CN105404726B - A kind of capacitor model inverting inter well connectivity method and device based on Gaussian Profile - Google Patents
A kind of capacitor model inverting inter well connectivity method and device based on Gaussian Profile Download PDFInfo
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Abstract
The invention discloses a kind of capacitor model inverting inter well connectivity method and device based on Gaussian Profile, including：The initial related data of water filling well group is obtained, including：Water injection rate, Liquid output, stream pressure, water filling section initial time and the connectivity relationship of the water filling well group；Target note, which is filtered out, from the initial related data adopts data；It is to be pressed with the water injection rate of initial fill date match, Liquid output and stream in water filling section that the target note, which adopts data,；Target note is adopted into data and brings capacitor model into, the capacitor model is solved by Gauss distribution method, so as to calculate connectivity parameters；The connectedness that the connectivity parameters are used to characterize between the water injection well and liquid producing well is strong and weak.For method and device provided by the invention solving the acquisition methods of oil deposit inter-well dynamic connectivity in the prior art, it is low the degree of accuracy to be present, influences the technical problems such as normal production and expense height.Realizing reduces cost, simplifies workload and ensure that the technique effect of accuracy.
Description
Technical Field
The invention relates to the technical field of geophysical prospecting development, in particular to a method and a device for inverting connectivity between wells based on a capacitance model with Gaussian distribution.
Background
The oil deposit is a dynamic balance system, in the development of oil fields, each well can be connected with one or more peripheral wells, each well is not completely isolated, a water injection well can cause certain fluctuation to a production well connected with the water injection well when being injected with water, and the fluctuation size of the production well has certain relation with the connectivity of the injection and production wells.
The interwell connectivity of the reservoir includes static connectivity and dynamic connectivity. Generally, static connectivity refers to the connectivity result obtained by applying geological and geophysical exploration methods, and is determined by reservoir geological characteristics and reservoir characteristics. Due to the characteristic that reservoir layers of the fracture-cavity oil reservoirs are complex, stratum connectivity obtained by traditional geological and geophysical exploration (such as well logging, well testing, geological modeling and other methods) belongs to a static category, and connectivity of fracture-cavity bodies cannot be effectively known. And the dynamic connectivity among the oil reservoir wells refers to the communication degree of reservoir fluid among the wells after the oil reservoir is developed. At present, the common reservoir inter-well dynamic connectivity research methods at home and abroad mainly comprise tracer tests, pressure tests, interference well testing, pulse well testing and other connectivity identification methods.
However, when the methods of tracer test, pressure test, interference well testing and pulse well testing are used for researching the injection-production communication relationship, the problems of interference among multiple wells, production system difference or difficulty in determining the communication degree in the multiple wells exist; meanwhile, normal production is influenced, and the cost is high; the use frequency of wells is limited, the data is not abundant, and the problem that whether the low water-cut wells are communicated or not can not be determined is solved; and can also affect the normal production operation of the oil field.
That is to say, the acquisition of dynamic connectivity among oil reservoir wells in the prior art has the technical problems of low accuracy, influence on normal production, high cost and the like.
Disclosure of Invention
The embodiment of the application provides a method and a device for inverting the connectivity among wells by using a capacitance model based on Gaussian distribution, and solves the technical problems that the accuracy is low, normal production is influenced, the cost is high and the like in the method for acquiring the dynamic connectivity among oil reservoir wells in the prior art.
On one hand, the embodiment of the application provides the following technical scheme:
a capacitance model inversion interwell connectivity method based on Gaussian distribution comprises the following steps:
acquiring initial relevant data of a water injection well group; the initial correlation data includes: the water injection quantity, the liquid production quantity, the flow pressure, the initial time of a water injection section and the connectivity relation of the water injection well group;
screening target injection and production data from the initial relevant data; the target injection and production data are water injection amount, liquid production amount and flowing pressure which are matched with the initial water injection date in the water injection section;
bringing the target injection and production data into a capacitance model, and solving the capacitance model through a Gaussian distribution algorithm so as to calculate connectivity parameters; and the connectivity parameters are used for representing the strength of the connectivity between the water injection well and the liquid production well in the water injection well group.
Optionally, the method further includes: judging whether the flow pressure is used as the target injection-production data for calculating the connectivity parameter or not, and obtaining a first judgment result; if the first judgment result is yes, the initial relevant data further comprises: the flowing pressure of the water injection well group and the acquisition time corresponding to the flowing pressure; the capacitance model is a capacitance model considering the flow pressure; and if the first judgment result is negative, the capacitance model is a capacitance model without considering the flow pressure.
