Analog oil reservoir recommendation method based on dynamic and static data sets

Abstract

The invention discloses an analog reservoir recommendation method based on dynamic and static data sets, and the method comprises the specific steps: extracting dynamic data and static data from an oil well professional database, and sequentially carrying out the standardized cleaning and dynamic data normalization, and obtaining a temporary data table; specifying a target oil reservoir, and according to the temporary data table, constructing an oil reservoir static similar model through a K-means clustering algorithm to obtain a plurality of oil reservoirs with similar geologic features; discriminating parameters are selected, a discriminating model is constructed from the multiple oil reservoirs with the similar geological features through an expert fuzzy system, and the oil reservoirs with the similar geological features in the multiple development stages are obtained; determining main control factors and weights which influence the yield from similar oil reservoirs in a plurality of development stages through a random forest algorithm to obtain a dynamic curve similarity calculation model, determining the oil reservoir with the highest coefficient by curve similarity, namely the oil reservoir with the most similar development effect with the target oil reservoir, and completing the recommendation of analogy oil reservoirs; the problem that in the prior art, it is difficult to manually judge the similarity degree between oil reservoirs in all development stages is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of oilfield analogy methods, and in particular relates to an analogy reservoir recommendation method based on dynamic and static data sets. Background Art

[0002] Due to the variety of geological parameters, production parameters, and mining parameters of oil and gas fields, the importance of reservoir characteristics under different geological conditions has different effects on production. How to define and judge whether reservoirs are similar has become a difficult problem. The reservoir analogy method is an important technical means for SEC reserve assessment, recoverable reserve calibration, development plan preparation, reservoir development dynamic prediction and other businesses. However, in the face of differences in multiple factors such as reservoir properties, development stages, and development effects, it is particularly difficult to accurately and quickly find similar reservoirs.

[0003] In the prior art, the patent with publication number CN107133879A discloses a method for screening similar oil fields. The method is based on the grey theory and similarity theory, and according to the essential association between the analog parameters and the analog targets, the analog parameters and weights are quantitatively determined. According to the reservoir type of the research object, the parameter threshold normal distribution is introduced to process the analog parameters, and the similarity is calculated. The similar oil fields are screened by comprehensive evaluation. However, only the static data association relationship of the oil fields is considered, and the analog parameters are relatively simple. The patent with publication number CN113240321A discloses an oil field analog evaluation method and system based on deep learning, and proposes to use one of the static characteristic parameters of the oil field to be evaluated or the oil field development and production parameters as the analog target function, and compare and screen the parameters one by one. However, the development and production parameters only involve the characteristics at a single time point, and the similarity of the reservoirs is not considered from the time series.

[0004] Therefore, there is an urgent need for a method that is easy to use, highly accurate, and suitable for recommending similar reservoirs at various development stages. Summary of the invention

[0005] The purpose of the present invention is to provide an analog reservoir recommendation method based on dynamic and static data sets, which solves the problem in the prior art that it is difficult for humans to judge the similarity between reservoirs at different development stages.

[0006] The technical solution of the present invention is an analog reservoir recommendation method based on dynamic and static data sets, which is specifically implemented according to the following steps: Step 1, extracting dynamic data and static data from a professional database of oil wells and performing standardized cleaning and dynamic data normalization in sequence to obtain a temporary data table;

[0007] Step 2, specify the target reservoir and build a reservoir static similarity model based on the temporary data table through the K-means clustering algorithm to obtain multiple reservoirs with similar geological characteristics;

[0008] Step 3, selecting discrimination parameters and constructing a discrimination model from multiple reservoirs with similar geological characteristics through an expert fuzzy system to obtain similar reservoirs at multiple development stages;

[0009] Step 4: From similar reservoirs at multiple development stages, the main controlling factors and weights that affect production are determined by the random forest algorithm to obtain a dynamic curve similarity calculation model. The reservoir with the highest curve similarity determination coefficient is the reservoir that is most similar to the target reservoir in development effect, thus completing the recommendation of analog reservoirs.

[0010] The present invention is also characterized in that the dynamic data in step 1 include decline rate, water content, oil production and recovery degree of recoverable reserves; the static data in step 1 include development strata, permeability, effective thickness, fracture development, edge and bottom water development, formation coefficient, mobility and porosity ratio.

[0011] The normalization and cleaning in step 1 includes filling missing values, removing outliers, and converting text categorical variables.

