Well group adjustment measure generation method, system, equipment, medium and product

Abstract

The invention discloses a well group adjustment measure generation method, system and device, a medium and a product, and relates to the field of oilfield development. The chemical flooding production data comprises reservoir physical property static data, water well development dynamic data, oil well development dynamic data and well group development dynamic data; on the basis of the time sequence data, the daily oil production of the well group is predicted through a daily oil production prediction model, and a daily oil production curve in a preset time period is obtained; forward reasoning is carried out according to the daily oil production curve and a knowledge base, and adjustment measures of the well group are generated; standardized rules for adjusting the chemical flooding injection amount, the chemical flooding injection concentration and the chemical flooding injection speed are stored in the knowledge base. The generation efficiency and accuracy of chemical flooding measures can be improved.

Description

Technical Field

[0001] The present application relates to the field of oilfield development, and in particular to a method, system, equipment, medium and product for generating well group adjustment measures. Background Art

[0002] As an important means to improve oil recovery, chemical flooding technology has been widely used in major oil fields in China, and the development and application of chemical flooding technology has made significant progress. Chemical flooding technology improves oil-water fluidity and reduces oil-water interfacial tension by injecting chemical agents (such as polymers, surfactants, alkali, etc.), thereby effectively improving the recovery rate of oil fields.

[0003] The existing implementation of chemical flooding mainly relies on manual experience to evaluate the chemical agents that need to be injected. However, as oilfield development enters a complex stage, the traditional manual experience evaluation method can no longer cope with the complex and changeable reservoir conditions. It has problems such as low evaluation efficiency and low accuracy, and it is difficult to meet the actual production needs. Summary of the invention

[0004] The purpose of this application is to provide a method, system, equipment, medium and product for generating well group adjustment measures, which can improve the generation efficiency and accuracy of chemical flooding measures.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for generating well group adjustment measures, comprising:

[0007] Acquire time series data consisting of chemical flooding production data, wherein the chemical flooding production data includes static data of reservoir physical properties, dynamic data of water well development, dynamic data of oil well development, and dynamic data of well group development;

[0008] Based on the time series data, a daily oil production prediction model is used to predict the daily oil production of the well group, and a daily oil production curve within a preset time period is obtained; the daily oil production prediction model includes a time convolution network and a long short-term memory network;

[0009] Forward reasoning is performed based on the daily oil production curve and a knowledge base to generate adjustment measures for the well group; the knowledge base stores standardized rules for adjusting the chemical flooding injection amount, chemical flooding injection concentration and chemical flooding injection speed.

[0010] Furthermore, the process of constructing the knowledge base includes:

[0011] Calculating the effectiveness data of chemical flooding measures after implementation according to the water well development dynamic data, oil well development dynamic data and well group development dynamic data;

[0012] Determine the effectiveness of the well group according to the effectiveness data, the water well development dynamic data and the oil well development dynamic data, wherein the effectiveness is divided into four levels: good, relatively good, medium and poor;

[0013] Determine the daily oil production related characteristics according to the reservoir physical property static data, water well development dynamic data and well group development dynamic data;

[0014] The correlation analysis method is used to analyze the correlation between the daily oil production related characteristics, which affect the daily oil production characteristics;

[0015] Using big data technology to determine the degree of influence of the characteristics affecting daily oil production on the cumulative oil production increase, and determining key characteristics and the weight of each key characteristic according to the degree of influence;

[0016] Determine the standardization rules according to the effectiveness and the weights of the key features; and store the standardization rules to obtain a knowledge base.

[0017] Furthermore, the effectiveness data include effectiveness time, validity period, moisture content before measures, moisture content after measures and cumulative oil increase;

[0018] Determining the effectiveness of the well group according to the effectiveness data, the water well development dynamic data and the oil well development dynamic data includes:

[0019] When the validity period is greater than or equal to the first time period, and the rising amplitude of the slope of the Hall curve in the water well development dynamic data is greater than or equal to the first preset value and the decreasing amplitude of the apparent water absorption index is greater than or equal to the first preset percentage, and the cumulative oil increase in the oil well development dynamic data is greater than or equal to the first oil volume value and the decreasing amplitude of the water content is greater than or equal to the first water content, it is determined that the effectiveness of the well group is good;

