A reserve model optimization fitting method and system

Abstract

The present invention provides a reserve model optimization fitting method and system. The method designs a reserve data standard template covering comprehensive extraction requirements based on the attribute characteristics of reserve data, extracts the calculated reserve data of a set period based on multiple data extraction requirements, and stores the actual reserve data within the corresponding period in the standard template to achieve targeted data fusion; then reads the calculated reserve data and the actual reserve data in the standard template, and comprehensively determines the effective pore volume fitting parameters of the reserve numerical model based on the difference between the single-layer reserve and the total stratum reserve; based on the automatic assignment and update of the reserve numerical model file, the reserve model is optimized. The scheme organically combines data fusion, file update and reserve fitting technology, overcomes the defects of cumbersome data processing and high error probability in the prior art, and makes the layered reserve numerical model fitting more automatic and fast.

Description

Technical Field

[0001] The invention relates to the technical field of numerical optimization processing of oil and gas reservoirs, and in particular to a reserve model optimization fitting method and system, which are applied to numerical simulation engineering of sandstone oil and gas reservoirs. Background Art

[0002] According to reliable research in the field, the main influencing factors in the process of calculating reserve values ​​are: oil-bearing area, porosity, effective thickness, and original oil saturation. Among them, the saturation is mainly affected by the following two aspects: the change of water compressibility has a significant impact on the change of saturation; the height of the oil-water interface can directly affect the original oil saturation, thereby affecting the reserves. The adjustable degree of the fitting parameters related to reserves is from large to small, namely oil-bearing area, saturation, effective thickness, and porosity. Porosity is an important parameter that affects the storage and flow properties of reservoir fluids. Among them, the effective pore volume parameter represents the ratio of the interconnected pore volume in the microscopic pore space of the reservoir to the total volume of the reservoir, which has an important control relationship for the movement of fluids in the reservoir.

[0003] The historical data of oil and gas field development show that effective pore volume is the core parameter for reliably evaluating the storage and flow performance of an oil and gas reservoir. It is reflected as a function of oil-bearing area, saturation, effective thickness, and porosity, and can reflect the comprehensive effect of the impact on reserves. At present, the existing digital model platform is mainly based on the trial and error method to achieve reserve fitting. By manually modifying and adjusting adjustable parameters such as permeability according to the fitting principle, the fitting of digital model personnel requires certain experience. The existing trial and error technology data preparation and fitting process efficiency are relatively low. It cannot meet the requirements of oil and gas field engineering for technical data.

[0004] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a method for optimizing and fitting a reserve model. The rapid reserve fitting technology based on effective pore volume adjustment of the present invention is based on the actual stratified reserve data of oil and gas reservoirs and the output data of professional numerical simulation software, based on the error between actual and model stratified reserves, and uses effective pore volume adjustment as a means to achieve rapid approximation and fitting of model stratification and overall reserves. In one embodiment, the method includes:

[0006] Integrate the template design steps and design a standard template for reserves data that covers comprehensive extraction requirements based on the attribute characteristics of reserves data; each standard template includes multiple sub-templates, each of which corresponds to a type of data extraction requirement;

[0007] The multi-source data extraction step extracts the calculated reserve data of a set period from the numerical model database according to various data extraction requirements, and extracts the actual reserve data within the corresponding period from the multi-source measured database and stores them in the corresponding sub-template of the standard template to achieve targeted target data fusion;

[0008] The fitting parameter decision step is to read the calculated reserve data and actual reserve data in the standard template according to the field, and decide the effective pore volume adjustment coefficient of the reserve numerical model based on the difference between the single-layer reserve and the total layer reserve as the fitting parameter;

[0009] Model optimization steps, automatically assign values ​​and update the reserve numerical model file using the calculated fitting parameters, and verify the integrity and validity of the numerical model file.

[0010] Preferably, as a further improvement of the present invention, in the integration template design step, different sub-templates are set to include different data storage areas, corresponding to the storage of reserve data objects of different fields and data types, and the fields are set according to the source category fields of the source data.

[0011] Furthermore, in one embodiment, in the multi-source data extraction step, corresponding objects are extracted from the source database using the data category keyword or field of the source data to be extracted as an index and stored in the corresponding sub-template of the standard template.

