Hydraulic fracturing effect main control factor analysis method and device based on causal inference

Abstract

The invention relates to a hydraulic fracturing effect main control factor analysis method and device based on causal inference. Factors influencing the yield increase of an oil and gas well after hydraulic fracturing are researched by using a causal inference method, and the causal effect value of each influence factor on the yield is determined, so that workers can better understand the factors influencing the yield, and then field workers are guided to carry out hydraulic fracturing parameter optimization design.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of oil and gas extraction, and in particular to a method and device for analyzing main controlling factors of hydraulic fracturing effects based on causal inference. Background Art

[0002] There are many factors that affect the effect of hydraulic fracturing in the development of tight oil and gas reservoirs, but it is currently impossible to accurately determine the main controlling factors that affect the effect of hydraulic fracturing, thereby making it impossible to improve the production capacity of hydraulic fracturing. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the embodiments of this specification provide a method and device for analyzing the main controlling factors of hydraulic fracturing effects based on causal inference. The causal inference method is used to study the factors affecting the increase in oil and gas well production after hydraulic fracturing, and to clarify the causal effect value of each influencing factor on the output. This helps staff to better understand the factors affecting the output, and then guide on-site staff to optimize the design of hydraulic fracturing parameters.

[0004] The specific technical solutions of the embodiments of this specification are as follows:

[0005] On the one hand, the embodiments of this specification provide a method for analyzing the main controlling factors of hydraulic fracturing effects for causal inference, the method comprising:

[0006] Obtaining values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells;

[0007] Taking a type of post-intervention variable as the intervention variable to be analyzed, clustering the values ​​of the intervention variable to be analyzed of the plurality of produced wells to obtain a plurality of categories, and dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories;

[0008] For each produced well in the intervention group and the control group, the endogenous control coefficients of the multiple types of background variables and the multiple types of post-intervention variables of the produced well are calculated according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the value of the production data;

[0009] For each produced well in the intervention group and the control group, the propensity score of the intervention variable to be analyzed of the produced well is calculated according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the endogenous control coefficients of the respective variables;

[0010] Calculating the weight coefficient of the intervention variable to be analyzed for each produced well according to the propensity score of the intervention variable to be analyzed for each produced well in the intervention group and the control group;

[0011] Matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matched groups;

[0012] The intervention effect of the intervention variable to be analyzed on the production data is calculated based on the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups, so that the staff can optimize the design of construction parameters based on the intervention effect.

[0013] Furthermore, dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories further comprises:

[0014] The category belonging to the intervention group and the category belonging to the control group are determined according to the mean value of the intervention variable to be analyzed in each category.

[0015] Further, calculating the endogenous control coefficients of the multiple types of background variables and the multiple types of post-intervention variables of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the value of the production data further includes:

[0016] Determine at least one instrumental variable from the plurality of types of the background variables and other types of post-intervention variables except the intervention variable to be analyzed;

[0017] The least square method is used to solve the formula Solve to obtain the endogenous control coefficients of the background variables of multiple types and the post-intervention variables of multiple types, where Y represents the value of the output data, X i represents the background variable of the i-th type or the post-intervention variable other than the intervention variable to be analyzed, β0, ..., β i , ..., β D are the endogenous control coefficients, among which β i is the endogenous control coefficient of the background variable or the post-intervention variable of the ith type, η is a constant term, in is the alternative value of the intervention variable to be analyzed, Z i represents the value of the instrumental variable of the ith type, α0, ..., α i is the coefficient, ε is the constant term; the loss function of the least squares method is Wherein, Loss is the loss value, and n represents the total number of background variables or post-intervention variables other than the intervention variables to be analyzed.

[0018] Furthermore, the formula for calculating the propensity score of the intervention variable to be analyzed of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the endogenous control coefficients of the respective variables is:

[0019]

[0020] Where e(D) represents the propensity score of the intervention variable D to be analyzed, X1, X2, ..., X n are the values ​​of the background variables of multiple types, the values ​​of other types of post-intervention variables except the intervention variables to be analyzed, β1, β2, ..., β n are respectively the endogenous control coefficients of the corresponding background variables or other types of post-intervention variables except the intervention variables to be analyzed.

[0021] Furthermore, calculating the weight coefficient of the intervention variable to be analyzed for each produced well according to the propensity score of the intervention variable to be analyzed for each produced well in the intervention group and the control group further includes:

[0022] If the produced well belongs to the intervention group, then according to the formula Calculate the weight coefficient, where w i represents the weight coefficient of the intervention variable D to be analyzed for the i-th well in the intervention group, e(D i ) represents the propensity score of the intervention variable D to be analyzed for the i-th well in the intervention group;

[0023] If the produced well belongs to the control group, then according to the formula Calculate the weight coefficient, where w j represents the weight coefficient of the intervention variable D to be analyzed for the jth well in the control group, e(D j ) represents the propensity score of the intervention variable D to be analyzed for the j-th well in the control group.

[0024] Furthermore, matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups further includes:

[0025] Calculate the difference between the propensity score of each produced well in the intervention group and the propensity score of each produced well in the control group, and determine the produced wells in the control group whose difference is less than a threshold, and regard the produced wells in the intervention group and the produced wells in the control group as a matching group;

[0026] If an already produced well in the intervention group has no matching already produced well in the control group, the already produced well in the intervention group will be discarded.

