A method for targeted identification of oil recovery functional microorganisms in crude oil

Abstract

This invention relates to a targeted identification method for oil recovery functional microorganisms in crude oil, comprising the following steps: analyzing gene modules related to oil recovery function based on metagenomic data and calculating an integrity score; simultaneously analyzing related metabolites based on metabolomics data and calculating a metabolic activity score; fusing the two scores to calculate a comprehensive targeted identification value; and ranking the microorganisms according to this value. Compared with existing technologies, this invention significantly improves the accuracy and reliability of identification through dual verification of genomic function prediction and metabolic activity validation, providing a targeted indicator for efficient isolation and culture.

Description

Technical Field

[0001] This invention relates to the technical field of microbial enhanced oil recovery, and in particular to a targeted identification method for microorganisms with oil recovery function in crude oil. Background Technology

[0002] Microbial enhanced oil recovery (MEOR) technology is an important tertiary oil recovery technique. Traditional research has focused on the microbial communities in reservoir formation water or produced water, neglecting the importance of the crude oil phase itself as a potential microbial habitat. Recent studies have found that crude oil is not absolutely water-free, often existing in the form of "water-in-oil" droplets, forming a unique microenvironment. These droplets may be enriched with specialized microorganisms adapted to high oil pressure and oligotrophic environments. Their effects on crude oil degradation and emulsification may be more direct and efficient, representing a potential key microbial resource for MEOR.

[0003] However, research on the microenvironment of water droplets within crude oil currently faces bottlenecks in analytical methods. For example, prior art document CN115678978A discloses a method for identifying reservoir-driven oil displacement microorganisms based on metagenomics and metatranscriptomics. However, this method relies on reservoir-produced water samples, which have relatively sufficient sample sizes. In contrast, water droplets within crude oil are small and dispersed, making it difficult to routinely collect sufficient biological samples to meet the requirements of multidimensional omics analysis. This hinders the implementation of key activity analyses requiring a certain biomass, such as metatranscriptomics sequencing. Furthermore, prior art document CN117925871A discloses a feed assessment method based on fish fecal metagenomics and metabolomics analysis. While combining metagenomics and metabolomics, this method targets fish gut microbiota, not the reservoir environment, and fails to address the challenge of directly linking gene function and metabolic activity in minute sample volumes.

[0004] Existing technologies generally suffer from a disconnect between functional validation and practical application: DNA-based metagenomic sequencing can predict metabolically functional genes, but it cannot distinguish whether these genes are actively expressed in situ; while metabolomics analysis can detect metabolites, it is difficult to directly correlate them with the gene functions of specific microorganisms. More importantly, existing technologies lack a standardized process for converting omics data into a priority list of high-value strains for isolation, resulting in a lack of targeted resource discovery.

[0005] Therefore, there is an urgent need in this field to establish a microbial identification method that can overcome the above-mentioned bottlenecks, directly link gene function and metabolic activity from trace samples, and accurately guide the isolation of targets. Summary of the Invention

[0006] The purpose of this invention is to overcome the defects of the prior art by providing a targeted identification method for functional microorganisms in crude oil recovery, aiming to solve the problems of inaccurate functional identification and the disconnect between genes and metabolism in the prior art.

[0007] The objective of this invention can be achieved through the following technical solutions: This invention provides a targeted identification method for functional microorganisms in crude oil recovery, see [link to relevant documentation]. Figure 1 This includes the following steps: S1. Based on the metagenomic data, analyze the oil-producing functional microorganisms in the microenvironment of oil droplets in crude oil to identify the gene modules related to oil production function and calculate the integrity score of the gene modules. At the same time, based on the metabolomics data, analyze the metabolites related to the oil production function and calculate the metabolic activity score. S2. By integrating the gene module integrity score and the metabolic activity score, a comprehensive target recognition value for each microorganism is calculated. The microorganisms are then ranked according to the comprehensive target recognition value to achieve targeted identification of high-value oil recovery functional microorganisms in the water droplet microenvironment within crude oil.

[0008] Furthermore, the specific process of separating and obtaining the tiny water droplets contained within a crude oil sample includes: The crude oil sample was spread into a thin layer on the surface of a sterile carrier, and then the tiny water droplets distributed in the crude oil were aspirated using a micropipette.