[0012] In a preferred embodiment, the method further comprises:

[0013] Data verification step: After the multi-source data extraction step in the first stage, the integrity and validity of the data in the sub-template corresponding to the standard template are checked according to the preset data verification condition list to see if they meet the requirements. If both meet the requirements, it is determined to have passed the verification. If not, the multi-source data extraction step is re-executed to re-extract all the data corresponding to the current extraction task that has not passed the verification until all the current reserve data meet the requirements.

[0014] On the other hand, in a specific embodiment, the fitting parameter decision step includes the following operations:

[0015] Calculate the error value between the actual reserves and the calculated reserves data corresponding to each layer respectively. If there is a situation where the error value exceeds the first set threshold, calculate the corresponding effective pore volume adjustment coefficient based on the actual reserves and the calculated reserves data of the layer;

[0016] Otherwise, the error value between the actual reserves of the total layer in the corresponding period and the calculated reserves data is further calculated. When the error value exceeds the second set threshold, the corresponding effective pore volume adjustment coefficient is calculated based on the actual reserves of the total layer and the calculated reserves data; if the error value does not exceed the set threshold, the current model is maintained.

[0017] Optionally, in one embodiment, the method includes:

[0018] Based on the actual energy storage data and calculated energy storage data of different time periods under various working conditions, multiple rounds of fitting parameter decisions are made, and the reserve numerical model file is continuously updated to the set calculation times. When there is no error value exceeding the set threshold, the obtained reserve numerical model is put into use as the final reserve model.

[0019] Specifically, in one embodiment, the fitting parameter decision operation is implemented by reading the reserve data in the standard template through an interface or path corresponding to the application modeling conditions.

[0020] Furthermore, in one embodiment, when the same source data needs to be extracted for different application scenarios, the source data is extracted independently and stored in a matching sub-template in an organizational format that matches the application scenario conditions.

[0021] Based on other aspects of the method described in any one or more of the above embodiments, the present invention further provides a storage medium storing program codes that can implement the method described in any one or more of the above embodiments.

[0022] Based on the application aspects of the method described in any one or more of the above embodiments, the present invention also provides a reserve model optimization fitting system, which executes the method described in any one or more of the above embodiments.

[0023] Compared with the closest prior art, the present invention also has the following beneficial effects:

[0024] The present invention provides a reserve model optimization fitting method and system. The method designs a reserve data standard template covering comprehensive extraction requirements based on the attribute characteristics of reserve data, extracts calculated reserve data of a set period based on multiple data extraction requirements, and stores the actual reserve data within the corresponding period in the standard template, thereby realizing targeted data fusion. The method overcomes the defects of cumbersome data processing and high error probability in the prior art, and provides reliable and convenient data support for subsequent model fitting parameter calculation and model file optimization.

[0025] Then, the calculated reserve data and actual reserve data in the standard template are read, and the difference between the single-layer reserve and the total layer reserve is used to determine the effective pore volume fitting parameters of the reserve numerical model; based on the automatic assignment and update of the reserve numerical model file, the reserve model is optimized. The organic combination of model file update and reserve fitting technology makes layered reserve fitting more automatic and fast, and realizes the rapid approximation and fitting of model layers and overall reserves. Non-numerical simulation professionals can also quickly fit reserves, and also lay the model foundation for the subsequent fitting of oil and gas reservoir blocks and single wells.

[0026] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0028] Figure 1 It is a schematic flow chart of a reserve model optimization fitting method provided in one embodiment of the present invention;

[0029] Figure 2 It is a detailed diagram of the operation flow of the reserve model optimization fitting method provided in an embodiment of the present invention;

[0030] Figure 3 It is a schematic diagram of a data extraction process of a reserve model optimization fitting method provided in an embodiment of the present invention;

[0031] Figure 4 is an example diagram of comparison of reserve errors in the optimal fitting of the reserve model provided in the embodiment of the present invention;

[0032] Figure 5 It is a structural schematic diagram of a reserve model optimization fitting system provided by an embodiment of the present invention.

[0033] Figure 6 It is a functional structural relationship principle diagram of the reserve model optimization fitting system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will describe the implementation methods of the present invention in detail in conjunction with the accompanying drawings and embodiments, so that the implementers of the present invention can fully understand how the present invention applies technical means to solve technical problems and achieve the implementation process of technical effects and implement the present invention specifically according to the above implementation process. It should be noted that as long as there is no conflict, the various embodiments and various features of the embodiments in the present invention can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0035] Although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently, or simultaneously. The order of the operations may be rearranged. A process may be terminated when its operations are completed, but may also have additional steps not included in the accompanying drawings. A process may correspond to a method, function, procedure, subroutine, subprogram, etc.

