Method, system and equipment for analyzing broken-solution reservoir and storage medium

Abstract

The embodiment of the invention provides a broken-karst reservoir analysis method, system and equipment and a storage medium, and belongs to the technical field of oil exploration and development. The fault-karst reservoir analysis method comprises the steps of obtaining target logging data and well-side seismic data of an oil field, performing horizon calibration according to the target logging data and the well-side seismic data, and determining a mapping relation between the target logging data and the well-side seismic data, based on different preset models and the mapping relation between the target logging data and the well-side seismic data, wave impedance inversion is carried out according to the target logging data and the well-side seismic data, and a target wave impedance body is obtained. According to the embodiment of the invention, the wave impedance iterative inversion is carried out by using the preset model, so that the accuracy of reservoir prediction is improved, and the risk of drilling is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum exploration and development, and in particular to a fault-karst reservoir analysis method, a fault-karst reservoir analysis system, an electronic device and a readable storage medium. Background Art

[0002] my country's oil exploration and development is facing huge challenges with the fluctuation of international oil prices, and fault-melt reservoirs have gradually become the research focus of exploration and development. However, fault-melt reservoirs are deeply buried, highly heterogeneous, and have short "leakage" and "empty" sections, which makes it difficult to describe the reservoir. At the same time, the collected core data, logging data, and seismic signal-to-noise ratio are low, which increases the difficulty of exploration.

[0003] At present, the prediction of fault-karst reservoirs is mainly based on extracting abnormal seismic reflection characteristics through seismic data processing to identify and predict fault-karst bodies. Among them, seismic attribute prediction and post-stack seismic inversion are the main means of carving and predicting fault-karst oil reservoirs. Seismic attribute prediction mainly calculates different types of seismic attributes through mathematical methods, and uses drilling logging, drilling "emptying" and "leakage" data to calibrate the seismic reflection anomaly boundary, and then clarify the fault-karst boundary (contour) threshold. However, seismic attribute prediction is often affected by the top interface of the Middle Ordovician target layer and the seismic amplitude energy of the tight layer, and cannot accurately calibrate the fault-karst reservoir boundary. At the same time, seismic attribute values ​​do not have strict rock physical meanings, and the porosity converted from seismic attribute predictions is difficult to meet the needs of strong heterogeneous reservoir prediction and modeling; post-stack seismic inversion is mainly divided into deterministic inversion and random inversion, among which conventional deterministic inversion is a recursive inversion method that uses a sparse pulse deconvolution algorithm and is constrained by a low-frequency model based on well point information; random inversion is based on deterministic inversion, combined with probability density function and variation function, using Markov chain-Monte Carlo algorithm, and finally obtains an inversion data body that meets the geostatistical characteristics of the input data through multiple equal probability realizations. However, the abundance of well logging data of fault-karst reservoirs is difficult to meet the needs of these two inversion methods. Moreover, the low-frequency model based on well point information is established by the combination of strata, faults, target logging data, and geological knowledge. It is affected by the seismic strata and is distributed in layers, which does not match the characteristics of the vertical distribution of fault-karst reservoirs. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a fault-karst reservoir analysis method, system, device and storage medium to solve the above-mentioned problems.

[0005] In order to achieve the above object, an embodiment of the present invention provides a fault-karst reservoir analysis method, the method comprising:

[0006] Obtain target logging data and wellside seismic data of oil fields;

[0007] Performing layer calibration based on target well logging data and wellside seismic data to determine the mapping relationship between target well logging data and wellside seismic data;

[0008] Based on different preset models and the mapping relationship between target well logging data and wellside seismic data, wave impedance inversion is performed according to the target well logging data and wellside seismic data to obtain the target wave impedance body.

[0009] Optionally, obtaining target logging data includes:

[0010] Obtain initial oilfield logging data;

[0011] Analyzing the initial well logging data of the oil field to determine invalid well logging data;

[0012] Eliminate invalid logging data from the initial logging data of the oil field to obtain valid logging data;

[0013] Based on the effective logging data, a logging curve is obtained;

[0014] According to the well logging curve, judging whether the dense layer baseline in the well logging curve is unified;

[0015] If they are not unified, the dense layer baseline is adjusted using the histogram method until all dense layer baselines in the logging curve are unified, and the effective logging data corresponding to the logging curve with unified dense layer baseline is used as the target logging data.

[0016] Optionally, the preset model includes a low-frequency model and a phase trend model;

[0017] The method of performing wave impedance inversion based on the target well logging data and the well seismic data based on different preset models and the mapping relationship between the target well logging data and the well seismic data to obtain the target wave impedance body includes:

[0018] Based on the mapping relationship between the low-frequency model and the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain a first wave impedance body;

[0019] updating parameters of the phase trend model according to the first wave impedance body;

[0020] Based on the updated phase trend model and the mapping relationship between the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain the target wave impedance body.