[0027] Furthermore, if the values ​​of the intervention variables to be analyzed of the plurality of produced wells are clustered to obtain two or more categories, dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories further includes:

[0028] Determine a plurality of categories belonging to the intervention group and a category belonging to the control group according to the mean value of the intervention variable to be analyzed in each category;

[0029] Matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups further comprises:

[0030] Calculate the difference between the propensity score of each produced well in each intervention group and the propensity score of each produced well in the control group, and determine the produced wells in the control group whose difference is less than a threshold value, and regard the produced wells in the intervention group and the produced wells in the control group as a matching group;

[0031] If a produced well in any intervention group has no matching produced well in the control group, the produced well in the intervention group will be discarded.

[0032] Furthermore, the formula for calculating the intervention effect of the intervention variable to be analyzed on the production data according to the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups is:

[0033]

[0034] Where ATE represents the intervention effect of the intervention variable to be analyzed on the production data, k represents the total number of matching groups, T represents the set of intervention groups, C represents the set of control groups, and Y i represents the numerical value of the production data of the ith well belonging to the intervention group T, Y j Represents the numerical value of the production data of the jth well in the control group C that belongs to the same matching group as the i-th well in the intervention group T.

[0035] Furthermore, after obtaining the values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells, the method further includes:

[0036] The multiple produced wells are grouped according to the values ​​of the background variables to obtain multiple groups, so as to perform the step of using a type of post-intervention variable as the intervention variable to be analyzed for the multiple produced wells in each group, and finally obtain the intervention effect of the intervention variable to be analyzed in each group on the production data.

[0037] On the other hand, the embodiment of this specification also provides a device for analyzing the main controlling factors of hydraulic fracturing effects based on causal inference, the device comprising:

[0038] A data acquisition unit, used to acquire values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells;

[0039] A clustering unit, used for taking a type of post-intervention variable as the intervention variable to be analyzed, clustering the values ​​of the intervention variable to be analyzed of the plurality of produced wells to obtain a plurality of categories, and dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories;

[0040] an endogenous control coefficient calculation unit, for calculating, for each produced well in the intervention group and the control group, the endogenous control coefficients of the multiple types of background variables and the multiple types of post-intervention variables of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the value of the production data;

[0041] a propensity score calculation unit, for calculating, for each produced well in the intervention group and the control group, the propensity score of the intervention variable to be analyzed of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the endogenous control coefficients of the respective variables;

[0042] A weight coefficient calculation unit, used to calculate the weight coefficient of the intervention variable to be analyzed of each produced well according to the propensity score of the intervention variable to be analyzed of each produced well in the intervention group and the control group;

[0043] a matching unit, configured to match the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups;

[0044] The intervention effect calculation unit is used to calculate the intervention effect of the intervention variable to be analyzed on the production data based on the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups, so as to facilitate the staff to optimize the design of construction parameters according to the intervention effect.

[0045] The embodiments of this specification pre-set the types of background variables and post-intervention variables, and then obtain the values ​​of multiple types of background variables, post-intervention variables and production data of multiple produced wells, and then use causal inference methods to form a multi-level and comprehensive causal inference framework, which not only effectively solves the problems of confounding factors, endogeneity and intervention heterogeneity, but also provides accurate causal effect analysis and optimization guidance, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a schematic diagram of an implementation system of a method for analyzing main controlling factors of hydraulic fracturing effect based on causal inference in an embodiment of this specification;

[0048] Figure 2 It is a schematic diagram of a flow chart of a method for analyzing main controlling factors of hydraulic fracturing effect based on causal inference in an embodiment of this specification;

[0049] Figure 3 It is a schematic diagram of a process of dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories in an embodiment of this specification;

[0050] Figure 4 It is a flow chart of dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories in another embodiment of the present specification;

[0051] Figure 5 It is a schematic diagram of the structure of a hydraulic fracturing effect main control factor analysis device based on causal inference in an embodiment of this specification;

[0052] Figure 6 The figure is a schematic diagram of the structure of a computer device in an embodiment of the present specification.

[0053]

Description of reference numerals

[0054] 101. Terminal;

[0055] 102. Server;

[0056] 501. Data acquisition unit;

[0057] 502, clustering unit;

[0058] 503. Endogenous control coefficient calculation unit;

[0059] 504. Propensity score calculation unit;

[0060] 505. Weight coefficient calculation unit;

[0061] 506. Matching unit;

[0062] 507: Intervention effect calculation unit;

[0063] 602. Computer equipment;

[0064] 604, processor;

[0065] 606. Memory;

[0066] 608, driving mechanism;

[0067] 610, input / output module;

[0068] 612. Input devices;

[0069] 614. Output devices;

[0070] 616. Presentation equipment;

[0071] 618. Graphical user interface;

[0072] 620, network interface;

[0073] 622, communication link;

[0074] 624. Communication bus. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the embodiments of this specification.

[0076] It should be noted that the terms "first", "second", etc. in the description and claims of the embodiments of this specification and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of this specification described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0077] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of the embodiments of this specification comply with the relevant provisions of national laws and regulations.