[0012] In step 1, the dynamic data is normalized to transform the dynamic data into a dimensionless expression between [0,1].

[0013] Step 2 is implemented as follows:

[0014] Step 2.1, specifying a target reservoir from the entire reservoir and constructing a reservoir static similarity model using a one-dimensional or multi-dimensional K-means clustering algorithm based on a temporary data table and several static parameters;

[0015] Step 2.2: Extract other reservoirs in the cluster where the target reservoir is located in the reservoir static similarity model, that is, the reservoir group with similar geological characteristics.

[0016] Step 3 is implemented as follows:

[0017] Step 3.1, selecting a discriminant parameter from the temporary data table, the discriminant parameter including one or more of the three parameters of water content, oil production, and recoverable reserves recovery degree;

[0018] Step 3.2: From multiple reservoirs with similar geological characteristics, a discrimination model is constructed through an expert fuzzy system to extract similar reservoirs at multiple development stages.

[0019] Step 3.2 is implemented as follows:

[0020] Step 3.2.1: Determine the oil production stage based on the change rate of oil production in the reservoir in the last two months. The calculation formula for the change rate is:

[0021]

[0022] Among them, ρ cis the oil production change rate, m2 is the oil production of the latest month, and m1 is the oil production of the previous month; when ρ c Greater than 5% is considered as high yield. c Less than 5% is a decreasing rate, and a change rate of 5% is a stable yield;

[0023] Step 3.2.2: Determine the extent of recoverable reserves. The calculation formula is:

[0024]

[0025] Among them, ρ k is the recoverable reserves rate, V y is the geological reserves of crude oil, V k is recoverable reserves; when ρ k When <20%, ​​it is low recoverable reserves rate. When 20%≤ρ k When <60%, it is medium recoverable reserves rate. When 60%≤ρ k When <80%, it is a high recoverable reserve rate. When 80%≤ρ k When is the ultra-high recoverable reserves rate;

[0026] Step 3.2.3: Based on the comprehensive water content of the reservoir f w Divide, when f w When <20%, it is low moisture content. When 20%≤f w When the moisture content is less than 60%, it is medium moisture content. When 60%≤f w When <80%, it is high moisture content. When 80%≤f w When the moisture content is very high;

[0027] Step 3.2.4: According to the oil production change rate ρ in step 3.2.1 c , the recoverable reserve rate ρ in step 3.2.2 k and the comprehensive reservoir water content f in step 3.2.3 w Label naming is performed, a discriminant model is constructed, and multiple reservoirs with similar geological characteristics are selected, that is, similar reservoirs in multiple development stages.

[0028] Step 4 is implemented according to the following steps:

[0029] Step 4.1, based on similar reservoirs at multiple development stages, determine the main controlling factors and weights that affect production changes through the random forest algorithm;

[0030] Step 4.2: Draw a curve based on one or more parameters of the main control factors affecting yield changes, and determine the similarity of the curves. The curve similarity coefficient R 2 The calculation formula is

[0031]

[0032] Among them, y is the data of the specified parameters of the target reservoir, The data of parameters specified for other comparative reservoirs, i is the sequence number of the i-th value in the parameter data, n is the number of parameter data values, is the mean value of y;

[0033] Curve similarity coefficient R 2 is the weighted average of the similarity and influence weight of each main control factor curve, that is,

[0034]

[0035] Among them, R 2 is the curve similarity coefficient, R i is the similarity coefficient of each main control factor curve, V i is the weight of the impact of the main control factors on output, and the sum of the weights of all main control factors is equal to 1;

[0036] Step 4.3: Based on the reservoir similarity R 2 The reservoir with the highest curve similarity determination coefficient among similar reservoirs at multiple development stages is the reservoir with the most similar development effect to the target reservoir, thus completing the recommendation of analog reservoirs.

