Microorganism DNA sequencing-based long-time inter-well injection and production connectivity discrimination method

Abstract

The invention discloses a long-time inter-well injection and production connectivity discrimination method based on microorganism DNA sequencing in the technical field of oil-water well connectivity detection, which comprises the following steps of: sampling from a water injection well and a production well according to a set time interval and circulation time respectively, pretreating the sample, and then extracting DNA and sequencing DNA; based on the sequencing results of the water injection well produced fluid sample and the production well produced fluid sample, comparing and analyzing the difference of the two samples under the time scale so as to judge the injection-production connectivity; according to the method, sampling, sequencing and data analysis are carried out in a mode of screening and tracing strains and in a mode of endogenous tracing, main coordinate analysis is carried out on a sequencing result to reduce the dimension of a high-dimensional similarity matrix into a low-dimensional space, the decoupling difficulty of effective information is effectively reduced, and on the basis, the accuracy of data analysis is improved. By comparing and analyzing composition differences of microbial communities in production fluid samples of a water injection well and a production well under different time scales, the connectivity between the injection wells and the production wells is judged, and dynamic monitoring of the connectivity between the wells in a long-term and large-range mode is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of oil and water well connectivity detection, and in particular to a method for distinguishing long-term well-to-well injection and production connectivity based on microbial DNA sequencing. Background Art

[0002] Interwell connectivity evaluation is an important task in reservoir dynamic analysis, and is also the basis for studying the distribution law of remaining oil in high water-cut reservoirs and formulating development adjustment plans. Interwell connectivity judgment methods are mainly divided into three types: production dynamic characteristics method, interwell interference well test analysis method, and tracer analysis method.

[0003] Production dynamics characteristic method Production dynamics refers to the process in which underground fluids move and redistribute according to certain rules as oil production and water injection proceed after the oil field is put into development. It is mainly manifested in changes in the production wells' fluid production, oil production, water content, formation pressure, dynamic liquid level, and fluid properties. Inter-well connectivity analysis can be performed by analyzing changes in relevant indicators of production wells. It can be analyzed through production data without the need for additional experiments and tests. However, it has certain limitations in terms of large data demand, high computational complexity, high technical requirements for operators, and strong dependence on the properties of bottom hole fluids.

[0004] Well-to-well interference test analysis is a test method that determines the connectivity between wells and the formation parameters of well-to-well connectivity by testing the pressure interference caused by opening or closing adjacent wells. It can obtain data through field tests and reduce dependence on production data. The advantage is that it can directly measure the pressure response between wells and provide a more direct basis for connectivity judgment, but the cost is high, the time to obtain results is long, and it causes certain interference to the production of the oil field. The test results are affected by factors such as geological conditions and hydraulic fracturing, and there may be certain uncertainties.

[0005] The inter-well tracer test is to judge the inter-well connectivity by comparing the tracer plug injected into the injection well with the fluid output from the production well, analyzing the concentration of the injected tracer in the water sample taken from the production well, the breakthrough time, the shape of the output curve and other information. This technology has high sensitivity, high precision and a wide range of applications, but there are problems such as polluting the formation, taking a long time, being complicated in actual operation and not being able to monitor for a long time.

[0006] In order to overcome the defects of traditional well connectivity analysis methods, in the existing technology, the research on well connectivity analysis using microbial DNA information has been gradually carried out. At present, the method of using DNA information to judge well connectivity is to inject DNA wrapped in exogenous nanomaterials into the injection well, and evaluate the degree of well connectivity by measuring the concentration of injected DNA in the production well production fluid. However, on the one hand, this method has problems such as the instability of injected DNA, susceptibility to underground microorganisms, and difficulty in survival of injected DNA. On the other hand, there is a lack of large-scale block monitoring and analysis based on time scale, and the current continental sedimentary old oil fields are characterized by strong heterogeneity and multiple layers. The "circular reinjection" water injection development model is mostly adopted, which makes the DNA information of oil and water flora superimposed in time and space, and it is difficult to effectively decouple information. Summary of the invention

[0007] The purpose of the present invention is to provide a method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing, so as to solve the technical problems in the prior art of instability of exogenous DNA, lack of time scale, and difficulty in decoupling effective information due to spatiotemporal superposition of oil and water bacterial community DNA information.

