A method for calculating inter-well connectivity based on time-step response relationship

Abstract

The present invention relates to the technical field of oil development, and discloses a method for calculating inter-well connectivity based on the time-step response relationship, which includes collecting and sequencing the bacterial genomic DNA of the injection well and the production well of the injection-production well pair to determine the corresponding bacterial species and bacterial abundance; constructing a pulse response model, in which the measured values of the bacterial species and bacterial abundance of the injection well and the production well are used as input signals and output signals respectively in the pulse response model, introducing the Kalman filtering method to construct the response relationship based on time steps between the injection well and the production well, and using the Laplace transform to solve the response relationship to obtain the strength of the response relationship between the injection well and the production well; and using the strength of the response relationship to characterize the size of the connectivity between the injection well and the production well, and determining the connectivity between the injection well and the production well; the present invention combines the microbial DNA sequencing results and the Kalman filtering calculation method to reflect the dynamic change law of the inter-well connectivity between the injection and production wells in real time and efficiently.

Description

Technical Field

[0001] The present invention relates to the technical field of oil extraction, and specifically relates to a method for calculating inter-well connectivity based on time-step response relationships. Background Art

[0002] At present, there are various methods for describing inter-well connectivity and judging fracture height in China, which are mainly divided into two categories: static connectivity and dynamic connectivity. Static connectivity uses techniques such as small-layer comparison and seismic identification to describe connectivity. The determination methods of dynamic connectivity mainly include tracer monitoring, interference well testing, and production dynamic analysis. Traditional tracer techniques have high usage requirements, large dosages, high costs, are easily degradable, and are prone to polluting reservoirs.

[0003] In the process of oil and gas field development, judging the connectivity between injection and production wells is a key link in optimizing the water injection development plan and improving the recovery rate. Traditional methods (such as tracer testing, pressure interference analysis, geophysical logging, etc.) have problems such as high costs, long cycles, or insufficient resolution. In recent years, the technology for analyzing inter-well connectivity based on microbial DNA sequencing has gradually emerged. Its principle is to infer the migration path of underground fluids by comparing the compositional similarity of microbial communities in injection wells and production wells. Since the distribution and migration of microorganisms in the reservoir environment are highly correlated with fluid movement, and DNA sequencing technology has high sensitivity and high-throughput characteristics, this method is considered to have great potential.

[0004] However, the existing methods for using microbial DNA sequencing technology to judge inter-well connectivity still have the following significant defects. Although high-depth metagenomic sequencing can provide more comprehensive microbial information, it is costly and has a long data analysis cycle, making it difficult to meet the needs of rapid decision-making in the oilfield field. Existing simplified schemes such as 16S rRNA gene sequencing may lose key information at the functional gene level. At the same time, during the injection-production process, there is a complex non-linear coupling relationship between dynamic parameters such as reservoir pressure fluctuations, fluid phase changes, and chemical agent injection (such as oil displacement agents) and microbial community succession.

[0005] Therefore, existing models mostly assume a simple linear correlation between microbial distribution and connectivity, ignoring the influence of the synergistic effect of multiple physical fields on DNA signals, resulting in prediction deviations of dynamic laws. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for calculating inter-well connectivity based on time-step response relationships to solve the technical problem in the prior art that existing models mostly assume a simple linear correlation between microbial distribution and connectivity, ignoring the influence of the synergistic effect of multiple physical fields on DNA signals, resulting in prediction deviations of dynamic laws.