[0037] The beneficial effects of the present invention are as follows: the present invention automatically extracts static parameters of oil reservoirs and dynamic parameters based on time series, applies K-means clustering algorithm, fuzzy expert system and random forest algorithm to construct an integrated model, and comprehensively recommends other oil reservoirs similar to the target oil reservoir from multiple dimensions and multiple levels; the present invention overcomes the shortcomings of single comparison index and insufficient consideration of dynamic development rules, has the characteristics of convenient application and high precision, is suitable for the recommendation of similar oil reservoirs in various development stages, solves the problem that it is difficult for humans to judge the degree of similarity between oil reservoirs, and provides technical support for in-depth research on the relationship between multiple oil reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the analog reservoir recommendation method based on dynamic and static data sets of the present invention;

[0039] Figure 2 is an operational flow chart of the recommended method for analog reservoirs of the present invention;

[0040] Figure 3 It is a cluster analysis diagram of Example 1 in the analog reservoir recommendation method of the present invention;

[0041] Figure 4 It is a similar reservoir in the development stage of Example 1 in the analog reservoir recommendation method of the present invention;

[0042] Figure 5 is a similarity curve between the recommended reservoir and the target reservoir in Example 1 of the analog reservoir recommendation method of the present invention;

[0043] Figure 6 It is a cluster analysis diagram of Example 2 in the analog reservoir recommendation method of the present invention;

[0044] Figure 7 It is a similar reservoir in the development stage of Example 2 in the analog reservoir recommendation method of the present invention;

[0045] Figure 8 is a similarity curve of water drive reserve production between the recommended reservoir and the target reservoir in Example 2 of the analog reservoir recommendation method of the present invention;

[0046] Fig. 9 is a similarity curve between the recoverability of the recommended reservoir and the target reservoir in Example 2 of the analog reservoir recommendation method of the present invention;

[0047] Fig.10 It is a cluster analysis diagram of Example 3 in the analog reservoir recommendation method of the present invention;

[0048] Fig.11 It is a similar reservoir in the development stage of Example 3 in the analog reservoir recommendation method of the present invention;

[0049] Fig.12 is a similarity curve of water drive reserve production between the recommended reservoir and the target reservoir in Example 3 of the analog reservoir recommendation method of the present invention;

[0050] Fig.13 is a similarity curve of monthly comprehensive decline rate of old wells in the recommended reservoir and the target reservoir in Example 3 of the analog reservoir recommendation method of the present invention;

[0051] Fig.14 It is a cluster analysis diagram of Example 4 in the analog reservoir recommendation method of the present invention;

[0052] Fig.15 It is a similar reservoir in the development stage of Example 4 in the analog reservoir recommendation method of the present invention;

[0053] Fig.16 is a similarity curve of water drive reserve production between the recommended reservoir and the target reservoir in Example 4 of the analog reservoir recommendation method of the present invention;

[0054] Fig.17 It is a similarity curve of the monthly comprehensive decline rate of old wells in the recommended reservoir and the target reservoir in Example 4 of the analog reservoir recommendation method of the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] The present invention is based on the analog reservoir recommendation method of dynamic and static data sets, such as Figure 1 , Figure 2As shown, the specific implementation steps are as follows:

[0057] Step 1, data preparation stage: extract dynamic data and static data from the oil well professional database and perform standardized cleaning and dynamic data normalization in turn to obtain a temporary data table;

[0058] The dynamic data in step 1 include decline rate, water content, oil production and recovery degree of recoverable reserves; the static data in step 1 include development strata, permeability, effective thickness, fracture development, edge and bottom water development, formation coefficient, mobility and porosity ratio;

[0059] The normalization and cleaning in step 1 includes filling missing values, removing outliers, and converting text categorical variables.

[0060] In step 1, the dynamic data is normalized to transform the dynamic data into a dimensionless expression between [0,1].

[0061] Step 2, Recommendation of reservoirs with similar geological characteristics: Specify the target reservoir and build a reservoir static similarity model based on the temporary data table through the K-means clustering algorithm to obtain multiple reservoirs with similar geological characteristics;

[0062] Step 2.1, specifying a target reservoir from the entire reservoir and constructing a reservoir static similarity model using a one-dimensional or multi-dimensional K-means clustering algorithm based on a temporary data table and several static parameters;

[0063] Step 2.2, extracting other reservoirs in the cluster where the target reservoir is located in the reservoir static similarity model, that is, the reservoir group with similar geological characteristics;

[0064] Step 3, similar reservoir recommendation stage in development stage: select discrimination parameters and construct a discrimination model from multiple reservoirs with similar geological characteristics through an expert fuzzy system to obtain multiple similar reservoirs in development stages;

[0065] Step 3.1, selecting a discriminant parameter from the temporary data table, the discriminant parameter including one or more of the three parameters of water content, oil production, and recoverable reserves recovery degree;

[0066] Step 3.2, from multiple reservoirs with similar geological characteristics, a discrimination model is constructed through an expert fuzzy system to extract similar reservoirs at multiple development stages;