[0009] Furthermore, the specific process of simultaneously obtaining the metagenomic and metabolomics data of the microorganisms includes: The water droplet sample was processed to extract total DNA and total metabolites of the microorganisms therein. Metagenomic sequencing was performed on the extracted total DNA to obtain the metagenomic data; The extracted total metabolites were subjected to chromatographic or mass spectrometric analysis to obtain the metabolomics data.

[0010] Furthermore, in S1, the gene module related to oil recovery function includes at least one of the following: alkane degradation gene module, aromatics degradation gene module, biosurfactant synthesis gene module, and methanogenic gene module.

[0011] These gene modules directly correspond to the key physiological functions performed by microorganisms in the crude oil environment. For example, the alkane degradation gene module and the aromatic hydrocarbon degradation gene module endow microorganisms with the ability to degrade straight-chain alkanes and aromatic hydrocarbons in crude oil, respectively. The biosurfactant synthesis gene module enables microorganisms to produce surface-active substances that emulsify crude oil, while the methanogenic gene module supports microorganisms in increasing reservoir pressure through methanogenesis. By analyzing the existence and integrity of these modules through metagenomic data, it is possible to objectively predict at the genetic level whether microorganisms have the essential functional potential to drive crude oil flow or improve oil recovery.

[0012] Furthermore, in S1, the specific process for calculating the integrity score of the gene module includes: For a target gene module, based on the annotation results of the metagenomic data, the number of genes detected that belong to the gene module is counted, and then the proportion of this number to the total number of known genes in the gene module is calculated. This proportion is used as the integrity score of the gene module.

[0013] Furthermore, in S1, the specific process for calculating the metabolic activity score includes: For the target oil recovery function, a variety of target metabolites related to the function are identified from the metabolomics data. Then, the relative abundance values ​​of the identified target metabolites are summed, and the sum is used as the metabolic activity score of the function. The target metabolites include biosurfactant precursors, short-chain organic acids, petroleum hydrocarbon degradation intermediates, and metabolic gases.

[0014] Furthermore, in S2, the specific process of calculating the comprehensive target recognition value for each microorganism by integrating the gene module integrity score and the metabolic activity score includes: Using a weighted summation method, the gene module integrity score and metabolic activity score of the same microorganism for the same oil recovery function are fused together according to a predetermined weight coefficient. The calculation formula for the target identification comprehensive value is: Target identification comprehensive value = Gene module integrity score × Weight coefficient A + Metabolic activity score × Weight coefficient B, where the sum of weight coefficient A and weight coefficient B is 1.

[0015] Furthermore, in S2, the specific process of ranking microorganisms according to their comprehensive target recognition values ​​to achieve targeted identification of high-value oil recovery microorganisms in the water droplet microenvironment includes: All microorganisms in the microenvironment of water droplets within crude oil are sorted from high to low according to their calculated comprehensive target recognition values, generating an ordered list of microorganisms. The microorganisms ranked at the top of the list are identified as the high-value oil recovery functional microorganisms.

[0016] Based on the water droplet ion composition and the metabolic characteristics of the microorganisms reflected in the multi-omics data, a separation and culture strategy is recommended for the high-value oil recovery functional microorganisms. The strategy includes at least the selection of culture medium salinity conditions, oxygen conditions, electron acceptor type, and substrate, and verifies the correctness of the targeted recognition of high-value oil recovery functional microorganisms.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention, by constructing a quantitative identification model integrating three elements—"genomic function prediction, metabolic activity verification, and niche-specific assessment"—achieves for the first time precise and efficient targeted localization of oil-producing functional microorganisms within the microenvironment of water droplets inside crude oil. Compared to existing technologies, its beneficial effects are mainly reflected in: overcoming the limitations of traditional methods that can only perform macro-community descriptions or single-gene predictions, and elevating functional identification from "probability speculation" to the practical level of "activity verification and priority ranking." Specifically, the invention's unique dual verification mechanism of genomic functional potential (GMI) and metabolic activity (MAI) effectively links the functional gene reserves and actual metabolic output of microorganisms, significantly improving the accuracy and reliability of target identification. Based on this, by introducing niche-specific factors and constructing a quantifiable and rankable target identification comprehensive value (TIV) model, high-value core bacterial targets with high functional potential, high niche activity, and environmental adaptability can be accurately screened from complex microbial communities. This provides an accurate "navigation map" for subsequent high-efficiency and low-cost targeted isolation culture and functional verification, greatly accelerating the discovery and application of novel oil recovery functional bacterial resources in the special habitat of crude oil droplets. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the targeted identification method for intra-crude oil recovery functional microorganisms in this invention. Detailed Implementation

