfactant production, 10 µL of the cell-free supernatant was gently added to the center of the oil layer. The supernatant was obtained by centrifuging the bacterial culture grown in an MSS medium at 10,000× g for 10 min at 4 °C. The presence of biosurfactants was indicated by the displacement of oil, forming a clear zone on the surface. To determine the ability of bacteria to emulsify oil, the Cooper method [28], based on the determination of the emulsification index, was used. The cell-free supernatant was mixed with oil in a 3:2 ratio and vigorously agitated on a laboratory vortex at 1000 rpm for 1 min to form a stable emulsion. The test tubes were then left at room temperature in an upright position. After 24 h, the height of the stable emulsion layer was measured. The emulsification index (E24) was expressed as a percentage, calculated as the ratio of the emulsion layer height to the total liquid height in the test tube using Formula (1), where V_e is the height of the emulsion layer and V_n is the total height of the liquid in the test tube.

E₂₄ % = V_e/V_n × 100

(1)

The surface tension of selected bacterial supernatant was measured using an SFZL-A2 tensiometer (Shandong Dekon Import Export Co., Ltd., Jinan, China) with the Wilhelmy plate method [29]. Distilled water was used as the control in this procedure. All surface tension measurements were performed in triplicate, and the average value was used to represent the surface tension of each sample.

2.5. FT-IR Spectroscopic Analysis of Crude Biosurfactant

Biosurfactants were extracted using the acid precipitation method outlined by Zhang et al. [30]. The cell-free supernatant was harvested through centrifugation at 10,000× g for 10 min at 4 °C. In a nutshell, the cell-free supernatant was adjusted to pH 2.0 with 6M HCl and left overnight at 4 °C to ensure complete biosurfactant precipitation. The resulting precipitate was gathered by centrifugation at 10,000× g for 10 min at 4 °C and underwent two rinses with water adjusted to an acidic pH of 2.0. The unrefined biosurfactants were dried in a drying cabinet at 110 °C for 24 h and weighed.

The FT-IR spectrum of dried biosurfactants was obtained on a ALPHA II spectrophotometer (Bruker Corporation, Billerica, MA, USA) in a dry atmosphere. The FT-IR spectra were collected in the range of 400–4000 wavenumbers (cm⁻¹).

2.6. Genetic Identification of Biosurfactants

Screening for the presence of surfactin, lichenysin and rhamnolipid genes was carried out using the primers listed in

Table 1. Primers used for the detection of biosurfactants genes.

Biosurfactant	Gene	Sequence	PCR Product Size (bp)	Annealing Temperature (°C)	References
Surfactin	srfAA	F-5′ AAGGGCCATTGCCAATACGA 3′ R-5′ ACTTTGCCGTTTGCCGTAAC 3′	501	56–58	[31]
	srfp	F-5′ ATGAAGATTTACGGAATTTA 3′ R-5′ TTATAAAAGCTCTTCGTACG 3′	635	48–52	[32]
Lichenysin	lchAA	F-5′ TGAACGGCACAAAATGCAGG 3′ R-5′ CGTTTGATCGATTCGCGCTT 3′	678	56–60	[31]
Rhamnolipid	rhlAA	F-5′ ATGCGGCGCGAAAGTCTGTTGGTA 3′ R-5′ TCAGGCGTAGCCGATGGCCAT 3′	887	68	In this study

.

The PCR reaction was performed in a final volume of 12 µL containing 6.25 µL Master Mix (ThermoFisher Scientific, Waltham, MA, USA), 1.0 µL forward primer, 1.0 µL reverse primer, 2.75 nuclease-free sterile H₂O (Cytiva, Marlborough, MA, USA) and 1 µL DNA of each strain. Thermal cycling was carried out in the Mastercycler nexus (Eppendorf, Hamburg, Germany) with an initial denaturation of 95 °C for 5 min followed by 30 cycles at 95 °C for 1 min, 56–64 °C at 1 min and 72 °C for 1 min, followed by a final extension of 72 °C for 5 min. Each amplification product was analyzed by electrophoresis using a 1% agarose gel followed by MIDORI Green Advance (Nippon Genetics Europe, Düren, Germany) staining and UV visualization. GeneRuler 1 kb DNA Ladder was used as a marker (ThermoFisher Scientific, Waltham, MA, USA).