[0007] To solve the above technical problems, the present invention specifically provides the following technical solutions:

[0008] A method for calculating inter-well connectivity based on a time-step response relationship includes the following steps:

[0009] Step 100: Collect and sequence bacterial genomic DNA from the water injection wells and oil production wells, and determine the bacterial species and bacterial abundance corresponding to the water injection wells and oil production wells based on the sequencing results;

[0010] Step 200: Construct an impulse response model. In the impulse response model, the measured values of the bacterial species and bacterial abundance of the water injection well are used as input signals, and the measured values of the bacterial species and bacterial abundance of the oil production well are used as output signals. The Kalman filter method is introduced to construct a time-step-based response relationship between the water injection well and the oil production well. The specific response relationship is:

[0011] ;

[0012] in, represents a water injection well, express Output signal of the oil well at the moment; express Input signal of water injection well at all times; express Output signal of oil well at all times; represents the time step; 、 Indicates the state quantity that changes over time;

[0013] The response relationship is solved using Laplace transform to obtain the strength of the response relationship between the water injection well and the oil production well;

[0014] Obtain the strength of the response relationship between water injection wells and oil production wells;

[0015] Step 300: Use the strength of the response relationship between the water injection well and the oil production well to characterize the connectivity between the water injection well and the oil production well, and determine the connectivity between the water injection well and the oil production well. , connectivity The expression is:

[0016] .

[0017] As a preferred embodiment of the present invention, in step 100, the weighted values of the bacterial species and bacterial abundance of the water injection well and the oil production well are determined by adopting the entropy weight method, and the weighted sum of the bacterial species and bacterial abundance is used as the measurement value of the water injection well and the oil production well. The calculation formula of the measurement value is:

[0018] ;

[0019] in, Represents the measured value, , ,…, Represents The abundances of the common bacterial species, , ,…, Represents the weights of each bacterial species.

[0020] As a preferred embodiment of the present invention, a specific method for obtaining the strength of the response relationship between the injection well and the production well by performing a Taylor expansion on the autoregressive model between the injection well and the production well in the real number domain includes:

[0021] Transform the response relationship based on time steps into a complex frequency domain transfer function through mathematical transformation; perform corresponding conversions on the various parameters of the input signal and the output signal; finally, invert the solution result of the complex frequency domain transfer function to the corresponding result in the real number domain of the response relationship based on time steps;

[0022] The complex frequency domain transfer function is specifically:

[0023] ;

[0024] The specific process of inverting the solution result of the complex frequency domain transfer function is:

[0025] ; [[ID=3⑥]]Invert to The state vector at the moment;

[0026] Invert to ; Invert to ; Invert to The output signal at the moment; Invert to The input signal at the moment.

[0027] As a preferred embodiment of the present invention, the method for collecting and sequencing the bacterial genomic DNA of the injection well and the production well of the injection-production well specifically includes:

[0028] Step 101: Collect and extract the bacterial genomic DNA of the bacteria in the fluid samples at the wellheads of the injection well and the production well and the bacteria in the cuttings at different downhole depths;

[0029] Step 102: Using the bacterial genomic DNA in the fluid sample as a template, amplify respectively with primers targeting the 16srRNA gene to obtain the bacterial amplification products of the cuttings and the fluid samples, and sequence the bacterial amplification products of the cuttings and the fluid samples respectively;

[0030] Step 103: Determine the bacterial species and bacterial abundance of the injection well and the production well based on the sequencing results of the bacterial amplification products of the cuttings and fluid samples.

[0031] As a preferred embodiment of the present invention, high-throughput sequencing is used to sequence the bacterial genomic DNA of the injection well and the production well of the injection-production well collected. The short fragment sequences generated by high-throughput sequencing are spliced into longer continuous sequences through the overlapping regions between the short fragment sequences, and the longer continuous sequences are clustered into bacteria at a given similarity. The bacteria are annotated by comparing with the existing bacterial database; the bacterial species complexity and the inter-group species differences are analyzed based on the bacteria and the species annotation results.