[0078] It should be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0079] like Figure 1 The diagram shows a schematic diagram of an implementation system of a method for analyzing the main controlling factors of hydraulic fracturing effects based on causal inference in an embodiment of the specification, including a terminal 101 and a server 102. The terminal 101 and the server 102 can communicate with each other through a network, and the network can include a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof, and is connected to a website, a user device (such as a computing device), and a back-end system.

[0080] The staff inputs the values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells to the server 102 through the terminal 101. The server 102 uses the causal inference method to analyze and calculate the values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells to obtain the intervention effect of the specified intervention variable to be analyzed on the production data. The server 102 can provide the intervention effect of the intervention variable to be analyzed on the production data to the staff through the terminal 101, so that the staff can optimize the design of construction parameters according to the intervention effect.

[0081] Alternatively, the server 102 may be a node of a cloud computing system (not shown), or each server may be a separate cloud computing system including a plurality of computers interconnected by a network and operating as a distributed processing system.

[0082] In addition, it should be noted that Figure 1 What is shown is only one application environment provided by the embodiment of this specification. In actual application, other application environments may also be included, and this specification does not limit it.

[0083] In view of the problems existing in the prior art, the embodiments of this specification provide a method for analyzing the main controlling factors of hydraulic fracturing effects based on causal inference. Figure 2 The flowchart of the method for analyzing the main controlling factors of hydraulic fracturing effect based on causal inference in the embodiment of this specification is shown. The figure describes the process of analyzing the intervention effect of the intervention variable to be analyzed on the production data. The order of steps listed in the embodiment is only one way of executing the order of many steps, and does not represent the only execution order. When the system or device product is executed in practice, it can be executed in the order of the method shown in the embodiment or the accompanying drawings or in parallel.

[0084] Specific as Figure 2 As shown, the method may include:

[0085] Step 201: obtaining values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells;

[0086] Step 202: taking a type of post-intervention variable as the intervention variable to be analyzed, clustering the values ​​of the intervention variable to be analyzed of the plurality of produced wells to obtain a plurality of categories, and dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories;

[0087] Step 203: for each produced well in the intervention group and the control group, calculate the endogenous control coefficients of the multiple types of background variables and the multiple types of post-intervention variables of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the value of the production data;

[0088] Step 204: for each produced well in the intervention group and the control group, calculate the propensity score of the intervention variable to be analyzed of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the endogenous control coefficients of the respective variables;

[0089] Step 205: Calculate the weight coefficient of the intervention variable to be analyzed for each produced well according to the propensity score of the intervention variable to be analyzed for each produced well in the intervention group and the control group;

[0090] Step 206: Matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups;

[0091] Step 207: Calculate the intervention effect of the intervention variable to be analyzed on the production data based on the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups, so that the staff can optimize the design of construction parameters based on the intervention effect.

[0092] The embodiments of this specification pre-set the types of background variables and post-intervention variables, and then obtain the values ​​of multiple types of background variables, post-intervention variables and production data of multiple produced wells, and then use causal inference methods to form a multi-level and comprehensive causal inference framework, which not only effectively solves the problems of confounding factors, endogeneity and intervention heterogeneity, but also provides accurate causal effect analysis and optimization guidance, and has broad application prospects.

[0093] In the embodiments of this specification, background variables are variables that represent the inherent characteristics of geology and wells, which are not affected by other variables but may affect the post-intervention variables, including but not limited to:

[0094] Oil layer thickness (H), unit: m;

[0095] Porosity (φ), unit: %;

[0096] Oil layer permeability (k), unit: mD;

[0097] Oil layer drilling rate (R), unit: %;

[0098] Horizontal section length (L h ), unit: m.

[0099] Post-intervention variables can be construction parameters, which are parameters that are affected by background variables and can be adjusted through intervention, including but not limited to:

[0100] Liquid strength (Q f ), unit: m 3 / min;

[0101] Sand addition strength (Q s ), unit: kg / min;

[0102] Sand ratio (S), unit: %;

[0103] Segment spacing (D), unit: m;

[0104] Cluster spacing (C), unit: m.

[0105] Yield data include but are not limited to yield increase factor (ΔP), unit: m 3 / d (increase in production after fracturing).

[0106] The embodiments of this specification may first pre-process the above raw data, clean, standardize and normalize the collected raw data to meet the requirements of the causal inference model. The data normalization and continuous variable conversion method is as follows:

[0107] (1) Data standardization and normalization

[0108] For continuous variables, standardization (Z-score standardization) is performed, and the formula is as follows:

[0109]

[0110] Where X is the original data value, μ is the sample mean, and σ is the sample standard deviation.

[0111] Suppose there are several data points, each of which represents the data of a produced well:

[0112] Oil layer thickness (H) = [30, 45, 50, 65, 70, 55, 60, 48, 40, 72];

[0113] Porosity (φ) = [15, 18, 20, 22, 23, 19, 17, 21, 20, 24];

[0114] Reservoir permeability (k) = [80, 120, 140, 160, 110, 95, 105, 130, 140, 125];

[0115] These data are standardized to obtain the standardized data:

[0116] Data after normalization of oil layer thickness:

[0117] H std =[0.25,0.85,1.05,1.65,1.95,1.15,1.45,0.75,0.05,2.05];

[0118] Porosity normalized data:

[0119] φ std =[-0.95,-0.15,0.15,0.55,0.75,0.25,-0.35,0.45,0.15,0.95];

[0120] Normalized data of oil layer permeability:

[0121] k std =[-1.31,1.07,1.67,2.17,0.57,-0.11,0.07,1.17,1.67,1.07];

[0122] (2) Continuous variables are converted into categorical variables to facilitate user viewing

[0123] For continuous variables (such as liquid intensity, sand addition intensity, etc.), unsupervised clustering methods (such as K-means) are used for classification. f ) and sand strength (Q s ) to group.