3. Results and Discussion

3.1. Chemical Composition of Formation Water

Oil reservoirs are considered extreme ecosystems, where the use of MEOR technologies to recover residual oil requires the consideration of various parameters. Key factors include residual oil saturation, reservoir chemical composition, depth, temperature, salinity, expected oil recovery increase and economic factors [33].

In this study, the physicochemical characteristics of formation water were analyzed using samples collected from several wells (No. 129, No. 15, No. 142) in oil fields in Western Kazakhstan (

Table 2. Chemical parameters of formation water collected in oil fields in Western Kazakhstan.

Parameters	Units of Measurement	No. 129	No. 15	No. 142
HCO3−	mg/L	24.4	210	52
SO42−		Not detected	Not detected	Not detected
Cl−		160,322.6	149,634	156,760
Ca2+		9018	4058	2806
Mg2+		2675.2	2128	1824
Na+ + K+		88,616.7	88,480	95,056
Fe3+	mg-eq/L	11.76	Not detected	Not detected
Fe2+	mg-eq/L	17.6	30.9	Not detected
Total mineralisation	mg/L	261,760	245,305	257,880
Suspended solids		Not detected	Not detected	Not detected
Density at 20 °C	g/cm3	1.1730	1.1673	1.1722
pH	-	5.76	6.48	6.59
J−	mg-eq/L	3.78	3.15	1.68
Br−	mg-eq/L	197.08	159.9	98.28
Temperature	°C	42.1	33.5	41

). The analysis revealed that the pH ranged from 5.76 to 6.59, while temperatures varied between 33.5 °C and 42.1 °C, which are conditions suitable for microbial growth. However, the high salinity of the water, which significantly exceeds optimal parameters, may negatively affect microbial activity and biosurfactant efficiency. Nevertheless, according to the literature, certain microbial strains demonstrate adaptability to extremely high salinity levels [34,35].

The success of MEOR is primarily influenced by three critical factors: salinity, temperature and pH. According to Sayyou et al. [36], MEOR is feasible at salinity levels up to 100,000 ppm, while Safdel et al. [37] reported that the salinity of formation water can range from <10 g/L to over 273 g/L. Guo et al. [38] identified the optimal temperature range for MEOR as 30–60 °C, and near-neutral pH values have been shown to support microbial growth [33].

The formation water analyzed in this study generally falls within the acceptable range for MEOR, although it exhibits elevated salinity. This underscores the potential for applying halotolerant microbial strains capable of functioning under extreme conditions. Further research will aim to assess the performance of such strains in both laboratory and field settings and to develop approaches for improving MEOR efficiency in high-salinity reservoirs.

3.2. Distribution Characteristics of the Microbial Community

The diversity analysis of formation water provided insights into the microbial community structure and its taxonomic composition. A total of 963,957 sequences were obtained, of which 62.6% (603,665) passed quality filtering. At the taxonomic classification level, 99.25% of sequences were assigned to kingdoms, and 66.44% were classified at the species level.

The taxonomic structure of the microbial community revealed the dominance of bacteria (Bacteria, 98.61%), with minor contributions from archaea (Archaea, 0.63%) and unclassified sequences (0.75%). The predominant phylum was Proteobacteria (92.45%), followed by Firmicutes (1.76%) and Actinobacteria (0.90%). At the class level, Gammaproteobacteria (79.82%) and Deltaproteobacteria (5.36%) were dominant, while Alteromonadales (66.65%) prevailed at the order level (Figure 1).

At the genus level, the community was primarily composed of Marinobacter (65.95%), followed by Halomonas (4.66%) and Desulforhopalus (2.43%). Among the species, Marinobacter santoriniensis accounted for the largest proportion (41.42%), along with Marinobacter sediminum (5.86%) and Marinobacter haloterrigenus (3.51%).