[0032] As a preferred embodiment of the present invention, the specific method for determining the weight values of the bacterial species and bacterial abundance of the injection well and the production well by using the entropy weight method includes:

[0033] The bacterial species and bacterial abundance after sequencing analysis are standardized through a standard formula, and then the standardization formula is redefined to calculate the index information entropy value, the information utility value and the corresponding weights:

[0034] Among them, the redefined standardization formula is:

[0035] ;

[0036] represents the dimensionless processed th unit of the th index value; represents the total amount of processed units;

[0037] The specific formula for calculating the index information entropy value is:

[0038] ;

[0039] The specific formula for calculating the information utility value is:

[0040] ;

[0041] The specific formula for calculating the weight is:

[0042] ;

[0043] Among them, represents the information entropy value of the th index, which is used to measure the uncertainty of the th index; represents the probability that the th possible result occurs under the th index;

[0044] Indicates the information utility value of the th index, reflecting the contribution degree of the index to the decision-making;

[0045] Indicates the weight of the th index, ranging from 0 to 1; Indicates the total number of processes.

[0046] As a preferred embodiment of the present invention, in the process of introducing the Kalman filtering method to construct the time-step based response relationship between the water injection well and the oil production well, the optimal estimation of the state vector at the time is used to predict the predicted value of the state vector at the time and the measured value of the input signal state vector corresponding to the time, and based on the predicted value and the measured value of the state vector of the input signal at the time, the

[0047] optimal estimation of the state vector at the time is calculated. The specific calculation formula is:

[0048] where, indicates the optimal estimation of the state vector at the time, indicates the predicted value of the state vector of the input signal at the time, indicates the Kalman gain coefficient.

[0049] As a preferred embodiment of the present invention, in the process of calculating the optimal estimation of the state vector at the time based on the predicted value and the measured value of the state vector of the input signal at the time, the

[0050] optimal estimation of the state vector at the time is controlled by the optimal estimation process covariance. The specific formula is:

[0051] where, represents the matrix of 1; K represents the Kalman gain; represents the observation matrix; indicates the optimal estimation process covariance at the time; indicates the

[0052] The present invention has the following beneficial effects compared with the prior art:

[0053] The present invention samples and performs DNA sequencing on the bacterial microorganisms in injection wells and production wells, determines the species and bacterial abundance of the corresponding strains in the injection wells and production wells according to the sequencing results, constructs a pulse response model, and constructs the response relationship of the intermediate states of the injection wells and production wells by introducing the Kalman filtering method, dynamically characterizing the strength of the response relationship between the injection wells and production wells, thereby judging the effectiveness of characterizing the connectivity between the injection wells and production wells by the strength of the response relationship between the injection wells and production wells, and expressing the connectivity between the injection wells and production wells based on the response relationship of the pulse response model. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained according to the provided drawings.

[0055] Figure 1 It is a schematic flow chart of calculating the dynamic inter-well connectivity by Kalman filtering calculation according to an embodiment of the present invention;

[0056] Figure 2 It is a schematic diagram of the dynamic inter-well connectivity calculated by Kalman filtering calculation according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0058] As Figure 1 and Figure 2 shown, the present invention provides a method for calculating inter-well connectivity based on the time-step response relationship, including the following steps:

[0059] Step 100: Collect and sequence the bacterial genomic DNA of the injection well and the production well, and determine the species and bacterial abundance of the corresponding strains in the injection well and the production well according to the sequencing results;

[0060] Step 200: Construct a pulse response model. In the pulse response model, use the measured values of the strain species and bacterial abundance in the water injection well as input signals, and use the measured values of the strain species and bacterial abundance in the production well as output signals. Introduce the Kalman filtering method to construct the response relationship based on time steps between the water injection well and the production well. The response relationship is specifically as follows:

[0061] ;

[0062] Among them, represents a certain water injection well, represents the output signal of the production well at time represents the input signal of the water injection well at time represents the output signal of the production well at time represents the time step; , represent the state variables that change with time;

[0063] Use the Laplace transform to solve the response relationship and obtain the strength of the response relationship between the water injection well and the production well;

[0064] Step 300: Characterize the connectivity between the water injection well and the production well using the strength of the response relationship between them, and determine the connectivity between the water injection well and the production well , the connectivity has the following expression:

[0065] .