Optionally, the capacitance model without considering the flow pressure is as follows:the capacitance model considering the flow pressure is as follows:wherein,the water injection amount of the water injection well i at the moment n is obtained; i.e. iij(n) is the liquid production amount of the oil production well j in the water injection well group i at the moment of n; n is0Is the initial time;for the amount of the liquid production iij(n) convolution of the (n); λ is the cross-flow coefficient; τ is a time lag constant; lambda [ alpha ]ijIs the cross-flow coefficient between the water injection well i and the oil production well j; tau isijIs the time lag constant between the water injection well i and the oil production well j;the influence of the first water injection of the water injection well,is equal to the imbalance constant; wherein,the flowing pressure of a corresponding oil production well k in the water injection well group i is measured;to the pressure of flowConvolution of (2); upsilon iskiIs a weight; upsilon iskiIs equal to λki；λkiIs the cross-flow coefficient between the water injection well i and the oil production well k; tau iskiIs the time lag constant between the water injection well i and the production well k.
Optionally, the acquiring of the initial related data of the water injection well group specifically includes: acquiring initial relevant data of a water injection well group by an injection-production response method; or obtaining initial correlation data for the water flooding well group through the tracer.
Optionally, the connectivity parameters include: a cross-flow coefficient of the water injection well group.
Optionally, the bringing the target injection-production data into a capacitance model, and solving the capacitance model through a gaussian distribution algorithm, so as to calculate connectivity parameters includes: randomly generating an estimated channeling coefficient and an estimated time lag constant of each oil production well by using Gaussian distribution as initial parameters; screening out data meeting the requirements from the initial parameters according to a preset range to serve as target parameters; partial data in the target injection and production data and the target parameters are brought into the capacitance model, and a water injection amount estimated value is calculated; calculating an error value between the water injection amount and the water injection amount in the target injection and production data; and determining the channeling coefficient of the water injection well group from the target parameters through a Gaussian distribution algorithm according to the error value.
On the other hand, the embodiment of the present application further provides a device for inverting connectivity between wells based on a capacitance model with gaussian distribution, including:
the acquisition module is used for acquiring initial relevant data of the water injection well group; the initial correlation data includes: the water injection amount and the liquid production amount of the water injection well group and the acquisition time corresponding to the water injection amount and the liquid production amount;
the screening module is used for screening target injection and production data from the initial relevant data; the target injection-production data is data matched with the initial water injection time of the water injection well group in the initial relevant data;
the calculation module is used for substituting the target injection and extraction data into a capacitance model, and solving the capacitance model through a Gaussian distribution algorithm so as to calculate connectivity parameters; and the connectivity parameters are used for representing the strength of the connectivity between the water injection well and the liquid production well in the water injection well group.
Optionally, the apparatus further comprises: the judging module is used for judging whether the flow pressure is used as the target injection-production data for calculating the connectivity parameters or not and obtaining a first judging result; if the first judgment result is yes, the capacitance model is a capacitance model considering the flow pressure; and if the first judgment result is negative, the capacitance model is a capacitance model without considering the flow pressure.
Optionally, the obtaining module includes: the first acquisition unit is used for acquiring initial related data of the water injection well group by an injection-production response method; or a second acquisition unit for acquiring initial correlation data of the water injection well group by the tracer.
Optionally, the apparatus further comprises: the Gaussian calculation module is used for randomly generating an estimated channeling coefficient and an estimated time lag constant of each oil production well by applying Gaussian distribution as initial parameters; screening out data meeting the requirements from the initial parameters according to a preset range to serve as target parameters; partial data in the target injection and production data and the target parameters are brought into the capacitance model, and a water injection amount estimated value is calculated; calculating an error value between the water injection amount and the water injection amount in the target injection and production data; and determining a channeling coefficient and a time lag constant of the water injection well group from the target parameters through a Gaussian distribution algorithm according to the error value.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
1. the method and the device provided by the embodiment of the application carry out quantitative analysis on the strength of well-to-well communication based on production dynamic data, namely the obtained initial relevant data, and calculate the water injection effect and the spread condition based on a capacitance model and combined with Gaussian distribution and other algorithms, so that the problem that the normal production construction operation is influenced by the traditional well-to-well communication judgment method is solved, the cost for judging the strength of injection-production communication is greatly reduced, and the workload for judging the traditional injection-production communication relation is simplified. Furthermore, the method utilizes the thought of probability statistics, and uses a Gaussian distribution algorithm to solve the capacitance model, so as to calculate the connectivity parameters, thereby ensuring the accuracy of the quantitative analysis of the connectivity among wells.
2. According to the method and the device provided by the embodiment of the application, the capacitance model is solved by using a Gaussian distribution algorithm through the concept of probability statistics, the estimated channeling coefficient and the estimated time lag constant of each oil production well are randomly generated, the optimal solution is found out from random values, the channeling coefficient and the time lag constant of the water injection well group are determined, the combination with practical application is tighter, and the accuracy of the quantitative analysis of the connectivity among wells is ensured.
Drawings
FIG. 1 is a flow chart of a method for inverting connectivity between wells based on a Gaussian-distribution capacitance model in an embodiment of the present application;
FIG. 2 is a flow chart of Gaussian distribution calculation in an embodiment of the present application;
FIG. 3 is a flow chart of solving a capacitance equation by Gaussian distribution in the embodiment of the present application;
FIG. 4 is a structural diagram of an apparatus for inverting connectivity between wells based on a Gaussian-distribution capacitance model according to an embodiment of the present disclosure.