[0019] Overall, the method for targeted identification of oil-producing functional microorganisms in the microenvironment of water droplets in crude oil in this invention includes: achieving targeted identification of oil-producing functional microorganisms in water droplets based on multi-omics information.

[0020] Compared with existing technologies, this invention first analyzes the metabolic potential of oil-producing functional microorganisms in the microenvironment of water droplets within crude oil to identify the dominant oil-producing functional microorganisms. Then, based on the metabolomics information of the dominant oil-producing functional microorganisms, it targets and identifies high-value oil-producing functional microorganisms, thereby providing recommended isolation and culture strategies.

[0021] In practice, the water droplets in the crude oil are collected by spreading the crude oil flat on an aluminum foil and using a pipette.

[0022] In practice, the multi-omics data of microorganisms in the water droplets within the crude oil are obtained through metagenomic sequencing and metabolomics analysis.

[0023] In practice, the oil recovery functional microorganisms mentioned include, but are not limited to, hydrocarbon-degrading bacteria, biosurfactant-producing bacteria, and methanogenic bacteria.

[0024] In practice, the metagenomic analysis is used to identify gene module information related to oil recovery function in the microbial genome, and to determine the dominant oil recovery function microorganism in the droplet based on the integrity of the gene module and its abundance in the genome.

[0025] In specific implementation, the integrity (GMI) of the oil recovery function gene module is the percentage of the detected genes in all genes in the gene module. If more than 50% of the genes in the module are detected, it is considered "complete". If 20%-50% of the genes are detected, it is considered "partial". If less than 20% of the genes are detected, it is considered that the microorganism has no corresponding metabolic potential.

[0026] In specific implementation, the gene modules related to oil recovery function include, but are not limited to: alkane degradation gene module, aromatic degradation gene module, biosurfactant synthesis gene module, organic acid fermentation gene module, methanogenic gene module, etc., to determine the metabolic potential of microorganisms.

[0027] In practice, the metabolomics analysis aims to identify metabolites involved in the gene module, and sum the relative abundance of the detected target metabolites to obtain the metabolic activity index (MAI).

[0028] In specific implementation, the metabolites include, but are not limited to: biosurfactant precursors, short-chain organic acids, petroleum hydrocarbon degradation intermediates, and metabolic gases.

[0029] In practice, the metabolomics analysis includes, but is not limited to, qualitative and quantitative analysis of target metabolites using techniques such as chromatography-mass spectrometry, nuclear magnetic resonance, and high-resolution tandem mass spectrometry.

[0030] In specific implementation, the targeted identification of oil-producing functional microorganisms in step 3) is based on the integrity of the oil-producing functional gene module (GMI) and the metabolic activity index (MAI) of the microorganisms in the sample. The target identification comprehensive value (TIV) is calculated, and the formula is: TIV = 0.5*GMI + 0.5*MAI. Based on the TIV value, the microorganisms are sorted to generate a list of high-value target microorganisms.

[0031] The key to this invention lies in accurately identifying the dominant oil-producing microorganisms in water droplets. Existing technologies typically analyze the composition and metabolic potential of microorganisms in water droplets at the DNA level, but they cannot distinguish the metabolic state of microorganisms in the water droplet microenvironment. The above method introduces the analysis of metabolite composition at the metabolomics level and identifies the metabolically active dominant oil-producing microorganisms based on their relative abundance, which is both scientific and innovative.

[0032] In practice, the proposed isolation and culture strategy based on multi-omics data accurately reconstructs the in-situ living environment and metabolic state of the target microorganism, and reversely derives the key culture conditions for its optimal growth, including but not limited to the selection of co-culture system, electron acceptor, oxygen conditions, substrate, etc., thereby verifying the correctness of the targeted identification of high-value oil recovery functional microorganisms.