[0008] In order to solve the above technical problems, the present invention specifically provides a method for determining the connectivity of long-term well-to-well injection and production based on microbial DNA sequencing, comprising the following steps: Collecting production fluid samples from the injection wells from the wellhead valves at set time intervals, and performing DNA extraction and DNA sequencing on the collected production fluid samples from the injection wells after pretreatment; Calculate the circulation time of the fluid from the injection well to the production well underground when the injection well is sampled based on the sampling time of the production fluid sample and the waterline advancement speed; After the flow time of the injection well production fluid sample sampling interval, the production well production fluid samples are collected at the wellheads of all production wells, and DNA extraction and DNA sequencing are performed on the collected production well production fluid samples after pretreatment; Based on the sequencing results of the injection well fluid samples and the production well fluid samples, the differences between the two were compared and analyzed on a time scale to determine the injection-production connectivity; in: The composition information of the microbial community was obtained by analyzing the sequencing results of the production samples of injection wells at different time intervals; The UPGMA cluster tree was used to analyze the differences in microbial community composition between injection well fluid samples and production well fluid samples on a time scale and to screen tracer strains. The principal coordinate analysis of the tracer bacteria in the fluid samples of the injection wells and all the production wells after the interval circulation time was performed to determine the connectivity between the wells.

[0009] Furthermore, the 16s rRNA gene sequence sequencing method was used in DNA sequencing.

[0010] Furthermore, after DNA sequencing is completed, the original sequencing data is processed: Splice the DNA sequence to obtain tags of the hypervariable region; The spliced ​​tags were clustered as OUT, and clustered under the set similarity to obtain the OTU representative sequence, and the chimeras generated by PCR amplification were removed from the OTU representative sequence; The OTU representative sequences were compared with the database for species annotation, and the confidence threshold was set.

[0011] Further, a time scale is set, and the interval of the time scale is not greater than the minimum time for the microbial community to change; The dynamic changes of microbial communities in the production fluid samples of injection wells at different time points are analyzed on the time scale.

[0012] Furthermore, production samples of injection wells were collected at N different time points, and the composition information of the microbial community was obtained using 16s rRNA gene sequencing method.

[0013] Furthermore, the microbial community composition information of the injection well fluid samples and production well fluid samples at N different time points was analyzed by UPGMA cluster tree analysis, and the microbial flora shared by the injection wells and production wells were annotated; Under the water injection development mode of "circulating reinjection and multi-well common source", tracer strains are screened according to the principles of screening tracer strains.

[0014] Furthermore, the principles for screening tracer strains are: The strains are stably present in both injection wells and production wells; The abundance of bacterial species in injection wells was higher than that in production wells.

[0015] Furthermore, when the principal coordinate analysis is performed on the tracer bacteria of the injection well fluid samples and all production well fluid samples after the interval circulation time, specifically: The principal coordinate analysis was performed on the tracer bacterial species of the production fluid samples of the injection wells and all the production wells after the interval circulation time. The bacterial species of the production fluid samples were clustered to analyze the composition and abundance differences of the microbial communities in different production fluid samples. According to the abundance of each microbial community, the similarity metric is calculated using the Euclidean distance to perform principal coordinate analysis on the similarity matrix, and the coordinates of different production samples on the principal coordinate axis are obtained. The principal coordinate analysis will reduce the high-dimensional similarity matrix to a low-dimensional space, and the similarity of bacterial species of different production samples can be judged by observing the distribution of production samples in the low-dimensional space to determine the injection-production connectivity. Among them, the similarity of bacterial species in different production samples was positively correlated with the injection-production connectivity.