Detailed Description
The embodiment of the application provides a method and a device for inverting the connectivity among wells by using a capacitance model based on Gaussian distribution, and solves the technical problems that the accuracy is low, normal production is influenced, the cost is high and the like in the prior art for acquiring the dynamic connectivity among oil reservoir wells. The technical effects of reducing cost, simplifying workload and ensuring accuracy are achieved.
In order to solve the technical problems in the prior art, the general idea of the technical scheme provided by the embodiment of the application is as follows:
a capacitance model inversion interwell connectivity method based on Gaussian distribution comprises the following steps:
acquiring initial relevant data of a water injection well group; the initial correlation data includes: the water injection quantity, the liquid production quantity, the flow pressure, the initial time of a water injection section and the connectivity relation of the water injection well group;
screening target injection and production data from the initial relevant data; the target injection and production data are water injection amount, liquid production amount and flowing pressure which are matched with the initial water injection date in the water injection section;
bringing the target injection and production data into a capacitance model, and solving the capacitance model through a Gaussian distribution algorithm so as to calculate connectivity parameters; and the connectivity parameters are used for representing the strength of the connectivity between the water injection well and the liquid production well in the water injection well group.
According to the method, the strength of the inter-well injection-production communication is quantitatively analyzed based on production dynamic data, namely the obtained initial relevant data, the water injection effect and the spread condition are calculated based on a capacitance model and combined with Gaussian distribution and other algorithms, the problem that the normal production construction operation is influenced by the traditional inter-well communication judgment method is solved, the cost for judging the strength of the injection-production communication is greatly reduced, and the workload for judging the traditional injection-production communication relation is simplified. Furthermore, the method utilizes the thought of probability statistics, and uses a Gaussian distribution algorithm to solve the capacitance model, so as to calculate the connectivity parameters, thereby ensuring the accuracy of the quantitative analysis of the connectivity among wells.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
The first embodiment is as follows:
in an embodiment, a method for inverting connectivity between wells based on a capacitance model of gaussian distribution is provided, please refer to fig. 1, as shown in fig. 1, the method includes:
s101, acquiring initial relevant data of a water injection well group; the initial correlation data includes: the water injection quantity, the liquid production quantity, the flow pressure, the initial time of a water injection section and the connectivity relation of the water injection well group;
s102, screening target injection and production data from the initial relevant data; the target injection and production data are water injection amount, liquid production amount and flowing pressure which are matched with the initial water injection date in the water injection section;
step S103, bringing the target injection and extraction data into a capacitance model, and solving the capacitance model through a Gaussian distribution algorithm so as to calculate connectivity parameters; and the connectivity parameters are used for representing the strength of the connectivity between the water injection well and the liquid production well in the water injection well group.
The basic principle of the method provided by the application is that the change of the injection amount of the water injection well can cause the fluctuation of the liquid production amount of the surrounding oil well, the larger the fluctuation range is, the better the communication degree is, and the communication relation between injection and production can be quantitatively represented by using production dynamic data, namely initial relevant data through a mathematical method.
For a water-drive reservoir, the water injection amount of a water well and the liquid production amount of a surrounding oil well have a certain relationship, the reservoir can be regarded as a system that the water injection well sends out stimulation and the surrounding oil well receives the stimulation, namely an injection-production system, and quantitative representation of the injection-production communication relationship is realized by adopting a mathematical method. And (3) inverting the injection-production communication relation by utilizing oil field development data and combining a statistical method such as a multivariate regression model.
Based on the principle, the method provided by the application is used for carrying out qualitative and quantitative dynamic inversion algorithm research on the fractured-vuggy oil reservoir with strong heterogeneity and well pattern irregularity on the basis of the conventional inter-well connectivity research. And calculating the inter-well communication parameters by establishing a new inter-well communication model, and quantitatively describing the inter-well communication degree.
The following describes in detail the implementation steps of the capacitance model based on gaussian distribution for inverting the connectivity between wells with reference to fig. 1:
firstly, executing step S101 to obtain initial relevant data of a water injection well group; the initial correlation data includes: and the water injection amount, the liquid production amount, the flow pressure, the initial time of the water injection section and the connectivity relation of the water injection well group.
In the embodiment of the application, before the step S101 is executed, it may be further determined whether to add the flow pressure data to invert the inter-well connectivity, so as to obtain a first determination result;
if the first judgment result is yes, the capacitance model is a capacitance model considering the flow pressure;
and if the first judgment result is negative, the capacitance model is a capacitance model without considering the flow pressure.
In a specific implementation process, a user may pre-select whether to use the flow pressure as the target injection and production data for calculating the connectivity parameter according to needs.
In this embodiment of the application, before step S101 is executed, a method for obtaining the initial related data may be further selected, and initial related data of the water injection well group may be obtained by an injection-production response method; or obtaining initial correlation data for the water flooding well group through the tracer.
In the specific implementation process, the method for initiating the relevant data can be preset by the user according to the needs.
