Fracture parameter inversion method for fractured-porous carbonate reservoir
By acquiring multiple sets of fracture parameters and performing iterative optimization, the problem of inaccurate inversion results for fracture-porous carbonate reservoirs was solved, and high-precision inversion of fracture parameters was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-12-18
- Publication Date
- 2026-06-19
AI Technical Summary
In existing technologies, fracture parameter inversion methods for fracture-porous carbonate reservoirs are complex and have low accuracy, making it difficult to accurately obtain fracture parameters.
By acquiring a set of parameters including crack length, crack azimuth, crack dip angle, crack aperture, crack permeability, and crack density, an initial state vector is constructed. A numerical simulation model is used for prediction, and the data is corrected by combining actual production data to form an iterative optimization loop, ultimately determining the target crack parameter set.
This improved the accuracy of fracture parameter inversion results in fracture-porous carbonate reservoirs, ensuring the comprehensiveness and precision of parameter inversion.
Smart Images
Figure CN122241951A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of fracture parameter detection technology for fracture-porous carbonate reservoirs, and particularly relates to a fracture parameter inversion method for fracture-porous carbonate reservoirs. Background Technology
[0002] In fractured porous carbonate reservoirs, fractures serve as important channels for underground fluid flow, and fracture parameters have a certain impact on the production and development of fractured porous carbonate reservoirs. However, the existing inversion methods for obtaining fracture parameters are relatively complex and have low accuracy. Therefore, how to improve the accuracy of fracture parameter inversion results for fractured porous carbonate reservoirs is an urgent technical problem to be solved. Summary of the Invention
[0003] The embodiments of this application provide a method for inverting fracture parameters in fracture-porous carbonate reservoirs, which can improve the accuracy of fracture parameter inversion results for fracture-porous carbonate reservoirs.
[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.
[0005] According to a first aspect of the embodiments of this application, a method for inverting fracture parameters in fractured porous carbonate reservoirs is provided. The method comprises: obtaining a preset number of fracture parameter sets, the fracture parameter sets including fracture length, fracture azimuth, fracture dip angle, fracture aperture, fracture permeability, and fracture density; constructing a preset number of initial state vectors based on the preset number of fracture parameter sets; determining a preset number of predicted state vectors based on the preset number of initial state vectors using numerical simulation models; acquiring actual production data of the fractured porous carbonate reservoir, and correcting the preset number of predicted state vectors based on the actual production data to obtain a preset number of updated state vectors; using the preset number of updated state vectors as new preset number of initial state vectors, returning to the step of determining a preset number of predicted state vectors based on the preset number of initial state vectors using numerical simulation models, until a preset number of iterations is satisfied to obtain a preset number of final state vectors; and determining a target fracture parameter set based on the preset number of final state vectors.
[0006] In some embodiments of this application, based on the aforementioned scheme, obtaining a preset number of fracture parameter sets includes: determining preset fracture parameter intervals for each parameter dimension based on the actual production data of the fracture-porous carbonate reservoir; and extracting a preset number of fracture parameters within the preset fracture parameter intervals for each parameter dimension using the Latin hypercube sampling method to obtain a preset number of fracture parameter sets.
[0007] In some embodiments of this application, based on the foregoing scheme, constructing a preset number of initial state vectors based on the preset number of fracture parameter groups includes: obtaining the actual state parameters of the fracture-porous carbonate reservoir, and determining simulated state parameters corresponding one-to-one with the preset number of fracture parameter groups based on the actual state parameters and the preset number of fracture parameter groups; determining corresponding predicted production data based on the fracture parameter groups and the corresponding simulated state parameters; and determining the initial state vector based on the fracture parameter groups, the simulated state parameters, and the predicted production data using the following formula:
[0008] z = [m s m p ,d] T
[0009] Where z represents the initial state vector, m s Represents the crack parameter set, m p d represents the simulated state parameters, and d represents the predicted production data.