[0067] Step 3.2.1: Determine the oil production stage based on the change rate of oil production in the reservoir in the last two months. The calculation formula for the change rate is:

[0068] ρ c =(m2-m1) / m2×100% (1)

[0069] Among them, ρ cis the oil production change rate, m2 is the oil production of the latest month, and m1 is the oil production of the previous month; when ρ c Greater than 5% is considered as high yield. c Less than 5% is a decreasing rate, and a change rate of 5% is a stable yield;

[0070] Step 3.2.2: Determine the extent of recoverable reserves. The calculation formula is:

[0071] ρ k =(V y -V k ) / V y ×100% (2)

[0072] Among them, ρ k is the recoverable reserves rate, V y is the geological reserves of crude oil, V k is recoverable reserves; when ρ k When <20%, ​​it is low recoverable reserves rate. When 20%≤ρ k When <60%, it is medium recoverable reserves rate. When 60%≤ρ k When <80%, it is a high recoverable reserve rate. When 80%≤ρ k When is the ultra-high recoverable reserves rate;

[0073] Step 3.2.3: Based on the comprehensive water content of the reservoir f w Divide, when f w When <20%, it is low moisture content. When 20%≤f w When the moisture content is less than 60%, it is medium moisture content. When 60%≤f w When <80%, it is high moisture content. When 80%≤f w When the moisture content is very high;

[0074] Step 3.2.4: According to the oil production change rate ρ in step 3.2.1 c , the recoverable reserve rate ρ in step 3.2.2 k and the comprehensive reservoir water content f in step 3.2.3 w The label naming rule for the reservoir development stage is "comprehensive water content + technically recoverable reserves recovery degree + oil production", for example: high water content and declining recovery stage. A discrimination model is constructed to select from multiple reservoirs with similar geological characteristics, that is, reservoirs with similar development stages.

[0075] Step 4, recommendation of reservoirs with similar development effects: From similar reservoirs at multiple development stages, the main controlling factors and weights affecting production are determined by the random forest algorithm, and a dynamic curve similarity calculation model is obtained. The reservoir with the highest curve similarity determination coefficient is the reservoir with the most similar development effect to the target reservoir, and the analog reservoir is recommended.

[0076] Step 4.1, based on similar reservoirs at multiple development stages, determine the main controlling factors and weights that affect production changes through the random forest algorithm;

[0077] Step 4.2: Draw a curve based on one or more parameters of the main control factors affecting yield changes, and determine the similarity of the curves. The curve similarity coefficient R 2 The calculation formula is

[0078]

[0079] Among them, y is the data of the specified parameters of the target reservoir, The data of parameters specified for other comparative reservoirs, i is the sequence number of the i-th value in the parameter data, n is the number of parameter data values, is the mean value of y;

[0080] Curve similarity coefficient R 2 is the weighted average of the similarity and influence weight of each main control factor curve, that is,

[0081]

[0082] Among them, R 2 is the curve similarity coefficient, R i is the similarity coefficient of each main control factor curve, V i is the weight of the impact of the main control factors on output, and the sum of the weights of all main control factors is equal to 1;

[0083] Step 4.3: According to the curve similarity coefficient R 2 The reservoir with the highest curve similarity determination coefficient among similar reservoirs at multiple development stages is the reservoir with the most similar development effect to the target reservoir, thus completing the recommendation of analog reservoirs.

[0084] Example 1

[0085] This embodiment provides an analog reservoir recommendation method based on dynamic and static data sets, which is specifically implemented according to the following steps:

[0086] Step 1: extract dynamic data and static data from the oil well professional database and perform standardized cleaning and dynamic data normalization in turn to obtain a temporary data table;

[0087] The data extractor automatically extracts relevant data from the reservoir static database and the oil, gas and water well production database. The data cleaner fills missing values, removes outliers, and converts text classification variables. The data processor performs dimensionless transformation of dynamic data to [0,1], and then saves all processed data to the data storage device.

[0088] Step 2, specify the target reservoir and build a reservoir static similarity model based on the temporary data table through the K-means clustering algorithm to obtain multiple reservoirs with similar geological characteristics;

[0089] like Figure 3 As shown, the analog object, reservoir range and static parameters (one or more) are specified through the parameter selector 1, the clustering analysis is performed on the relevant reservoir parameters in the data storage device through the clustering analyzer, and the best cluster and clustering results are displayed through the recommender. The other reservoirs in the cluster where the target reservoir is located are the reservoirs with similar geological characteristics.