[0124] Assuming liquid strength data (unit: m 3 / min) is:

[0125] Q f =[5,7,8,6,7,5,9,6,8,7];

[0126] Use K-means clustering method to divide them into 3 categories. After clustering, the following classification results are obtained:

[0127] Category 1: Q f =[5,6,5];

[0128] Category 2: Q f =[7,6,7,7];

[0129] Category 3: Q f =[8,9,8];

[0130] Next, the sand strength (Q s ) are divided into 4 categories. Through similar clustering processing, the following classification results are obtained:

[0131] Category 1: Q s = [100,120];

[0132] Category 2: Q s =[130,140,145];

[0133] Category 3: Q s =[150,155,160];

[0134] Category 4: Q s =[180,185];

[0135] At this point, these variables are converted into categorical variables for easier viewing by users.

[0136] In addition, the embodiment of this specification uses a type of post-intervention variable as the intervention variable to be analyzed. The intervention variable to be analyzed can be specified by the staff, indicating the intervention effect of the variable on the production to be analyzed. The stronger the intervention effect, the more obvious the improvement of the production by adjusting the construction parameters corresponding to the variable. After clustering the post-intervention variables, multiple produced wells are divided into an intervention group and a control group according to the clustering results corresponding to the intervention variables to be analyzed in the post-intervention variables.

[0137] Specifically, dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories further comprises:

[0138] The category belonging to the intervention group and the category belonging to the control group are determined according to the mean value of the intervention variable to be analyzed in each category.

[0139] For example, the clustering results of liquid intensity are: Category 1: Q f =[5,6,5]; Category 2: Q f =[7,6,7,7]; Category 3: Q f =[8,9,8]. The exploited wells corresponding to category 1 with the lowest mean are divided into the control group, and the exploited wells corresponding to category 2 and category 3 are divided into two intervention groups with different intervention degrees. Alternatively, the exploited wells corresponding to category 3 with the highest mean are divided into the control group, and the exploited wells corresponding to category 1 and category 2 are divided into two intervention groups with different intervention degrees.

[0140] In addition, the staff may also divide the produced wells into one or more intervention groups and a control group according to the clustering results, which is not limited in the embodiments of this specification.

[0141] According to one embodiment of the present specification, since the intervention effect of the same intervention variable to be analyzed on different formation characteristics is unbalanced, after obtaining the values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells, the embodiment of the present specification can also group the multiple produced wells according to the values ​​of the background variables to obtain multiple groups, so as to execute the step of using one type of post-intervention variable as the intervention variable to be analyzed for the multiple produced wells in each group, and finally obtain the intervention effect of the intervention variable to be analyzed on the production data in each group.

[0142] For example, oil and gas wells are grouped according to formation characteristics (such as porosity, permeability, etc.). For simplicity, the samples are divided into three groups, representing low porosity (φ<18%), medium porosity (18%≤φ<21%), and high porosity (φ≥21%) oil and gas wells.

[0143] Group 1 (low porosity): φ = [15, 17];

[0144] Group 2 (mesoprosity): φ = [18, 19, 20, 21];

[0145] Group 3 (high porosity): φ = [22, 23, 24, 24];

[0146] By grouping, it is ensured that the background variables such as porosity and permeability of the oil layer in each group have similar distributions, so that the steps of using a type of post-intervention variable as the intervention variable to be analyzed are performed for multiple produced wells in each group, and finally the intervention effect of the intervention variable to be analyzed on the production data in each group is obtained, which helps to reduce the impact of the imbalance of background variables on the analysis of intervention effects. In practice, it can also be divided only by blocks, development strata, and development technologies, which are not limited in the embodiments of this specification.

[0147] Then, a quantitative description and multi-method synthesis of the intervention effect are conducted:

[0148] (1) First level: To control endogeneity, the instrumental variable method (IV) and the two-stage least squares method (2SLS) are used to calculate the endogeneity control coefficient.

[0149] Specify an intervention variable to be analyzed. If the intervention variable has endogeneity problems, use the instrumental variable method (IV) to eliminate bias in the estimation of intervention effects. Endogeneity may come from problems such as omitted variables, measurement errors, or reverse causal relationships. The steps are as follows:

[0150] ① Select instrumental variables: It is necessary to select an instrumental variable (selected manually, generally from background variables) that is related to the intervention variable to be analyzed but not directly related to the dependent variable (production increase factor). In the embodiments of this specification, the geological characteristics of the well (such as oil layer thickness, porosity, etc.) can be used as instrumental variables because they determine the design of hydraulic fracturing but do not directly affect the production after fracturing.