[0020] When the validity period is within the first time period, and the rising amplitude of the slope of the Hall curve in the water well development dynamic data is within the first preset range and the decreasing amplitude of the apparent water absorption index is within the first percentage range, and the cumulative oil increase in the oil well development dynamic data is within the first oil volume value range and the water cut decrease amplitude is within the second water cut range, it is determined that the effectiveness of the well group is good;

[0021] When the validity period is within the first time period, and the rising amplitude of the slope of the Hall curve in the water well development dynamic data is within the second preset range and the decreasing amplitude of the apparent water absorption index is within the second percentage range, and the cumulative oil increase in the oil well development dynamic data is within the first oil volume value range and the water cut decrease amplitude is within the second water cut range, it is determined that the effectiveness of the well group is medium;

[0022] When the validity period is less than the second time period, and the water well development dynamic data and the oil well development dynamic data do not change, it is determined that the effectiveness of the well group is poor.

[0023] Furthermore, the correlation analysis method is any one of Pearson correlation analysis method, Spearman rank correlation analysis method, Kendall correlation analysis method and Hoefding D correlation analysis method.

[0024] Furthermore, the big data technology adopts any one of a random forest algorithm, an extreme gradient algorithm, a support vector machine algorithm and a neighbor algorithm.

[0025] Furthermore, the static data of reservoir physical properties include average permeability of target layer, breakthrough coefficient, target layer thickness, effective porosity, reservoir pore throat radius, formation water salinity, chemical flooding injection concentration and chemical flooding injection rate;

[0026] The water well development dynamic data include wellhead pressure, casing pressure, water nozzle, daily water injection volume, daily injection volume, total water injection volume, daily injection volume, cumulative water injection, development notes, cumulative injection volume and acquisition date;

[0027] The well group development dynamic data includes daily liquid production, daily water production, daily oil production, water cut, accumulated oil, pressure and acquisition date;

[0028] The oil well development dynamic data include daily liquid production, daily water production, daily gas production, daily oil production, water content, pump inlet pressure, pump outlet pressure, casing pressure, technical casing pressure, surface casing pressure, motor temperature, wellhead temperature, flow temperature, cumulative oil production, cumulative liquid production and cumulative gas production and acquisition date.

[0029] In a first aspect, the present application provides a well group adjustment measure generation system, comprising:

[0030] The acquisition module is used to acquire time series data composed of chemical flooding production data, wherein the chemical flooding production data includes static data of reservoir physical properties, dynamic data of water well development, dynamic data of oil well development and dynamic data of well group development.

[0031] The prediction module is used to predict the daily oil production of the well group based on the time series data using a daily oil production prediction model to obtain a daily oil production curve within a preset time period; the daily oil production prediction model includes a time convolution network and a long short-term memory network.

[0032] A generation module is used to perform forward reasoning based on the daily oil production curve and a knowledge base to generate adjustment measures for the well group; the knowledge base stores a series of standardized rules for adjusting the chemical flooding injection amount, chemical flooding injection concentration and chemical flooding injection speed.

[0033] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for generating well group adjustment measures.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for generating well group adjustment measures.

[0035] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for generating well group adjustment measures.

[0036] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0037] The present application provides a method, system, equipment, medium and product for generating adjustment measures for a well group. The daily oil production within a preset time period in the future is predicted by a daily oil production prediction model, and then the daily oil production and standardized rules in a knowledge base are used for forward reasoning to automatically generate adjustment measures. This process generates adjustment measures based on future oil production demand, does not rely on manual experience, and can quickly generate adjustment measures, thereby improving the efficiency and accuracy of measure generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 This is an application environment diagram of a method for generating well group adjustment measures in one embodiment of the present application;

[0040] Figure 2 A schematic diagram of a flow chart of a method for generating well group adjustment measures provided in one embodiment of the present application;

[0041] Figure 3 A schematic diagram of functional modules of a well group adjustment measure generation system provided in one embodiment of the present application;

[0042] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0045] The method for generating well group adjustment measures provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the time series data composed of chemical drive production data to the server 104. After the server 104 receives the time series data, for the time series data, the server 104 uses the daily oil production prediction model based on the time series data to predict the daily oil production of the well group, and obtains the daily oil production curve within the preset time period; and forward reasoning is performed based on the daily oil production curve and the knowledge base to generate the adjustment measures of the well group. The server 104 can feedback the obtained adjustment measures of the well group to the terminal 102. In addition, in some embodiments, the method for generating the adjustment measures of the well group can also be implemented separately by the server 104 or the terminal 102, such as the terminal 102 can directly process the time series data, or the server 104 can obtain the time series data from the data storage system and process the time series data.

