CN114066666A - Method for analyzing connectivity among wells through injection-production profile monitoring data - Google Patents
Method for analyzing connectivity among wells through injection-production profile monitoring data Download PDFInfo
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Abstract
The invention discloses a method for analyzing connectivity among wells through injection-production profile monitoring data, which comprises the following steps of: s1: collecting basic data of the injection and production well group of the target block, judging the communication condition of each small layer of the injection and production well, and removing the non-communication layer; s2: establishing a longitudinal splitting coefficient of the injection and production well, and splitting the injection quantity of the water injection well and the output of the production well to each communicated small layer; s3: respectively substituting the split liquid amount of each communicated small layer of the injection and production well into a gray correlation model, a linear regression model and a resistance-capacitance model to obtain the correlation coefficient of the injection and production amount of each communicated small layer under each model; s4: determining the weight coefficient of the correlation coefficient under different mathematical models; s5: calculating a comprehensive correlation coefficient; s6: and judging the inter-well connectivity of the injection and production wells according to the magnitude of the comprehensive correlation coefficient. The method can quantitatively describe the inter-well connectivity, accurately classify the reservoir connectivity and provide a theoretical basis for making and adjusting the water drive development scheme of the sandstone reservoir.
Description
Technical Field
The invention relates to the technical field of sandstone oil reservoir water injection development, in particular to a method for analyzing the connectivity among wells through injection-production profile monitoring data.
Background
Sandstone reservoir water injection development is a main technology of oil field development, and has the advantages that while the development effect is obvious, most oil fields enter a high-water-content and ultra-high-water-content stage, a reservoir layer is subjected to long-term scouring development of a large number of dominant seepage channels through pore fluid, so that the oil-water relationship is more complex, the heterogeneity of water injection displacement effect is enhanced, the water injection development effect is seriously influenced, the oil and water control is stabilized, and the utilization efficiency of injected water is improved, so that the problem becomes the focus of the water injection development of the reservoir. The inter-well connectivity is used as an important evaluation index for reservoir description, directly influences the water drive efficiency of an oil reservoir, and is a premise for realizing water control and yield increase.
At present, the common methods for identifying and describing the communication degree between injection and production wells at home and abroad mainly comprise a well logging method, a well testing method and a tracer monitoring method, but all the methods need to carry out special tests, the test workload is large, the construction cost is high, the test period is long, the popularization and application are greatly limited, and how to efficiently and accurately identify and describe the communication degree between the injection and production wells still remains a technical problem in the field of water injection development of oil and gas fields.
Disclosure of Invention
In view of the above, the present invention is directed to a method for analyzing interwell connectivity through injection-production profile monitoring data.
The technical scheme of the invention is as follows:
a method of analyzing interwell connectivity from injection and production profile monitoring data, comprising the steps of:
s1: collecting basic data of the injection and production well group of the target block, judging the communication condition of each small layer of the injection and production well, and removing the non-communication layer;
s2: establishing a longitudinal splitting coefficient of the injection and production well, and splitting the injection quantity of the water injection well and the output of the production well to each communicated small layer;
s3: respectively substituting split liquid quantities of all communicated small layers of the injection and production well into a gray correlation model, a linear regression model and a resistance-capacitance model to obtain correlation coefficients of injection and production quantities of all communicated small layers under different mathematical models;
s4: determining the weight coefficient of the correlation coefficient under each different mathematical model according to the influence factors considered by each different mathematical model;
s5: calculating to obtain a comprehensive correlation coefficient according to the correlation coefficient and the weight corresponding to the correlation coefficient;
s6: and judging the inter-well connectivity of the injection and production wells according to the magnitude of the comprehensive correlation coefficient, wherein the larger the correlation coefficient is, the higher the inter-well connectivity is.