[0033] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, circuit structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0034] Example 1 The crude oil sample was taken from well A in Daqing Oilfield. The reservoir temperature was about 45 ℃. The water content in the oil sample was determined by Karl Fischer water content determination method to be 22.5%, and the average diameter of water droplets in the crude oil was about 84 μm.

[0035] Place the serum bottle containing crude oil in a water bath at 46 ℃ and let it stand for 24 h. Open the serum bottle in a laminar flow hood, scoop out the crude oil with the end of a sterile weighing spoon, spread the crude oil evenly on sterile aluminum foil, remove the water droplets on the surface with a 2.5-μL pipette, and transfer it to a sterile centrifuge tube.

[0036] The collected water droplets were filtered through a 0.22 μm filter membrane to remove bacteria. The water sample was analyzed in triplicate using ion chromatography to obtain the ionic composition of the water droplets: 984 mg / L Cl. - 0.39 mg / L NO3 - 1372 mg / LCO3 2- 0.9 mg / L SO4 2- 1729 mg / L Na + 7.2 mg / LK + 7.0 mg / L Mg 2+ 21.8 mg / L Ca 2+ 0.045 mg / L formic acid, 0.13 mg / L acetic acid, <0.04 mg / L propionic acid, <0.01 mg / L butyric acid.

[0037] Following the procedures of the DNA extraction kit, DNA was extracted from 15 water droplets containing microorganisms. The DNA was then amplified using universal primers for bacterial and archaea 16S rRNA genes (bacterial primers: front primer sequence GTGCCAGCMGCCGCGGTAA, see SEQ ID NO.1; back primer sequence GGACTACHVGGGTWTCTAAT, see SEQ ID NO.2; archaea primers: front primer sequence CAGYMGCCRCGGKAAHACC, see SEQ ID NO.3; back primer sequence GGACTACNSGGGTMTCTAAT, see SEQ ID NO.4). Sequencing was performed, followed by microbial community composition analysis. The results showed that in archaea… Methanocalculus (71.4%) accounted for an absolute dominant position among bacteria. Warm-hearted (67.2%) was the dominant bacterial genus, followed by Quadrisphere (6.6%). Based on the results of metagenomic annotation, Warm-hearted It also holds an absolute advantage, with a relative abundance of approximately 19.3%. (Confirmed) Warm-hearted This is the dominant bacterial genus in this oil well. A high-quality sample was obtained through separate tanks. Warm-hearted Metagenomic assembly of the genome (MAG-01).

[0038] Functional annotation of MAG-01 revealed that it carries a complete rhamnolipin synthesis gene cluster ( rhlA , rhlB , rhlC , rhlR The GMI index of the rhamnolipid synthesis gene module was 66.7%. Several water droplets were combined, concentrated by centrifugation, vacuum dried, and then methanol was added before direct injection for UPLC-MS / MS detection. The direct precursor of rhamnolipid synthesis—3-(3-hydroxyalkyloxy)alkyl acid—and other intermediates of rhamnolipid synthesis were successfully detected, with a MAI index of 13.1%. The calculated target recognition composite value (TIV) was 0.40, ranking first. Warm-hearted It is a dominant rhamnolipid-producing bacterium with active metabolism in water droplets and is a high-value target microorganism for oil recovery.

[0039] Based on the ionic composition information and genome annotation of water droplets, recommendations are given. Warm-heartedThe culture conditions were as follows: anaerobic or oxygen-limited conditions, cultured at 35–65 °C. The culture medium specifically consisted of: 10 mL / L glycerol, 3 g / L NaCl, 1.5 g / L KCl, 0.1 g / L CaCl2, 2 g / L (NH4)2SO4, 2 g / L Na2SO4, 0.1 g / L MgSO4, 0.15 g / L KH2PO4, 0.8 g / L NaHCO3, 0.2 g / L NaS2, 1.0 mL / L trace elements and vitamins, with a pH of 7–7.2.