[0016] Furthermore, the Euclidean distance is calculated as: ; ; In the formula, represents the abundance of the i-th bacterial species in different wells, and n identifies the species number of the bacterial species; represents the distance between different wells and determines the connectivity between wells; X represents a vector containing the abundance information of different bacterial species in the well, and |X| represents the modulus of this vector to measure the overall abundance and diversity of the microbial composition in the sample; Indicates the abundance of a specific bacterial species.

[0017] Furthermore, the injection-production connectivity is positively verified by the permeability of the formation between the injection well and the production well.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention samples, sequences and performs data analysis on the microorganisms of the injection wells and production wells producing fluid at different time points by screening tracer strains and using endogenous tracers. The high-dimensional similarity matrix is ​​reduced to a low-dimensional space by performing principal coordinate analysis on the sequencing results. The similarity of the strains of different producing fluid samples is judged by observing the distribution of the producing fluid samples in the low-dimensional space to determine the injection-production connectivity, thereby effectively reducing the difficulty of decoupling effective information. On this basis, the composition differences of the microbial communities in the producing fluid samples of the injection wells and production wells at different time scales are compared and analyzed to determine the connectivity between the injection and production wells, thereby achieving long-term dynamic monitoring of the connectivity between wells over a large range. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0020] Figure 1 It is a schematic diagram of a flow chart of an embodiment of the present invention; Figure 2 It is a design chart of four samples in the embodiment of the present invention; Figure 3 This is a schematic diagram of the abundance of various bacterial species in the first sampling of injection wells and production wells in the embodiment of the present invention; Figure 4 This is a schematic diagram of the abundance of various bacterial species in the second sampling of injection wells and production wells in the embodiment of the present invention; Figure 5 This is a schematic diagram of the abundance of various bacterial species in the third sampling of injection wells and production wells in the embodiment of the present invention; Figure 6 This is a schematic diagram of the abundance of various bacterial species in the fourth sampling of injection wells and production wells in the embodiment of the present invention; Figure 7 The principal coordinate analysis result diagram of the abundance of tracer bacteria in the production fluid samples of the water injection well and the production well in the embodiment of the present invention is shown in FIG. Figure 8 It is a Venn diagram of the liquid samples of the water injection well 7N3 and the production well in the embodiment of the present invention; Fig. 9 It is a Venn diagram of the liquid production samples of the water injection well 5-4 and the production well in the embodiment of the present invention; Fig.10 It is a Venn diagram of the liquid samples of the water injection well 5N5 and the production well in the embodiment of the present invention; Fig.11 is a well location map in an embodiment of the present invention; Fig.12 Schematic diagram of the abundance changes of the screened tracer bacterial species in four samplings. DETAILED DESCRIPTION

[0021] 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.

[0022] The present invention evaluates the connectivity between wells by sampling the production samples of injection wells and production wells and performing DNA sequencing, and then comparing the DNA similarity between the two. That is, in the present invention, the connectivity between wells is determined based on endogenous tracing, and a large-scale dynamic monitoring study of the connectivity between wells based on time scale is carried out.

[0023] Research has shown that the method of evaluating connectivity using microbial DNA information in the production fluid of injection wells and production wells has the advantages of higher accuracy, easier operation, and long-term effective dynamic monitoring compared to traditional identification methods. Moreover, with the significant reduction in the cost of gene sequencing, the feasibility of this technology has been further improved, thereby significantly reducing costs.

[0024] Based on the endogenous tracer method, the present invention samples, sequences and analyzes the microorganisms of the injection well and production well fluid at different time points. First, the rationality of sampling is verified by analyzing the microbial community composition of the injection well fluid samples at different time scales; secondly, the differences in the microbial community composition of the injection well and production well fluid samples at different time scales are compared and analyzed to evaluate the feasibility of the analysis method. Finally, the proposed method is used to determine the connectivity between injection and production wells, and the accuracy of the results is verified, thereby realizing the dynamic monitoring of long-term and large-scale well connectivity.