Preferably, in step S2, the amount of cleavage liquid for each small layer is calculated by the following formula:
in the formula: qi/j,kThe injection quantity of the ith injection well in the kth layer or the liquid production quantity of the jth production well in the kth layer is determined; qi/jThe injection quantity of the ith injection well or the liquid production quantity of the jth production well; ri,jThe total seepage resistance coefficient between the ith injection well and the jth production well is shown; ri,j,kThe seepage resistance of the k layer between the ith injection well and the jth production wellA force coefficient; lambda [ alpha ]i,j,kApparent viscosity of a k-th small layer between an i-th injection well and a j-th production well; l isi,j,kAn effective well spacing between the ith injection well and the jth production well in the kth sub-zone; hi,j,kThe average thickness of a k-th small layer between an ith injection well and a jth production well; ki,j,kThe average permeability of a k-th small layer between an i-th injection well and a j-th production well; kro、Krw、μo、μwRelative permeability of oil phase, relative permeability of water phase, viscosity of crude oil and viscosity of water in the kth small layer between the ith injection well and the jth production well.
Preferably, in step S3, the correlation coefficient of the injection yield of each connected stratum under the gray correlation model is calculated by the following formula:
in the formula: m1 is a correlation coefficient under a grey correlation model; n is the closing time; s is the development time of the injection and production wells; gamma rayi,j,kThe weight of the correlation coefficient at different time points; delta (Q)i/j,k,s) The correlation coefficient of the injection quantity and the liquid production quantity of the kth small layer between the injection well i and the production well j at the s moment; delta (min) is the minimum difference of two stages of injection quantities of the injection-production unit; rho is a resolution coefficient, is a front coefficient of a maximum value delta (max), and takes a value between 0 and 1; delta (max) is the two-stage maximum difference of the injection amount of the injection-production unit; and delta i, j, k is the absolute difference value of the injection quantity and the liquid production quantity at the corresponding time point of the kth layer between the injection well i and the production well j.
Preferably, the resolution factor is 0.5.
Preferably, in step S3, the correlation coefficient of each connected-stratum injection yield under the linear regression model is calculated by the following formula:
in the formula: qj,kThe liquid production amount of the kth layer of the jth production well; xiojThe number of water drive reservoir injection and production imbalances is adopted; m2 is a correlation coefficient under linear regression; qi,kThe injection quantity of the k layer of the ith injection well is shown.
Preferably, in step S3, the correlation coefficient of the injection yield of each connected small layer in the resistance-capacitance model is calculated by the following formula:
in the formula: m0Is a weight coefficient formed under the influence of the initial liquid amount; qj,k(s＝0)The initial fluid production for production well j; e is a natural base number; s0Is the initial time; tau is0Is the time constant under the influence of the initial liquid amount; k is the total number of layers of communication between injection wells and production wells; m3 is a correlation coefficient under a resistance-capacitance model; q'i,j,k(s)The injection quantity after convolution for time step s; vjThe degree coefficient of the liquid production quantity influenced by the bottom hole flowing pressure fluctuation; p is a radical ofwfj(s＝0)Initiating a bottom hole flow pressure for the production well; tau isjIs a time constant; p is a radical ofwfj(s)Bottom hole flow pressure at s time for the producing well; p'wfj(s)The bottom hole flow pressure of the production well after time step s convolution is taken.
Preferably, in step S3, when the bottom hole pressure is stable or the influence of bottom hole flow pressure fluctuation is ignored, the correlation coefficient of the injection and production amount of each connected small layer under the resistance-capacitance model is calculated by the following formula:
preferably, in step S4, the weighting factor of the correlation factor under each different mathematical model is determined by the following substeps:
s41: determining influence factors of the well connectivity, and determining influence weights of the influence factors on the well connectivity, wherein the sum of the influence weights is equal to 1;
s42: determining influence factors considered by different mathematical models, and adding influence weights of the influence factors considered by the mathematical models on the basis of one third to obtain influence coefficients of the corresponding mathematical models;
s43: the ratio of the influence coefficient corresponding to each mathematical model to the sum of all the influence coefficients is the weight coefficient of the correlation coefficient corresponding to the model.
Preferably, in step S5, the comprehensive correlation coefficient is calculated by the following formula:
ψ＝α×|M1|+β×|M2|+γ×|M3| (10)
in the formula: psi is the comprehensive correlation coefficient; alpha, beta and gamma are weight coefficients of correlation coefficients under a grey correlation model, a linear regression model and a resistance-capacitance model respectively.