These results highlight the predominance of marine and halophilic microorganisms adapted to the extreme conditions of oil reservoirs, emphasizing their potential significance in biotechnological processes, such as MEOR.

The characterization of formation water microbial communities from petroleum reservoirs revealed extensive phylogenetic diversity, with taxa exhibiting specialized physiological adaptations to extreme physicochemical parameters including hypersaline conditions, elevated hydrostatic pressure and oligotrophic environments. Taxonomic profiling identified Proteobacteria as the predominant phylum, with Gammaproteobacteria representing the most abundant class and Marinobacter constituting the dominant genus, underscoring their pivotal role in anaerobic hydrocarbon biodegradation pathways. Analogous taxonomic distributions have been reported in comparable subsurface ecosystems, wherein Gammaproteobacteria, particularly Marinobacter isolates, have demonstrated facultative anaerobic metabolism including dissimilatory nitrate and thiosulfate reduction, halotolerant mechanisms enabling survival under hypersaline conditions, radioresistance adaptations and extracellular polymeric substance production facilitating biofilm architecture, collectively conferring ecological fitness within the geochemically extreme oil reservoir environments [39].

Comparisons of reservoirs with varying sulfate concentrations showed that low-sulfate environments are dominated by methanogenic archaea, whereas high-sulfate waters are enriched with sulfate-reducing bacteria such as Desulfobacterota. These bacteria contribute to equipment corrosion and oil quality degradation [40]. On a global scale, metagenomic studies have identified a “core microbiome” that includes Clostridia, Methanomicrobia and other groups involved in hydrocarbon metabolism, maintaining shared functional traits across geographically diverse reservoirs [41]. Marinobacter and other Gammaproteobacteria predominate in reservoirs with a high salt content, demonstrating the possibilities of aerobic and anaerobic degradation of hydrocarbons. This highlights their role in the biotransformation of hydrocarbons and the potential for biotechnologies, including MEOR [42].

A total of 40 bacterial isolates were obtained during the study, with all indigenous microorganisms characterized based on their macro- and micromorphological features. Most of the isolates are Gram-positive bacilli, while Gram-negative bacilli are observed less frequently.

The analysis of formation water from oil reservoirs in Western Kazakhstan revealed a diverse microbial community dominated by Proteobacteria, particularly the Marinobacter genus, which demonstrates key capabilities such as hydrocarbon transformation, nitrate reduction and adaptation to high salinity. These findings highlight the potential of leveraging indigenous microorganisms for MEOR, despite challenges posed by extreme conditions, such as high salinity and the risk of equipment corrosion from sulfate-reducing bacteria. The isolation and characterization of 40 bacterial strains provide a foundation for targeted applications, enabling the selection of robust strains for specific reservoir conditions. Subsequent research initiatives should prioritize the metabolic characterization of dominant microbial groups, the formulation of synergistic microbial communities and the incorporation of cutting-edge biotechnological approaches to enhance MEOR efficiency while simultaneously implementing controlled microbial management strategies to prevent adverse operational consequences.

3.3. Screening of Biosurfactant-Producing Isolates

Screening methods for evaluating the ability of a biosurfactant include the oil-displacement method and the emulsification method. The oil-spreading method is used to measure the ability of a biosurfactant to reduce surface and interfacial tension, which improves the mobility of oil (Figure 2). The emulsification method evaluates the ability to stabilize emulsions, which is important for increasing the bioavailability of hydrocarbons. Both methods play a key role in MEOR.

On the other hand, the oil emulsification index determination method, also known as the Cooper method, evaluates the ability to stabilize emulsions, which is important for increasing the bioavailability of hydrocarbons. The evaluation of oil-emulsifying activity was performed after 24 h of incubation of the culture liquid without centrifugation. Both methods play a key role in MEOR.

The results of the study of oil spreading and oil emulsifying properties of the investigated bacterial isolates are presented in

Table 3. Screening results of biosurfactant-producing bacteria from oil fields in West Kazakhstan.