[0025] In the present invention, compared with the combined analysis of all bacterial species in the production fluid of the injection and production wells, it is more accurate and scientific to screen out the tracer bacterial species that are stably present above and below the ground during the circulation process for injection and production connectivity analysis. Secondly, based on the consideration of circulation time, by analyzing the injection well and production well samples at different time points, the influence of the advancement speed of the water line can be overcome.

[0026] like Figure 1 As shown, the present invention specifically provides a method for determining the connectivity of long-term well-to-well injection and production based on microbial DNA sequencing, comprising the following steps: Collecting production fluid samples from the injection wells from the wellhead valves at set time intervals, and performing DNA extraction and DNA sequencing on the collected production fluid samples from the injection wells after pretreatment; Calculate the circulation time of the fluid from the injection well to the production well underground when the injection well is sampled based on the sampling time of the production fluid sample and the waterline advancement speed; After the flow time of the injection well production fluid sample sampling interval, the production well production fluid samples are collected at the wellheads of all production wells, and DNA extraction and DNA sequencing are performed on the collected production well production fluid samples after pretreatment; Based on the sequencing results of the injection well fluid samples and the production well fluid samples, the differences between the two were compared and analyzed on a time scale to determine the injection-production connectivity.

[0027] When sampling and pre-treating the produced fluid samples of the injection well and the production well, attention should be paid to bacterial contamination. In a specific embodiment, the produced fluid samples are collected in sterile conical bottles, each container has a volume of 30 to 50 ml, and each produced fluid sample does not exceed 70 ml. Gloves should be worn during sampling to reduce the contamination of the produced fluid samples by skin microorganisms.

[0028] The sterile conical flask should be fully cleaned and disinfected with ultraviolet light before sampling. After sampling, it should be placed in an incubator containing freezing liquid and ice packs immediately to keep the sample in a frozen state, stop its metabolism, and cool it to a temperature close to -20°C. The liquid sample should be quickly transported to the laboratory and quickly placed in a -80°C refrigerator for freezing and storage until extraction.

[0029] In the present invention, there is no need to deploy downhole tools or inject exogenous tracers during the sampling of production fluid samples in water injection wells and production wells. Therefore, the sampling process does not pollute or damage the concentration of formation bacteria, does not require downtime, and does not affect actual production operations.

[0030] During DNA sequencing, agarose gel electrophoresis analysis is performed after amplification. After the sample is melted on ice, it is thoroughly mixed and centrifuged, and an appropriate amount of sample is taken for testing.

[0031] The present invention adopts the 16s rRNA sequencing method. After sequencing, the original sequencing data is processed, and the software FLASH (Fast Length Adjustment of Short reads, v1.2.11) is used to splice the sequence to obtain the tags of the highly variable region. The spliced ​​tags are clustered into OTUs using the software USEARCH (v7.0.1090). UPARSE is used to cluster at 97% similarity to obtain the representative sequence of OTU, and UCHIME (v4.2.40) is used to remove the chimeras generated by PCR amplification from the OTU representative sequence. After obtaining the OTU representative sequence, the OTU representative sequence is compared with the database by RDP classifer (v2.2) software for species annotation, and the confidence threshold is set to 0.6.

[0032] The comparative analysis process is described in detail below.

[0033] Based on the sequencing results of the injection well fluid samples and the production well fluid samples, the differences between the two were compared and analyzed on a time scale to determine the injection-production connectivity, which mainly includes three aspects: First, the community composition of injection well production fluid samples was analyzed on a time scale.

[0034] If the production samples of water injection wells maintain a relatively stable change trend over a period of time, this indicates that most of the production samples of water injection wells have similar characteristics. Therefore, several water injection well samples obtained from the water injection station may represent the characteristics of the production samples of all water injection wells. In this case, the characteristics and change patterns of the production samples of the entire water injection wells can be understood by analyzing these representative samples.