Preferably, in step S6, the inter-well connectivity determination criterion of the injection and production well is determined as follows:
in the formula: a. b is a critical value for judging the communication among wells.
The invention has the beneficial effects that:
the method can solve the problem of efficient identification and description of the inter-well communication condition in the water injection development process of the sandstone reservoir, and by establishing a set of injection-production well-well communication inversion method based on a real-time injection-production profile, and according to the principle of conservation of substances, by utilizing the one-to-one correspondence relationship between the injected water and the liquid production amount of each small layer, the injected water and the liquid production amount can show the corresponding correlation according to the communication degree of each small layer between the injection-production wells, so that the communication degree of the reservoir can be reversely shown according to the production dynamic data of the injection-production wells.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a block diagram of a target block according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples. It should be noted that, in the present application, the embodiments and the technical features of the embodiments may be combined with each other without conflict. It is noted that, unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The use of the terms "comprising" or "including" and the like in the present disclosure is intended to mean that the elements or items listed before the term cover the elements or items listed after the term and their equivalents, but not to exclude other elements or items.
The invention provides a method for analyzing connectivity among wells through injection-production profile monitoring data, which comprises the following steps of:
s1: collecting basic data of the injection and production well group of the target block, judging the communication condition of each small layer of the injection and production well, and removing the non-communication layer.
In a specific embodiment, the basic data comprise geological data, production dynamic data, perforation data and the like, the communication condition of each small layer of the injection and production well can be preliminarily judged through the basic data, the communication value is 1, the non-communication value is 0, and the non-communication layer is removed during inversion; all connected layers are uniformly numbered n (i, j, k) according to injection wells, production wells and layers, and the average thickness H of the reservoir is calculatedi,j,kAverage permeability Ki,j,k。
S2: and establishing a longitudinal splitting coefficient of the injection and production well, and splitting the injection quantity of the water injection well and the yield of the production well to each communicated small layer.
In a specific embodiment, according to the hydropower similarity principle and the parallel circuit shunting theorem, by utilizing physical parameters, fluid parameters and injection-production well distances of all small layers and fully considering two seepage relations of crude oil viscosity, injected water viscosity and oil-water, a small layer seepage resistance coefficient is introduced to evaluate the small layer seepage resistance of all injection-production units, and for the injection-production units, the small layer seepage resistance coefficient among the injection-production wells is as follows:
the apparent viscosity considering the oil-water two-phase flow relationship is:
splitting the water injection amount of an injection well and the liquid production amount of a production well into small layers according to the shunt theorem of the parallel circuit, wherein the total seepage resistance coefficient between the injection wells and the production wells is as follows:
the splitting liquid separating amount of each small layer of the injection and production unit is as follows:
formula (1) to formula (4): qi/j,kThe injection quantity of the ith injection well in the kth layer or the liquid production quantity of the jth production well in the kth layer is determined; qi/jThe injection quantity of the ith injection well or the liquid production quantity of the jth production well; ri,jThe total seepage resistance coefficient between the ith injection well and the jth production well is shown; ri,j,kThe seepage resistance coefficient of a k layer between an ith injection well and a jth production well; lambda [ alpha ]i,j,kIs the ith injection well and theApparent viscosity at kth interval between j production wells; l isi,j,kAn effective well spacing between the ith injection well and the jth production well in the kth sub-zone; hi,j,kThe average thickness of a k-th small layer between an ith injection well and a jth production well; ki,j,kThe average permeability of a k-th small layer between an i-th injection well and a j-th production well; kro、Krw、μo、μwRelative permeability of oil phase, relative permeability of water phase, viscosity of crude oil and viscosity of water in the kth small layer between the ith injection well and the jth production well.
S3: and respectively substituting the split liquid amount of each communicated small layer of the injection and production well into a gray correlation model, a linear regression model and a resistance-capacitance model to obtain the correlation coefficient of the injection and production amount of each communicated small layer under different mathematical models.