[0046] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, and tablet computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0047] In an exemplary embodiment, Figure 2 As shown, a method for generating well group adjustment measures is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 203. Among them:

[0048] Step 201, obtaining time series data consisting of chemical flooding production data, wherein the chemical flooding production data includes static data of reservoir physical properties, dynamic data of water well development, dynamic data of oil well development and dynamic data of well group development. The chemical flooding production data is stored in a database.

[0049] Step 202: Based on the time series data, a daily oil production prediction model is used to predict the daily oil production of the well group to obtain a daily oil production curve within a preset time period; the daily oil production prediction model includes a time convolution network and a long short-term memory network.

[0050] Specifically, the daily oil production prediction model is composed of a temporal convolutional network and a long short-term memory network.

[0051] Temporal Convolutional Network (TCN) extracts local features from time series data through convolution operations.

[0052] The Long Short-Term Memory (LSTM) network predicts daily oil production based on local features. The genetic algorithm (GA) is used to continuously optimize the hyperparameters of TCN and LSTM. The GA algorithm will continuously adjust the hyperparameters of TCN and LSTM, including the number of hidden layer units, the number of layers of TCN and LSTM, and then find the best set of hyperparameters through selection, crossover and other operations, and use the network model corresponding to the best set of hyperparameters as the daily oil production prediction model.

[0053] Step 203, forward reasoning is performed based on the daily oil production curve and a knowledge base to generate adjustment measures for the well group; the knowledge base stores standardized rules for adjusting the chemical flooding injection amount, chemical flooding injection concentration and chemical flooding injection speed.

[0054] Based on the effectiveness data, wellhead pressure, chemical flooding injection amount, chemical flooding injection concentration, chemical flooding injection rate in water well development dynamic data, and the weights corresponding to the chemical flooding injection amount, chemical flooding injection concentration, and chemical flooding injection rate, standardized rules for adjusting the chemical flooding injection amount, chemical flooding injection concentration, and chemical flooding injection rate are formed.

[0055] The principle of forward reasoning is to use the predicted daily oil production, effectiveness data, wellhead pressure and key features in the dynamic data of water well development to infer the basis data. Starting from the existing standardized rules, the basis data is matched with the rules in the knowledge base, and the chemical flooding measures are gradually deduced to determine how to adjust them, and then the adjustment measures are obtained. The key features include the chemical flooding injection volume, chemical flooding injection concentration and chemical flooding injection rate. For example, if the daily oil production of a well group is predicted to be lower than the daily oil production before the measure for three consecutive days, forward reasoning is used according to the standardized rules in the knowledge base, and the adjustment measure generated by reasoning is to increase the injection volume.

[0056] The method for generating well group adjustment measures provided in the present application predicts the future trend of daily oil production based on the time series data composed of chemical flooding production data, and directly obtains the adjustment measures of the well group based on the predicted daily oil production combined with the knowledge base. This process requires manual work, which greatly improves the evaluation efficiency and accuracy of chemical flooding measures.

[0057] In another exemplary embodiment of the present application, the chemical flooding production data, namely, the profile adjustment measures data, includes static data of reservoir physical properties, dynamic data of water well development, dynamic data of oil well development and dynamic data of well group development.

[0058] The data of profile control measures are stored in the database in the form of data tables. The data of profile control measures are stored in corresponding tables, including well group static physical property table, oil well data table, water well information table, well group data table and oil-water well correspondence. The well group static physical property table is used to store static data of reservoir physical properties, the oil well data table is used to store dynamic data of oil well development, the water well information table is used to store dynamic data of water well development, and the oil-water well correspondence is used to store the correspondence between oil wells and water wells. Generally, one oil well corresponds to one water well, and different oil wells and water wells are distinguished by oil well number and water well number.

[0059] According to the profile control measures data in the database, a line graph of the corresponding data can be generated, such as pressure curve, oil and gas production curve, injection volume curve and Hall curve.

[0060] Specifically, the static data of reservoir physical properties include the average permeability of the target layer, the breakthrough coefficient, the target layer thickness, the effective porosity, the reservoir pore throat radius, the formation water salinity, the chemical flooding injection concentration and the chemical flooding injection rate.