[0151] ② Instrumental variable method: Use instrumental variables to replace the intervention variables to be analyzed (such as liquid intensity, sand addition intensity, etc.). Estimation is performed through the following two-stage process:

[0152] The first stage: The predicted value of the intervention variable to be analyzed (such as liquid intensity and sand addition intensity) is estimated through instrumental variables. The estimation methods include but are not limited to linear regression, machine learning, Bayesian regression, polynomial regression and other regression prediction methods. Taking linear regression as an example:

[0153]

[0154] in is the alternative value of the intervention variable to be analyzed, Zi represents the value of the instrumental variable of the ith type, α0, ..., α i is the coefficient, and ε is the constant term. The value of the α coefficient is calculated by the least squares method, and we get result.

[0155] ③ Endogenous control: Use Conduct causal effect estimation to ensure that the intervention effect is not affected by endogeneity bias, using linear regression:

[0156]

[0157] Among them, Y represents the value of production data, X i represents the background variable of the i-th type or the post-intervention variable other than the intervention variable to be analyzed, β0, ..., β i , ..., β D are the endogenous control coefficients, among which β i is the endogenous control coefficient of the background variable or the post-intervention variable of the ith type, and η is a constant term.

[0158] The estimation is performed using the two-stage least squares method (2SLS). The loss function of the least squares method in this step is:

[0159]

[0160] Wherein, Loss is the loss value, and n represents the total number of background variables or post-intervention variables other than the intervention variables to be analyzed.

[0161] (2) The second layer: data preprocessing, propensity score matching (PSM) and inverse probability weighting (IPW) were used to match the produced wells in the intervention group and the control group.

[0162] In order to ensure the accuracy of the intervention effect estimation and reduce the impact of confounding factors, the propensity score matching method (PSM) is first used to deal with the confounding variables in the sample. The goal of PSM is to balance the experimental group and the control group on the confounding variables so that the samples are as similar as possible on the background variables. The basic steps are as follows:

[0163] ① Calculate the propensity score: The propensity score refers to the probability of a sample accepting an intervention under given background variables (such as porosity, permeability, etc.). The propensity score can be estimated using a logistic regression model:

[0164]

[0165] Where e(D) represents the propensity score of the intervention variable D to be analyzed, X1, X2, ..., X nare the values ​​of the background variables of multiple types, the values ​​of other types of post-intervention variables except the intervention variables to be analyzed, β1, β2, ..., β n are respectively the endogenous control coefficients of the corresponding background variables or other types of post-intervention variables except the intervention variables to be analyzed.

[0166] ② Use the inverse probability weighting method (IPW) to determine the weight: In order to further reduce the bias of the intervention score, the inverse probability weighting method (IPW) is used for weight adjustment. The weight of each sample is determined by the inverse of its propensity score. The weight adjustment formula is as follows:

[0167] If the produced well belongs to the intervention group, then according to the formula Calculate the weight coefficient, where w i represents the weight coefficient of the intervention variable D to be analyzed for the i-th well in the intervention group, e(D i ) represents the propensity score of the intervention variable D to be analyzed for the i-th well in the intervention group;

[0168] If the produced well belongs to the control group, then according to the formula Calculate the weight coefficient, where w j represents the weight coefficient of the intervention variable D to be analyzed for the jth well in the control group, e(D j ) represents the propensity score of the intervention variable D to be analyzed for the j-th well in the control group.

[0169] These weights can be used to make weighted estimates of the intervention effects, thereby further reducing the bias caused by sample imbalance.

[0170] ③ Matching samples: Match the samples in the intervention group and the control group according to the calculated propensity score. The matching method can be nearest neighbor matching, caliper matching, etc. The goal is to make the intervention group and the control group as similar as possible in propensity scores, thereby reducing the bias caused by confounding variables.

[0171] like Figure 3 As shown, matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain multiple matching groups further includes:

[0172] Step 301: Calculate the difference between the propensity score of each produced well in the intervention group and the propensity score of each produced well in the control group, and determine the produced wells in the control group whose difference is less than a threshold, and treat the produced wells in the intervention group and the produced wells in the control group as a matching group;

[0173] Step 302: If a produced well in the intervention group has no matching produced well in the control group, the produced well in the intervention group is discarded.

[0174] For example, including samples 1 to 6 and their propensity scores:

[0175] For each sample in the intervention group, find a control group sample with the closest propensity score (within 0.05). For example:

[0176] The mined well 1 in the intervention group (propensity score = 0.014) was closest to the mined well 4 in the control group (propensity score = 0.018), and was matched as a matched group.

[0177] The mined well 3 in the intervention group (propensity score = 0.25) is closest to the mined well 2 in the control group (propensity score = 0.23) and is matched as a matched group.

[0178] The mined well 5 in the intervention group (propensity score = 0.15) is closest to the mined well 6 in the control group (propensity score = 0.18) and is matched as a matched group.

[0179] When calculating intervention effect analysis, using mutually matched samples for calculation (discarding unmatched samples) can effectively reduce the impact of confounding factors on the intervention effect estimate.