[0035] In the present invention, the impact of the 44-day time scale on the microbial community needs to be considered, and the dynamic changes of the microbial community in the injection well at different time points in the short-term time scale of 7-15 days are analyzed in detail. In the study, taking the production samples of the injection well at four different time points as an example, by sequencing and analyzing the 16S rRNA gene sequences of the microorganisms in the samples, the composition information of the microbial community can be obtained, which can reveal the composition and change rules of the microbial community at different time scales, and reveal the succession rules and stability of the microbial community.

[0036] Second, the differences in community composition of production fluid samples from injection wells and production wells were analyzed over time scales.

[0037] Taking the analysis of microbial community composition of production samples from injection wells and production wells at four different time points as an example, the similarities and differences between the production samples from injection wells and production wells were analyzed by UPGMA clustering tree, specifically including two aspects: (1) Determine the dynamic changes in the composition of the microbial communities in the injection well and production well fluid samples at different time points, and explore the common microbial flora in the injected and produced fluids.

[0038] (2) High-water-content oil fields usually adopt a "circular reinjection, multi-well common source" water injection development model, that is, the oil well produced fluid is reinjected into the water well, so that the oil and water wells contain common bacterial communities. In this "circular reinjection, multi-well common source" model, the water sources of adjacent wells come from the same water distribution station, resulting in a high degree of similarity in the bacterial communities of the injected water. Therefore, the common bacterial communities between injection and production wells may originate from the connection between the wells, or may be caused by the connection between the adjacent water wells and the oil wells. In order to determine the connectivity between injection and production, the bacterial species selected as tracers in this study should be stable throughout the entire circular reinjection process, that is, whether above or below the ground, and have tracer characteristics.

[0039] Therefore, the selection of representative endogenous bacterial species must follow two principles: 1) they are stable both above and below the ground, that is, they appear in both injection and production wells; 2) the abundance of the bacteria in injection wells is higher than that in production wells. Based on the above conditions, representative tracer bacterial species were selected for subsequent analysis of injection-production connectivity.

[0040] Third, the connectivity between injection and production wells is determined and verified based on the DNA sequencing results of production fluid samples. Connectivity determination: First, the principal coordinate analysis was performed on the DNA sequencing results of the first injection well fluid sample and the tracer species of the fourth production well fluid sample. The reason for choosing such a time-staggered analysis is that according to the data that the waterline advances at a speed of about 300m / 15 days, it is inferred that the fluid sampled from the first water well was transmitted underground to the production well at about the time of the fourth sampling.

[0041] The principal coordinate analysis cluster analysis method is used to cluster the strains of each production liquid sample. According to the similarities and differences between the principal coordinate analysis samples, the similarity of the production liquid samples of the injection and production wells is judged, so as to achieve the purpose of injection and production connectivity judgment. According to the abundance of each bacterial species, the similarity metric is calculated using the Euclidean distance, and the principal coordinate analysis is performed on the similarity matrix to obtain the coordinates of the samples on the principal coordinate axis. The principal coordinate analysis will reduce the high-dimensional similarity matrix to a low-dimensional space, and judge the similarity of strains of different production liquid samples by observing the distribution of samples in the low-dimensional space. The closer the distance between the samples, the stronger the correlation. The closer the point to the injection well in the principal coordinate analysis diagram is, the stronger the effect is on the production well, which is manifested as a stronger injection and production connectivity.

[0042] The Euclidean distance is calculated as: ; ; In the formula, represents the abundance of the i-th bacterial species in different wells, and n identifies the species number of the bacterial species; represents the distance between different wells and determines the connectivity between wells; X represents a vector containing the abundance information of different bacterial species in the well, and |X| represents the modulus of this vector to measure the overall abundance and diversity of the microbial composition in the sample; Indicates the abundance of a specific bacterial species. Result verification: Generally, the connectivity between production wells and injection wells is proportional to their distance. This means that the closer the production well is to the injection well, the higher its permeability is usually, while the farther the production well is, the lower its permeability is. However, after taking into account factors such as special geological structures, rock formation characteristics, and fracture systems, sometimes the connectivity between production wells and injection wells that are far away may be very strong, resulting in higher permeability. Therefore, the method of evaluating injection-production connectivity through microbial DNA sequencing can be verified. If the production wells far away from the injection wells show a strong connectivity with the injection wells through DNA sequencing, and the permeability of these production wells is also high, this will verify the accuracy of the technology.