[0061] The water well development dynamic data include wellhead pressure, casing pressure, water nozzle, daily water injection volume, daily polymer injection volume (i.e., chemical flooding injection volume), total water and polymer, daily injection volume, cumulative water injection, development notes, cumulative polymer injection, input-output ratio and collection date, among which the measure points marked in the development notes record the start time and end time of the chemical flooding measure.

[0062] The well group development performance data includes daily liquid production, daily water production, daily oil production, water cut, accumulated oil, pressure and acquisition date.

[0063] The oil well development dynamic data include daily liquid production, daily water production, daily gas production, daily oil production, water content, pump inlet pressure, pump outlet pressure, casing pressure, technical casing pressure, surface casing pressure, motor temperature, wellhead temperature, flow temperature, cumulative oil production, cumulative liquid production and cumulative gas production and acquisition date.

[0064] In another exemplary embodiment of the present application, the process of building a knowledge base includes the following steps 301 to 306. Among them:

[0065] Step 301, calculating the effectiveness data of chemical flooding measures after implementation according to the water well development dynamic data, oil well development dynamic data and well group development dynamic data.

[0066] Specifically, the effectiveness data includes effectiveness time, validity period, water content before measures, water content after measures, cumulative oil production, daily liquid production before measures, daily oil production before measures, daily liquid production after measures, daily liquid production after measures, average daily oil production and daily oil production peak. The effectiveness data is stored in the database.

[0067] The effective time is the time within 90 days from the date of implementation of the chemical flooding measures, when the daily oil production in the oil well development dynamic data exceeds the daily oil production before the measures for the first time.

[0068] The validity period is the difference between the time when the daily oil production of the chemical flooding measure becomes effective and the time when the daily oil production becomes lower than the daily oil production before the measure and remains lower than the daily oil production before the measure for 15 consecutive days and the time when the daily oil production first becomes lower than the daily oil production before the measure and the time when the chemical flooding measure becomes effective.

[0069] The daily liquid production before the measures is the average daily liquid production within 30 days before the implementation of the chemical flooding measures, calculated based on the daily liquid production in the well group development dynamic data.

[0070] The daily oil production before the measures is the average daily oil production within 30 days before the implementation of the chemical flooding measures, calculated based on the daily oil production in the well group development dynamics data.

[0071] The water content before the measure is the average water content within 30 days before the implementation of the chemical flooding measure, calculated based on the water content in the well group development dynamic data.

[0072] The daily liquid production after the measures is the average daily liquid production during the effective period calculated based on the daily liquid production in the well group development dynamic data.

[0073] The daily oil production after the measures is the average daily oil production during the effective period calculated based on the daily oil production in the well group development dynamic data.

[0074] The water content after the measure is the average water content during the effective period calculated based on the water content in the well group development dynamic data.

[0075] The average daily oil increase is the total amount of daily cumulative oil increase (cumulative oil increase in the well group development dynamic data) during the validity period divided by the validity period.

[0076] The cumulative oil increase is the sum of the daily cumulative oil increase during the validity period (the cumulative oil increase in the well group development dynamic data).

[0077] The daily oil production peak value is the maximum value of the daily oil production during the validity period (the daily oil production in the well group development dynamic data).

[0078] Step 302, determining the effectiveness of the well group according to the effectiveness data, the water well development dynamic data and the oil well development dynamic data, wherein the effectiveness is divided into four levels: good, relatively good, medium and poor.

[0079] There are four different levels of effectiveness, which are divided into "good", "relatively good", "medium" and "poor". Each level corresponds to different assessment contents and indicators. The effectiveness of the well group is assessed according to the corresponding assessment contents and indicators.

[0080] In this embodiment, the indicators include the validity period in the effectiveness data, the cumulative oil increase and water content in the oil well development dynamic data, the Hall curve slope and apparent water absorption index in the water well development dynamic data, the apparent water absorption index is the ratio of the daily injection volume to the wellhead pressure in the water well development dynamic data, and the Hall curve slope is the slope of the daily injection volume and wellhead pressure curve in the water well development dynamic data.