[0036] Computer devices include user devices and network devices. Among them, user devices or clients include but are not limited to computers, smart phones, PDAs, etc.; network devices include but are not limited to a single network server, a server group consisting of multiple network servers, or a cloud composed of a large number of computers or network servers based on cloud computing. Computer devices can be operated alone to implement the present invention, or they can be connected to the network and implement the present invention through interactive operations with other computer devices in the network. The network where the computer device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, VPN network, etc.

[0037] The terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "one", "one" and "item" used herein are also intended to include plural numbers. It should also be understood that the terms "include" and / or "comprise" used herein specify the existence of stated features, integers, steps, operations, units and / or components, without excluding the existence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0038] According to reliable research in the field, the main influencing factors in the process of calculating reserve values ​​are: oil-bearing area, porosity, effective thickness, and original oil saturation. Among them, the saturation is mainly affected by the following two aspects: the change of water compressibility has a significant impact on the change of saturation; the height of the oil-water interface can directly affect the original oil saturation, thereby affecting the reserves. The adjustable degree of the fitting parameters related to reserves is from large to small, namely oil-bearing area, saturation, effective thickness, and porosity. Porosity is an important parameter that affects the storage and flow properties of reservoir fluids. Among them, the effective pore volume parameter represents the ratio of the interconnected pore volume in the microscopic pore space of the reservoir to the total volume of the reservoir, which has an important control relationship for the movement of fluids in the reservoir.

[0039] The historical data of oil and gas field development show that the effective pore volume is the core parameter for reliably evaluating the storage and flow performance of an oil and gas reservoir. It is reflected as a function of oil-bearing area, saturation, effective thickness, and porosity, and can reflect the comprehensive effect of the impact on reserves. At present, the existing digital model platform is mainly based on the trial and error method to achieve reserve fitting. By manually modifying and adjusting the adjustable parameters such as permeability according to the fitting principle, the digital model personnel have certain experience requirements for fitting. The existing trial and error technology data preparation and fitting process efficiency is relatively low. It is difficult for non-surface engineering professionals to reliably fit and obtain an accurate reserve model, and it is highly dependent on the reliability of the previous data preparation. The processing process for achieving system model optimization is cumbersome and inefficient, and cannot well meet the requirements of oil and gas field development engineering and acoustic field control.

[0040] The existing research methods for integrating distributed heterogeneous information resources include the federated database method, data warehouse method, virtual database method, virtual data method, distributed component integration method, direct database access interface method, grid-based, ontology, WebService and extensible markup language method, etc. Among them, the federated database technology is proposed to achieve the interoperability of multiple databases that run independently of each other. The database system that runs independently of each other is usually called a unit database system. The so-called federated database system is a collection of unit database systems that cooperate with each other and are independent of each other. It integrates the unit database systems to different degrees. The most fundamental feature of the data warehouse method is to store data physically. The construction of the data warehouse is realized by cleaning, converting, integrating, loading and regularly refreshing the data from multiple data sources. It is stored in a unified data model. The general data warehouse includes metadata, data sources, data conversion tools, data collection bodies and data query and analysis tools. Data integration based on virtual data centers refers to the integration, transparent access and transparent management of heterogeneous data based on the data integration model of virtual data centers. It can uniformly access structured data, semi-structured data and unstructured data, and the access method is independent of the data source. The distributed component integration method transforms data access into independent distributed objects through encapsulation, sets a single or multiple servers as data access agents between data accessors and data sources, completes the operation of accessing data sources, and finally realizes the integration of heterogeneous data. The direct database access interface method is relatively simple in technology. Different programming languages ​​have different database access interfaces. Programming languages ​​execute SQL statements through these interfaces to manage databases. The grid, XML, ontology and Web Service methods use extensible markup language, which has the characteristics of cross-language, cross-platform, scalable, efficient, and separation of display and content.