[0180] like Figure 4 As shown, if the values ​​of the intervention variables to be analyzed of the multiple produced wells are clustered to obtain more than two categories, dividing the multiple produced wells into an intervention group and a control group according to the multiple categories further includes:

[0181] Step 401: determining a plurality of categories belonging to the intervention group and a category belonging to the control group according to the mean values ​​of the intervention variables to be analyzed in each category;

[0182] Matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups further comprises:

[0183] Step 402: Calculate the difference between the propensity score of each produced well in each intervention group and the propensity score of each produced well in the control group, and determine the produced wells in the control group whose difference is less than a threshold, and treat the produced wells in the intervention group and the produced wells in the control group as a matching group;

[0184] Step 403: If a produced well in any intervention group has no matching produced well in the control group, the produced well in the intervention group is discarded.

[0185] For example, the exploited well 7 in the intervention group (propensity score = 0.16, intervention level two) is closest to the exploited well 6 in the control group (propensity score = 0.18), and is matched as a matched group.

[0186] The mined well 8 in the intervention group (propensity score = 0.99, intervention level two) is closest to the mined well 9 in the control group (propensity score = 0.98), and they are matched as a matched group.

[0187] The matching relationship here is shown in Table 1 below, and there are 5 matching groups in total.

[0188] Table 1

[0189]

[0190] ④ Conduct intervention effect analysis: Evaluate the effect of the intervention by calculating the weighted average treatment effect (ATE) of the matched experimental and control groups:

[0191] The formula for calculating the intervention effect of the intervention variable to be analyzed on the production data according to the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups is:

[0192]

[0193] Where ATE represents the intervention effect of the intervention variable to be analyzed on the production data, k represents the total number of matching groups, T represents the set of intervention groups, C represents the set of control groups, and Y i represents the numerical value of the production data of the ith well belonging to the intervention group T, Y j Represents the numerical value of the production data of the jth well in the control group C that belongs to the same matching group as the i-th well in the intervention group T.

[0194] For example, the relationship between the endogenous control coefficient β, the propensity score e(D), the grouping (intervention group and control group), the weight coefficient w, and the intervention effect ATE is shown in Table 2 below, where T=0 represents the control group and T=1 represents the intervention group:

[0195] Table 2

[0196]

[0197] In the examples of this specification, the influence of the intervention variable on the target parameter is explained based on the results of the intervention effect. For example, the intervention effect of the intervention variable to be analyzed (sand addition intensity) on the production data is 0.17, indicating that the adjustment of the sand addition intensity can increase the production by 17%.

[0198] In addition, based on the absolute value of the intervention effect, the importance of variables is ranked, for example: sand addition intensity > fluid intensity > porosity > reservoir thickness. This means that sand addition intensity has the greatest impact on the hydraulic fracturing effect, while porosity and reservoir thickness also play an auxiliary role to a certain extent.

[0199] Through the above analysis results, we can understand how each variable affects the production increase of hydraulic fracturing effect and further reveal its causal relationship. Specifically, the causal explanation can be made from the following aspects:

[0200] ① The causal effect of intervention variables on yield increase:

[0201] Sanding intensity: The results show that sanding intensity has the greatest impact on production increase. This conclusion can be explained by causal inference analysis: increasing the sanding intensity increases the amount of sand injected during hydraulic fracturing, thereby forming more fractures in the formation, improving oil and gas mobility, and ultimately leading to increased production.

[0202] Fluid intensity: Increasing fluid intensity can also promote the fracturing effect. Increasing the amount of fluid injected helps to expand and maintain the cracks, thereby increasing production.

[0203] ②The influence of background variables:

[0204] Porosity: The increase in porosity means that the gaps in the rock are larger, making it easier to store oil and gas. Therefore, porosity also plays a certain role in promoting the effect of hydraulic fracturing.

[0205] Oil layer thickness: The thickness of the oil layer affects the coverage and effect of fracturing. Thicker oil layers may require stronger fracturing force and more sophisticated parameter design, which affects the final effect of hydraulic fracturing.

[0206] After analyzing the intervention effects of multiple intervention variables on the production data, the staff can optimize the construction parameters according to the following steps:

[0207] Optimizing sand addition intensity: Given the importance of sand addition intensity to the production stimulation effect, it is recommended to give priority to optimizing sand addition intensity in hydraulic fracturing design, especially in shallow oil layers.

[0208] Adjust the fluid intensity: Optimizing the fluid intensity can also effectively improve the fracturing effect, but it should be flexibly adjusted according to the characteristics of the reservoir.

[0209] Optimization for deep oil layers: For deep oil layers, it is recommended to increase the segment spacing and sand addition intensity through refined design to compensate for the lack of fracturing effect.

[0210] Robustness and interpretability of the model: The robustness of the estimation results is ensured through cross-validation and comparison of different models. Through the interpretative analysis of the model (such as the importance ranking of the decision tree model), the staff can clearly understand the mechanism of action of each factor, so as to better optimize the parameter design.