[0081] In an exemplary embodiment, the corresponding relationship between the evaluation content, indicators and four levels is as follows:

[0082] When the validity period is greater than or equal to the first time period, and the rising amplitude of the slope of the Hall curve in the water well development dynamic data is greater than or equal to the first preset value and the decreasing amplitude of the apparent water absorption index is greater than or equal to the first preset percentage, and the cumulative oil increase in the oil well development dynamic data is greater than or equal to the first oil volume value and the decreasing amplitude of the water content is greater than or equal to the first water content, it is determined that the effectiveness of the well group is good. In this embodiment, the first time period is 12 months, the first preset value is 20%, the first preset percentage is 30%, and the first oil volume value is 4000m 3 , the first moisture content is 3%.

[0083] When the validity period is within the first time period, and the slope of the Hall curve in the water well development dynamic data increases within the first preset range and the apparent water absorption index decreases within the first percentage range, and the cumulative oil increase in the oil well development dynamic data is within the first oil volume value range and the water content decreases within the second water content range, it is determined that the effectiveness of the well group is good. In this embodiment, the first time period is 5 to 12 months, the first preset range is 5% to 20, the first percentage range is 10% to 30%, and the first oil volume value range is 2000 to 4000m 3 , the second moisture content range is 1% to 3%.

[0084] When the validity period is within the first time period, and the rising amplitude of the slope of the Hall curve in the water well development dynamic data is between the second preset range and the decreasing amplitude of the apparent water absorption index is between the second percentage range, and the cumulative oil increase in the oil well development dynamic data is between the first oil value range and the water content decrease amplitude is between the second water content range, it is determined that the effectiveness of the well group is medium. In this embodiment, the second preset range is 0% to 5%, and the second percentage range is 0% to 10%.

[0085] When the validity period is less than the second time period, and the water well development dynamic data and the oil well development dynamic data have not changed, the effectiveness of the well group is determined to be poor. In this embodiment, the second time period is 1 month.

[0086] Step 303, determining daily oil production related features based on the reservoir physical property static data, water well development dynamic data and well group development dynamic data. Specifically, the daily oil production related features are artificially divided from the reservoir physical property static data, water well development dynamic data and well group development dynamic data. In this embodiment, the daily oil production related features include chemical flooding injection volume, chemical flooding injection concentration, chemical flooding injection rate, pressure difference before and after the measure one month, cumulative oil increase of the well group and pressure difference before and after the operation.

[0087] Step 304: Use correlation analysis to analyze the correlation between the daily oil production related characteristics to obtain the characteristics that affect the daily oil production.

[0088] The characteristics that affect daily oil production include chemical flooding injection volume, chemical flooding injection concentration and chemical flooding injection rate, the pressure difference one month before and after the measure, and the cumulative oil increase of the well group.

[0089] Specifically, the correlation analysis method is any one of Pearson correlation analysis method, Spearman rank correlation analysis method, Kendall correlation analysis method and Hoefding D correlation analysis method. In this embodiment, Pearson correlation analysis method is used to evaluate the correlation between the features.

[0090] The Pearson correlation coefficient between each daily oil production related feature is calculated. There is a significant correlation between the daily oil production related features whose Pearson correlation coefficient meets the preset value. Among the daily oil production related features with significant correlation, one feature is retained and the others are eliminated. For example, the correlation coefficient between the pressure difference one month before and after the measure and the pressure difference before and after the operation is close to 1, indicating that the pressure difference one month before and after the measure and the pressure difference before and after the operation are significantly correlated. In this case, the pressure difference one month before and after the measure is retained and the pressure difference before and after the operation is eliminated.

[0091] Step 305, using big data technology to determine the degree of influence of the characteristics affecting daily oil production on the cumulative oil increase, and determine key features and the weight of each key feature according to the degree of influence. The key features include chemical flooding injection liquid volume, chemical flooding injection concentration and chemical flooding injection speed.

[0092] Specifically, the big data technology uses any one of the random forest algorithm, extreme gradient algorithm, support vector machine algorithm and neighbor algorithm. The big data technology is used to obtain the influence of each feature on the cumulative oil increase, and the importance of each feature is ranked. At the same time, the weight of each feature affecting the cumulative oil increase is obtained, and the features with less influence are eliminated. Finally, the chemical flooding injection volume, chemical flooding injection concentration and chemical flooding injection speed are determined as the key features affecting the daily oil production, and the weight of each key feature is obtained.

[0093] Step 306: determine the standardization rules according to the effectiveness and the weights of the key features; and store the standardization rules to obtain a knowledge base.