[0040] Water droplet samples were anaerobically enriched at 46 °C according to the recommended culture medium formulation based on the ionic composition of water droplets. After 30 days, the dominant enriched material was obtained and used for anaerobic roll tube isolation of single bacteria. The selected single bacteria were then expanded in the customized culture medium, and DNA was extracted and sequenced for identification after 14 days. A pure culture strain was successfully isolated by 16S rRNA gene full-length sequencing and alignment analysis. Warm-hearted The sequence similarity with the target MAG-01 was as high as 98.3%. In contrast, the control group was cultured in a common inorganic salt medium (10 mL / L crude oil, 10 g / L NaCl, 1.5 g / L KCl, 0.22 g / L CaCl2, 0.25 g / L NH4Cl, 2 g / L Na2SO4, 2 g / L NaNO3, 0.09 g / L MgSO4, 0.15 g / L MgCl2·6H2O, 0.19 g / L K2HPO4, 0.8 g / L NaHCO3, 0.2 g / L NaS2, 5 mL / L trace elements, and 1.0 mL / L vitamin). After 30 days, 16S rRNA gene sequencing of the enriched samples revealed that the dominant bacteria had shifted to Acholeplasma (19.7%), which was difficult to pick during subsequent anaerobic roll tube selection. Warm-hearted Single strain.

[0041] Functional validation of this strain was performed: the surface tension of its fermentation broth decreased to 28.817 ± 0.03 mN / m after 7 days. Spot spray mass spectrometry analysis of the purified fermentation product confirmed it to be a monorhamnolipid homologue. This is the first demonstration that a functional strain with high surface activity can be directly isolated from water droplets inside crude oil, and its function highly matches the predicted target recognition.

[0042] The invention involved in Embodiment 1 Warm-hearted , Methanocalculus These strains are all common reservoir microorganisms already disclosed in existing technologies. For example, Warm-heartedAs a thermophilic bacterium, it has been reported in oil reservoir microbiology research both domestically and internationally, and its presence in the crude oil environment is not a new discovery. This embodiment is merely to illustrate how the method provided by the present invention can achieve precise targeted identification of dominant functional microorganisms (such as rhamnolipid-producing bacteria) in the microenvironment of water droplets inside crude oil by analyzing water droplet ion composition, genome annotation, and metabolites, thereby demonstrating the effectiveness of the method of the present invention.

[0043] Example 2: The difference from Example 1 is that the crude oil sample collected was from shale oil well A in Jiangsu Oilfield. The average water content of the oil sample was 2.3%, and the average diameter of the water droplets in the shale oil was 21.5 μm. The ionic composition of the water droplets was 5982 mg / L. - 8.49 mg / L NO3 - 207 mg / L CO3 2- 399 mg / L SO4 2- 5037 mg / L Na + 252 mg / LK + <0.2 mg / L Mg 2+ <0.2 mg / L Ca 2+ 8.84 mg / L formic acid, 66 mg / L acetic acid, 11.2 mg / L propionic acid, 0.86 mg / L butyric acid.

[0044] DNA was extracted from microorganisms in a water droplet, and 16S rRNA gene sequence analysis was performed to determine the microbial community structure. The main dominant bacteria in the water droplet were... Pseudomonas (22.9%) Tistrella (14.9%) Shewanella Typical reservoir microorganisms, such as (3.9%), were observed. High-quality samples were obtained through separate tanks. Pseudomonas (MAG-195) and Shewanella (MAG-60) metagenomic assembly of genome.

[0045] Functional annotations were performed on the MAG-195 and MAG-60, and it was found that... Pseudomonas (MAG-195) carries a more complete petroleum hydrocarbon degradation gene cluster. badH、benA、pcaCDFThe GMI index was 68.6%. Several water droplets were vacuum dried, dissolved in 60 μL CD3OD, transferred to a 5 mm NMR tube, and analyzed by 1H NMR. The NMR spectrum of the water droplets showed a clear olefin peak (not detected in the oil phase), indicating metabolites from the microbial degradation of aromatic compounds. Methanol was added, and the sample was directly injected for UPLC-MS / MS analysis. Several metabolites from the anaerobic degradation of alkanes and aromatics, including dodecylbenzenesulfonate (6.5%), N-undecylbenzenesulfonic acid (4.7%), and 3-carbonyloctadecanoic acid (1.9%), were successfully detected, with a MAI index of 15.2%. Combined with genomic and metabolomics analysis, the TIV index was calculated to be 0.42, indicating that the high-value target oil recovery microorganisms in the water droplets were hydrocarbon-degrading bacteria. Pseudomonas Provide recommendations. Pseudomonas Culture conditions: Under aerobic or anaerobic conditions, culture with shaking at 25-37℃. The culture medium specifically includes: 10 g / L crude oil, 5 g / L NaCl, 1.5 g / L KCl, 0.22 g / L CaCl2, 4 g / L NH4NO3, 0.15 g / L MgSO4, 3.45 g / L KH2PO4, 5.64 g / L Na2HPO4, 0.015 g / L Na·EDTA, with a pH of 7-7.5.

