A method, device, medium and product for identifying well connectivity

Abstract

The present invention discloses a method, device, medium and product for identifying well-to-well connectivity, and relates to the technical field of underground resource energy development. The method comprises: collecting fluid microbial samples from injection wells and each production well in a selected well field; performing 16S rRNA gene amplicon sequencing on the fluid microbial samples to obtain the original sequencing data of the injection fluid and each production fluid respectively; extracting amplified sequence variants from the original sequencing data to obtain the amplified sequence variant feature table of the injection fluid and each production fluid respectively; performing binary Venn diagram analysis on the amplified sequence variant feature table of each injection-production well pair to obtain the amplified sequence variant superposition number of each injection-production well pair, and then determining the well-to-well connectivity between each injection-production well pair. The present invention utilizes the characteristics that exogenous injection water and produced formation water breed completely different microbial communities to efficiently and conveniently identify the relative connectivity between the injection well and each production well and monitor its dynamic changes over time.

Description

Technical Field

[0001] The present invention relates to the field of underground resource energy development, and in particular to a method, device, medium and product for identifying well connectivity based on natural microbial community signals in reservoirs. Background Art

[0002] In the process of underground resource and energy development, such as oil and gas development, water resource development, underground energy storage, carbon dioxide geological storage, geothermal development and other scenarios, it is necessary to inject water into deep reservoirs to maintain formation pressure, increase oil and gas production, exchange heat, and store resources. The characterization of the inter-well connectivity between injection wells and production wells during reservoir injection and production is an important part of the efficient development and reasonable planning of the above-mentioned resources and energy, and is also a necessary prerequisite for reservoir modeling and production capacity prediction.

[0003] Since the inter-well reservoir permeability structure is unknown, highly heterogeneous, and changes dynamically over time, the connectivity between the injection well and each production well is often difficult to determine, and needs to be identified with the help of artificial tracer technology. Artificial tracer technology refers to the process of placing markers (i.e., tracers) in the injection well, allowing them to enter the reservoir and migrate along with the reinjection fluid, and continuously sampling and monitoring in the production well to obtain the tracer concentration change curve over time (i.e., breakthrough curve). The tracer recovery rate can be obtained by fitting and integrating the breakthrough curve, and the size of the recovery rate of each production well reflects its relative connectivity with the injection well. However, since tracers can only be injected in pulses rather than continuously in field tracer experiments, and due to the dilution effect of formation water, tracer experiments often encounter problems in the actual process, such as production wells not capturing injected tracers, or only generating noise data, and breakthrough curves being difficult to interpret. Therefore, the failure rate of field tracer experiments is high. In addition, high-frequency and continuous monitoring of observation wells is required during each tracer experiment, and field sampling often lasts for weeks or even months to depict a complete breakthrough curve, which is costly in terms of manpower and economy. Finally, tracers are mostly chemical reagents, which will contaminate formation water after being injected into the formation. The above problems have severely limited the application of artificial tracer experiments. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device, medium and product for identifying well connectivity, which utilizes the characteristics that exogenous injection water and produced formation water breed completely different microbial communities, and by finding the number of microbial species from the injection fluid in the produced fluid, the relative connectivity between the injection well and each production well can be efficiently and conveniently identified and its dynamic changes over time can be monitored.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for identifying well connectivity, the method comprising:

[0007] Collecting microbial samples of injection fluid from injection wells in the selected well field and microbial samples of output fluid from corresponding production wells;

[0008] Extract genomic DNA from each microbial sample;

[0009] Determine universal primers for 16S rRNA gene amplicon sequencing, and perform 16S rRNA gene amplicon sequencing on the genomic DNA of the injection liquid microbial sample and the output liquid microbial sample to obtain original sequencing data of the injection liquid and original sequencing data of each output liquid;

[0010] Extracting amplified sequence variants from the original sequencing data of the injection liquid and the original sequencing data of each of the output liquids to obtain an injection liquid amplified sequence variant feature table and an output liquid amplified sequence variant feature table;

[0011] Perform binary Venn diagram analysis on the amplified sequence variant feature table of each injection-production well pair to obtain the amplified sequence variant superposition number of each injection-production well pair; the amplified sequence variant superposition number refers to the number of microbial species common to the injection fluid and the production fluid;

[0012] The inter-well connectivity between each injection-production well pair is determined based on the number of amplified sequence variants in each injection-production well pair.

[0013] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-mentioned methods for identifying well connectivity.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for identifying well connectivity.