Next, step S102 is executed to screen target injection-production data from the initial relevant data; and the target injection and production data comprise water injection amount, liquid production amount and flowing pressure matched with the initial water injection date in the water injection section.
Specifically, the water injection well can be divided into a plurality of water injection sections through initial related data, matched water injection quantity and liquid production quantity read-in are selected by reading the peak value and the water injection time of a related oil production well in each water injection section, and the flow pressure of the oil production well with a matched date is read according to related information of the water injection sections, namely the water injection quantity, the liquid production quantity and the flow pressure matched with the initial water injection quantity date in the water injection sections are selected.
Then, step S103 is executed, the target injection and extraction data are brought into a capacitance model, the capacitance model is solved through a Gaussian distribution algorithm, and therefore connectivity parameters are calculated; and the connectivity parameters are used for representing the strength of the connectivity between the water injection well and the liquid production well in the water injection well group.
Specifically, the gaussian distribution algorithm mainly works on the selection of the cross-flow coefficient and the time lag constant. The principle is that according to the mean value mu and the standard deviation sigma of Gaussian distribution, the Gaussian distribution randomly generates a channeling coefficient and a time-lag constant of each oil production well; the effective part is selected, and data exceeding a certain range, such as-0.3, is discarded. The flow-through coefficient and the time-lag constant of the optimal solution for each production well of each generation are averaged and set as the mean μ of the gaussian distribution of this production well.
Specifically, the capacitance model is primarily used to screen optimal solutions. The basic principle is that proper relevant data including water injection amount, liquid production amount and flowing pressure matched with the initial water injection date of the water injection section are selected; randomly generating a cross-flow coefficient and a time-lag constant of each group of each round by using a Gaussian distributor; the data is put into a model to be solved, and the data is matched with the real water injection amount to obtain each group of accumulated errors in each round; selecting N optimal solutions, namely minimum errors, from each group of accumulated errors in each round; solving N optimal solution parameter average numbers and assigning the N optimal solution parameter average numbers to an average value mu in a Gaussian distributor; and repeating the loop algebra until the loop algebra is finished.
In the embodiment of the present application, the capacitance model in step S103 can be divided into two types, i.e., a capacitance model without considering the flow pressure and a capacitance model with considering the flow pressure, which are described below:
first, the capacitance model without considering the flow pressure is:
wherein,the water injection amount of the water injection well i at the moment n is obtained; i.e. iij(n) is the liquid production amount of the oil production well j in the water injection well group i at the moment of n; n is0Is the initial time;for the amount of the liquid production iij(n) convolution of the (n); λ is the cross-flow coefficient; τ is a time lag constant; lambda [ alpha ]ijThe channeling coefficient between a water injection well i and a production well j represents the connectivity between the well i and the well j; tau isijIs the time lag constant between the water injection well i and the oil production well j; lambda [ alpha ]ijAnd τijFor estimating the water injection rate of a water injection well i The influence of the first water injection of the water injection well,is equal to the imbalance constant.
Secondly, the capacitance model considering the flow pressure is:
the method for analyzing the connectivity among wells regards the communication relation among the water injection wells, the oil production wells and the wells as a complex system, describes the characteristics of the oil reservoirs by using the new application of a capacitance model based on the similar characteristics of oil reservoir seepage and current flow, namely the water and electricity similarity, and considers the influence of water body invasion at the same time.
Wherein,for injection of water injection well i at time nWater quantity; i.e. iij(n) is the liquid production amount of the oil production well j in the water injection well group i at the moment of n; n is0Is the initial time;for the amount of the liquid production iij(n) convolution of the (n); λ is the cross-flow coefficient; τ is a time lag constant; lambda [ alpha ]ijThe channeling coefficient between a water injection well i and a production well j represents the connectivity between the well i and the well j; tau isijIs the time lag constant between the water injection well i and the oil production well j; lambda [ alpha ]ijAnd τijFor estimating the water injection rate of a water injection well i The influence of the first water injection of the water injection well,is equal to the imbalance constant;
wherein,the flowing pressure of a corresponding oil production well k in the water injection well group i is measured;to the pressure of flowConvolution of (2); upsilon iskiIs a weight; upsilon iskiIs equal to λki；λkiIs the cross-flow coefficient between the water injection well i and the oil production well k; tau iskiIs the time lag constant between the water injection well i and the production well k.
Specifically, the imbalance constant can be obtained by error calculation, mainly acts to correct model errors, and is divided into a random mode and a proportional mode. The unbalance constant of the random mode is used for correcting the model error by random everyday errors; the imbalance constant of the proportional mode is the daily water injection quantity of the water injection well, and is used for correcting the daily error of the model after calculation according to a certain proportion.
Specifically, the cross-flow coefficient λ, characterizes: the connectivity, i.e. the weight of the connectivity of each production well to the injection well, is quantified. Time lag constant τ, characterization: and (3) the dissipation degree of the signals between the injection wells and the production wells. The initialization of the two coefficients is based on a Gaussian distribution algorithm to randomly generate a batch of initial seeds, and simultaneously, according to a capacitance model estimated by recursion, N generations of evolution are compared with real data, a series of seeds with smaller errors are selected, so that continuous recursion is carried out, the two coefficients are modified, an optimal solution matched with production history fitting is solved, and the flow channeling coefficients of each oil producing well and each water injection well of each water injection well group are obtained.