[0041] However, the existing data processing methods are mainly concentrated in the fields of informatization and digitization, and are less integrated with the application of professional numerical technology in oil and gas field development and production, and have high requirements for data access rights. When applied, the overall data backup is usually adopted, and then the data required for operation is obtained by extracting them separately. The operation is complicated, time-consuming and labor-intensive, and the matching degree between the data extraction results and the operation application is difficult to guarantee. In summary, in the management of reservoir and production engineering data, it is urgent to integrate data, that is, it is necessary to merge data objects in multiple relatively independent data sources and recombine them into a new organic whole, so as to provide support for the construction and fitting optimization of reserve numerical models in an orderly and accurate manner.

[0042] In order to obtain higher quality and more efficient historical matching in a timely manner with as little manpower as possible, the rapid reserve fitting technology mentioned in the present invention is based on the characteristics of reserve stratification data and reserve fitting, and organically combines data extraction, file updating and reserve fitting technology to achieve rapid reserve fitting based on effective pore volume multiples.

[0043] Based on the above needs, the purpose of the present invention is to achieve rapid approximation and fitting of model stratification and overall reserves based on the rapid reserve fitting technology based on effective pore volume adjustment, which is based on the actual stratified reserve data of oil and gas reservoirs and the output data of professional numerical simulation software, based on the error between actual and model stratified reserves, and uses effective pore volume adjustment as a means.

[0044] The present invention realizes the rapid fitting optimization of the reserve model through the following ideas: (1) establishing a standard template for comparison and adjustment of reserve stratification data to provide a medium for rapid fitting of reserve stratification; (2) reading the actual strata and reserves into the standard template according to fields in the source data table after geological understanding and logging interpretation; (3) reading the model strata and reserves into the standard template from the numerical model calculation result file; (4) calculating the error between the actual and model strata reserves, and if the error in the stratified and total strata reserves is greater than the set constraint value, calculating the effective pore volume adjustment coefficient according to the actual and model strata reserves.

[0045] Next, the detailed process of the method of the embodiment of the present invention is described in detail based on the accompanying drawings. The steps shown in the flowchart of the accompanying drawings can be executed in a computer system including a set of computer executable instructions. Although the logical order of each step is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0046] Embodiment 1

[0047] Figure 1 The schematic diagram of the flow chart of the reserve model optimization fitting method provided in the first embodiment of the present invention is shown. Figure 1 It can be seen that the method includes the following steps.

[0048] Integrate the template design steps and design a standard template for reserves data that covers comprehensive extraction requirements based on the attribute characteristics of reserves data; each standard template includes multiple sub-templates, each of which corresponds to a type of data extraction requirement;

[0049] The multi-source data extraction step extracts the calculated reserve data of a set period from the numerical model database according to various data extraction requirements, and extracts the actual reserve data within the corresponding period from the multi-source measured database and stores them in the corresponding sub-template of the standard template to achieve targeted target data fusion;

[0050] The fitting parameter decision step is to read the calculated reserve data and actual reserve data in the standard template according to the field, and decide the effective pore volume adjustment coefficient of the reserve numerical model based on the difference between the single-layer reserve and the total layer reserve as the fitting parameter;

[0051] Model optimization steps, automatically assign values ​​and update the reserve numerical model file using the calculated fitting parameters, and verify the integrity and validity of the numerical model file.

[0052] The logic of the above embodiment is used to optimize the fitting of the reserve numerical model. First, according to the characteristics of data time, reserve stratification data and reserve fitting, data extraction, file update and reserve fitting technology are organically combined to realize the rapid reserve fitting technology based on effective pore volume multiples. In this way, the standardization and structure of reserve stratification data and fitting are fully utilized, and the stratified reserve fitting becomes more automatic and fast by quickly adjusting the stratified effective pore volume multiples, so that non-numerical simulation professionals can also quickly fit the reserves, and also lay the model foundation for the subsequent fitting of oil and gas reservoir blocks and single wells.

[0053] After the reserve calculation model is constructed based on the available modeling means, the method proposed in the present invention can be used to implement rapid reserve fitting in high water-cut blocks. Rapid reserve fitting based on effective pore volume multiple adjustment mainly includes model calculation, data reading, reserve comparison, coefficient calculation, model update and result output. The rapid reserve model fitting method can lay a model foundation for the subsequent fitting of oil and gas reservoir blocks and single wells.