[0010] In some embodiments of this application, based on the foregoing scheme, the preset number of predicted state vectors are determined by the following formula:
[0011] z pre =G[z]+v w
[0012]
[0013] Where z represents the initial state vector, z pre Let v represent the predicted state vector. w Let G represent process noise, and Ψ represent the numerical simulation model. pre N represents a preset number of predicted state parameters. e Indicates the preset number.
[0014] In some embodiments of this application, based on the foregoing scheme, the updated state vector is determined by the following formula:
[0015]
[0016] d j =d obs +v j
[0017] H = [O|I]
[0018]
[0019] Among them, z ju K represents updating the state vector. e Let H represent the Kalman gain matrix, H represent the observation matrix, and d represent the Kalman gain matrix. j d represents the actual production data after adding observation error. obs Represents actual production data, v j O represents the observation error, O represents the zero matrix, and I represents the identity matrix. C represents the error covariance matrix of a preset number of predicted state vector sets; D H represents the error covariance matrix of the predicted production data for a predetermined number of samples. T This represents the transpose of the observation matrix. The mean of the predicted state vectors is a preset number.
[0020] In some embodiments of this application, based on the foregoing scheme, determining the target crack parameter group based on the preset number of final state vectors includes: determining the preset number of final crack parameter groups based on the preset number of final state vectors; and calculating the average value of the crack parameters for each parameter dimension in the preset number of final crack parameter groups to obtain the target crack parameter group.
[0021] In some embodiments of this application, based on the aforementioned scheme, before determining the preset number of predicted state vectors using numerical simulation models based on the preset number of initial state vectors, the method further includes: acquiring actual reservoir data, well logging data, and microseismic data of the fractured porous carbonate reservoir; establishing a basic model based on the actual reservoir data, the well logging data, and the microseismic data; the basic model being used to characterize the reservoir information in the fractured porous carbonate reservoir; and optimizing the basic model based on the actual production data and the well locations in the fractured porous carbonate reservoir to obtain a numerical simulation model of the fractured porous carbonate reservoir.
[0022] According to a second aspect of the embodiments of this application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform an operation as described in any of the embodiments of the first aspect above.
[0023] According to a third aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by a processor to perform the operation performed by the method described in any of the embodiments of the first aspect above.
[0024] According to a fourth aspect of the present application, an electronic device is provided, the electronic device including one or more processors and one or more memories, the one or more memories storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by the one or more processors to perform the operation performed by the method as described in any of the embodiments of the first aspect above.
[0025] Based on the technical solution proposed in this application, firstly, by acquiring fracture parameters across multiple dimensions, including fracture length, fracture azimuth, fracture dip angle, fracture aperture, fracture permeability, and fracture density, the fracture parameters comprehensively cover the characteristics of various aspects of the fracture, which helps improve the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir. Secondly, by constructing an initial state vector, then obtaining a predicted state vector through a numerical simulation model, and correcting the predicted state vector using actual production data from the fracture-porous carbonate reservoir to obtain an updated state vector, and then using the updated state vector as a new initial state vector, an iterative optimization loop can be formed. Through repeated iterative optimization, the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir can be further improved. Thirdly, determining the target fracture parameter group based on a preset number of final state vectors can make the simulation results more accurate, thereby improving the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir.
[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:
[0028] Figure 1 A flowchart of a fracture parameter inversion method for fractured porous carbonate reservoirs according to one embodiment of this application is shown;
[0029] Figure 2 A numerical simulation model of fracture parameters in a fractured-pore carbonate reservoir is shown in one embodiment of this application;
[0030] Figure 3 A schematic diagram of the structure of an electronic device according to one embodiment of this application is shown. Detailed Implementation
[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0032] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.
[0033] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0034] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0035] It should also be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.
[0036] To enable those skilled in the art to better understand this application, the fractured porous carbonate reservoir proposed in this application will be briefly described below.
[0037] In fractured-porosity carbonate reservoirs, naturally developed fractures exist, which are important channels for the outflow of underground oil and have a certain impact on reservoir production and development. Fracture parameters such as fracture length, fracture azimuth, fracture dip angle, fracture aperture, fracture permeability, and fracture density are important criteria for judging the quality of these fractures. However, the fracture parameters of naturally developed fractures are difficult to measure directly. Based on this, the inventors of this application propose a fracture parameter inversion method for fractured-porosity carbonate reservoirs to improve the accuracy of the fracture parameter inversion results.