[0094] Specifically, a series of standardized rules were established by conducting correlation analysis on the weights of key features such as chemical flooding injection volume, chemical flooding injection concentration and chemical flooding injection velocity, combined with profile control measures data. Some of the rules are as follows:

[0095] Chemical flooding injection parameter rules:

[0096] Rule 1: If the chemical flooding injection volume exceeds a certain upper limit, it is marked as "high injection volume". It is recommended to reduce the result of excessive pressure caused by high injection volume by adjusting the weight of the feature.

[0097] Rule 2: If the chemical flooding injection concentration is lower than the set lower limit, it is marked as "low concentration" and it is recommended to increase the injection concentration to achieve the best oil recovery effect.

[0098] Production dynamic data rules:

[0099] Rule 3: If the daily oil production is lower than the daily oil production before the measure for three consecutive days, it will be adjusted in sequence according to the weight of the features.

[0100] Rule 4: If the daily oil production fluctuates by more than 10%, it will be readjusted based on the weights of each feature.

[0101] Effect evaluation rules:

[0102] Rule 5: Based on the statistics of cumulative oil production, if the cumulative oil production of a well group is higher than the set threshold (such as 4000m3) and the water cut decreases by more than 3%, it is evaluated as a "good" effect and it is recommended to continue to maintain the existing measures.

[0103] Rule 6: If the slope of the Hall curve increases by less than 5%, it is rated as “poor” and it is recommended to re-evaluate the effectiveness of the water injection measures.

[0104] This application achieves an accurate prediction of the future trend of daily oil production. On this basis, the knowledge base can be used to quickly obtain adjustment measures for the well group, no longer relying on inefficient and time-consuming manual experience judgment, with high accuracy and fast timeliness.

[0105] Based on the same inventive concept, the embodiment of the present application also provides a well group adjustment measure generation system for implementing the well group adjustment measure generation method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more well group adjustment measure generation systems provided below can refer to the limitations of the well group adjustment measure generation method above, and will not be repeated here.

[0106] In an exemplary embodiment, Figure 3 As shown, a well group adjustment measure generation system is provided, comprising:

[0107] The acquisition module 31 is used to acquire time series data consisting of chemical flooding production data, wherein the chemical flooding production data includes static data of reservoir physical properties, dynamic data of water well development, dynamic data of oil well development and dynamic data of well group development.

[0108] The prediction module 32 is used to predict the daily oil production of the well group based on the time series data using a daily oil production prediction model to obtain a daily oil production curve within a preset time period; the daily oil production prediction model includes a time convolution network and a long short-term memory network.

[0109] The generation module 33 is used to perform forward reasoning based on the daily oil production curve and the knowledge base to generate adjustment measures for the well group; the knowledge base stores a series of standardized rules for adjusting the chemical flooding injection amount, chemical flooding injection concentration and chemical flooding injection speed.

[0110] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store time series data composed of chemical drive production data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for generating well group adjustment measures is implemented.

[0111] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0112] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above-mentioned method embodiments when executing the computer program.

[0113] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0114] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0115] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0116] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0117] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0118] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for generating well group adjustment measures, characterized in that: The method for generating well group adjustment measures comprises: Acquire time series data consisting of chemical flooding production data, wherein the chemical flooding production data includes static data of reservoir physical properties, dynamic data of water well development, dynamic data of oil well development, and dynamic data of well group development; Based on the time series data, a daily oil production prediction model is used to predict the daily oil production of the well group, and a daily oil production curve within a preset time period is obtained; the daily oil production prediction model includes a time convolution network and a long short-term memory network; Forward reasoning is performed based on the daily oil production curve and a knowledge base to generate adjustment measures for the well group; the knowledge base stores standardized rules for adjusting the chemical flooding injection amount, chemical flooding injection concentration and chemical flooding injection speed.

2. The method for generating well group adjustment measures according to claim 1, characterized in that: The process of constructing the knowledge base includes: Calculating the effectiveness data of chemical flooding measures after implementation according to the water well development dynamic data, oil well development dynamic data and well group development dynamic data; Determine the effectiveness of the well group according to the effectiveness data, the water well development dynamic data and the oil well development dynamic data, wherein the effectiveness is divided into four levels: good, relatively good, medium and poor; Determine the daily oil production related characteristics according to the reservoir physical property static data, water well development dynamic data and well group development dynamic data; The correlation analysis method is used to analyze the correlation between the characteristics related to daily oil production, and the characteristics affecting daily oil production are obtained; Using big data technology to determine the degree of influence of the characteristics affecting daily oil production on the cumulative oil production increase, and determining key characteristics and the weight of each key characteristic according to the degree of influence; Determine the standardization rules according to the effectiveness and the weights of the key features; and store the standardization rules to obtain a knowledge base.