In the embodiment of the present application, the solution idea of the capacitance model is as follows:
is provided withFor the water injection amount of the water injection well i at the moment of n, t is the total inversion duration, and the model parameter solving can be generalized and optimized as follows:
wherein,is the effect of the flow pressure;
in the embodiment of the application, the capacitance model parameter solving principle based on the gaussian distribution algorithm is as follows:
the gaussian distribution, also known as the normal distribution, acts to generate random variables, thereby avoiding the generation of local minima. Two parameters, namely a mean mu and a standard deviation sigma, are distributed in the Gaussian, and the mean mu determines the central position of a normal curve; the standard deviation σ determines how steep the normal curve is, and is expressed as follows:
wherein x is a variable.
The 34.1% region in the gaussian distribution is a range of values within less than one standard deviation from the mean. In normal distribution, the ratio of this range is 68% of the total number, and according to normal distribution, the ratio within two standard deviations together is 95%; the ratios within the three standard deviations, taken together, are 99%, which has two benefits:
1. the optimal mean value selected in each round is effectively protected from deviating too much;
2. meanwhile, the danger of falling into a local optimal value is avoided due to the fact that the random generation variables are different along with normal distribution.
And combining a Gaussian distribution algorithm, randomly generating first-round seeds, and solving the capacitance model by introducing parameters. And then selecting a series of optimal solutions, calculating the mean of the optimal solutions, assigning the mean to the mean mu in Gaussian distribution, calling the module again to perform a second round of selective directional random generation of a second batch of seeds, and recursing until the optimal solution is found, namely convergence of the inter-well communication splitting numerical algorithm.
In an embodiment of the present application, the connectivity parameters include: a cross-flow coefficient of the water injection well group.
In the embodiment of the present application, the capacitance model parameter solving method based on the gaussian distribution algorithm includes:
randomly generating an estimated channeling coefficient and an estimated time lag constant of each oil production well by using Gaussian distribution as initial parameters;
screening out data meeting the requirements from the initial parameters according to a preset range to serve as target parameters;
partial data in the target injection and production data and the target parameters are brought into the capacitance model, and a water injection amount estimated value is calculated;
calculating an error value between the water injection amount and the water injection amount in the target injection and production data;
and determining the channeling coefficient of the water injection well group from the target parameters through a Gaussian distribution algorithm according to the error value.
Specifically, solving the capacitance model based on the gaussian distribution algorithm may be:
data preprocessing is carried out firstly:
firstly, reading initial relevant data (including water injection quantity, liquid production quantity, flow pressure, initial time of a water injection section and connectivity relation); screening water injection amount, liquid production amount and flow pressure matched with the initial water injection amount date in the water injection section as target injection and production data; and then, bringing the screened data into the model for calculation, and screening target injection and production data again if the water injection section is not calculated after calculation until the calculation result of each water injection section is output and stored.
As shown in fig. 2, the specific implementation steps of the gaussian distribution based algorithm may be:
firstly, according to the mean value mu and the standard deviation sigma of Gaussian distribution, the Gaussian distribution randomly generates an estimated channeling coefficient and an estimated time lag constant of each oil production well; the effective part is selected, and data exceeding a certain range, such as-0.3, is discarded. And (3) solving the average value of the cross-flow coefficient and the time lag constant of the optimal solution of each oil production well, setting the average value to be the average value mu of Gaussian distribution of the oil production well, and randomly generating the estimated cross-flow coefficient and the estimated time lag constant of each oil production well if the module still needs to initialize the next generation, or ending.
Still further, as shown in fig. 3, a more detailed calculation method for solving the capacitance model based on gaussian distribution may be:
selecting appropriate related data including water injection amount, liquid production amount and flow pressure matched with the initial water injection date of the water injection section; then, randomly generating an estimated channeling coefficient and an estimated time lag constant of each group of each round by using a Gaussian distributor; partial data in the target injection and production data and the target parameters are brought into a model to be solved, and the model is matched with the real water injection amount to obtain each group of accumulated errors in each round; if the round still has the group without accumulated errors, returning to generate the target parameters; selecting a plurality of optimal solutions, namely minimum errors, from each set of accumulated errors in each round; solving a plurality of optimal solution parameter average numbers and assigning the optimal solution parameter average numbers to an average value mu in a Gaussian distributor; and (4) checking whether a loop algebra is reached (the module is set to 10 generations, and the loop is ensured to be converged certainly when the loop reaches 10 generations through continuous analysis and practice), and returning if the loop does not reach 10 generations, otherwise, ending.
According to practice, by adopting the method provided by the application, the influence of water intrusion is considered, invalid signals are removed, and a capacitance model can be optimized; and solving the model by using Gaussian distribution, and avoiding trapping in the local optimal solution on the premise of keeping the optimal solution.