In a specific embodiment, the correlation coefficient M1 is first found using a gray correlation method. The degree of correlation is substantially the degree of difference in geometry between curves. Therefore, the difference between the curves can be used as a measure of the correlation degree, and the correlation coefficient of the injection and production of each connected small layer under the gray correlation model is calculated by the following formula:
in the formula: m1 is a correlation coefficient under a grey correlation model; n is the closing time; s is the development time of the injection and production wells; gamma rayi,j,kThe weight of the correlation coefficient at different time points; delta (Q)i/j,k,s) The correlation coefficient of the injection quantity and the liquid production quantity of the kth small layer between the injection well i and the production well j at the s moment; delta (min) is the minimum difference of two stages of injection quantities of the injection-production unit; rho is a resolution coefficient, is a front coefficient of a maximum value delta (max), and takes a value between 0 and 1; delta (max) is the two-stage maximum difference of the injection amount of the injection-production unit; Δ i, j, k is between injection well i and production well jAnd the absolute difference value of the injection quantity and the liquid production quantity of the kth layer corresponding to the time point.
In a specific embodiment, the resolution factor is 0.5.
Then, a correlation coefficient M2 is obtained using a linear regression method. Regarding the production data of the production well as the result of the combined action of the injection data of all the surrounding water injection wells, regarding an injection and production system consisting of the production well and the water injection wells together, based on a multiple linear regression model, according to different injection and production balance conditions, the liquid production amount Q of the kth layer of the ith production wellj,kCan be expressed as:
in the formula: qj,kThe liquid production amount of the kth layer of the jth production well; xiojThe number of water drive reservoir injection and production imbalances is adopted; m2 is a correlation coefficient under linear regression; qi,kThe injection quantity of the k layer of the ith injection well is shown.
And (4) calculating the correlation coefficient of the injection and production of each connected small layer under the linear regression model by the formula (7).
Finally, a correlation coefficient M3 is obtained by using a resistance-capacitance model method. According to the principle of water and electricity similarity, the corresponding process of fluctuation of an injection and production well is similar to the process of capacitance charging, the injected water of an injection well is used as the input parameter of the whole system, the liquid production amount of a production well is used as the output parameter of the whole system, and the resistance-capacitance model of an actual oil reservoir can be obtained by utilizing a material balance equation and a superposition principle based on a signal processing and system analysis method:
in the formula: m0Is a weight coefficient formed under the influence of the initial liquid amount; qj,k(s＝0)The initial fluid production for production well j; e is a natural base number; s0Is the initial time; tau is0Is the time constant under the influence of the initial liquid amount; k is the total number of layers of communication between injection wells and production wells; m3 is resistance-capacitance dieCorrelation coefficient under type; q'i,j,k(s)The injection quantity after convolution for time step s; vjThe degree coefficient of the liquid production quantity influenced by the bottom hole flowing pressure fluctuation; p is a radical ofwfj(s＝0)Initiating a bottom hole flow pressure for the production well; tau isjIs a time constant; p is a radical ofwfj(s)Bottom hole flow pressure at s time for the producing well; p'wfj(s)The bottom hole flow pressure of the production well after time step s convolution is taken.
When the bottom hole pressure is stable or the influence of bottom hole flow pressure fluctuation is neglected, the formula (8) can be simplified as follows:
and calculating the correlation coefficient of the injection and production amount of each connected small layer under the resistance-capacitance model by using the formula (8) or the formula (9).
S4: determining the weight coefficient of the correlation coefficient under each different mathematical model according to the influence factors considered by each different mathematical model; the method specifically comprises the following substeps:
s41: determining influence factors of the well connectivity, and determining influence weights of the influence factors on the well connectivity, wherein the sum of the influence weights is equal to 1;
s42: determining influence factors considered by different mathematical models, and adding influence weights of the influence factors considered by the mathematical models on the basis of one third to obtain influence coefficients of the corresponding mathematical models;
s43: the ratio of the influence coefficient corresponding to each mathematical model to the sum of all the influence coefficients is the weight coefficient of the correlation coefficient corresponding to the model.