[0090] Step 3, selecting discrimination parameters and constructing a discrimination model from multiple reservoirs with similar geological characteristics through an expert fuzzy system to obtain similar reservoirs at multiple development stages;

[0091] like Figure 4 As shown, one or more parameters including annual oil production, technically recoverable reserves recovery rate and comprehensive water content are specified through parameter selector 2, and all reservoir-related parameters of the cluster where the target reservoir is located in the data storage device are identified through the expert fuzzy system, and reservoirs with the same name are similar reservoirs in the development stage.

[0092] Step 4: From similar reservoirs at multiple development stages, the main controlling factors and weights that affect production are determined by the random forest algorithm to obtain a dynamic curve similarity calculation model. The reservoir with the highest curve similarity determination coefficient is the reservoir that is most similar to the target reservoir in development effect, thus completing the recommendation of analog reservoirs.

[0093] like Figure 5 As shown, the normalized time series data of the reservoir is subjected to random forest learning through the main controlling factor analyzer, and the production parameters are used as the input set and other parameters are used as the input set to perform feature importance analysis on the changes in the production data, and the main controlling factors (one or more) are determined. The main controlling factor curve is drawn by the graph plotter, and the similarity of each main controlling factor and the reservoir similarity are calculated by the similarity calculator. The reservoir with the highest score is the reservoir with similar development effect.

[0094] Example 2

[0095] This embodiment provides an analog reservoir recommendation method based on dynamic and static data sets, which is specifically implemented according to the following steps:

[0096] Step 1: extract dynamic data and static data from the oil well professional database and perform standardized cleaning and dynamic data normalization in turn to obtain a temporary data table;

[0097] Step 2, specify the target reservoir and build a reservoir static similarity model based on the temporary data table through the K-means clustering algorithm to obtain multiple reservoirs with similar geological characteristics;

[0098] like Figure 6 As shown, taking the analog reservoir of recommended reservoir 46 as an example, the fracture development status, development degree, formation coefficient, mobility, and average roar radius are selected as clustering parameters, as shown in Table 1, and the best clustering clusters are determined to be 4 clusters.

[0099] Table 1

[0100]

[0101]

[0102] Step 3, selecting discrimination parameters and constructing a discrimination model from multiple reservoirs with similar geological characteristics through an expert fuzzy system to obtain similar reservoirs at multiple development stages;

[0103] like Figure 7 As shown, taking the cluster where oil reservoir 46 is located as input, the fuzzy expert system is used to judge the similarity of development stages, and the oil reservoirs with similar development stages are obtained.

[0104] Step 4: From similar reservoirs at multiple development stages, the main controlling factors and weights that affect production are determined by the random forest algorithm to obtain a dynamic curve similarity calculation model. The reservoir with the highest curve similarity determination coefficient is the reservoir that is most similar to the target reservoir in development effect, thus completing the recommendation of analog reservoirs.

[0105] like Figure 8 , Fig. 9 As shown, the reservoir 46 in the "high water content and ultra-high recovery reduction stage" is used as input to calculate the curve similarity, and it is determined that the reservoir 43 has a similar development effect.

[0106] Example 3

[0107] Step 1: extract dynamic data and static data from the oil well professional database and perform standardized cleaning and dynamic data normalization in turn to obtain a temporary data table;

[0108] Step 2, specify the target reservoir and build a reservoir static similarity model based on the temporary data table through the K-means clustering algorithm to obtain multiple reservoirs with similar geological characteristics;

[0109] like Fig.10 As shown in Table 2, taking the analogous reservoir of recommended reservoir 28 as an example, matrix permeability_air permeability, fracture development status_development degree, edge and bottom water development status_development degree are selected as clustering parameters, as shown in Table 2, and 6 is selected as the best cluster, and reservoirs with similar geological characteristics are obtained.

[0110] Table 2

[0111]

[0112] Step 3, selecting discrimination parameters and constructing a discrimination model from multiple reservoirs with similar geological characteristics through an expert fuzzy system to obtain similar reservoirs at multiple development stages;

[0113] like Fig.11 As shown, taking the cluster where reservoir 28 is located as input, the fuzzy expert system is used to judge the similarity of development stages, and the reservoirs with similar development stages are obtained.