3. The method for generating well group adjustment measures according to claim 2, characterized in that: The effectiveness data include the effective time, validity period, moisture content before measures, moisture content after measures and cumulative oil increase; Determining the effectiveness of the well group according to the effectiveness data, the water well development dynamic data and the oil well development dynamic data includes: When the validity period is greater than or equal to the first time period, and the rising amplitude of the slope of the Hall curve in the water well development dynamic data is greater than or equal to the first preset value and the decreasing amplitude of the apparent water absorption index is greater than or equal to the first preset percentage, and the cumulative oil increase in the oil well development dynamic data is greater than or equal to the first oil volume value and the decreasing amplitude of the water content is greater than or equal to the first water content, it is determined that the effectiveness of the well group is good; When the validity period is within the first time period, and the rising amplitude of the slope of the Hall curve in the water well development dynamic data is within the first preset range and the decreasing amplitude of the apparent water absorption index is within the first percentage range, and the cumulative oil increase in the oil well development dynamic data is within the first oil volume value range and the water cut decrease amplitude is within the second water cut range, it is determined that the effectiveness of the well group is good; When the validity period is within the first time period, and the rising amplitude of the slope of the Hall curve in the water well development dynamic data is within the second preset range and the decreasing amplitude of the apparent water absorption index is within the second percentage range, and the cumulative oil increase in the oil well development dynamic data is within the first oil volume value range and the water cut decrease amplitude is within the second water cut range, it is determined that the effectiveness of the well group is medium; When the validity period is less than the second time period, and the water well development dynamic data and the oil well development dynamic data do not change, it is determined that the effectiveness of the well group is poor.

4. The method for generating well group adjustment measures according to claim 2, characterized in that: The correlation analysis method is any one of Pearson correlation analysis method, Spearman rank correlation analysis method, Kendall correlation analysis method and Hoefding D correlation analysis method.

5. The method for generating well group adjustment measures according to claim 2, characterized in that: The big data technology adopts any one of a random forest algorithm, an extreme gradient algorithm, a support vector machine algorithm and a neighbor algorithm.

6. The method for generating well group adjustment measures according to claim 1, characterized in that: The static data of reservoir physical properties include average permeability of target layer, breakthrough coefficient, target layer thickness, effective porosity, reservoir pore throat radius, formation water salinity, chemical flooding injection concentration and chemical flooding injection rate; The water well development dynamic data include wellhead pressure, casing pressure, water nozzle, daily water injection volume, daily injection and polymerization volume, total water and polymerization volume, daily injection volume, cumulative water injection, development notes, cumulative injection and polymerization, input-output ratio and acquisition date; The well group development dynamic data includes daily liquid production, daily water production, daily oil production, water cut, accumulated oil, pressure and acquisition date; The oil well development dynamic data include daily liquid production, daily water production, daily gas production, daily oil production, water content, pump inlet pressure, pump outlet pressure, casing pressure, technical casing pressure, surface casing pressure, motor temperature, wellhead temperature, flow temperature, cumulative oil production, cumulative liquid production and cumulative gas production and acquisition date.

7. A well group adjustment measure generation system, characterized in that: The well group adjustment measure generation system comprises: An acquisition module, used to acquire time series data consisting of chemical flooding production data, wherein the chemical flooding production data includes static data of reservoir physical properties, dynamic data of water well development, dynamic data of oil well development and dynamic data of well group development; A prediction module, used to predict the daily oil production of the well group based on the time series data using a daily oil production prediction model to obtain a daily oil production curve within a preset time period; the daily oil production prediction model includes a time convolution network and a long short-term memory network; A generation module is used to perform forward reasoning based on the daily oil production curve and a knowledge base to generate adjustment measures for the well group; the knowledge base stores a series of standardized rules for adjusting the chemical flooding injection amount, chemical flooding injection concentration and chemical flooding injection speed.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for generating well group adjustment measures according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for generating well group adjustment measures according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, a method for generating well group adjustment measures according to any one of claims 1 to 6 is implemented.

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