The following is a specific application example of the method for inverting the connectivity between wells by using the capacitance model based on the gaussian distribution provided by the embodiment of the application:
according to the inversion comparison of the tracer report and the filtering method of TK663 well group in 08 years
After the water injection quantity of a certain water injection section of the TK663 water injection well is brought into the water injection well and the liquid production quantity of the four liquid production wells around the water injection section is matched with the time of the four liquid production wellsSolving the model, wherein the concrete solving method comprises the following steps:
firstly, initializing lambda and tau through Gaussian distribution and substituting the lambda and the tau into the formula; then inputting the liquid production amount and the flow pressure to carry out calculation; subtracting the calculation result from the real water injection amount to calculate the minimum value under the absolute value; continuously optimizing lambda and tau by a Gaussian distribution method according to the minimum value; and finally obtaining the optimal solution lambda and tau.
The results of the calculations are shown in tables 1 and 2:
TABLE 1 TK663 well group communication relation filter method and tracer method compare TABLE 1
TABLE 2 TK663 well group communication relation filter method and tracer method compare TABLE 2
And through inspection, the well connectivity result is matched with the tracer test result. The water distribution condition of the tracer in the well group, the maximum peak concentration of the tracer and the sequence of the results obtained by filtering analysis are basically consistent.
On the other hand, based on the same concept, a device corresponding to the method in the first embodiment is provided, and the details are shown in the second embodiment.
Example two:
in this embodiment, a device for inverting connectivity between wells based on a capacitance model of gaussian distribution is provided, please refer to fig. 4, where fig. 4 is a structural diagram of the device, and the device includes:
an obtaining module 401, configured to obtain initial relevant data of a water injection well group; the initial correlation data includes: the water injection quantity, the liquid production quantity, the flow pressure, the initial time of a water injection section and the connectivity relation of the water injection well group;
a screening module 402, configured to screen target injection-production data from the initial relevant data; the target injection and production data are water injection amount, liquid production amount and flowing pressure which are matched with the initial water injection date in the water injection section;
a calculation module 403, configured to bring the target injection and production data into a capacitance model, and solve the capacitance model through a gaussian distribution algorithm, so as to calculate connectivity parameters; and the connectivity parameters are used for representing the strength of the connectivity between the water injection well and the liquid production well in the water injection well group.
In an embodiment of the present application, the apparatus further includes:
the judging module is used for judging whether the flow pressure data is added to invert the connectivity among wells or not to obtain a first judging result; if the first judgment result is yes, the capacitance model is a capacitance model considering the flow pressure; and if the first judgment result is negative, the capacitance model is a capacitance model without considering the flow pressure.
In an embodiment of the present application, the obtaining module includes:
the first acquisition unit is used for acquiring initial related data of the water injection well group by an injection-production response method; or
And the second acquisition unit is used for acquiring initial related data of the water injection well group through the tracer.
In an embodiment of the present application, the apparatus further includes:
the Gaussian calculation module is used for randomly generating an estimated channeling coefficient and an estimated time lag constant of each oil production well by applying Gaussian distribution as initial parameters; screening out data meeting the requirements from the initial parameters according to a preset range to serve as target parameters; partial data in the target injection and production data and the target parameters are brought into the capacitance model, and a water injection amount estimated value is calculated; calculating an error value between the water injection amount and the water injection amount in the target injection and production data; and determining a channeling coefficient and a time lag constant of the water injection well group from the target parameters through a Gaussian distribution algorithm according to the error value.
The apparatus provided in this embodiment has been described in detail in the first embodiment, so that those skilled in the art can clearly understand the structure of the apparatus in this embodiment from the foregoing description, and for the sake of brevity of the description, the detailed description is omitted here.
The technical scheme in the embodiment of the application at least has the following technical effects or advantages:
1. the method and the device provided by the embodiment of the application carry out quantitative analysis on the communication strength between injection and production between wells based on production dynamic data, namely the obtained initial relevant data, and calculate the water injection effect and the spread condition based on a capacitance model and combined with Gaussian distribution and other algorithms, so that the problem that the conventional inter-well communication judgment method influences normal production construction operation is solved, the cost for judging the strength of the injection and production communication is greatly reduced, and the workload for judging the conventional injection and production communication relation is simplified. Furthermore, the method utilizes the thought of probability statistics, and uses a Gaussian distribution algorithm to solve the capacitance model, so as to calculate the connectivity parameters, thereby ensuring the accuracy of the quantitative analysis of the connectivity among wells.