[0038] Next, we will combine Figure 1 The fracture parameter inversion method for fractured porous carbonate reservoirs proposed in this application is described in detail.
[0039] See Figure 1 This document illustrates a flowchart of a fracture parameter inversion method for fractured porous carbonate reservoirs according to one embodiment of this application. The method can be executed by a device with computational processing capabilities, such as... Figure 1 As shown, the method may include at least steps 110 to 160:
[0040] Step 110: Obtain a preset number of crack parameter sets, which include crack length, crack azimuth angle, crack dip angle, crack aperture, crack permeability, and crack density.
[0041] Step 120: Based on the preset number of crack parameter groups, construct a preset number of initial state vectors respectively.
[0042] Step 130: Based on the preset number of initial state vectors, determine the preset number of predicted state vectors through numerical simulation models.
[0043] Step 140: Obtain the actual production data of the fractured porous carbonate reservoir, and correct the preset number of predicted state vectors based on the actual production data to obtain the preset number of updated state vectors.
[0044] Step 150: Use the preset number of updated state vectors as the new preset number of initial state vectors, return to execute the step of determining the preset number of predicted state vectors based on the preset number of initial state vectors through numerical simulation models, until the preset number of iterations is satisfied, and obtain the preset number of final state vectors.
[0045] Step 160: Determine the target crack parameter group based on the preset number of final state vectors.
[0046] In this application, the preset number can be 50 or 60, or other numbers depending on actual needs. This application does not make any specific limitation on this.
[0047] In this application, the phrase "until the preset number of cycles is met" can specifically mean 5 cycles, or other numbers of cycles depending on actual needs. This application does not impose any specific limitations on this.
[0048] In this application, firstly, by acquiring fracture parameters across multiple dimensions, including fracture length, fracture azimuth, fracture dip angle, fracture aperture, fracture permeability, and fracture density, the fracture parameters comprehensively cover the characteristics of various aspects of the fracture, thus improving the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir. Secondly, by constructing an initial state vector, obtaining a predicted state vector through a numerical simulation model, and then correcting the predicted state vector using actual production data from the fracture-porous carbonate reservoir to obtain an updated state vector, which is then used as a new initial state vector, an iterative optimization loop can be formed. Through repeated iterative optimization, the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir can be further improved. Thirdly, determining the target fracture parameter group based on a preset number of final state vectors can make the simulation results more accurate, thereby improving the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir.
[0049] In step 110 above, obtaining a preset number of crack parameter groups can be specifically performed according to steps 111 to 112 as follows:
[0050] Step 111: Determine the preset fracture parameter ranges for each parameter dimension based on the actual production data of the fractured porous carbonate reservoir.
[0051] Step 112: According to the Latin hypercube sampling method, a preset number of crack parameters are extracted from the preset crack parameter intervals of each parameter dimension to obtain a preset number of crack parameter groups.
[0052] In a specific embodiment of this application, according to the Latin hypercube sampling method, three crack parameters can be extracted from the preset crack parameter range of each parameter dimension, namely crack length (A1, A2, A3), crack azimuth (B1, B2, B3), crack dip angle (C1, C2, C3), crack aperture (D1, D2, D3), crack permeability (E1, E2, E3), and crack density (F1, F2, F3). By further randomly combining the crack parameters of each dimension according to the Latin hypercube random sampling method, three crack parameter groups can be obtained, such as (A1, B2, C1, D3, E2, F1) as a crack parameter group.
[0053] In this application, preset fracture parameter ranges for each parameter dimension are determined using actual production data from the fractured porous carbonate reservoir. This ensures that the preset fracture parameter ranges better match the actual fracture parameters of the fractured porous carbonate reservoir, thereby improving the rationality and accuracy of the fracture parameter set obtained through sampling to a certain extent. Furthermore, by employing the Latin hypercube sampling method, it is ensured that the sample distribution of fracture parameters extracted in each parameter dimension is uniform, avoiding situations where the samples are too concentrated or too sparse, thus improving the accuracy of the fracture parameter inversion results for the fractured porous carbonate reservoir.