[0054] The key is to extract the layered reserve data calculated by the model, calculate the error between the actual and model layered reserves, and correct the model reserves, including the following processing ideas:

[0055] (1) Establish a standard template for comparison and adjustment of reserve stratification data to provide a medium for rapid fitting of reserve stratification;

[0056] (2) Read the actual horizon and reserves from the source data table after geological understanding and logging interpretation into the standard template according to the fields;

[0057] (3) Read the model layers and reserves from the numerical model calculation result file into the standard template;

[0058] (4) Calculate the error between the actual and model stratum reserves. If the error between the stratum reserves and the total stratum reserves is greater than the set value, calculate the effective pore volume adjustment factor based on the actual and model stratum reserves;

[0059] Figure 2 The detailed flow chart of the embodiment of the present invention to realize the reserve model fitting optimization is shown. Figure 2In one embodiment, in the integrated template design step, different sub-templates are provided with different data storage areas, which correspond to the storage of reserve data objects of different fields and data types, and the fields are set according to the source category fields of the source data. For example, the data contents corresponding to the fields such as production time, stratification information, and reserve data are stored separately. In actual application, the reserve error data and effective porosity volume adjustment coefficient calculated in the fitting process are also correspondingly saved in the standard template.

[0060] A reserve stratification data table template is established, as shown in Tables 1 and 2 below, including the stratum name, actual reserves, simulated reserves, adjustment coefficient, relative error, etc. Then, the actual and simulated stratified reserves are read according to the stratum number using the present invention. Next, the stratified effective pore volume adjustment coefficient and relative error are calculated. If the error is greater than the set value, the above steps are repeated, otherwise the result comparison histogram is output.

[0061] Table 1 Example of standard template file for comparison and adjustment of reserve stratification data

[0062]

[0063] Table 2 Comparison and adjustment of reserve stratification data

[0064]

[0065] Since there are inevitably various development and production-related data in addition to reserve data in the multi-source database (for example, actual production reserve data needs to be extracted from the well logging database), it is necessary to identify the actual reserve data that meets the extraction requirements. Accordingly, in one embodiment of the present invention, in the multi-source data extraction step, the corresponding object is extracted from the source database using the data category keyword or field of the source data to be extracted as an index and stored in the corresponding sub-template of the standard template.

[0066] In the process of implementing multi-source data extraction, in one embodiment, for different application scenarios where the same source data needs to be extracted, the data is extracted independently and stored in a matching sub-template in an organizational format that matches the application scenario. The process of implementing multi-source data extraction in the embodiment of the present invention is as follows: Figure 3 As shown, in addition, after confirming that the data of the current batch has been successfully read and the fitting optimization is completed, the corresponding storage space can be cleared as needed.

[0067] Furthermore, in order to ensure the reliability of the extracted data and provide accurate data support for subsequent fitting operations, in a preferred embodiment, the method further includes:

[0068] Data verification step: After the multi-source data extraction step in the first stage, the integrity and validity of the data in the sub-template corresponding to the standard template are checked according to the preset data verification condition list to see if they meet the requirements. If both meet the requirements, it is determined to have passed the verification. If not, the multi-source data extraction step is re-executed to re-extract all the data corresponding to the current extraction task that has not passed the verification until all the current reserve data meet the requirements.

[0069] Specifically, in a preferred embodiment, the reserve data in the standard template is read through an interface or path corresponding to the application modeling conditions to implement the fitting parameter decision operation, and specifically, the actual and simulated stratified reserves can be read according to the layer number.

[0070] The purpose of the present invention is to achieve the stratification and overall rapid fitting of the model reserves by comparing and approximating the stratified reserves calculated by the numerical model with the actual stratified reserves, improve the efficiency of oil and gas reservoir model fitting, and enable non-numerical simulation professionals to quickly fit the reserves. Considering that the oil layer is divided into many small layers vertically, in order to accurately simulate, it is necessary to first ensure that the error between the reserves of each small layer and the reserves determined by the logging interpretation is less than 3%, and at the same time, it is also necessary to ensure that the total reserve error of all layers in the block is less than 3%. Therefore, in one embodiment, in the fitting parameter decision step, the following operations are included:

[0071] Calculate the error value between the actual reserves and the calculated reserves data corresponding to each layer respectively. If there is a situation where the error value exceeds the first set threshold, calculate the corresponding effective pore volume adjustment coefficient based on the actual reserves and the calculated reserves data of the layer;

[0072] Otherwise, the error value between the actual reserves of the total layer in the corresponding period and the calculated reserves data is further calculated. When the error value exceeds the second set threshold, the corresponding effective pore volume adjustment coefficient is calculated based on the actual reserves of the total layer and the calculated reserves data; if the error value does not exceed the set threshold, the current model is maintained.