2. According to the method and the device provided by the embodiment of the application, the capacitance model is solved by using a Gaussian distribution algorithm through the concept of probability statistics, the estimated channeling coefficient and the estimated time lag constant of each oil production well are randomly generated, the optimal solution is found out from random values, the channeling coefficient and the time lag constant of the water injection well group are determined, the combination with practical application is tighter, and the accuracy of the quantitative analysis of the connectivity among wells is ensured.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (4)
1. A method for inverting the connectivity between wells based on a capacitance model with Gaussian distribution is characterized by comprising the following steps:
acquiring initial relevant data of a water injection well group; the initial correlation data includes: the water injection quantity, the liquid production quantity, the flow pressure, the initial time of a water injection section and the connectivity relation of the water injection well group;
screening target injection and production data from the initial relevant data; the target injection and production data are water injection amount, liquid production amount and flowing pressure which are matched with the initial water injection date in the water injection section;
randomly generating an estimated channeling coefficient and an estimated time lag constant of each oil production well by using Gaussian distribution as initial parameters;
screening out data meeting the requirements from the initial parameters according to a preset range to serve as target parameters;
partial data in the target injection and production data and the target parameters are brought into a capacitance model, and a water injection amount estimated value is calculated;
calculating an error value between the water injection amount and the water injection amount in the target injection and production data;
determining the channeling coefficient of the water injection well group from the target parameters through a Gaussian distribution algorithm according to the error value;
wherein the method further comprises:
judging whether the flow pressure data is added to invert the inter-well connectivity or not, and obtaining a first judgment result;
if the first judgment result is yes, the capacitance model is a capacitance model considering the flow pressure;
if the first judgment result is negative, the capacitance model is a capacitance model without considering the flow pressure;
the capacitance model without considering the flow pressure is as follows:
<mrow> <msub> <mover> <mi>q</mi> <mo>^</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>&lambda;</mi> <mi>p</mi> </msub> <mi>q</mi> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mfrac> <mrow> <mo>-</mo> <mrow> <mo>(</mo> <mrow> <mi>n</mi> <mo>-</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <msub> <mi>T</mi> <mi>p</mi> </msub> </mfrac> </msup> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>i</mi> <mo>=</mo> <mi>l</mi> </mrow> </munderover> <msub> <mi>&lambda;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <msubsup> <mi>i</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>&prime;</mo> </msubsup> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
the capacitance model considering the flow pressure is as follows:
<mrow> <msub> <mover> <mi>q</mi> <mo>^</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>&lambda;</mi> <mi>p</mi> </msub> <mi>q</mi> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mfrac> <mrow> <mo>-</mo> <mrow> <mo>(</mo> <mrow> <mi>n</mi> <mo>-</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <msub> <mi>T</mi> <mi>p</mi> </msub> </mfrac> </msup> <mo>+</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>i</mi> <mo>=</mo> <mi>l</mi> </mrow> </munderover> <msub> <mi>&lambda;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <msubsup> <mi>i</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>&prime;</mo> </msubsup> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>k</mi> <mo>=</mo> <mi>K</mi> </mrow> </munderover> <msub> <mi>&upsi;</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> <mo>&lsqb;</mo> <msub> <mi>p</mi> <mrow> <msub> <mi>wf</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mfrac> <mrow> <mo>-</mo> <mrow> <mo>(</mo> <mrow> <mi>n</mi> <mo>-</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <msub> <mi>&tau;</mi> <mrow> <mi>k</mi> <mi>j</mi> </mrow> </msub> </mfrac> </msup> <mo>-</mo> <msub> <mi>p</mi> <mrow> <msub> <mi>wf</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>p</mi> <mrow> <msub> <mi>wf</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> </mrow> <mo>&prime;</mo> </msubsup> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>;</mo> </mrow>
wherein,the water injection amount of the water injection well i at the moment n is obtained; i.e. iij(n) is the liquid production amount of the oil production well j in the water injection well group i at the moment of n; n is0Is the initial time;for the amount of the liquid production iij(n) convolution of the (n); λ is the cross-flow coefficient; τ is a time lag constant; lambda [ alpha ]ijIs the cross-flow coefficient between the water injection well i and the oil production well j; tau isijIs the time lag constant between the water injection well i and the oil production well j;the influence of the first water injection of the water injection well,is equal to the imbalance constant;
wherein,the flowing pressure of a corresponding oil production well k in the water injection well group i is measured;to the pressure of flowConvolution of (2); upsilon iskiIs a weight; upsilon iskiIs equal to λki；λkiIs the cross-flow coefficient between the water injection well i and the oil production well k; tau iskiIs the time lag constant between the water injection well i and the production well k.
2. The method of claim 1, wherein the obtaining initial correlation data for the water flooding well group is specifically:
acquiring initial relevant data of a water injection well group by an injection-production response method; or
Initial correlation data for the water injection well group is obtained by the tracer.