[0054] In step 120 above, based on the preset number of crack parameter groups, a preset number of initial state vectors are constructed respectively. Specifically, this can be performed according to steps 121 to 123 as follows:
[0055] Step 121: Obtain the actual state parameters of the fractured porous carbonate reservoir, and based on the actual state parameters and the preset number of fracture parameter groups, determine the simulated state parameters that correspond one-to-one with the preset number of fracture parameter groups.
[0056] Step 122: Determine the corresponding predicted production data based on the crack parameter group and the corresponding simulated state parameters.
[0057] Step 123: Based on the crack parameter set, the simulated state parameters, and the predicted production data, determine the initial state vector using the following formula (1):
[0058] z = [m s m p ,d] T (1)
[0059] Where z represents the initial state vector, m s Represents the crack parameter set, m p d represents the simulated state parameters, and d represents the predicted production data.
[0060] In this application, the actual state parameters may specifically include the pressure and water saturation of the fractured porous carbonate reservoir, and the simulated state parameters may specifically include the simulated pressure and simulated water saturation derived from the actual state parameters and the fracture parameter set.
[0061] In this application, by obtaining the actual state parameters of the fractured porous carbonate reservoir, and based on the actual state parameters and the preset number of fracture parameter groups, the simulated state parameters are determined. In this way, by combining the actual state parameters with the fracture parameter groups, the accuracy and rationality of the simulated state parameters can be improved to a certain extent, thereby improving the accuracy of the fracture parameter inversion method for the fractured porous carbonate reservoir. In addition, based on the fracture parameter groups, the simulated state parameters, and various parameters of the predicted production data, the initial state vector is determined, which can improve the comprehensiveness of the initial state vector. By determining the initial vector through formula (1), the accuracy of the initial state vector can be improved, thereby improving the accuracy of the fracture parameter inversion results of the fractured porous carbonate reservoir.
[0062] In step 130 above, the preset number of predicted state vectors can be determined using the following formulas (2) to (3):
[0063] z pre =G[z]+v w (2)
[0064]
[0065] Where z represents the initial state vector, z pre Let v represent the predicted state vector. w Let G represent process noise, and Ψ represent the numerical simulation model. pre N represents a preset number of predicted state parameters. e Indicates the preset number.
[0066] In this application, the initial state vector is numerically simulated using a numerical simulation model to obtain a predicted state vector, which can improve the rationality and accuracy of the predicted state vector. During the numerical simulation process, process noise can also be added to avoid random errors that may occur during the numerical simulation, thereby further improving the accuracy of the predicted state vector. Ultimately, this can improve the accuracy of the fracture parameter inversion results of the fracture-porous carbonate reservoir.
[0067] In step 140 above, the updated state vector can be specifically determined using the following formulas (4) to (9):
[0068]
[0069] d j =d obs +v j (5)
[0070] H = [O | I] (6)
[0071]
[0072]
[0073]
[0074] Among them, z j u K represents updating the state vector. e Let H represent the Kalman gain matrix, H represent the observation matrix, and d represent the Kalman gain matrix. j d represents the actual production data after adding observation error. obs Represents actual production data, v j O represents the observation error, O represents the zero matrix, and I represents the identity matrix. C represents the error covariance matrix of a preset number of predicted state vector sets; D H represents the error covariance matrix of the predicted production data for a predetermined number of samples. T This represents the transpose of the observation matrix. The mean of the predicted state vectors is a preset number.
[0075] In this application, the updated state vector is calculated using formulas (4) to (9), which can improve the accuracy of the updated state vector. At the same time, the inclusion of actual production data in the calculation can also improve the rationality of the updated state vector. In addition, the addition of observation error to the actual production data can reduce the error that may occur when acquiring the actual production data, thereby further improving the accuracy of the updated state vector. Ultimately, this can improve the accuracy of the fracture parameter inversion results of the fracture-porous carbonate reservoir.