Isolates	Oil Spreading, cm	Emulsifying Index, %
M93-1	0	0
M93-2	0.5 ± 0.5	0
M93-3	0	0
M93-4	0	0
M93-5	0	0
M93-6	1.3 ± 1	0
M93-7B	0.5 ± 0.01	4 ± 1.0
M93-8C *	3.0 ± 0.5	50 ± 1.7
M121-1	0.2 ± 0.3	0
M121-2	0.3 ± 0.6	0
M121-3A	0.7 ± 0.3	0
M121-4C	2.9 ± 1.5	0
M121-5	1.5 ± 1	0
M83-1	0.5 ± 0.5	16 ± 1.5
M83-2P	0.4 ± 0.7	0
M83-3B	2.8 ± 1.0	25 ± 1.0
M83-4C	0.8 ± 0.3	15 ± 1.7
M22-1	0.2 ± 0.3	13 ± 1.5
M22-2B	0.7 ± 0.3	7 ± 1.0
M22-3B	0.3 ± 0.2	12 ± 1.5
M22-4A	0.7 ± 0.3	6 ± 0.1
M145-1	0.2 ± 0.3	0
M145-2P	0.7 ± 0.3	20 ± 0.1
M130-1	1.7 ± 1.5	0
M130-2	1.6 ± 0.5	21 ± 1.8
M130-3B	0.2 ± 0.3	6 ± 0.1
M130-4B	0.9 ± 0.2	20 ± 0.1
M130-5	0.7 ± 0.8	9 ± 1.5
M130-6	1.3 ± 0.8	9 ± 1.0
M130-7	0.8 ± 0.3	4 ± 1.5
M130-8	0.8 ± 0.6	16 ± 1.4
M15-1	0.7 ± 1.2	0
M22-5	0.9 ± 0.6	0
M22-6 *	2.7 ± 1.2	50 ± 0.8
M22-7 *	3.0 ± 2.4	46 ± 1.0
M22-8	0	0
M22-9	0.7 ± 0.8	2 ± 0.1
M142-1	1.1 ± 0.9	23 ± 0.5
M142-2 *	2.9 ± 0.8	50 ± 0.5
M142-3A	1.5 ± 0.9	34 ± 0.6

* The most active isolates.

.

The study evaluated the activity of microbial isolates based on their ability to spread oil and emulsifying activity. The data revealed isolates with the highest results in these parameters, suggesting their high potential for use in biosurfactant technologies and enhanced oil recovery. The isolates that showed the highest values include: M93-8C, M22-6; M142-2. Additionally, the M22-7 demonstrated an oil-spreading value of 3.0 ± 2.4 cm and an emulsifying index of 46 ± 1.0%, indicating significant activity; although, it did not reach the 50% emulsifying index. Microbial cultures that exhibited oil displacement above 2.6 cm are considered the most suitable candidates for biosurfactant production [43]. When the emulsifying index exceeds 50%, it indicates a high potential of biosurfactant-producing bacteria, which can be applied in the oil industry, including MEOR technologies [44].

After the initial screening, the surface tension of the selected microorganisms was examined on a tensiometer (Figure 3). According to Walter and co-authors [45], microorganisms promising for MEOR should reduce the surface tension of the liquid medium by 40 mN/m or less.

These results indicate that the surface tension of the isolates is significantly lower than that of distilled water (73.14 ± 0.9 mN/m), demonstrating their potential ability to reduce surface tension and act as biosurfactants. Among the isolates, M22-7 shows the lowest surface tension (28.81 ± 0.6 mN/m), suggesting it may be the most effective biosurfactant producer in this group.