In one specific embodiment, the obtaining of the interwell connectivity influencing factors from the mine site and experimental analysis comprises: permeability, oil saturation, oil-water viscosity ratio, seepage speed, water passing times and water storage rate; through designing a six-factor orthogonal experiment, determining the influence weight of each influence factor on the inter-well connectivity, as shown in table 1:
TABLE 1 impact weights of Interwell connectivity impact factors
| Influencing factor | Permeability rate of penetration | Degree of saturation of oil | Oil-water viscosity ratio | Velocity of seepage | Multiple of water passing | Water retention rate |
| Weight of influence | C1 | C2 | C3 | C4 | C5 | C6 |
Each impact weight satisfies the following requirement:
when a certain model considers a corresponding influence factor, adding a corresponding influence weight when a corresponding model configures a weight coefficient, and if the influence of permeability and oil saturation is considered in a gray correlation model, the influence coefficient corresponding to the gray correlation model is:
can be similarly obtained
And
then the calculation of α, β, and γ is:
s5: calculating to obtain a comprehensive correlation coefficient according to the correlation coefficient and the weight corresponding to the correlation coefficient, wherein the comprehensive correlation coefficient is calculated according to the following formula:
ψ＝α×|M1|+β×|M2|+γ×|M3| (10)
in the formula: psi is the comprehensive correlation coefficient; alpha, beta and gamma are weight coefficients of correlation coefficients under a grey correlation model, a linear regression model and a resistance-capacitance model respectively.
S6: and judging the inter-well connectivity of the injection and production wells according to the magnitude of the comprehensive correlation coefficient, wherein the larger the correlation coefficient is, the higher the inter-well connectivity is.
In a specific embodiment, the criterion for determining the connectivity between wells of the injection and production well is:
in the formula: a. and b, determining the critical value of the inter-well connectivity according to the actual geological condition of the target block.
In one embodiment, taking a target block shown in fig. 1 as an example, the present invention is used to determine the inter-well connectivity of the target block. The target block comprises two injection and production units, each injection and production unit comprises an injection well and a production well, each injection and production unit comprises 4 connected small layers, the results of the dynamic production monitoring data part of the two injection and production units of the target block from 8/1/2013 to 9/1/2020 are shown in table 2, and physical parameters of each small layer are shown in table 3.
TABLE 2 dynamic monitoring data for each small layer production of target block
TABLE 3 physical parameters of each small layer of target block injection-production unit
According to the data provided in tables 1 and 2, the injection and production amount at each moment is split into small layers by combining formulas (1) to (4), and the splitting result is shown in table 4:
TABLE 4 vertical stratified split results for pay-per-injection
In table 4, 0 represents that the injection well was not injected or the production well was shut in and not producing during that time period.
According to the injection and production amount of each small layer of the injection and production well at different moments, small layer correlation coefficients corresponding to a gray correlation model, a linear regression model and a resistance-capacitance model are obtained by calculation by combining formulas (5) to (9), and the calculation result is shown in a table 5:
TABLE 5 results of correlation coefficients for each small layer calculated by different methods
| Horizon | Grey correlation model | Linear regression model | Resistance-capacitance model |
| 1-1 | 0.68 | 0.74 | 0.56 |
| 1-2 | 0.67 | 0.59 | 0.52 |
| 1-3 | 0.78 | 0.79 | 0.80 |
| 1-4 | 0.67 | 0.54 | 0.73 |
| 2-1 | 0.71 | 0.66 | 0.79 |
| 2-2 | 0.68 | 0.59 | 0.62 |
| 2-3 | 0.78 | 0.73 | 0.70 |
| 2-4 | 0.76 | 0.89 | 0.68 |
In this embodiment, since each model considers the relevant influence factors of other inter-well connectivity, 1/3 are configured for the weight coefficients α, β, and γ of each sub-layer phase coefficient, and the comprehensive correlation coefficient is calculated as shown in table 6:
TABLE 6 comprehensive correlation coefficient calculation results
| Horizon | 1-1 | 1-2 | 1-3 | 1-4 | 2-1 | 2-2 | 2-3 | 2-4 |
| Integrated correlation coefficient | 0.66 | 0.59 | 0.79 | 0.65 | 0.72 | 0.63 | 0.73 | 0.77 |
According to the actual geological condition of the target block, determining that a critical value a of reservoir connectivity is 0.4, and b is 0.6, obtaining an unexveloped low-degree communication layer between injection and production wells of an injection and production unit 1 and an injection and production unit 2 of the target block by combining a formula (11), wherein the low-degree communication layer is medium-high-degree communication, only a second layer of the injection and production unit 1 is medium-degree communication, the rest of the injection and production units are high-degree communication, an advantageous seepage channel is easy to develop, the early-stage water flooding effect is good, and the middle and later stages of development need to pay attention.