[0066] In step 100, the weight values of the strain species and bacterial abundance of the water injection well and the production well are determined by using the entropy weight method, and the weighted sum of the weight values of the strain species and bacterial abundance is used as the measured value of the water injection well and the production well. The calculation formula of the measured value is:

[0067] ;

[0068] Among them, represents the measured value, , , …, represents the abundance of , , …, species of common strains,

[0069]

[0069] And perform Taylor expansion on the autoregressive model between the water injection well and the oil production well within the real number domain. The specific methods for obtaining the strength of the response relationship between the water injection well and the oil production well include:

[0070] Through mathematical transformation, convert the response relationship based on time steps into a complex frequency domain transfer function; perform corresponding conversions on the various parameters of the input signal and the output signal; finally, invert the solution result of the complex frequency domain transfer function to the corresponding result of the response relationship based on time steps in the real number domain;

[0071] The complex frequency domain transfer function is specifically:

[0072] ;

[0073] The specific inversion of the solution result of the complex frequency domain transfer function is:

[0074] ; Invert to The state vector at the moment;

[0075] Invert to ; Invert to ; Invert to The output signal at the moment; Invert to The input signal at the moment.

[0076] The specific methods for collecting and sequencing the bacterial genomic DNA of the water injection well and the oil production well of the injection-production wells include:

[0077] Step 101: Collect and extract the bacterial genomic DNA of the bacteria in the fluid samples at the wellheads of the water injection well and the oil production well and the bacteria in the cuttings at different downhole depths;

[0078] Step 102: Using the bacterial genomic DNA in the fluid sample as a template, amplify respectively with primers targeting the 16srRNA gene to obtain the bacterial amplification products of the cuttings and the fluid samples, and sequence the bacterial amplification products of the cuttings and the fluid samples respectively;

[0079] Step 103: Determine the bacterial species and bacterial abundance of the water injection well and the oil production well based on the sequencing results of the bacterial amplification products of the cuttings and the fluid samples.

[0080] The bacterial genomic DNA of the injection wells and production wells of the injection-production wells collected is sequenced using high-throughput sequencing. The short fragment sequences generated by high-throughput sequencing are spliced into longer continuous sequences through the overlapping regions between the short fragment sequences, and the longer continuous sequences are clustered into bacteria at a given similarity. The bacteria are annotated for species through comparison with the existing bacterial database. Analyses of the bacterial species complexity and the inter-group species differences are carried out based on the bacteria and the species annotation results.

[0081] The specific method for determining the weight values of the bacterial species and bacterial abundance of the injection wells and production wells by adopting the entropy weight method includes:

[0082] The bacterial species and bacterial abundance after sequencing analysis are standardized through a standard formula, and then the standardization formula is redefined to calculate the index information entropy value, the information utility value, and the corresponding weights:

[0083] Among them, the redefined standardization formula is:

[0084] ;

[0085] represents the dimensionless processed value of the th unit of the th index value; represents the total amount of processed units;

[0086] The specific formula for calculating the index information entropy value is:

[0087] ;

[0088] The specific formula for calculating the information utility value is:

[0089] ;

[0090] The specific formula for calculating the weight is:

[0091] ;

[0092] Among them, represents the information entropy value of the th index, which is used to measure the uncertainty of the th index; represents the probability that the th possible result occurs under the th index;

[0093] represents the information utility value of the th index, which reflects the contribution degree of the index to the decision-making;

[0094] represents the weight of the th index, which is between 0 and 1; represents the total number of processes.

[0095] In the process of introducing the Kalman filtering method to construct the time-step-based response relationship between injection wells and production wells, the optimal estimation prediction of the state vector at the time, the predicted value of the state vector at the time, and the measured value of the state vector of the input signal corresponding to the time are used. Based on the predicted value and the measured value of the state vector of the input signal at the time, the optimal estimation of the state vector at the

[0096] time is calculated. The specific calculation formula is:

[0097] Among them, represents the optimal estimation of the state vector at the time, represents the predicted value of the state vector of the input signal at the time, represents the Kalman gain coefficient.