[0046] Water droplet samples were aerobically enriched at 37 °C according to the recommended culture medium formulation based on the ionic composition of water droplets. After 7 days, the dominant enriched material was plate-spread for single-cell isolation. The selected single cells were then amplified in liquid culture medium, and DNA was extracted and sequenced for identification. A pure culture strain was successfully isolated through 16S rRNA gene sequencing and alignment analysis. Pseudomonas It has a sequence similarity of up to 97.8% with the target MAG-195.

[0047] In Example 2, the following was adopted: Pseudomonas , Shewanella These bacteria are also typical hydrocarbon-degrading bacteria disclosed in existing technologies, and have been extensively studied, especially in the field of reservoir bioremediation. This invention uses... Pseudomonas As an example, this study aims to demonstrate how to rapidly identify and target metabolically active hydrocarbon-degrading microorganisms in water droplets based on the ionic characteristics of the water droplet microenvironment and multi-omics data (such as genomics and metabolomics).

[0048] Example 3: The difference from Example 2 is that the crude oil sample collected was from shale oil well B in Jiangsu Oilfield. The average water content of the oil sample was 7.1%, and the average diameter of the water droplets in the shale oil was 17.3 μm. The ionic composition of the water droplets was 16602 mg / L. -2.53 mg / L NO3 - <0.1 mg / L CO3 2- 606 mg / L SO4 2- 10419 mg / L Na + 256 mg / LK + 34.1 mg / L Mg 2+ 83.9 mg / L Ca 2+ 1.53 mg / L formic acid, 133 mg / L acetic acid, 2.8 mg / L propionic acid, <0.93 mg / L butyric acid.

[0049] DNA was extracted from microorganisms in the water droplet, and 16S rRNA gene sequence analysis was performed to determine the microbial community structure. The dominant bacteria in the water droplet were identified as follows: Thermodesulphorhabdus (39.8%) and Thermometer (13.3%), the dominant archaea were Methermicoccus (68.3%). High quality is obtained through repackaging. Thermodesulphorhabdus (MAG-107) Thermometer (MAG-03) and Methermicoccus (MAG-362) Metagenomic assembly of genome.

[0050] Functional annotations were performed on MAG-107, MAG-03, and MAG-362, and it was found that... Thermodesulphorhabdus (MAG-107) and Thermometer In (MAG-03), the integrity of gene modules related to oil recovery function was all below 20%. Methermicoccus (MAG-362) carries a complete hydrogen-trophic methanogenic gene module with a GMI index of 96%. Recommended culture conditions based on genomic information are as follows: static culture at 45–60°C under strictly anaerobic conditions. The culture medium specifically includes: 10 g / L NaCl, 1.5 g / L KCl, 0.22 g / L CaCl2, 0.25 g / L NH4Cl, 2 g / L Na2SO4, 0.1 g / L MgSO4, 0.19 g / L K2HPO4, 0.8 g / L NaHCO3, 0.2 g / L NaS2, 5 mL / L trace elements, and 1.0 mL / L vitamin, with a pH of 7–7.2, and sufficient CO2 and H2 aeration.

[0051] Water droplet samples were anaerobically enriched at 56 °C according to the recommended culture medium formulation based on the ionic composition of water droplets. During the incubation period, gas chromatography was performed every 7 days, revealing an increase in CH4 concentration and a decrease in CO2 and H2 concentrations. After 30 days, the CH4 concentration reached 3.89 mmol / L, the CO2 concentration was 2.95 mmol / L, and H2 was not detected. The MAI index was 56.9%. Methermicoccus The TIV index was 0.76, and the high-value target oil recovery microorganism in the water droplets was a hydrogenotrophic methanogen. Methermicoccus .