[0076] In step 160 above, determining the target crack parameter group based on the preset number of final state vectors can be specifically performed according to steps 161 to 162 as follows:
[0077] Step 161: Based on the preset number of final state vectors, determine the preset number of final crack parameter groups.
[0078] Step 162: Calculate the average value of the crack parameters for each parameter dimension in the predetermined number of final crack parameter groups to obtain the target crack parameter group.
[0079] In this application, the final state vector is obtained by iterating a preset number of times, and the final fracture parameter set is determined by the final state vector. This can make full use of actual production data, thereby improving the rationality of the final state vector and thus improving the accuracy of the final fracture parameter set. In addition, by averaging the fracture parameters of each parameter dimension in the preset number of final fracture parameter sets, the target fracture parameter set is obtained, which can improve the accuracy and rationality of the obtained target fracture parameter set, thereby improving the accuracy of the fracture parameter inversion results of the fracture porosity carbonate reservoir.
[0080] Based on the fracture parameter inversion method for fractured and porous carbonate reservoirs proposed in this application, before determining the predetermined number of predicted state vectors using numerical simulation models based on the predetermined number of initial state vectors, the following steps 101 to 102 can be performed:
[0081] Step 101: Obtain actual reservoir data, well logging data, and microseismic data of the fractured porous carbonate reservoir, and establish a basic model based on the actual reservoir data, well logging data, and microseismic data. The basic model is used to characterize the reservoir information in the fractured porous carbonate reservoir.
[0082] Step 102: Based on the actual production data and the well locations of the fractured porous carbonate reservoir, the basic model is optimized to obtain a numerical simulation model of the fractured porous carbonate reservoir.
[0083] In this application, a basic model is established using actual reservoir data, well logging data, and microseismic data of the fractured porous carbonate reservoir. This model determines the reservoir information within the fractured porous carbonate reservoir, laying the foundation for the numerical simulation model. Then, the numerical simulation model is determined using the actual production data and the well locations within the fractured porous carbonate reservoir. For details, please refer to [reference needed]. Figure 2 This paper illustrates a numerical simulation model of fracture parameters in a fractured-porous carbonate reservoir according to one embodiment of the present application. This model can accurately determine the predicted production data by initializing the state vector, thereby obtaining a more accurate predicted state vector and improving the accuracy of the fracture parameter inversion results of the fractured-porous carbonate reservoir.
[0084] Based on the technical solution proposed in this application, firstly, by acquiring fracture parameters across multiple dimensions, including fracture length, fracture azimuth, fracture dip angle, fracture aperture, fracture permeability, and fracture density, the fracture parameters comprehensively cover the characteristics of various aspects of the fracture, which helps improve the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir. Secondly, by constructing an initial state vector, then obtaining a predicted state vector through a numerical simulation model, and correcting the predicted state vector using actual production data from the fracture-porous carbonate reservoir to obtain an updated state vector, and then using the updated state vector as a new initial state vector, an iterative optimization loop can be formed. Through repeated iterative optimization, the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir can be further improved. Thirdly, determining the target fracture parameter group based on a preset number of final state vectors can make the simulation results more accurate, thereby improving the accuracy of the fracture parameter inversion results for the fracture-porous carbonate reservoir.
[0085] Based on the same inventive concept, embodiments of this application provide a computer program product, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform the operations performed as described above.
[0086] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to perform the operations described above.
[0087] Figure 3 A schematic diagram of the structure of an electronic device according to one embodiment of this application is shown.
[0088] Based on the same inventive concept, embodiments of this application also provide an electronic device. (Reference) Figure 3 The diagram shows a schematic of the structure of an electronic device according to an embodiment of this application. The electronic device includes one or more memories 304, one or more processors 302, and at least one computer program (program code) stored in the memory 304 and executable on the processor 302. When the processor 302 executes the computer program, it implements the method described above.
[0089] Among them, Figure 3In this document, a bus architecture (represented by bus 300) is used. Bus 300 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.
[0090] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.
[0091] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.
[0092] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.
[0093] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.