[0021] Optionally, based on the mapping relationship between the low-frequency model and the target well logging data and the wellside seismic data, performing wave impedance inversion according to the target well logging data and the wellside seismic data to obtain a first wave impedance body includes:

[0022] Based on the mapping relationship between the low-frequency model and the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain a first inversion data volume;

[0023] A discrimination analysis is performed on the first inversion data volume to obtain a first wave impedance volume.

[0024] Optionally, performing discrimination analysis on the first inversion data volume to obtain a first wave impedance volume includes:

[0025] Using formula (1), discrimination analysis is performed on the first inversion data volume to obtain the first wave impedance volume:

[0026] F(x i ,ω i ,t)=f(x i ,ω i ,t)+α i g(x i ,ω i ,t) (1);

[0027] In formula (1), F(x i ,ω i ,t) is the first wave impedance body, f(x i ,ω i ,t) absolute wave impedance body, g(x i ,ω i ,t) is the relative wave impedance, α i is the superposition coefficient, i is the sampling point number, x i is the main survey line number for seismic data sampling, ω i is the earthquake channel number, and t is the time.

[0028] Optionally, the step of performing wave impedance inversion based on the target well logging data and the well seismic data based on the updated phase trend model and the mapping relationship between the target well logging data and the well seismic data to obtain the target wave impedance body includes:

[0029] Based on the updated phase trend model and the mapping relationship between the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain a second inversion data volume;

[0030] Determining whether the difference between the second inversion data volume and the first inversion data volume reaches a preset difference value;

[0031] If yes, taking the second inversion data volume as the target inversion data volume;

[0032] Performing discrimination analysis on the target inversion data body to obtain a second wave impedance body;

[0033] Based on the first wave impedance body and the second wave impedance body, a target wave impedance body is obtained.

[0034] Optionally, obtaining a target wave impedance body based on the first wave impedance body and the second wave impedance body comprises:

[0035] Based on the first wave impedance body and the second wave impedance body, using formula (2), the target wave impedance body is obtained:

[0036] F(u i )=α i Z(u i )+β i Y(u i ) (2);

[0037] In formula (2), F(u i ) is the target wave impedance body, Z(u i ) is the first wave impedance, Y(u i ) is the second wave impedance body; α i and β i is the difference coefficient, u i Contains x i ,ω i and t, x i is the main survey line number for seismic data sampling, ω i is the earthquake channel number, and t is the time.

[0038] In a second aspect of the embodiments of the present invention, a fault-karst reservoir analysis system is provided, the system comprising:

[0039] A data acquisition module is used to acquire target well logging data and wellside seismic data of the oil field;

[0040] The layer calibration module is used to perform layer calibration based on the target well logging data and the wellside seismic data, and determine the mapping relationship between the target well logging data and the wellside seismic data;

[0041] The wave impedance inversion module is used to perform wave impedance inversion according to the target well logging data and wellside seismic data based on different preset models and the mapping relationship between the target well logging data and the wellside seismic data to obtain the target wave impedance body.

[0042] A third aspect of the present application provides an electronic device configured to execute the above-mentioned fault-karst reservoir analysis method.

[0043] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the above-mentioned fault-karst reservoir analysis method.

[0044] The embodiment of the present invention obtains target logging data and wellside seismic data of the oil field, further performs layer calibration based on the target logging data and wellside seismic data, determines the mapping relationship between the target logging data and wellside seismic data, and then performs wave impedance inversion based on the target logging data and wellside seismic data based on different preset models and the mapping relationship between the target logging data and wellside seismic data to obtain the target wave impedance body. That is, the embodiment of the present invention uses the preset model to perform wave impedance iterative inversion, improves the accuracy of reservoir prediction, and reduces the risk of drilling.

[0045] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0047] Figure 1 It is a schematic flow chart of a fault-karst reservoir analysis method provided by an embodiment of the present invention;

[0048] Figure 2 is the cross-section of the low-frequency model;

[0049] Figure 3 is the cross-section diagram of the phase trend model;

[0050] Figure 4 is the seismic energy gradient profile;

[0051] Figure 5 is the target wave impedance inversion profile;

[0052] Figure 6 It is the posterior well-connected profile;

[0053] Figure 7 A schematic diagram of functional modules of a fault-karst reservoir analysis system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0056] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0057] Embodiment 1

[0058] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the flow of a fault-karst reservoir analysis method provided in this embodiment.

[0059] Step S100: Acquire target well logging data and wellside seismic data of the oil field.

[0060] It is understandable that due to the different logging sequences at the oil field site and the existence of drilling "emptying" and "leakage", there are invalid values ​​in the logging curve and drift of the dense layer baseline, which will affect the results of seismic inversion reservoir prediction. Therefore, this embodiment provides the following solutions to the above problems: obtain initial oil field logging data, further analyze the initial oil field logging data, determine invalid logging data, eliminate invalid logging data from the initial oil field logging data, obtain valid logging data, and then obtain a logging curve based on the valid logging data. According to the logging curve, it is determined whether the dense layer baseline in the logging curve is unified. If it is unified, the valid logging data corresponding to the logging curve is directly used as the target logging data; if it is not unified, the dense layer baseline is adjusted by using the histogram method until all the dense layer baselines in the logging curve are unified, and the valid logging data corresponding to the logging curve with the unified dense layer baseline is used as the target logging data.