3. A capacitance model based Gaussian distribution based device for inverting connectivity between wells, comprising:
the acquisition module is used for acquiring initial relevant data of the water injection well group; the initial correlation data includes: the water injection quantity, the liquid production quantity, the flow pressure, the initial time of a water injection section and the connectivity relation of the water injection well group;
the screening module is used for screening target injection and production data from the initial relevant data; the target injection and production data are water injection amount, liquid production amount and flowing pressure which are matched with the initial water injection date in the water injection section;
the Gaussian calculation module is used for randomly generating an estimated channeling coefficient and an estimated time lag constant of each oil production well by applying Gaussian distribution as initial parameters; screening out data meeting the requirements from the initial parameters according to a preset range to serve as target parameters; partial data in the target injection and production data and the target parameters are brought into the capacitance model, and a water injection amount estimated value is calculated; calculating an error value between the water injection amount and the water injection amount in the target injection and production data; determining a channeling coefficient of the water injection well group from the target parameters through a Gaussian distribution algorithm according to the error value;
the judging module is used for judging whether the flow pressure is used as the target injection-production data for calculating the connectivity parameters or not and obtaining a first judging result; if the first judgment result is yes, the initial relevant data further comprises: the flowing pressure of the water injection well group and the acquisition time corresponding to the flowing pressure; the capacitance model is a capacitance model considering the flow pressure; if the first judgment result is negative, the capacitance model is a capacitance model without considering the flow pressure;
wherein the capacitance model without considering the flow pressure is:
<mrow> <msub> <mover> <mi>q</mi> <mo>^</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>&lambda;</mi> <mi>p</mi> </msub> <mi>q</mi> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mfrac> <mrow> <mo>-</mo> <mrow> <mo>(</mo> <mrow> <mi>n</mi> <mo>-</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <msub> <mi>T</mi> <mi>p</mi> </msub> </mfrac> </msup> <mo>+</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>i</mi> <mo>=</mo> <mi>l</mi> </mrow> </munderover> <msub> <mi>&lambda;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <msubsup> <mi>i</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>&prime;</mo> </msubsup> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
the capacitance model considering the flow pressure is as follows:
<mrow> <msub> <mover> <mi>q</mi> <mo>^</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>&lambda;</mi> <mi>p</mi> </msub> <mi>q</mi> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mfrac> <mrow> <mo>-</mo> <mrow> <mo>(</mo> <mrow> <mi>n</mi> <mo>-</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <msub> <mi>T</mi> <mi>p</mi> </msub> </mfrac> </msup> <mo>+</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>i</mi> <mo>=</mo> <mi>l</mi> </mrow> </munderover> <msub> <mi>&lambda;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <msubsup> <mi>i</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>&prime;</mo> </msubsup> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>k</mi> <mo>=</mo> <mi>K</mi> </mrow> </munderover> <msub> <mi>&upsi;</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>&lsqb;</mo> <mrow> <msub> <mi>p</mi> <mrow> <msub> <mi>wf</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mfrac> <mrow> <mo>-</mo> <mrow> <mo>(</mo> <mrow> <mi>n</mi> <mo>-</mo> <msub> <mi>n</mi> <mn>0</mn> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <msub> <mi>&tau;</mi> <mrow> <mi>k</mi> <mi>j</mi> </mrow> </msub> </mfrac> </msup> <mo>-</mo> <msub> <mi>p</mi> <mrow> <msub> <mi>wf</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>p</mi> <mrow> <msub> <mi>wf</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> </mrow> <mo>&prime;</mo> </msubsup> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow> <mo>&rsqb;</mo> </mrow> <mo>;</mo> </mrow>
wherein,the water injection amount of the water injection well i at the moment n is obtained; i.e. iij(n) is the liquid production amount of the oil production well j in the water injection well group i at the moment of n; n is0Is the initial time;for the amount of the liquid production iij(n) convolution of the (n); λ is the cross-flow coefficient; τ is a time lag constant; lambda [ alpha ]ijIs the cross-flow coefficient between the water injection well i and the oil production well j; tau isijIs the time lag constant between the water injection well i and the oil production well j;the influence of the first water injection of the water injection well,is equal to the imbalance constant;
wherein,the flowing pressure of a corresponding oil production well k in the water injection well group i is measured;to the pressure of flowConvolution of (2); upsilon iskiIs a weight; upsilon iskiIs equal to λki；λkiIs the cross-flow coefficient between the water injection well i and the oil production well k; tau iskiIs the time lag constant between the water injection well i and the production well k.
4. The apparatus of claim 3, wherein the acquisition module comprises:
the first acquisition unit is used for acquiring initial related data of the water injection well group by an injection-production response method; or
And the second acquisition unit is used for acquiring initial related data of the water injection well group through the tracer.
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| CN107120111B (en) * | 2017-03-24 | 2020-07-07 | 中国地质大学（武汉） | Oil reservoir inter-well communication degree evaluation method and system based on multi-fractal |
| CN110965970B (en) * | 2018-09-29 | 2022-02-11 | 北京国双科技有限公司 | Method and device for determining correlation between water injection well and oil production well |
| CN109389307B (en) * | 2018-10-10 | 2022-03-01 | 中国石油天然气股份有限公司 | Method and device for determining channeling flow of oil reservoir water injection well |
| CN111126650B (en) * | 2018-10-31 | 2022-05-27 | 北京国双科技有限公司 | Model construction method and device for oil field simulation system |
| CN111506975B (en) * | 2020-04-10 | 2024-09-24 | 中国石油化工股份有限公司 | Method, device and computing equipment for judging communication relation between wells |
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