The inter-well connectivity of the target block is monitored by adopting a traditional method, the monitoring result of the method is consistent with the monitoring result of the traditional method, and the accuracy of the monitoring result of the method is verified. Compared with the traditional method, the method realizes efficient and convenient inversion of the connectivity of the reservoir stratum by utilizing the existing production dynamic data of the oil field, is more convenient and quicker on the basis of obtaining an accurate result, and avoids the complex construction process of the traditional method; in addition, the invention combines multiple mathematical methods, avoids system errors caused by solving a single mathematical model, considers relevant geological and development factors in the model, configures weight coefficients for different methods, considers models with more influence factors or stronger influence factor sensitivity, and correspondingly configures larger weight, so that the calculation result of the reservoir connectivity inversion model is more accurate. In conclusion, the present invention represents a significant advance over the prior art.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for analyzing the connectivity among wells through injection-production profile monitoring data is characterized by comprising the following steps of:
s1: collecting basic data of the injection and production well group of the target block, judging the communication condition of each small layer of the injection and production well, and removing the non-communication layer;
s2: establishing a longitudinal splitting coefficient of the injection and production well, and splitting the injection quantity of the water injection well and the output of the production well to each communicated small layer;
s3: respectively substituting split liquid quantities of all communicated small layers of the injection and production well into a gray correlation model, a linear regression model and a resistance-capacitance model to obtain correlation coefficients of injection and production quantities of all communicated small layers under different mathematical models;
s4: determining the weight coefficient of the correlation coefficient under each different mathematical model according to the influence factors considered by each different mathematical model;
s5: calculating to obtain a comprehensive correlation coefficient according to the correlation coefficient and the weight corresponding to the correlation coefficient;
s6: and judging the inter-well connectivity of the injection and production wells according to the magnitude of the comprehensive correlation coefficient, wherein the larger the correlation coefficient is, the higher the inter-well connectivity is.
2. The method for analyzing connectivity between wells according to the injection-production profile monitoring data of claim 1, wherein in step S2, the amount of split liquid in each small layer is calculated by the following formula:
in the formula: qi/j,kThe injection quantity of the ith injection well in the kth layer or the liquid production quantity of the jth production well in the kth layer is determined; qi/jThe injection quantity of the ith injection well or the liquid production quantity of the jth production well; ri,jThe total seepage resistance coefficient between the ith injection well and the jth production well is shown; ri,j,kThe seepage resistance coefficient of a k layer between an ith injection well and a jth production well; lambda [ alpha ]i,j,kApparent viscosity of a k-th small layer between an i-th injection well and a j-th production well; l isi,j,kAn effective well spacing between the ith injection well and the jth production well in the kth sub-zone; hi,j,kThe average thickness of a k-th small layer between an ith injection well and a jth production well; ki,j,kBetween the ith injection well and the jth production wellk average permeability of the small layer; kro、Krw、μo、μwRelative permeability of oil phase, relative permeability of water phase, viscosity of crude oil and viscosity of water in the kth small layer between the ith injection well and the jth production well.