[0211] Based on the same inventive concept, the embodiment of this specification also provides a hydraulic fracturing effect main control factor analysis device based on causal inference. Figure 5 As shown, the device comprises:

[0212] A data acquisition unit 501 is used to acquire values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells;

[0213] A clustering unit 502 is used to cluster the values ​​of the intervention variables to be analyzed of the plurality of produced wells by taking a type of post-intervention variables as the intervention variables to be analyzed to obtain a plurality of categories, and to divide the plurality of produced wells into an intervention group and a control group according to the plurality of categories;

[0214] An endogenous control coefficient calculation unit 503 is used to calculate, for each produced well in the intervention group and the control group, the endogenous control coefficients of the multiple types of background variables and the multiple types of post-intervention variables of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the value of the production data;

[0215] A propensity score calculation unit 504 is used to calculate, for each produced well in the intervention group and the control group, the propensity score of the intervention variable to be analyzed of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the endogenous control coefficients of the respective variables;

[0216] A weight coefficient calculation unit 505 is used to calculate the weight coefficient of the intervention variable to be analyzed of each produced well according to the propensity score of the intervention variable to be analyzed of each produced well in the intervention group and the control group;

[0217] A matching unit 506, configured to match the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups;

[0218] The intervention effect calculation unit 507 is used to calculate the intervention effect of the intervention variable to be analyzed on the production data based on the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups, so that the staff can optimize the design of construction parameters according to the intervention effect.

[0219] Since the principle of solving the problem by the above device is similar to that of the above method, the implementation of the above system can refer to the implementation of the above method, and the repeated parts will not be repeated.

[0220] like Figure 6 The figure is a schematic diagram of the structure of the computer device in the embodiment of this specification. The method in the embodiment of this specification can be applied to the computer device in this embodiment.

[0221] The computer device 602 may include one or more processors 604, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. The computer device 602 may also include any memory 606 for storing any type of information such as code, settings, data, etc. Without limitation, for example, the memory 606 may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, etc. More generally, any storage resource may use any technology to store information.

[0222] Further, any storage resource may provide volatile or non-volatile retention of information.

[0223] Further, any storage resource may represent a fixed or removable component of the computer device 602. In one case, when the processor 604 executes the associated instructions stored in any storage resource or combination of storage resources, the computer device 602 may perform any operation of the associated instructions. The computer device 602 also includes one or more drive mechanisms 608 for interacting with any storage resource, such as a hard disk drive system, an optical disk drive system, etc.

[0224] The computer device 602 may also include an input / output module 610 (I / O) for receiving various inputs (via input devices 612) and for providing various outputs (via output devices 614). A specific output mechanism may include a presentation device 616 and an associated graphical user interface (GUI) 618. In other embodiments, the input / output module 610 (I / O), input device 612, and output device 614 may not be included, and the computer device 602 may be used as a computer device in a network. The computer device 602 may also include one or more network interfaces 620 for exchanging data with other devices via one or more communication links 622. One or more communication buses 624 couple the components described above together.

[0225] The communication link 622 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 622 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0226] The embodiments of the present specification also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0227] The embodiments of the present specification also provide a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to execute the above method.

[0228] It should be understood that in the various embodiments of the present specification, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present specification.

[0229] It should also be understood that in the embodiments of this specification, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the embodiments of this specification generally indicates that the associated objects before and after are in an "or" relationship.

[0230] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of this specification can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the embodiments of this specification.

[0231] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0232] In the several embodiments provided in the embodiments of this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0233] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this specification.

[0234] In addition, each functional unit in each embodiment of the present specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0235] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of this specification is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the embodiment of this specification. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0236] The embodiments of this specification use specific embodiments to illustrate the principles and implementation methods of the embodiments of this specification. The description of the above embodiments is only used to help understand the methods and core ideas of the embodiments of this specification. At the same time, for those skilled in the art, according to the ideas of the embodiments of this specification, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the embodiments of this specification.

Claims

1. A method for analyzing the main controlling factors of hydraulic fracturing effects based on causal inference, characterized in that: The method comprises: obtaining values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells; Taking a type of post-intervention variable as the intervention variable to be analyzed, clustering the values ​​of the intervention variable to be analyzed of the plurality of produced wells to obtain a plurality of categories, and dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories; For each produced well in the intervention group and the control group, the endogenous control coefficients of the multiple types of background variables and the multiple types of post-intervention variables of the produced well are calculated according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the value of the production data; For each produced well in the intervention group and the control group, the propensity score of the intervention variable to be analyzed of the produced well is calculated according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the endogenous control coefficients of the respective variables; Calculating the weight coefficient of the intervention variable to be analyzed for each produced well according to the propensity score of the intervention variable to be analyzed for each produced well in the intervention group and the control group; Matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matched groups; The intervention effect of the intervention variable to be analyzed on the production data is calculated based on the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups, so that the staff can optimize the design of construction parameters based on the intervention effect.

2. The method according to claim 1, characterized in that Dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories further comprises: The category belonging to the intervention group and the category belonging to the control group are determined according to the mean value of the intervention variable to be analyzed in each category.

3. The method according to claim 1, characterized in that Calculating the endogenous control coefficients of the multiple types of background variables and the multiple types of post-intervention variables of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the value of the production data further includes: Determine at least one instrumental variable from the plurality of types of the background variables and other types of post-intervention variables except the intervention variable to be analyzed; The least square method is used to solve the formula Solve to obtain the endogenous control coefficients of the background variables of multiple types and the post-intervention variables of multiple types, where Y represents the value of the output data, X i represents the background variable of the i-th type or the post-intervention variable other than the intervention variable to be analyzed, β0, ..., β i , ..., β D are the endogenous control coefficients, among which β i is the endogenous control coefficient of the background variable or the post-intervention variable of the ith type, η is a constant term, in is the alternative value of the intervention variable to be analyzed, Z i represents the value of the instrumental variable of the ith type, α0, ..., α i is the coefficient, ε is the constant term; the loss function of the least squares method is Wherein, Loss is the loss value, and n represents the total number of background variables or post-intervention variables other than the intervention variables to be analyzed.