[0052] Therefore, the dominant enrichment obtained after 30 days of culture was used for anaerobic roll tube isolation of single bacteria. The selected single bacteria were then expanded in a customized culture medium, and DNA was extracted and sequenced after 14 days for identification. Through 16S rRNA gene full-length sequencing and alignment analysis, a pure culture strain was successfully isolated. Methermicoccus It has a sequence similarity of up to 97.5% with the target MAG-362.

[0053] The example involved in Example 3 Methermicoccus , Thermodesulphorhabdus These strains also belong to the known reservoir microorganisms in the prior art, such as... Methermicoccus Hydrogen-nutritive methanogenic archaea have been documented multiple times in anaerobic oil reservoir environments. This embodiment uses these published species as examples to illustrate how the method of the present invention can achieve targeted identification of high-value functional microorganisms (such as methanogens) in water droplet microenvironments through customized culture conditions and functional verification (such as methanogenic activity detection).

[0054] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A method for targeted identification of microorganisms for oil recovery in crude oil, characterized in that, Includes the following steps: S1. Based on metagenomic data, analyze the oil-producing functional microorganisms in the microenvironment of oil droplets in crude oil to identify the gene modules related to oil production function and calculate the integrity score of the gene modules. At the same time, based on metabolomics data, analyze the metabolites related to the oil production function and calculate the metabolic activity score. S2. By integrating the gene module integrity score and the metabolic activity score, a comprehensive target recognition value for each microorganism is calculated. The microorganisms are then ranked according to the comprehensive target recognition value to achieve targeted identification of high-value oil recovery functional microorganisms in the water droplet microenvironment within crude oil.

2. The targeted identification method for functional microorganisms in crude oil recovery according to claim 1, characterized in that, In S1, the gene modules related to oil recovery function include at least one of the following: alkane degradation gene module, aromatics degradation gene module, biosurfactant synthesis gene module, and methanogenic gene module.

3. The targeted identification method for functional microorganisms in crude oil recovery according to claim 1, characterized in that, In S1, the specific process for calculating the integrity score of the gene module includes: For a target gene module, based on the annotation results of the metagenomic data, the number of genes detected that belong to the gene module is counted, and then the proportion of this number to the total number of known genes in the gene module is calculated. This proportion is used as the integrity score of the gene module.

4. The targeted identification method for functional microorganisms in crude oil recovery according to claim 1, characterized in that, In S1, the specific process for calculating the metabolic activity score includes: For the target oil recovery function, a variety of target metabolites related to the function are identified from the metabolomics data. Then, the relative abundance values ​​of the identified target metabolites are summed, and the sum is used as the metabolic activity score of the function.

5. The targeted identification method for functional microorganisms in crude oil recovery according to claim 4, characterized in that, The target metabolites include biosurfactant precursors, short-chain organic acids, petroleum hydrocarbon degradation intermediates, and metabolic gases.

6. The targeted identification method for functional microorganisms in crude oil recovery according to claim 1, characterized in that, In S2, the specific process of calculating the comprehensive target recognition value for each microorganism by integrating the gene module integrity score and the metabolic activity score includes: Using a weighted summation method, the gene module integrity score and metabolic activity score of the same microorganism for the same oil recovery function are fused together according to a predetermined weight coefficient. The calculation formula for the target identification comprehensive value is: Target identification comprehensive value = Gene module integrity score × Weight coefficient A + Metabolic activity score × Weight coefficient B, where the sum of weight coefficient A and weight coefficient B is 1.

7. The targeted identification method for functional microorganisms in crude oil recovery according to claim 1, characterized in that, In S2, the specific process of ranking microorganisms according to their comprehensive target recognition values ​​to achieve targeted identification of high-value oil recovery microorganisms in the water droplet microenvironment includes: All microorganisms in the microenvironment of water droplets within crude oil are sorted from high to low according to their calculated comprehensive target recognition values, generating an ordered list of microorganisms. The microorganisms ranked at the top of the list are identified as the high-value oil recovery functional microorganisms.

8. The targeted identification method for functional microorganisms in crude oil recovery according to claim 1, characterized in that, Based on the water droplet ion composition and the metabolic characteristics of the microorganisms reflected in the multi-omics data, an isolation and culture strategy is recommended for the high-value oil recovery functional microorganisms to verify the correctness of the targeted identification of high-value oil recovery functional microorganisms.

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