[0015] A computer program product includes a computer program, which implements the steps of the above-mentioned well connectivity identification method when executed by a processor.

[0016] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0017] The present invention provides a method, device, medium and product for identifying well-to-well connectivity. The fluid microbial samples of the injection well and each production well in the selected well field are collected for 16S rRNA gene amplicon sequencing to obtain the original sequencing data; the amplified sequence variants are extracted from the original sequencing data to obtain the amplified sequence variant feature tables of the injection fluid and the production fluid respectively; the amplified sequence variant feature table of each injection-production well pair is analyzed by a binary Venn diagram to obtain the amplified sequence variant overlap number of each injection-production well pair, and then the well-to-well connectivity between each injection-production well pair is determined by the overlap number. The present invention utilizes the characteristics that the exogenous injection water and the produced formation water breed completely different microbial communities, and uses the microbial community in the exogenous injection water as a natural tracer. By finding the number of microbial species from the injection fluid in the production fluid, the relative connectivity between the injection well and each production well is efficiently and conveniently identified and its dynamic changes over time are monitored. Compared with traditional tracer technology, the present invention does not need to inject any chemical substances, but uses the microbial signals that have not been used in the fluid injected or produced during the injection-production operation. Therefore, the present invention is more environmentally friendly than the existing inter-well tracing technology and will not cause any pollution to the formation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic diagram of a flow chart of a method for identifying well connectivity provided in Example 1 of the present invention;

[0020] Figure 2 A schematic diagram of the definition of the nASV-Overlap index provided in Example 1 of the present invention;

[0021] Figure 3 A schematic diagram of relative connectivity identification of multiple injection-production well pairs at multiple times based on nASV-Overlap provided in Example 1 of the present invention;

[0022] Figure 4 This is a diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The purpose of the present invention is to provide a method, device, medium and product for identifying well connectivity, which utilizes the characteristics that exogenous injected water and produced formation water breed completely different microbial communities to efficiently and conveniently identify the relative connectivity between injection wells and various production wells and monitor their dynamic changes over time.

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Example 1

[0027] like Figure 1 As shown, a method for identifying well connectivity in this embodiment specifically includes the following steps:

[0028] Step 1: Select the injection wells and production wells that need to be continuously monitored in the well site, such as injection well I and production wells P1, P2, P3, and P4.

[0029] Step 2: Define the span of a single time point and the monitoring frequency. For example, a single time point spans 2 days and the monitoring frequency is once a week.

[0030] Step 3: Collect several liters (e.g., 1-10 L) of fluid microbial samples from the injection well and each production well at the current time point, including injection fluid microbial samples and corresponding output fluid microbial samples from each production well, enrich the naturally grown microbial cells in each sample and freeze them for storage to ensure sample representativeness.

[0031] For example: within 2 days at the beginning of the first week, complete the collection and storage of microbial samples of the injection fluid of injection well I and the output fluid of production wells P1, P2, P3, and P4.

[0032] Step 4: Extract and purify the genomic DNA of each microbial sample using conventional DNA extraction procedures.

[0033] Step 5: Determine universal primers for the highly variable regions of the 16S rRNA gene (such as V4 and V5), amplify the genomic DNA of each sample and establish a sequencing library. Keep the primers unchanged during the monitoring of subsequent time points. Sequence the 16S rRNA gene amplicons of microbial samples using common high-throughput sequencing platforms (such as Illumina) to obtain raw data files (such as .fastq files), including the raw sequencing data of the injection fluid and the raw sequencing data of each output fluid.

[0034] Step 6: Use a standard bioinformatics process (such as DADA2) in a conventional program or software (such as R or Qiime) to extract amplicon sequence variants (i.e., amplicon sequence variants, referred to as ASVs, also known as denoised DNA sequences) from the original sequencing data, and obtain an ASV feature table for each sample, including an injection fluid amplicon sequence variant feature table and an output fluid amplicon sequence variant feature table. The amplicon sequence variant ASV is the denoised DNA sequence.

[0035] Step 7: Draw a dilution curve to confirm that each sample has sufficient sequencing depth. If the sequencing depth is insufficient, remove the sample data or re-sequence.