[0114] Step 4: From similar reservoirs at multiple development stages, the main controlling factors and weights that affect production are determined by the random forest algorithm to obtain a dynamic curve similarity calculation model. The reservoir with the highest curve similarity determination coefficient is the reservoir that is most similar to the target reservoir in development effect, thus completing the recommendation of analog reservoirs.

[0115] like Fig.12 , Fig.13 As shown, taking the reservoir 28 in the "medium water content and high production reduction stage" as input, the curve similarity is calculated, and the reservoir 47 with similar development effect is determined.

[0116] Example 4

[0117] This embodiment provides an analog reservoir recommendation method based on dynamic and static data sets, which is specifically implemented according to the following steps:

[0118] Step 1: extract dynamic data and static data from the oil well professional database and perform standardized cleaning and dynamic data normalization in turn to obtain a temporary data table;

[0119] Step 2, specify the target reservoir and build a reservoir static similarity model based on the temporary data table through the K-means clustering algorithm to obtain multiple reservoirs with similar geological characteristics;

[0120] like Fig.14 As shown in Figure 3, taking the analogous reservoir of recommended reservoir 20 as an example, matrix permeability_air permeability, fracture development status_development degree, fracture development status_fracture direction, edge and bottom water development status_development degree, edge and bottom water development status_contact type, formation coefficient, mobility, average roar channel radius, and pore-roar ratio are selected as clustering parameters, and 10 is selected as the best clustering cluster, as shown in Table 3, and reservoirs with similar geological characteristics are obtained.

[0121] Table 3

[0122]

[0123]

[0124] Step 3, selecting discrimination parameters and constructing a discrimination model from multiple reservoirs with similar geological characteristics through an expert fuzzy system to obtain similar reservoirs at multiple development stages;

[0125] like Fig.15 As shown, taking the cluster where reservoir 20 is located as input, the fuzzy expert system is used to judge the similarity of development stages, and the reservoirs with similar development stages are obtained.

[0126] Step 4: From similar reservoirs at multiple development stages, the main controlling factors and weights that affect production are determined by the random forest algorithm to obtain a dynamic curve similarity calculation model. The reservoir with the highest curve similarity determination coefficient is the reservoir that is most similar to the target reservoir in development effect, thus completing the recommendation of analog reservoirs.

[0127] like Fig.16 , Fig.17 As shown, the reservoir 20, which is located in the "ultra-high water content and ultra-high production reduction stage", is used as input to calculate the curve similarity, and it is determined that the reservoir 14 has a similar development effect.

[0128] The advantage of the present invention is that it comprehensively considers the dynamic and static parameters of the reservoir, and based on machine learning methods such as K-means clustering algorithm, fuzzy expert system, random forest algorithm, etc., establishes a multi-dimensional, multi-level, progressive integrated analog reservoir recommendation method, which has the advantages of comprehensive consideration of indicators and high similarity of recommendation results. Data extraction, cleaning, and analysis are fully automated, and users can freely choose the operation steps according to the application purpose, which greatly reduces the difficulty of manual judgment of similar reservoirs, and effectively supports business applications such as recoverable reserves calibration, development plan preparation, and reservoir development effect evaluation.

Claims

1. The analog reservoir recommendation method based on dynamic and static data sets is characterized by: The specific implementation is as follows: Step 1, extracting dynamic data and static data from the oil well professional database and performing standardized cleaning and dynamic data normalization in turn to obtain a temporary data table; Step 2, specifying the target reservoir and constructing a reservoir static similarity model based on the temporary data table by using a K-means clustering algorithm to obtain multiple reservoirs with similar geological characteristics; Step 3, selecting discrimination parameters and constructing a discrimination model from a plurality of oil reservoirs with similar geological characteristics through an expert fuzzy system to obtain oil reservoirs with similar development stages at a plurality of development stages; Step 4: From multiple similar reservoirs in the development stage, determine the main controlling factors and weights that affect production through the random forest algorithm, and obtain a dynamic curve similarity calculation model. The reservoir with the highest curve similarity determination coefficient is the reservoir that is most similar to the development effect of the target reservoir, and the analog reservoir is recommended.

2. The analog reservoir recommendation method based on dynamic and static data sets according to claim 1, characterized in that: The dynamic data described in step 1 include decline rate, water content, oil production and recovery degree of recoverable reserves; the static data described in step 1 include development strata, permeability, effective thickness, fracture development, edge and bottom water development, formation coefficient, mobility and porosity ratio.