[0098] In the calculation of the optimal estimation of the state vector at the time based on the predicted value and the measured value of the state vector of the input signal at the time, the optimal estimation of the state vector at the time is controlled by the covariance of the optimal estimation process. The specific formula is:

[0099] ;

[0100] Among them, represents the matrix of 1 incorporated with the Kalman filtering algorithm into the above response relationship; K represents the Kalman gain; represents the observation matrix; represents the covariance of the optimal estimation process at the time; represents

[0101] Based on the response relationship, the optimal estimation of the state vector at the time is used to predict the state vector at the

[0102] time. Specifically:

[0103] Among them, represents the process noise.

[0104] The covariance of the prediction process is:

[0105] ;

[0106] represents the covariance of the optimal estimation result of the output signal at time

[0107] represents the covariance of the system process;

[0108] ; the measured value of the state vector of the input signal at time is , then:

[0109] ;

[0110] In this embodiment, to more clearly illustrate the DNA sequencing process of injection-production wells, taking the 9th sand group of the second member of the Shahejie Formation in the second district as an example, it is located in the southwestern wing of the Shengli Village dome anticline structure, and its north and east are adjacent to the Tuo 21 and Tuo 11 fault blocks by the No. 7 and No. 9 faults respectively. In 1986, the reported oil-bearing area was recalculated to be 2.4 km 2 , the effective thickness is 4.5 m, and the proven reserves are 1.38 million tons. The structure of the 9th sand group of the second member of the Shahejie Formation in the second district is relatively simple, which is a fan-shaped fault block gradually dipping from northeast to southwest clamped between two faults, and the top surface burial depth is 2030 - 2350 m. Vertically, it includes two small layers, 91 and 92, with a wide distribution area, and the single-layer sand thickness is about 2 m. The average porosity of the core in the main area is 29.4%, and the average permeability is 74.5×10-3 μm 2 , belonging to high-porosity and medium-low-permeability reservoirs.

[0111] The sampled wells are water well ST2-1-220 (E1), oil wells ST2-OXN-224 (E2), 21XN-207 (E3), STS2N-2 (E4), ST2-1-243 (E5), ST2-1XN-22 (E6), STS-2X-102 (E7), ST2-1XN-190 (E8) respectively.

[0112] Specifically, the method for collecting and sequencing the bacterial genomic DNA of injection wells and production wells in injection-production wells specifically includes:

[0113] Step 101: Collect and extract the bacterial genomic DNA of bacteria in the fluid samples at the wellheads of injection wells and production wells, as well as the cuttings bacteria at different downhole depths. In this embodiment, the number of bacterial genomic DNA extracted from the fluid samples is ≥ 10,000, preferably 10,000 - 20,000. When the number of bacterial genomic DNA extracted from the fluid samples < 10,000, the sequencing data of this group cannot be used for downstream analysis, and sampling can be carried out again and extraction can be performed.

[0114] Conduct specific sampling work. The sampling work includes:

[0115] Collect the produced fluid samples taken from different wellheads into sterile conical containers, and collect 23 - 45 ml of produced fluid in each conical container on average.

[0116] Immediately place the sterile conical containers into a cooler and transport them to the laboratory.

[0117] Add 100 μL of washing buffer into the conical container, and after mixing, obtain a uniform mixture. Concentrate the bacterial liquid by filtering with a filter membrane. Centrifuge the mixture using a centrifuge, set the rotation speed to 12,000 rpm, and the centrifugation duration to 15 min. The centrifuged bacterial precipitate is stored refrigerated until extraction.

[0118] Washing buffer: Dissolve 10 g of Na2HPO4, 1 g of KH2HPO4, 50 g of NaCl, and 1.5 g of KCl. Add KH2HPO4, NaCl, and KCl into Na2HPO4 to form a solution. Add 1% of the solution volume of sodium dodecyl sulfate into the solution and mix evenly to obtain the buffer. Adjust the pH of the buffer to 7.5, and then add 10 times the volume of distilled water of the buffer and mix evenly.