[0043] The following is an explanation of the screening method of tracer bacteria through more specific examples.

[0044] The X block with a simple structure was selected. It includes ten small layers in the vertical direction, with a wide distribution area and a single layer thickness of about 20m. By sampling the cuttings and the production fluid for up to 5 months, the production fluid of the injection well and the production well was sampled four times in a total of two months to obtain high-resolution reservoir microbial community data, such as Figure 2 shown.

[0045] The abundance of each bacterial species in the injection and production wells in the four samplings is as follows Figures 3 to 6 As shown, Inj represents injection wells and the rest are production wells.

[0046] The bacterial species that can be used as tracers should be stable in the entire recycling process, both above and below ground, and have tracer characteristics. Therefore, the screening of representative endogenous bacterial species should follow two principles: 1) they should be stable above and below ground, that is, they should appear in both injection and production wells; 2) the abundance of the bacteria in injection wells is higher than that in production wells.

[0047] Fig.12 The figure shows the changes in the DNA abundance of the selected coded bacterial species (tracer bacterial species) on the time axis during the four samplings. Inj represents the injection well, Pro represents the production well, and a, b, c, and d represent the results within the four sampling times. The figure indicates that the above bacterial species are common to both the injection well and the production well, and the abundance of this type of bacterial species in the injection well is greater than that in the production well. The changes on the time axis are shown in Figure 1. Fig.12 shown.

[0048] According to the above two conditions, nine bacterial species that can be used as tracers were screened out, including Methanococcus, Candidatus_Cloacamonas, Methanobacterium, Desulfacinum, Geobacter, Methanomethylovorans, Methanothrix, Fervidobacterium, and Acinetobacter. Their abundance in injection wells was higher than that in production wells. The above nine tracer bacterial species were used for subsequent well connectivity analysis.

[0049] According to the waterline advancement speed, the fluid sampled from the first injection well reaches the production well at the fourth sampling, so the principal coordinate analysis is performed on the first injection well production fluid sample and the fourth production well production fluid sample.

[0050] The analysis results are as follows Figure 7 As shown in the figure, based on the above-mentioned characteristic bacteria species and their abundance contained in the production fluid of each injection and production well, the high-dimensional DNA data is reduced in dimension. Based on the distance as sqeuclidean, standardization as PercentScaler, and std-direction as row, the principal coordinate analysis result is obtained.

[0051] The results are divided into 5 categories according to the confidence level of 0.9, where the blue interval of category 0 is the injection well, and the rest are production wells. The closer the production well result point is to the injection well production result point, the stronger the connectivity between the production well and the injection well. It is obvious from the figure that the production well with stronger connectivity to the injection well is the pink interval of category 2. According to the results of principal coordinate analysis Figure 7 , it can be judged that the production wells with strong connectivity with the water injection wells are 6X403, 8-3, 8P406, 6-0, 6N7, 8-06, 6-406, and 8-0. In addition, the three production wells 7C7, 6-8, and 6-405 have poor connectivity with the water injection wells.

[0052] according to Figure 7 As shown in the figure, the connectivity between injection well 7N3 and production well 6X403 is strong, the connectivity between injection well 5-4 and production well 8-3 is strong, and the connectivity between injection well 5N5 and production well 8P406 is strong. At the same time, the OTUs of the injection wells in the second pink interval with the production wells with strong and weak connectivity are used to make a Venn diagram. The Venn diagram is made using BMC (Bardou et al. 2014). Fig.11 It can be seen that production well 6X403 and injection well 7N3 have the highest similarity. The proportions of the same OTUs in production wells 6X403, 6-406, 8-0 and injection well 7N3 account for 35.3%, 31.1%, and 28.7% of the total OTU number of 7N3, respectively.