4. The method according to claim 3, characterized in that The formula for calculating the propensity score of the intervention variable to be analyzed of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the endogenous control coefficients of the respective variables is: Where e(D) represents the propensity score of the intervention variable D to be analyzed, X1, X2, ..., X n are the values ​​of the background variables of multiple types, the values ​​of other types of post-intervention variables except the intervention variables to be analyzed, β1, β2, ..., β n are respectively the endogenous control coefficients of the corresponding background variables or other types of post-intervention variables except the intervention variables to be analyzed.

5. The method according to claim 4, characterized in that Calculating the weight coefficient of the intervention variable to be analyzed for each produced well according to the propensity score of the intervention variable to be analyzed for each produced well in the intervention group and the control group further includes: If the produced well belongs to the intervention group, then according to the formula Calculate the weight coefficient, where w i represents the weight coefficient of the intervention variable D to be analyzed for the i-th well in the intervention group, e(D i ) represents the propensity score of the intervention variable D to be analyzed for the i-th well in the intervention group; If the produced well belongs to the control group, then according to the formula Calculate the weight coefficient, where w j represents the weight coefficient of the intervention variable D to be analyzed for the jth well in the control group, e(D j ) represents the propensity score of the intervention variable D to be analyzed for the j-th well in the control group.

6. The method according to claim 5, characterized in that Matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups further comprises: Calculate the difference between the propensity score of each produced well in the intervention group and the propensity score of each produced well in the control group, and determine the produced wells in the control group whose difference is less than a threshold, and regard the produced wells in the intervention group and the produced wells in the control group as a matching group; If an already produced well in the intervention group has no matching already produced well in the control group, the already produced well in the intervention group will be discarded.

7. The method according to claim 6, characterized in that If clustering is performed on the values ​​of the intervention variables to be analyzed of the plurality of produced wells to obtain two or more categories, dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories further comprises: Determine a plurality of categories belonging to the intervention group and a category belonging to the control group according to the mean value of the intervention variable to be analyzed in each category; Matching the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups further comprises: Calculate the difference between the propensity score of each produced well in each intervention group and the propensity score of each produced well in the control group, and determine the produced wells in the control group whose difference is less than a threshold value, and regard the produced wells in the intervention group and the produced wells in the control group as a matching group; If a produced well in any intervention group has no matching produced well in the control group, the produced well in the intervention group will be discarded.

8. The method according to claim 6, characterized in that The formula for calculating the intervention effect of the intervention variable to be analyzed on the production data according to the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups is: Where ATE represents the intervention effect of the intervention variable to be analyzed on the production data, k represents the total number of matching groups, T represents the set of intervention groups, C represents the set of control groups, and Y i represents the numerical value of the production data of the ith well belonging to the intervention group T, Y j Represents the numerical value of the production data of the jth well in the control group C that belongs to the same matching group as the i-th well in the intervention group T.

9. The method according to claim 1, characterized in that: After obtaining values ​​of multiple types of background variables, multiple types of post-intervention variables, and production data of multiple produced wells, the method further includes: The multiple produced wells are grouped according to the values ​​of the background variables to obtain multiple groups, so as to perform the step of using a type of post-intervention variable as the intervention variable to be analyzed for the multiple produced wells in each group, and finally obtain the intervention effect of the intervention variable to be analyzed in each group on the production data.

10. A device for analyzing the main controlling factors of hydraulic fracturing effects based on causal inference, characterized in that: The device comprises: A data acquisition unit, used to acquire values ​​of multiple types of background variables, multiple types of post-intervention variables and production data of multiple produced wells; A clustering unit, used for taking a type of post-intervention variable as the intervention variable to be analyzed, clustering the values ​​of the intervention variable to be analyzed of the plurality of produced wells to obtain a plurality of categories, and dividing the plurality of produced wells into an intervention group and a control group according to the plurality of categories; an endogenous control coefficient calculation unit, for calculating, for each produced well in the intervention group and the control group, the endogenous control coefficients of the multiple types of background variables and the multiple types of post-intervention variables of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the value of the production data; a propensity score calculation unit, for calculating, for each produced well in the intervention group and the control group, the propensity score of the intervention variable to be analyzed of the produced well according to the values ​​of the multiple types of background variables of the produced well, the values ​​of other types of post-intervention variables except the intervention variable to be analyzed, and the endogenous control coefficients of the respective variables; A weight coefficient calculation unit, used to calculate the weight coefficient of the intervention variable to be analyzed of each produced well according to the propensity score of the intervention variable to be analyzed of each produced well in the intervention group and the control group; a matching unit, configured to match the produced wells in the intervention group with the produced wells in the control group according to the propensity score to obtain a plurality of matching groups; The intervention effect calculation unit is used to calculate the intervention effect of the intervention variable to be analyzed on the production data based on the values ​​of the production data of the two produced wells in each matching group, the weight coefficient and the total number of the matching groups, so as to facilitate the staff to optimize the design of construction parameters according to the intervention effect.

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