[0119] Extract the bacterial genomic DNA of the fluid samples respectively. The extraction steps are as follows:

[0120] Add 10 - 50 μL of the sample into a centrifuge tube, and then add 100 μL of washing buffer into the centrifuge tube. After mixing, obtain a uniform mixture;

[0121] Use a cell disruptor to disrupt the mixture for 40 - 60 s;

[0122] Centrifuge the mixture using a centrifuge, set the rotation speed to 12,000 - 16,000 rpm, and the centrifugation duration to 10 - 15 min;

[0123] Remove the supernatant from the centrifuged mixture to obtain the solid phase;

[0124] Add 200 - 300 μL of extraction washing solution into the solid phase, then add 30 - 50 μL of lysozyme and mix evenly, and water-bath at 37 °C for 5 - 10 h;

[0125] Extraction cleaning solution: Mix chloroform and isoamyl alcohol together at a volume ratio of 24:1;

[0126] Add 2MOL / L sodium chloride solution to maximize the solubility of DNA. At this time, the impurities have been filtered out by filtration. Adjust the solution to 0.14MOL / L to precipitate DNA, and then filter to filter out DNA.

[0127] Step 102: Using the bacterial genomic DNA in the fluid sample as a template, primers targeting the 16srRNA gene are used for amplification respectively to obtain bacterial amplification products of the cuttings and the fluid sample, and the bacterial amplification products of the cuttings and the fluid sample are sequenced respectively;

[0128] Step 103: Determine the bacterial species and bacterial abundance of the injection well and the production well based on the sequencing results of the bacterial amplification products of the cuttings and the fluid sample.

[0129] After extracting the bacterial genomic DNA of the fluid sample and the cuttings at different underground depths, the present invention uses the bacterial genomic DNA of the fluid sample as a template, and primers targeting the 16srRNA gene are used for amplification respectively to obtain amplification products of the cuttings and the fluid sample, and the amplification products of the cuttings and the fluid sample are sequenced respectively.

[0130] The system of the PCR amplification includes: bacterial genomic DNA, 5PRIME MasterMix, and the "universal" 16srRNA targeting primers 515F-Y and 926R. The degree of the PCR amplification is preferably: pre-denaturation at 93°C for 5 min; denaturation for 30 s, annealing at 55°C for 40 s, extension at 72°C for 1 min, for 25 cycles. In the second reaction of the 8th cycle, unique forward and reverse indices are added to the amplicon of each sample by annealing with the adapter sequence of the amplicon to add sequencing barcodes for sample differentiation. This process is carried out under strict QA and QC procedures.

[0131] In this embodiment, the primers are preferably primers targeting the V4 and V5 hypervariable regions of the 16srRNA gene. The present invention has no special limitation on the amplification method, and the conventional amplification methods in the art can be used.

[0132] In this embodiment, after obtaining the amplification product of the fluid sample, it is preferably further included to purify the amplification product of the fluid sample, and the purification method is preferably to purify with agarose gel electrophoresis purification beads.

[0133] DNA extraction and purification were carried out, specifically including: loading samples into a 96-well test tube box, so that multiple samples can be tested by high-throughput at one time. Duplicate a single sample, break the cell wall by a combination of physical and chemical methods, and convert the DNA inside the microorganism into a neutral solution. After cell lysis, purification is carried out.

[0134] During the high-throughput test, high-throughput sequencing was carried out using an Illumina sequencer. Due to the previous purification, there is a special DNA sequence at the end of each DNA fragment of each sample, so that all samples are connected together and loaded into the machine.

[0135] During sequencing, the DNA strand binds to the surface of the closed cell and replicates the scaffold like PCR. However, in the DNA strand, each DNA sequence is fluorescently labeled, and the newly recognized nucleotides by the sequencer are added to the end of each of the 25 million DNA sequences suitable for flow.

[0136] After sequencing, the DNA sequence data can be converted into readable text, and the original sequence data is stored in the database of the biota and associated with the metadata of the original sample, such as well names and data.

[0137] In this embodiment, the fluid sample is preferably amplified and sequenced in duplicate.