[0053] according to Fig. 9 It can be seen that production well 5-4 has the highest similarity with injection well 8-3. The proportion of the same OTUs in production wells 8-3, 8-0, 6N7 and injection well 5-4 accounts for 36.2%, 27.3%, and 11.8% of the total OTU number in 5-4, respectively.

[0054] according to Fig.10 It can be seen that production well 5N5 has the highest similarity with injection well 8P406. The proportion of the same OTUs in production wells 8P406, 6-0, 8-0 and injection well 5N5 to the total OTU number of 5N5 is 36.2%, 31.9%, and 31.7%, respectively, which verifies the above view, indicating that there is a certain correlation between the composition of the microbial community and the connectivity between wells. Therefore, the difference in microbial communities can be used as an indicator to judge the connectivity of injection and production wells in oil fields.

[0055] Figure 7 This is the principal coordinate analysis diagram of the tracer strain. The blue area is the water injection well. The oil wells are divided into five categories according to the degree of connectivity with the water injection wells. Among them, the oil wells in the pink area are more connected with the water injection wells.

[0056] Figure 8 ~ Fig.10It is a Venn diagram. The numbers in the overlapping area of ​​the two sample ellipses represent the number of OTUs shared by the two samples, the non-overlapping area represents the number of unique OTUs in the sample, and the coordinate axis below represents the total number of OTUs contained in each sample.

[0057] Figure 8 A Venn diagram was made using the OTUs of the injection well 7N3 and its strongly connected 6X403, and less strongly connected 6-406 and 8-0 samples. The proportions of the same OTUs in production wells 6X403, 6-406, 8-0 and injection well 7N3 to the total number of OTUs in 7N3 were 35.3%, 31.1%, and 28.7%, respectively.

[0058] Fig. 9 The Venn diagram of injection well 5-4 and its strongly connected production well 8-3, and less strongly connected production wells 8-0 and 6N7 shows that the proportion of the same OTU as injection well 5-4 in the total number of OTUs is 36.2%, 27.3%, and 11.8%, respectively.

[0059] Fig.10 The Venn diagram of the injection well 5N5 and its relatively strong connected production well 8P406, and the less strongly connected production wells 8-0 and 6-0 shows that the proportion of the same OTU as the injection well 5N5 in the total number of OTUs is 36.2%, 31.9%, and 31.7%, respectively.

[0060] The specific result verification process is as follows: The distance between each production well and the nearest injection well is Fig.11 As shown. Fig.11 As shown, the production wells closest to the injection wells are 7C7, 8-3, 8P406, 6-0, 6-8, 8-0, 6XN3, 7N7, 6-406, 8-6, 8-01, 6-6, etc. By comparing the principal coordinate analysis results of the microbial DNA of the injection and production wells with the distance between the injection and production wells, the relationship between the microbial community composition and the distance between the wells can be explored, and the accuracy of the method can be verified.

[0061] The injection-production connectivity results obtained based on microbial DNA sequencing show that the injection-production connectivity of production wells 8-3, 8P406, and 6-0, which are closer to the injection wells, is stronger. Fig.11 As shown, the permeability of these three wells is also high, and the oil wells are well affected.

[0062] It should be noted that this relationship does not hold true in reverse. For example, if the distance between the injection and production wells is close, the microbial DNA of the production fluid of the injection and production wells may not be similar, and the injection-production connectivity relationship may not be strong. For example, the three production wells 7C7, 6-8, and 6-405 are very close to the injection wells, but the composition of the tracer bacteria in their production fluids is quite different from that of the injection wells, and the permeability of these three wells is low. However, the tracer microbial communities of production wells far from the injection wells, such as 6X403, 6N7, and 6XN3, also show extremely high similarity, and the permeability of these three wells is also high, and the effect is high.