3. The analog reservoir recommendation method based on dynamic and static data sets according to claim 1, characterized in that: The normalization cleaning described in step 1 includes filling missing values, removing outliers, and converting text categorical variables.

4. The analog reservoir recommendation method based on dynamic and static data sets according to claim 1, characterized in that: The dynamic data normalization in step 1 is to transform the dynamic data into a dimensionless expression between [0, 1].

5. The analog reservoir recommendation method based on dynamic and static data sets according to claim 1, characterized in that: The step 2 is specifically implemented according to the following steps: Step 2.1, specifying a target reservoir from the entire reservoir and constructing a reservoir static similarity model by performing a one-dimensional or multi-dimensional K-means clustering algorithm based on the temporary data table and a number of static parameters; Step 2.2: Extract other reservoirs in the cluster where the target reservoir is located in the reservoir static similarity model, that is, the reservoir group with similar geological characteristics.

6. The analog reservoir recommendation method based on dynamic and static data sets according to claim 1, characterized in that: The step 3 is specifically implemented according to the following steps: Step 3.1, selecting a discriminant parameter from the temporary data table, the discriminant parameter including one or more of the three parameters of water content, oil production, and recoverable reserves recovery degree; Step 3.2: From the multiple reservoirs with similar geological characteristics, a discrimination model is constructed through an expert fuzzy system to extract similar reservoirs in multiple development stages.

7. The analog reservoir recommendation method based on dynamic and static data sets according to claim 6, characterized in that: The step 3.2 is specifically implemented according to the following steps: Step 3.2.1: Determine the oil production stage based on the change rate of oil production in the reservoir in the last two months. The calculation formula for the change rate is: ρ c =(m2-m1) / m2×100% (1) Among them, ρ c is the oil production change rate, m2 is the oil production of the latest month, and m1 is the oil production of the previous month; when ρ c Greater than 5% is considered as high yield. c Less than 5% is a decreasing rate, and a change rate of 5% is a stable yield; Step 3.2.2: Determine the extent of recoverable reserves. The calculation formula is: ρ k =(V y -V k ) / V y ×100% (2) Among them, ρ k is the recoverable reserves rate, V y is the geological reserves of crude oil, V k is recoverable reserves; when ρ k When <20%, ​​it is low recoverable reserves rate. When 20%≤ρ k When <60%, it is medium recoverable reserves rate. When 60%≤ρ k When <80%, it is a high recoverable reserve rate. When 80%≤ρ k When is the ultra-high recoverable reserves rate; Step 3.2.3: Based on the comprehensive water content of the reservoir f w Divide, when f w When <20%, it is low moisture content. When 20%≤f w When the moisture content is less than 60%, it is medium moisture content. When 60%≤f w When <80%, it is high moisture content. When 80%≤f w When the moisture content is very high; Step 3.2.4: According to the oil production change rate ρ in step 3.2.1 c , the recoverable reserve rate ρ in step 3.2.2 k and the comprehensive reservoir water content f in step 3.2.3 w Label naming is performed, a discrimination model is constructed, and multiple reservoirs with similar geological characteristics are selected, that is, similar reservoirs in multiple development stages.

8. The analog reservoir recommendation method based on dynamic and static data sets according to claim 1, characterized in that: The step 4 is specifically implemented according to the following steps: Step 4.1, based on similar reservoirs at multiple development stages, determine the main controlling factors and weights that affect production changes through the random forest algorithm; Step 4.2: Draw a curve based on one or more parameters of the main control factors affecting yield changes, and determine the similarity of the curves. The curve similarity coefficient R 2 The calculation formula is Among them, y is the data of the specified parameters of the target reservoir, The data of parameters specified for other comparative reservoirs, i is the sequence number of the i-th value in the parameter data, n is the number of parameter data values, is the mean value of y; Curve similarity coefficient R 2 is the weighted average of the similarity and influence weight of each main control factor curve, that is, Among them, R 2 is the curve similarity coefficient, R i is the similarity coefficient of each main control factor curve, V i is the weight of the impact of the main control factors on output, and the sum of the weights of all main control factors is equal to 1; Step 4.3: According to the curve similarity coefficient R 2 The reservoir with the highest curve similarity determination coefficient among similar reservoirs at multiple development stages is the reservoir with the most similar development effect to the target reservoir, thus completing the recommendation of analog reservoirs.

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