[0036] The x-axis of the dilution curve is the sequencing amount, and the y-axis is the number of unique ASVs. As the sequencing amount increases, the number of unique ASVs measured (i.e., the number of species) will increase rapidly at first, and then tend to level off. The leveling off means that the current sequencing amount has fully captured all species in the sample, and further increasing the sequencing amount will not detect new ASVs. Therefore, when the actual sequencing amount has caused the dilution curve to reach a plateau, it means that the sequencing depth is sufficient. On the contrary, when the actual sequencing amount does not cause the dilution curve to reach a plateau, that is, if the number of unique ASVs is still on an upward trend, it means that the sequencing amount is not enough to capture all species in the sample and there are still many undetected species, then the data point is not representative enough and needs to be eliminated or resequenced.

[0037] Step 8: Use a Venn diagram analysis function (e.g., the venn() function of the eulerr package in R language) to perform a binary Venn diagram analysis on the ASV feature table of each injection-production well pair, and calculate the nASV-Overlap index of each injection-production well pair, i.e., the overlap number of amplified sequence variants. Determine the connectivity of each production well with the injection well based on the nASV-Overlap index.

[0038] Taking advantage of the fact that the microbial communities of exogenous injected water and produced formation water are very different during the injection and production operations of underground reservoirs, the DNA sequence data of the microbial community in the injection and production fluids are obtained and compared through high-throughput sequencing technology of microbial community genomic DNA to obtain the microbial DNA signals from the injection fluid in the production fluid. The new indicator "nASV-Overlap" (i.e., "number of Amplicon Sequence Variants Overlapped" / "number of amplified sequence variants overlapped") is defined to quantify the number of microbial species shared by the injection and production fluids, and it is proposed that nASV-Overlap is positively correlated with the connectivity between injection and production wells, and the nASV-Overlap index is used as a new indicator to characterize the connectivity between injection wells and production wells. The nASV-Overlap index is obtained by performing a binary Venn diagram analysis of the fluid microbial community between the injection well and each production well, such as Figure 2 As shown, its definition is as follows:

[0039] nASV-Overlap[I t -P i,t ]=n(I t )+n(P i,t )-n(I t ∪P i,t )=n(I t ∩P i,t );

[0040] Among them, I t represents the unique ASV set obtained by sequencing the microbial community of the injection fluid in injection well I at time t, P i,t represents the i-th production well P at time t i Unique ASV set obtained by sequencing the output fluid; nASV-Overlap[I t -P i,t ] represents the injection well I and production well P at time t i nASV-Overlap index; n(I t ) and n(P i,t ) represent I t and P i,t The number of ASVs in the population (equivalent to species richness); n(I t ∪P i,t ) represents I t and P i,t The number of ASVs in the union of t ∩P i,t ) represents I t and P i,t The number of ASVs in the intersection of .

[0041] The present invention proposes that the probability of naturally grown microbial cells in the injection fluid successfully migrating to a certain production well is proportional to the well-to-well connectivity between the injection well and the production well. Since the microorganisms in the injection fluid exist in the form of a multi-species community, the number of injection fluid species that successfully migrate to the production well is also proportional to the connectivity between the injection well and the production well. The number of injection fluid species that migrate to the production well (i.e., nASV-Overlap value) can be efficiently and conveniently obtained by high-throughput sequencing combined with binary Venn diagram analysis of injection and production fluids. Therefore, it is proposed to characterize the relative connectivity between the injection well and the surrounding production wells by the size of the nASV-Overlap value, and to monitor the changes in connectivity between the injection well and a certain production well over time.

[0042] Step 9: Compare the nASV-Overlap index of all injection-production well pairs. The higher the nASV-Overlap index, the better the connectivity between the production well and the injection well. Figure 3 shown.

[0043] nASV-Overlap[I t -P 4,t ]>nASV-Overlap[I t -P 1,t ]>nASV-Overlap[I t -P 3,t ]

[0044] >nASV-Overlap[I t -P 2,t ], then at time t, the connectivity between each production well and the injection well is P4>P1>P3>P2.

[0045] nASV-Overlap[I t+1 -P 1,t+1 ]>nASV-Overlap[I t+1 -P 4,t+1 ]>nASV-Overlap[

[0046] I t+1 -P 3,t+1 ]>nASV-Overlap[I t+1 -P 2,t+1 ], then at time t+1, the connectivity between production wells and injection wells is P1>P4>P3>P2.

[0047] nASV-Overlap[I t+2 -P 1,t+2 ]>nASV-Overlap[I t+2 -P 2,t+2 ]>nASV-Overlap[

[0048] I t+2 -P 3,t+2 ]>nASV-Overlap[I t+2 -P 4,t+2 ], then at time t+2, the connectivity between production wells and injection wells is P1>P2>P3>P4.