Biosurfactants produced by microorganisms are widely known for their ability to reduce surface tension in liquid media. For example, the species of microorganism Pseudomonas aeruginosa is one of the most studied producers of biosurfactants. It is known that strains of this species can reduce the surface tension of aqueous solutions to values less than 30–32 mN/m, due to the synthesis of rhamnolipids, which are effective biosurfactants [46,47]. Some strains of Bacillus subtilis, for example, synthesize surfactin, are also able to significantly reduce surface tension. In experiments, the value of the surface tension decreased to 27 mN/m [48]. Trehalose lipids derived from Rhodococcus erythropolis and Arthrobacter sp. have been shown to reduce surface tension in culture broth to values ranging from 25 to 40 mN/m [49].

In summary, the study identifies M22-7 as a promising biosurfactant producer, with the lowest surface tension (28.81 ± 0.6 mN/m) and notable oil-spreading and emulsifying activity, though slightly below the optimal emulsifying index (50%). These findings align with the performance of well-known biosurfactant-producing microorganisms like Pseudomonas aeruginosa and Bacillus subtilis, highlighting the potential of these isolates for MEOR. Further optimization could enhance their efficiency and broaden their use.

3.4. FT-IR Analysis of Biosurfactant

For the cultivation of biosurfactant-producing isolates, a defined mineral salt solution (MSS) was used, composed of (g/L): NH₄NO₃—4.0; Na₂HPO₄—7.12; KH₂PO₄—4.08; MgSO₄·7H₂O—0.2; and glucose—20.0 [26]. Glucose served as the sole carbon source, while ammonium nitrate and phosphate salts provided nitrogen and phosphorus, respectively.

Using this medium, the biosurfactant production potential of different isolates was evaluated (Figure 4). As illustrated, the isolates exhibited varying levels of productivity, with M142-2 demonstrating an exceptionally high yield of 0.3 g/L. In contrast, the lowest yield was observed for M22-7, with 0.06 g/L of crude biosurfactant.

The FT-IR spectrums of the analyzed biosurfactant reveal characteristic absorption bands corresponding to various functional groups, providing detailed insights into its chemical structure (Figure 5). A broad band observed in the region of ~3400–3600 cm⁻¹ corresponds to O-H stretching vibrations, indicating the presence of hydroxyl groups, commonly found in alcohols or carboxylic acids. This finding aligns with the literature, where similar peaks are associated with carboxylic acid functional groups in studies of functionalized carbon materials [50]. Peaks in the ~2800–3000 cm⁻¹ region reflect C-H stretching vibrations characteristic of aliphatic hydrocarbon chains, a hallmark of the lipid structures widely present in biosurfactants [51].

A sharp and strong absorption peak around ~1700 cm⁻¹ is attributed to C=O stretching vibrations, indicating the presence of carbonyl groups, such as those in carboxylic acids or esters. These groups are essential in forming the amphiphilic structure of biosurfactants, as corroborated by polymer and lipid studies [52]. The peaks in the ~1500–1650 cm⁻¹ range suggest N-H bending and C=O stretching vibrations, characteristic of amide bonds, which are commonly found in lipopeptides [53]. Additionally, absorption in the ~1000–1300 cm⁻¹ region corresponds to C-O stretching vibrations, indicative of alcohol or ester groups, which are crucial for the functionality of biosurfactants [54].

This FT-IR analysis confirms the complex amphiphilic structure of the biosurfactant, which comprises both hydrophilic (polar) and hydrophobic (non-polar) components. These structural features play a crucial role in the biosurfactant’s performance in diverse applications. For instance, its amphiphilic nature enables exceptional emulsification and dispersion properties, making it valuable in industries such as food processing, pharmaceuticals and cosmetics. In environmental remediation, biosurfactants facilitate the solubilization and mobilization of hydrophobic pollutants, such as hydrocarbons, proving effective in cleaning oil spills and treating contaminated soils and water. In MEOR, the biosurfactant reduces interfacial tension between oil and water, enhancing oil extraction while being eco-friendly due to its biodegradability.

Moreover, the presence of amide and ester groups highlights its potential for biomedical applications, including antimicrobial, antiviral and anti-inflammatory properties. These features make biosurfactants promising candidates for developing therapeutic agents or advanced drug delivery systems. Thus, the IR spectrum not only confirms the chemical structure but also underscores the biosurfactant’s versatility and potential for industrial and environmental applications. Future research should focus on optimizing production processes, tailoring functional properties for specific uses and scaling sustainable production to meet the growing industrial demand.