[0138] Using high-throughput sequencing to sequence the bacterial genomic DNA of the injection wells and production wells of the injection-production wells collected, splicing the short fragment sequences into longer continuous sequences through the overlapping regions between the short fragment sequences generated by high-throughput sequencing, and clustering the longer continuous sequences into bacteria at a given similarity, and annotating the species of bacteria by comparing the bacteria with the existing bacterial database; analyzing the bacterial species complexity and the inter-group species differences based on the bacteria and the species annotation results.

[0139] Specifically, the mothur software (traceable software) was used for the sequencing results to determine the species types and species abundances of bacteria contained in different fluid samples; the reads were spliced into Tags through the Overlap relationship between the reads; the Tags were clustered into bacteria at a given similarity, and then the bacteria were annotated by comparing with the database; based on the bacteria and the species annotation results, the sample species complexity analysis and the inter-group species difference analysis were carried out to determine the bacterial types contained in the fluid sample and their abundances.

[0140] Furthermore, the produced fluid was collected at the wellhead, and the volume of the fluid sample collected in each sterile container was preferably 23-45 mL; the fluid was collected as close to the wellhead as possible within the safety requirements, so as to maximize the representation of the reservoir by the fluid.

[0141] In this embodiment, the preservation and transportation temperature of the collected cuttings and fluid samples is preferably -10~0°C. This temperature range can inhibit the growth of microorganisms and avoid the influence of impurity microorganisms on the results.

[0142] In the specific implementation process of this embodiment, the sterile container is placed in a cooler ice bag to maintain its biological stability, and the collected fluid sample is transported to the laboratory.

[0143] As Figure 2 shown in the on-site tracer data comparison chart, the on-site tracer results show that in the ST2-1-220 well group, there is a connection relationship between the water well and the 21XN207(E3) oil well, which is consistent with the test results, proving the feasibility of this method.

[0144] The calculation process of the Kalman filter is edited through MATLAB software, and the measured data of the injection well and the production well are respectively input, that is, the measured values after processing the two factors of the strain type and the bacterial abundance.

[0145] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements within the essence and protection scope of the present application, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present application.

Claims

1. A method for calculating well connectivity based on time-step response relationship, characterized in that: The steps include: Step 100: Collect and sequence bacterial genomic DNA from the water injection wells and oil production wells, and determine the bacterial species and bacterial abundance corresponding to the water injection wells and oil production wells based on the sequencing results; Step 200: Construct an impulse response model. In the impulse response model, the measured values of the bacterial species and bacterial abundance of the water injection well are used as input signals, and the measured values of the bacterial species and bacterial abundance of the oil production well are used as output signals. The Kalman filter method is introduced to construct a time-step-based response relationship between the water injection well and the oil production well. The specific response relationship is: ； in, represents a water injection well, express Output signal of the oil well at the moment; express Input signal of water injection well at all times; express Output signal of oil well at all times; represents the time step; 、 Indicates the state quantity that changes over time; The response relationship is solved using Laplace transform to obtain the strength of the response relationship between the water injection well and the oil production well; Step 300: Use the strength of the response relationship between the water injection well and the oil production well to characterize the connectivity between the water injection well and the oil production well, and determine the connectivity between the water injection well and the oil production well. , connectivity The expression is: 。 2. The method for calculating inter-well connectivity based on time-step response relationship according to claim 1, characterized in that: In step 100, the entropy weight method is used to determine the weight values of the bacterial species and bacterial abundance of the water injection well and the oil production well, and the weighted sum of the weight values of the bacterial species and bacterial abundance is used as the measurement value of the water injection well and the oil production well. The calculation formula of the measurement value is: ； in, Indicates the measured value, , ,…, express The abundance of common bacterial species, , ,…, Indicates the weight of each bacterial species.