[0073] In actual application, the error between the actual reserves and the reserves calculated by the new model is calculated. If it is still greater than 3%, the fitting parameter decision step and the model optimization step are repeated until there is no situation where the reserve error exceeds 3%.

[0074] Among them, the calculation logic of reserves in the field includes: total effective pore volume = oil-bearing area * effective thickness * effective porosity (or connected porosity), crude oil effective pore volume = total effective pore volume * oil saturation, crude oil geological reserves = crude oil effective pore volume * underground crude oil density;

[0075] When the model needs to be adjusted, the ratio of the actual reserve data to the corresponding calculated reserve data is set as the effective pore volume adjustment coefficient, that is, the adjustment coefficient is the actual reserve data divided by the calculated reserve data before fitting. Assuming that the actual reserve data is A and the calculated reserve data is B, the calculated reserve after fitting = the calculated reserve before fitting * A / B.

[0076] In a preferred embodiment, in order to ensure the accuracy of the fitting results, the present invention performs multiple rounds of fitting parameter decisions based on actual energy storage data and calculated energy storage data in different time periods of multiple working conditions, continuously updates the reserve numerical model file to a set number of calculations, and when there is no error value exceeding a set threshold, the obtained reserve numerical model is put into use as the final reserve model.

[0077] Specifically, in one embodiment, in the model optimization step, the adjustment coefficient is automatically assigned and the numerical model .DATA file is modified, the integrity and validity of the numerical model file are checked, and the model calculation is re-performed. In actual application, the adjustment coefficient is automatically calculated by programming, and the adjustment coefficient assignment is multiplied by the calculated reserves before fitting, so as to realize the modification of the reserve numerical model in the computational model.

[0078] Furthermore, each time the reserve calculation model is adjusted, the integrity and validity of the reserve numerical model file are tested by verifying whether there are any operational measures through the operational model. If there are any abnormalities, the fitting parameter decision step and the model optimization step are re-executed based on the model before fitting.

[0079] The method further includes, after the model is optimized to meet the requirements and the integrity and validity are verified, further including:

[0080] Draw a comparison chart of the actual reserves data of each layer and the simulated reserves, as well as a comparison chart of the actual reserves data of each layer and the simulated reserves, to display to the user, so as to provide the user with a more intuitive display of the fitting effect, such as Figure 4 shown.

[0081] In actual application, the calculation of the numerical model DATA file in the embodiment of the present invention is the basis for reserve fitting. Each time the adjustment coefficient is modified, it is necessary to assign the value to the DATA file and recalculate the layered reserves. The rationality and effectiveness of the coefficient adjustment are judged by outputting the comparison result chart (comparison bar chart, the observation is more intuitive and clear when calculating the reserve error) and comparing the error. It is not that each layer of reserves can be fitted by simply modifying the adjustment coefficient once. It needs to be adjusted repeatedly to meet the 3% error requirement. After meeting the error requirement, the final data model DATA file can be used for subsequent block and single well reserve fitting.

[0082] The present invention can be implemented through software programming, and the specific process is described as follows:

[0083] (1) Template establishment: A standard template for stratified reserve fitting data is established. The data items include horizon, actual reserves, simulated reserves, adjustment coefficient, and relative error item. The stratified sub-layer number can be manually input or automatically extracted according to the data source.

[0084] (2) Data reading: The layer number, actual layer reserves, and simulated layer reserves data are automatically loaded into the data template to establish a reserve comparison data model.

[0085] (3) Reserve comparison: Calculate the error between actual and model stratum reserves and determine whether the error is less than 3%.

[0086] (4) Coefficient calculation: If the error is greater than 3%, the effective pore volume adjustment coefficient is calculated based on the actual and model stratum reserves.

[0087] (5) Model update: Use adjustment coefficients to automatically assign values ​​and modify the numerical model .DATA file, check the integrity and validity of the numerical model file, and recalculate the model.

[0088] (6) Result output: Finish and output the bar chart result plate comparing the actual layered reserves and the model layered reserves, and the final reserve fitting data file.

[0089] For the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the order of the actions described, because according to the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0090] It should be pointed out that, in other embodiments of the present invention, the method can also obtain a new reserve model optimization fitting method by combining one or several of the above embodiments.