[0049] Step 10: At the next time point, perform sample collection and data analysis through the above steps. For example, within 2 days starting from the second week, collect and store microbial samples of the injection fluid and output fluid of injection well I and production wells P1, P2, P3, and P4 again. And so on.

[0050] Step 11: Based on the data from all time points to date, a graph of the nASV-Overlap between each pair of injection and production wells is plotted to describe the changes in the connectivity between injection and production wells over time. Figure 3 shown.

[0051] nASV-Overlap[I t+2 -P 1,t+2 ]>nASV-Overlap[I t+1 -P 1,t+1 ]>nASV-Overlap[

[0052] I t -P 1,t ], then for the production well P1, the connectivity with the injection well I is at time t+2> at time t+1> at time t.

[0053] nASV-Overlap[I t+2 -P 2,t+2 ]>nASV-Overlap[I t+1 -P 2,t+1 ]>nASV-Overlap[

[0054] I t -P 2,t ], then for the production well P2, the connectivity with the injection well I is at time t+2> at time t+1> at time t.

[0055] nASV-Overlap[I t -P 3,t ]>nASV-Overlap[I t+1 -P 3,t+1 ]>nASV-Overlap[I t+2

[0056] -P 3,t+2], then for the production well P3, the connectivity with the injection well I is at time t> at time t+1> at time t+2.

[0057] nASV-Overlap[I t -P 4,t ]>nASV-Overlap[I t+1 -P 4,t+1 ]>nASV-Overlap[I t+2

[0058] -P 4,t+2 ], then for the production well P4, the connectivity with the injection well I is at time t> at time t+1> at time t+2.

[0059] This embodiment establishes a technical process for identifying well connectivity based on the natural microbial community DNA of the injection and production fluid and the new nASV-Overlap indicator. This technical process helps to fully standardize and control the quality of the present invention during application and promotion. The necessity of the following quality control steps in the process is proposed and emphasized: 1) The definition of the range of a single time point should be clear and as unified as possible; 2) Each group of injection and production well fluid samples must be collected within the defined time point range and frozen and stored as soon as possible to ensure the representativeness of the samples, so as to ensure that the group of samples is representative of the reservoir state at the current time point, and at the same time avoid reducing the accuracy of connectivity identification due to changes in the injection fluid microbial community over time; 3) After sequencing is completed, the original data needs to be quality controlled, especially to confirm whether the sequencing depth is sufficient, and data that does not meet the quality control requirements should be eliminated or re-tested.

[0060] In this embodiment, compared with the existing artificial tracer technology, the present invention does not need to put any chemical substances into the formation water, but uses the natural differences in microbial species caused by the differences in the environment between the exogenous injection water and the endogenous formation water in the injection and production operation, and identifies the microbial signals from the injection fluid in the production fluid through high-throughput sequencing and Venn diagram analysis, and uses the signals to characterize the connectivity between the injection and production wells. Its advantages are as follows:

[0061] 1. The existing tracer technology requires high-frequency sampling in each production well, the purpose of which is to obtain a complete breakthrough curve in each well (generally containing 30-50 data points, so 30-50 samples are required) so as to calculate the tracer recovery rate of each production well by integration. The connectivity between each production well and the injection well can be determined by the recovery rate (the higher the recovery rate, the better the connectivity with the injection well); while the present invention only needs to collect two samples of injection fluid and output fluid to obtain the nASV-Overlap value between the sample pairs through high-throughput sequencing and then determine the connectivity between the production well and each injection well, which greatly reduces the sampling frequency, and thus has the advantages of low cost and high efficiency.

[0062] 2. It is often difficult to capture the tracer plume in the production well using the existing traditional tracer method, so the field test is prone to failure; however, the present invention does not need to capture the tracer plume, and the field test success rate is high.

[0063] 3. Due to the above advantages of low cost and high success rate, when the connectivity between injection and production wells changes dynamically over time, the present invention can be easily repeated at the next time point. From the perspective of judging the connectivity between wells, one nASV-Overlap data point is equivalent to the information content of a whole breakthrough curve (generally containing 30-50 data points) of a traditional chemical tracer. Therefore, the present invention can realize long-term monitoring of connectivity between injection and production wells efficiently and at low cost, which cannot be achieved by existing traditional tracer methods.