[0094] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for inverting fracture parameters in fractured porous carbonate reservoirs, characterized in that, The method includes: Obtain a preset number of crack parameter sets, the crack parameter sets including crack length, crack azimuth angle, crack dip angle, crack aperture, crack permeability and crack density; Based on the preset number of crack parameter groups, a preset number of initial state vectors are constructed respectively; Based on the preset number of initial state vectors, the preset number of predicted state vectors are determined through numerical simulation models. Obtain actual production data of the fractured and porous carbonate reservoir, and correct the preset number of predicted state vectors based on the actual production data to obtain the preset number of updated state vectors; The preset number of updated state vectors are used as the new preset number of initial state vectors. The process of determining the preset number of predicted state vectors based on the preset number of initial state vectors is repeated until the preset number of iterations is satisfied, and the preset number of final state vectors are obtained. Based on the preset number of final state vectors, the target crack parameter set is determined.
2. The method according to claim 1, characterized in that, The process of obtaining a preset number of crack parameter sets includes: Based on the actual production data of the fractured porous carbonate reservoir, the preset fracture parameter ranges for each parameter dimension are determined; According to the Latin hypercube sampling method, a preset number of crack parameters are extracted from the preset crack parameter intervals of each parameter dimension to obtain a preset number of crack parameter groups.
3. The method according to claim 1, characterized in that, The method for constructing a preset number of initial state vectors based on the preset number of crack parameter groups includes: The actual state parameters of the fractured porous carbonate reservoir are obtained, and based on the actual state parameters and the preset number of fracture parameter groups, the simulated state parameters corresponding one-to-one with the preset number of fracture parameter groups are determined respectively. Based on the crack parameter set and the corresponding simulated state parameters, the corresponding predicted production data is determined. Based on the crack parameter set, the simulated state parameters, and the predicted production data, the initial state vector is determined using the following formula: z=[m s ,m p ,d] Where z represents the initial state vector, m s Represents the crack parameter set, m p d represents the simulated state parameters, and d represents the predicted production data.
4. The method according to claim 1, characterized in that, The preset number of predicted state vectors are determined using the following formula: with pre =G[z]+v w Where z represents the initial state vector, z pre Represents the predicted state vector, v w Let G represent process noise, and Ψ represent the numerical simulation model. pre N represents a preset number of predicted state parameters. e Indicates the preset number.
5. The method according to claim 1, characterized in that, The updated state vector is determined by the following formula: d j =d obs +v j H = [O | I] Among them, z j u K represents updating the state vector. e Let H represent the Kalman gain matrix, H represent the observation matrix, and d represent the Kalman gain matrix. j d represents the actual production data after adding observation error. obs Represents actual production data, v j O represents the observation error, O represents the zero matrix, and I represents the identity matrix. C represents the error covariance matrix of a preset number of predicted state vector sets; D H represents the error covariance matrix of the predicted production data for a predetermined number of samples. T This represents the transpose of the observation matrix. The mean of the predicted state vectors is a preset number.
6. The method according to claim 1, characterized in that, The determination of the target crack parameter set based on the preset number of final state vectors includes: Based on the preset number of final state vectors, determine the preset number of final crack parameter groups; The average value of the crack parameters in each parameter dimension of the predetermined number of final crack parameter groups is calculated to obtain the target crack parameter group.
7. The method according to claim 1, characterized in that, Before determining the preset number of predicted state vectors based on the preset number of initial state vectors using numerical simulation models, the method further includes: Obtain actual reservoir data, well logging data, and microseismic data of the fractured porous carbonate reservoir, and establish a basic model based on the actual reservoir data, well logging data, and microseismic data. The basic model is used to characterize the reservoir information in the fractured porous carbonate reservoir. Based on the actual production data and the well locations of the fractured porous carbonate reservoir, the basic model is optimized to obtain a numerical simulation model of the fractured porous carbonate reservoir.
8. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform the method as claimed in any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations performed by the method as described in any one of claims 1 to 7.
10. An electronic device, characterized in that, The method includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to implement the method as claimed in any one of claims 1 to 7.