[0063] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing, characterized in that: The following steps are involved: Collecting production fluid samples from the injection wells from the wellhead valves at set time intervals, and performing DNA extraction and DNA sequencing on the collected production fluid samples from the injection wells after pretreatment; Calculate the circulation time of the fluid from the injection well to the production well underground when the injection well is sampled based on the sampling time of the production fluid sample and the waterline advancement speed; After the flow time of the injection well production fluid sample sampling interval, the production well production fluid samples are collected at the wellheads of all production wells, and DNA extraction and DNA sequencing are performed on the collected production well production fluid samples after pretreatment; Based on the sequencing results of the injection well fluid samples and the production well fluid samples, the differences between the two were compared and analyzed on a time scale to determine the injection-production connectivity; in: The composition information of the microbial community was obtained by analyzing the sequencing results of the production samples of injection wells at different time intervals; The UPGMA cluster tree was used to analyze the differences in microbial community composition between injection well fluid samples and production well fluid samples on a time scale and to screen tracer strains. The principal coordinate analysis of the tracer bacteria in the fluid samples of the injection wells and all the production wells after the interval circulation time was performed to determine the connectivity between the wells.

2. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 1, characterized in that: The 16s rRNA gene sequence sequencing method was used for DNA sequencing.

3. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 2, characterized in that: After DNA sequencing is completed, the raw sequencing data is processed: Splice the DNA sequence to obtain tags of the hypervariable region; The spliced ​​tags were clustered as OUT, and clustered under the set similarity to obtain the OTU representative sequence, and the chimeras generated by PCR amplification were removed from the OTU representative sequence; The OTU representative sequences were compared with the database for species annotation, and the confidence threshold was set.

4. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 2, characterized in that: Setting a time scale, wherein the interval of the time scale is no greater than the minimum time for a microbial community to change; The dynamic changes of microbial communities in the production fluid samples of injection wells at different time points are analyzed on the said time scale.

5. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 4, characterized in that: The production samples of injection wells were collected at N different time points, and the composition information of microbial communities was obtained by 16s rRNA gene sequencing.

6. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 4, characterized in that: The microbial community composition information of the injection well fluid samples and production well fluid samples at N different time points was analyzed by UPGMA cluster tree analysis, and the microbial flora shared by the injection wells and production wells were annotated; Under the water injection development mode of "circulating reinjection and multiple wells with the same source", tracer strains are selected according to the principles of selecting tracer strains.

7. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 6, characterized in that: Principles for screening tracer strains: The strains are stably present in both injection wells and production wells; The abundance of bacterial species in injection wells was higher than that in production wells.

8. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 4, characterized in that: When the principal coordinate analysis is performed on the tracer bacteria of the injection well fluid samples and all production well fluid samples after the interval circulation time, the specific analysis is as follows: The principal coordinate analysis was performed on the tracer bacterial species of the production fluid samples of the injection wells and all the production wells after the interval circulation time. The bacterial species of the production fluid samples were clustered to analyze the composition and abundance differences of the microbial communities in different production fluid samples. According to the abundance of each microbial community, the similarity metric is calculated using the Euclidean distance to perform principal coordinate analysis on the similarity matrix, and the coordinates of different production samples on the principal coordinate axis are obtained. The principal coordinate analysis will reduce the high-dimensional similarity matrix to a low-dimensional space, and the similarity of bacterial species of different production samples can be judged by observing the distribution of production samples in the low-dimensional space to determine the injection-production connectivity. Among them, the similarity of bacterial species in different production samples was positively correlated with the injection-production connectivity.

9. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 8, characterized in that: The Euclidean distance is calculated as: ； ； In the formula, represents the abundance of the i-th bacterial species in different wells, and n identifies the species number of the bacterial species; represents the distance between different wells and determines the connectivity between wells; X represents a vector containing the abundance information of different bacterial species in the well, and |X| represents the modulus of this vector to measure the overall abundance and diversity of the microbial composition in the sample; Indicates the abundance of a specific bacterial species.

10. The method for determining long-term well-to-well injection-production connectivity based on microbial DNA sequencing according to claim 8, characterized in that: The injection-production connectivity is positively verified by the permeability of the formation between the injection well and the production well.

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