3.5. Genetic Markers of Biosurfactant Synthesis

The presence of genes encoding biosurfactants, such as srfAA, srfp, lchAA and rhlAA, is critical for understanding microbial contributions to metabolic processes and their potential applications in environmental and industrial settings. These genes govern the production of biosurfactants—surface-active agents synthesized by microorganisms—that reduce surface tension, enhance emulsification and increase the bioavailability of hydrophobic compounds.

The srfAA and srfp genes are integral to the biosynthesis of surfactin, a potent lipopeptide biosurfactant. Surfactin, produced via a nonribosomal peptide synthetase system, is catalyzed by a modular multienzyme complex called surfactin synthetase, which is composed of four modules [32]. This biosurfactant effectively reduces water’s surface tension and demonstrates significant emulsifying properties, making it highly suitable for MEOR applications.

The lchAA gene, responsible for the synthesis of lichenysin, encodes a lipopeptide structurally related to surfactin but with superior thermal and emulsification properties, which are advantageous for applications in oilfield conditions [55]. Meanwhile, the rhlAA gene, found in Pseudomonas aeruginosa, facilitates the production of rhamnolipids, glycolipid biosurfactants that play a crucial role in biofilm development and the reduction in interfacial tension. Rhamnolipids have versatile applications, ranging from bioremediation to antimicrobial activity, making them relevant for both environmental and industrial purposes [56].

As shown in

Table 4. Presence of genes encoding biosurfactants (srfAA, srfp, lchAA, rhlAA) in isolates from the Western Kazakhstan oil field. Detailed results of agarose gel electrophoresis of PCR products for biosurfactant-related genes are provided in Supplementary Figure S1.

Isolate	Gene a
	srfAA	srfp	lchAA	rhlAA
M22-6	−	−	−	−
M22-7	+	+	−	−
M93-8C	+	+	−	−
M142-2	+	+	−	−

^a +, detected; −, not detected.

, the surfactin biosynthesis genes srfAA and srfp were detected in isolates M22-7, M93-8C and M142-2, while none of these isolates possessed the lchAA or rhlAA genes. In contrast, isolate M22-6 lacked all four biosurfactant-related genes. This genetic profile suggests that while several isolates have the capacity to produce surfactin, they do not possess the genetic potential to synthesize lichenysin or rhamnolipids, highlighting limited biosurfactant diversity in this microbial community.

Genes encoding biosurfactants such as srfAA, srfp, lchAA and rhlAA are an integral part of the development of sustainable MEOR technologies. Understanding their genetic basis not only expands our knowledge of microbial metabolism but also paves the way for innovative applications in bioremediation and hydrocarbon production. Further studies of these genetic pathways can optimize the production of biosurfactants and increase their efficiency in oil production operations.

4. Conclusions

The study confirmed the high potential of using microorganisms from the oil reservoirs of Western Kazakhstan in MEOR technologies. The analysis of formation water revealed conditions favorable for the growth and activity of salt-tolerant bacteria capable of synthesizing biosurfactants. Microbial community analysis revealed a bacterial assemblage characterized by the dominance of marine and halophilic taxa specifically adapted to the harsh environmental conditions prevalent in petroleum reservoir ecosystems. Strain screening identified strain M22-7 as the most promising bioproducer of surfactin with a low surface tension value. Genetic analysis confirmed the presence of srfAA and srfp genes, corroborating the strain’s capacity for lipopeptide production with applications in petroleum biotechnology.

Further research should focus on studying the functional activity of key microbial groups, developing consortia adapted to specific reservoir conditions and optimizing MEOR processes using biosurfactants to improve oil production efficiency and minimize environmental risks, including the evaluation of various types of carbon sources, both hydrophilic and hydrophobic, to enhance biosurfactant yield and overall process efficiency.