[0091] It should be noted that, based on the method in any one or more of the above embodiments of the present invention, the present invention also provides a storage medium, on which is stored a program code that can implement the method as described in any one or more of the above embodiments, and when the code is executed by an operating system, the reserve model optimization fitting method as described above can be implemented.

[0092] Embodiment 2

[0093] The above embodiments disclosed in the present invention describe the method in detail. The method of the present invention can be implemented by various devices or systems. Therefore, based on other aspects of the method described in any one or more embodiments, the present invention also provides a reserve model optimization fitting system, which is used to execute the reserve model optimization fitting method described in any one or more embodiments. Specific embodiments are given below for detailed description.

[0094] Specifically, Figure 5 The structural schematic diagram of the reserve model optimization fitting system provided in the embodiment of the present invention is shown in FIG. Figure 5 As shown, the system includes:

[0095] A user interface module is configured to realize human-computer interaction with a user, through which the user opens, sets, reads, and generates data parameters and data files of a data standard template, and on the other hand, displays and realizes the reading of data, storage results, and update results of comparison charts during the model fitting optimization process;

[0096] An integrated template design module is configured to design a standard template for reserve data that covers comprehensive extraction requirements based on the attribute characteristics of reserve data; each standard template includes multiple sub-templates, each sub-template corresponding to a type of data extraction requirement;

[0097] A multi-source data extraction module is configured to extract the calculated reserve data of a set period from the numerical model database according to various data extraction requirements, and to extract the actual reserve data within the corresponding period from the multi-source measured database and store them in the corresponding sub-template of the standard template to achieve targeted target data fusion;

[0098] A fitting parameter decision module is configured to read the calculated reserve data and the actual reserve data in the standard template according to the field, and decide the effective pore volume adjustment coefficient of the reserve numerical model based on the difference between the single-layer reserve and the total layer reserve as a fitting parameter;

[0099] The model optimization module is configured to automatically assign values ​​and update the reserve numerical model file using the calculated fitting parameters and to verify the integrity and validity of the numerical model file.

[0100] The present invention is divided into three functional parts: data template, model fitting and result display. The relationship between the parts is as follows: Figure 6As shown. (1) Data template: This part can store the stratified horizons, stratified reserves, calculation errors, adjustment coefficients, etc. read from the source data and model result files. (2) Model fitting: This part is the core of the present method. Through this module, users can read and store source data files and model data files, calculate the actual and model reserve errors, calculate the effective pore volume adjustment coefficient and modify and calculate the numerical model DATA file, output the comparison result chart, etc., so as to realize the human-computer interaction of the present invention. (3) Result display: Display the bar chart result chart comparing the actual stratified reserves and the model stratified reserves.

[0101] Furthermore, in one embodiment, the integrated template design module sets different sub-templates to include different data storage areas, which respectively correspond to the storage of reserve data objects of different fields and data types, and the fields are set according to the source category fields of the source data.

[0102] Preferably, in one embodiment, the multi-source data extraction module is configured to extract corresponding objects from the source database using the data category keyword or field of the source data to be extracted as an index and store them in the corresponding sub-template of the standard template.

[0103] In a preferred embodiment, the system further comprises:

[0104] The data verification module is configured to detect whether the integrity and validity of the data in the sub-template corresponding to the standard template meet the requirements according to the preset data verification condition list after realizing the first stage of multi-source data extraction. If both meet the requirements, it is determined to have passed the verification. If not, the multi-source data extraction is re-executed, and all data corresponding to the current extraction task that has not passed the verification are re-extracted until all current reserve data meet the requirements.

[0105] Further, in one embodiment, the fitting parameter decision module is configured to perform the following operations:

[0106] Calculate the error value between the actual reserves and the calculated reserves data corresponding to each layer respectively. If there is a situation where the error value exceeds the first set threshold, calculate the corresponding effective pore volume adjustment coefficient based on the actual reserves and the calculated reserves data of the layer;

[0107] Otherwise, the error value between the actual reserves of the total layer in the corresponding period and the calculated reserves data is further calculated. When the error value exceeds the second set threshold, the corresponding effective pore volume adjustment coefficient is calculated based on the actual reserves of the total layer and the calculated reserves data; if the error value does not exceed the set threshold, the current model is maintained.