[0064] 4. Based on the above-mentioned advantage of "being able to realize long-term monitoring of connectivity between injection and production wells", the cross-comparison of nASV-Overlap monitoring data of each production well and injection well can be used to determine whether the uptake of injection fluid between production wells is a synergistic or competitive relationship, which is impossible to achieve with existing tracer technology.

[0065] Example 2

[0066] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a well-to-well connectivity identification method in Example 1.

[0067] Example 3

[0068] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a well-to-well connectivity identification method in Example 1.

[0069] Example 4

[0070] A computer program product includes a computer program, which implements the steps of a well connectivity identification method in embodiment 1 when executed by a processor.

[0071] Example 5

[0072] A computer device, which may be a database, may have an internal structure as shown in Figure 4As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a well-to-well connectivity identification method in Example 1 is implemented.

[0073] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0074] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by guiding the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0075] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for identifying well connectivity, characterized in that: The method comprises: Collecting microbial samples of injection fluid from injection wells in the selected well field and microbial samples of output fluid from corresponding production wells; Extract genomic DNA from each microbial sample; Determine universal primers for 16S rRNA gene amplicon sequencing, and perform 16S rRNA gene amplicon sequencing on the genomic DNA of the injection liquid microbial sample and the output liquid microbial sample to obtain original sequencing data of the injection liquid and original sequencing data of each output liquid; Extracting amplified sequence variants from the original sequencing data of the injection liquid and the original sequencing data of each of the output liquids to obtain an injection liquid amplified sequence variant feature table and an output liquid amplified sequence variant feature table; Perform binary Venn diagram analysis on the amplified sequence variant feature table of each injection-production well pair to obtain the amplified sequence variant superposition number of each injection-production well pair; the amplified sequence variant superposition number refers to the number of microbial species common to the injection fluid and the production fluid; Determine the inter-well connectivity between each injection-production well pair according to the superposition number of amplified sequence variants of each injection-production well pair; Taking advantage of the natural differences in microbial species between exogenous injection water and endogenous formation water due to differences in the environment during injection and production operations, high-throughput sequencing and Venn diagram analysis are used to identify microbial signals from the injection fluid in the produced fluid and use the signals to characterize the connectivity between the injection and production wells.

2. A method for identifying well connectivity according to claim 1, characterized in that: Before executing the step of "performing a binary Venn diagram analysis on the amplified sequence variant feature table of each injection-production well pair", the method further includes: Dilution curves are drawn according to the characteristic table of amplified sequence variants of the injection fluid and the characteristic table of amplified sequence variants of each output fluid; Judging whether the sequencing depth of the microbial sample is sufficient according to the dilution curve; when the dilution curve tends to be flat or has reached a plateau, it is considered that the sequencing depth is sufficient; the microbial sample includes the injection liquid microbial sample and the corresponding output liquid microbial sample of each production well; If so, a bivariate Venn diagram analysis was performed; If not, delete the corresponding microbial samples or re-sequence the 16S rRNA gene amplicon for the microbial samples that do not meet the sequencing depth.

3. A method for identifying well connectivity according to claim 1, characterized in that: The step of "determining the inter-well connectivity between each injection-production well pair according to the number of amplified sequence variants of each injection-production well pair" also includes: For each injection-sample well pair, obtain the combined number of amplified sequence variants at all time points; Plot the variation of the number of amplified sequence variants reunion between each injection and production well pair over time; According to the change diagram, the change pattern of connectivity between each pair of injection and production wells over time is determined.

4. A method for identifying well connectivity according to claim 1, characterized in that: The calculation formula for the superposition number of amplified sequence variants for each injection-collection well pair is: nASV-Overlap[I t -P i,t ]=n(I t ∩P i,t ); Among them, nASV-Overlap[I t -P i,t ] represents the injection well I and production well P at time t i The number of amplified sequence variants in the t represents the set of amplified sequence variants of the injection fluid in well I at time t, P i,t represents the i-th production well P at time t i The set of variant sequences of the amplified sequence of the output fluid; n(I t ∩P i,t ) represents I t and P i,t The number of amplified sequence variants in the intersection of 5. A method for identifying well connectivity according to claim 1, characterized in that: The 16SrRNA gene amplicon sequencing is completed using a high-throughput sequencing platform.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a well-to-well connectivity identification method as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for identifying well connectivity described in any one of claims 1 to 5 are implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of a method for identifying well connectivity described in any one of claims 1 to 5 are implemented.

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