3. The method for analyzing connectivity between wells according to the injection-production profile monitoring data of claim 2, wherein in step S3, the correlation coefficient of injection-production of each connected small layer under the gray correlation model is calculated by the following formula:
in the formula: m1 is a correlation coefficient under a grey correlation model; n is the closing time; s is the development time of the injection and production wells; gamma rayi,j,kThe weight of the correlation coefficient at different time points; delta (Q)i/j,k,s) The correlation coefficient of the injection quantity and the liquid production quantity of the kth small layer between the injection well i and the production well j at the s moment; delta (min) is the minimum difference of two stages of injection quantities of the injection-production unit; rho is a resolution coefficient, is a front coefficient of a maximum value delta (max), and takes a value between 0 and 1; delta (max) is the two-stage maximum difference of the injection amount of the injection-production unit; and delta i, j, k is the absolute difference value of the injection quantity and the liquid production quantity at the corresponding time point of the kth layer between the injection well i and the production well j.
4. The method of analyzing connectivity between wells via injection-production profile monitoring data of claim 3, wherein the resolution factor takes on a value of 0.5.
5. The method for analyzing connectivity between wells according to the injection-production profile monitoring data of claim 3, wherein in step S3, the correlation coefficient of injection-production of each connected stratum under the linear regression model is calculated by the following formula:
in the formula: qj,kThe liquid production amount of the kth layer of the jth production well; xiojThe number of water drive reservoir injection and production imbalances is adopted; m2 is a correlation coefficient under linear regression; qi,kThe injection quantity of the k layer of the ith injection well is shown.
6. The method for analyzing connectivity between wells according to the injection-production profile monitoring data of claim 5, wherein in step S3, the correlation coefficient of the injection-production of each connected small layer under the resistance-capacitance model is calculated by the following formula:
in the formula: m0Is a weight coefficient formed under the influence of the initial liquid amount; qj,k(s＝0)The initial fluid production for production well j; e is a natural base number; s0Is the initial time; tau is0Is the time constant under the influence of the initial liquid amount; k is the total number of layers of communication between injection wells and production wells; m3 is a correlation coefficient under a resistance-capacitance model; q'i,j,k(s)The injection quantity after convolution for time step s; vjThe degree coefficient of the liquid production quantity influenced by the bottom hole flowing pressure fluctuation; p is a radical ofwfj(s＝0)Initiating a bottom hole flow pressure for the production well; tau isjIs a time constant; p is a radical ofwfj(s)Bottom hole flow pressure at s time for the producing well; p'wfj(s)The bottom hole flow pressure of the production well after time step s convolution is taken.
7. The method for analyzing the connectivity between wells according to the injection-production profile monitoring data of claim 6, wherein in step S3, when the bottom hole pressure is stable or the influence of bottom hole flow pressure fluctuation is ignored, the correlation coefficient of the injection-production quantity of each connected small layer under the resistance-capacitance model is calculated by the following formula:
8. the method for analyzing connectivity between wells by monitoring data through an injection-production profile according to claim 6 or 7, wherein in step S4, the weight coefficients of the correlation coefficients under each different mathematical model are determined by the following sub-steps:
s41: determining influence factors of the well connectivity, and determining influence weights of the influence factors on the well connectivity, wherein the sum of the influence weights is equal to 1;
s42: determining influence factors considered by different mathematical models, and adding influence weights of the influence factors considered by the mathematical models on the basis of one third to obtain influence coefficients of the corresponding mathematical models;
s43: the ratio of the influence coefficient corresponding to each mathematical model to the sum of all the influence coefficients is the weight coefficient of the correlation coefficient corresponding to the model.
9. The method for analyzing connectivity between wells via injection-production profile monitoring data of claim 8, wherein in step S5, the comprehensive correlation coefficient is calculated by:
ψ＝α×|M1|+β×|M2|+γ×|M3| (10)
in the formula: psi is the comprehensive correlation coefficient; alpha, beta and gamma are weight coefficients of correlation coefficients under a grey correlation model, a linear regression model and a resistance-capacitance model respectively.
10. The method for analyzing the connectivity between wells through the injection-production profile monitoring data as claimed in claim 9, wherein in step S6, the criteria for determining the connectivity between wells of the injection-production well are:
in the formula: a. b is a critical value for judging the communication among wells.
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