3. The method for calculating inter-well connectivity based on time-step response relationship according to claim 1, characterized in that: Taylor expansion is performed on the autoregressive model between the water injection well and the oil production well in the real number domain. The specific method for obtaining the strength of the response relationship between the water injection well and the oil production well includes: The time-step-based response relationship is converted into a complex frequency domain transfer function through mathematical transformation; the various parameters of the input signal and the output signal are converted accordingly; and finally, the solution of the complex frequency domain transfer function is inverted to the corresponding result of the time-step-based response relationship in the real domain; The complex frequency domain transfer function is specifically: ； The inverse of the solution of the complex frequency domain transfer function is: ; Invert to The state vector at the moment; Invert to ; Invert to ; Invert to Output signal at all times; Invert to Input signal at all times.

4. The method for calculating inter-well connectivity based on time-step response relationship according to claim 1, characterized in that: The method for collecting and sequencing bacterial genomic DNA from water injection wells and oil production wells specifically includes: Step 101: collecting and extracting bacterial genomic DNA from fluid samples at the wellheads of water injection wells and oil production wells, as well as from rock debris bacteria at different downhole depths; Step 102: using the bacterial genomic DNA in the fluid sample as a template, amplifying the bacterial DNA using primers targeting the 16s rRNA gene to obtain bacterial amplification products of the rock cuttings and fluid samples, respectively, and sequencing the bacterial amplification products of the rock cuttings and fluid samples, respectively; Step 103: Determine the bacterial species and bacterial abundances in the water injection wells and oil production wells based on the sequencing results of the bacterial amplification products of the rock cuttings and fluid samples.

5. The method for calculating inter-well connectivity based on time-step response relationship according to claim 4, characterized in that: High-throughput sequencing was used to sequence the bacterial genomic DNA collected from the injection wells and oil production wells. The short fragment sequences generated by high-throughput sequencing were spliced into longer continuous sequences by bridging the overlapping regions. The longer continuous sequences were clustered into bacteria at a given similarity. The bacteria were compared with the existing bacterial database and the species annotation was performed. Based on the bacterial and species annotation results, the bacterial species complexity and species differences between groups were analyzed.

6. The method for calculating inter-well connectivity based on time-step response relationship according to claim 1, characterized in that: The specific method of determining the weight values of bacterial species and bacterial abundance in water injection wells and oil production wells by using the entropy weight method includes: The bacterial species and bacterial abundance after sequencing analysis were standardized using the standard formula. The standardization formula was then redefined to calculate the indicator information entropy value and information utility value as well as the corresponding weights: Among them, the redefined normalization formula is: ； represents the dimensionless The unit's indicator values; Indicates the total amount of units processed; The specific formula for calculating the information entropy value of the indicator is: ； The specific formula for calculating the information utility value is: ； The specific formula for calculating weight is: ； in, Indicates the The information entropy value of the indicator is used to measure the The uncertainty of each indicator; Indicates the The possible outcome is The probability of occurrence under the indicator; Indicates the The information utility value of an indicator reflects the contribution of the indicator to decision-making; Indicates the The weight of each indicator is between 0 and 1; Indicates the total number of processes.

7. The method for calculating inter-well connectivity based on time-step response relationship according to claim 1, characterized in that: In the process of introducing the Kalman filter method to construct the time-step response relationship between water injection wells and oil production wells, the The optimal estimate of the state vector at time The state vector prediction value at time The measured value of the input signal state vector at time , and based on The state vector prediction value and measured value of the input signal at time The optimal estimate of the state vector at time t is calculated as follows: ； in, express The optimal estimate of the state vector at time , express The state vector prediction value of the input signal at time t, express The measured value of the state vector of the input signal at time t, represents the Kalman gain coefficient.

8. The method for calculating inter-well connectivity based on time-step response relationship according to claim 7, characterized in that: Based on The state vector prediction value and measured value of the input signal at time In the optimal estimation of the state vector at time t, the covariance control of the optimal valuation process is achieved The optimal estimate of the state vector at time t is: ； in, It is represented by a matrix of 1; K represents the Kalman gain; represents the observation matrix; express The optimal estimation process covariance at time t; express The forecast process covariance at time t.

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