[0108] After making multiple rounds of fitting parameter decisions based on actual energy storage data and calculated energy storage data of different time periods under various working conditions, the present invention continuously updates the reserve numerical model file to a set number of calculations, and when there is no error value exceeding a set threshold, the obtained reserve numerical model is put into use as the final reserve model.

[0109] Before the fitting parameter decision calculation, the reserve data in the standard template is read through the interface or path corresponding to the application modeling conditions to realize the fitting parameter decision operation.

[0110] In one optional embodiment, the multi-source data extraction module is configured as follows: for different application scenarios where the same source data needs to be extracted, the data is extracted independently and stored in a matching sub-template in an organizational format that matches the application scenario conditions.

[0111] In the reserve model optimization fitting system provided by the embodiment of the present invention, each module or unit structure can be operated independently or in combination according to actual extraction and calculation requirements to achieve corresponding technical effects.

[0112] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0113] The "one embodiment" mentioned in the specification means that a particular feature, structure or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the phrase "one embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0114] Although the embodiments disclosed in the present invention are as above, the above contents are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A reserve model optimization fitting method, characterized in that: The method comprises: Integrate the template design steps and design a standard template for reserves data that covers comprehensive extraction requirements based on the attribute characteristics of reserves data; each standard template includes multiple sub-templates, each of which corresponds to a type of data extraction requirement; The multi-source data extraction step extracts the calculated reserve data of a set period from the numerical model database according to various data extraction requirements, and extracts the actual reserve data within the corresponding period from the multi-source measured database and stores them in the corresponding sub-template of the standard template to achieve targeted target data fusion; The fitting parameter decision step is to read the calculated reserve data and actual reserve data in the standard template according to the field, and decide the effective pore volume adjustment coefficient of the reserve numerical model based on the difference between the single-layer reserve and the total layer reserve as the fitting parameter; Model optimization steps, automatically assign values ​​and update the reserve numerical model file using the calculated fitting parameters, and verify the integrity and validity of the numerical model file; The fitting parameter decision step includes the following operations: Calculate the error value between the actual reserves and the calculated reserves data corresponding to each layer respectively. If there is a situation where the error value exceeds the first set threshold, calculate the corresponding effective pore volume adjustment coefficient based on the actual reserves and the calculated reserves data of the layer; Otherwise, further calculate the error value between the actual reserves of the total layer and the calculated reserves data within the corresponding period. When the error value exceeds the second set threshold, calculate the corresponding effective pore volume adjustment coefficient according to the actual reserves of the total layer and the calculated reserves data; if the error value does not exceed the set threshold, keep the current model; Among them, the ratio of actual reserve data to corresponding calculated reserve data is set as the effective pore volume adjustment coefficient.

2. The method according to claim 1, characterized in that In the integration template design step, different sub-templates are set to include different data storage areas, which correspond to the storage of reserve data objects of different fields and data types, and the fields are set according to the source category fields of the source data.

3. The method according to claim 1, characterized in that In the multi-source data extraction step, the corresponding objects are extracted from the source database using the data category keyword or field of the source data to be extracted as an index and stored in the corresponding sub-template of the standard template.

4. The method according to claim 1, characterized in that: The method further comprises: Data verification step: After the multi-source data extraction step in the first stage, the integrity and validity of the data in the sub-template corresponding to the standard template are checked according to the preset data verification condition list to see if they meet the requirements. If both meet the requirements, it is determined to have passed the verification. If not, the multi-source data extraction step is re-executed to re-extract all the data corresponding to the current extraction task that has not passed the verification until all the current reserve data meet the requirements.

5. The method according to claim 1, characterized in that: The method comprises: Based on the actual energy storage data and calculated energy storage data of different time periods under various working conditions, multiple rounds of fitting parameter decisions are made, and the reserve numerical model file is continuously updated to the set calculation times. When there is no error value exceeding the set threshold, the obtained reserve numerical model is put into use as the final reserve model.

6. The method according to claim 1, characterized in that The reserve data in the standard template is read through the interface or path corresponding to the application modeling conditions to realize the fitting parameter decision operation.

7. The method according to claim 1, characterized in that For situations where the same source data needs to be extracted for different application scenarios, they are extracted separately and stored in matching sub-templates in an organizational format that matches the application scenario conditions.

8. A storage medium, characterized in that: The storage medium stores program codes that can implement the method as claimed in any one of claims 1 to 7.

9. A reserve model optimization fitting system, characterized in that: The system executes the method according to any one of claims 1 to 7.

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