[0061] Step S200: perform layer calibration according to target well logging data and wellside seismic data to determine a mapping relationship between the target well logging data and wellside seismic data.

[0062] Layer calibration is a bridge between depth-domain well logging data and time-domain seismic data. This embodiment performs time-depth conversion based on target well logging data, calculates the reflection coefficient of each layer using formula (3), and then determines the seismic profile information based on the wellside seismic data, extracts the seismic wavelet from the seismic profile, and calculates the synthetic seismic record using formula (4). The synthetic seismic record is compared with the wellside seismic data, and when the correlation coefficient between the synthetic seismic record and the wellside seismic data is the largest, the mapping relationship between the target well logging data and the wellside seismic data is determined. Wherein, formula (3) and formula (4) are as follows:

[0063]

[0064] In formula (3), R i is the reflection coefficient of layer i, ρ i is the density of layer i, v i is the seismic wave velocity of layer i;

[0065] S(t i )=W(t i )×R(t i )+σ(t i ) (4);

[0066] In formula (4), S(t i ) is the synthetic seismic record, W(t i ) is the seismic wavelet, R(t i ) is the reflection coefficient, σ(t i ) is noise.

[0067] Step S300: Based on different preset models and the mapping relationship between the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain a target wave impedance body.

[0068] The preset models include low frequency model and phase trend model.

[0069] Since seismic data are attenuated and scattered by different reflection interfaces during the acquisition process, high-frequency signals decay faster than low-frequency signals, causing the spectrum of the seismic data to shift toward low frequencies. However, seismic data are affected by filtering, denoising, stacking and other operations during processing, resulting in high-frequency signals being enhanced or retained, while low-frequency signals being weakened or eliminated. The post-stack seismic data that ultimately participate in the inversion often lack data below 10 Hz. The lack of low-frequency information will affect the accuracy of low-frequency and high-frequency signals in the inversion results, thereby reducing the accuracy of the wave impedance inversion. Therefore, this embodiment proposes a low-frequency model, which is constructed by establishing a low-frequency model by inter-well interpolation under the constraints of the sequence stratigraphic grid during the first wave impedance inversion (such as Figure 2 ), use the sequence framework to constrain the inversion results, and increase the low-frequency signal of seismic inversion through the low-frequency model.

[0070] Since the low-frequency model is not ideal for seismic inversion of fault-karst reservoirs, and the layered distribution does not conform to the reservoir distribution law of fault-karst reservoirs, this embodiment proposes a phase trend model (such as Figure 3 ) instead of the conventional low-frequency model, the seismic phase is used to constrain the inversion results and increase the consistency of seismic inversion prediction. The phase trend model is established by using the seismic energy gradient (such as Figure 4) extracts the fault-karst boundary, with a threshold of 22000 (dimensionless). At the same time, the fault-karst interior is assigned the value of the first wave impedance body processed by discrimination analysis, and the dense layer is assigned the gypsum wave impedance value, which can be 1.8×107kg / m3*m / s. ​​The phase trend model uses the concept of seismic phase and reasonably uses seismic attributes to characterize the fault-karst boundary, reducing the impact of layered reflection characteristics in the original seismic on the prediction of fault-karst reservoirs.

[0071] It can be understood that the present embodiment improves the accuracy of inversion prediction by means of wave impedance iterative inversion, which specifically includes: based on the mapping relationship between the low-frequency model and the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data, and the first inversion data body is calculated by using formula (4), and the first inversion data body is subjected to discrimination analysis by using formula (1) to obtain the first wave impedance body, and then the parameters of the phase trend model are updated according to the first wave impedance body, and the wave impedance inversion is performed according to the target well logging data and the wellside seismic data based on the updated phase trend model and the mapping relationship between the target well logging data and the wellside seismic data, Using formula (5), the second inversion data volume is obtained, and it is determined whether the difference between the second inversion data volume and the first inversion data volume reaches a preset difference value. If not, the phase trend model is updated based on the second inversion data volume, and then the wave impedance inversion is performed based on the updated phase trend model until the difference between the second inversion data volume and the first inversion data volume reaches a preset difference value, and the second inversion data volume is used as the target inversion data volume; if so, the second inversion data volume is used as the target inversion data volume, and the target inversion data volume is subjected to discrimination analysis using formula (1) to obtain the second wave impedance volume. Based on the first wave impedance volume and the second wave impedance volume, the target wave impedance volume (such as Figure 5 ). Among them, formula (5) specifically expresses that the three-dimensional wave impedance body is converted into a three-dimensional reflection coefficient body, and then convolved with the seismic wavelet obtained by horizon calibration to generate a new synthetic seismic record, and the reflector data is calculated using the least squares optimization algorithm; formula (1), formula (2) and formula (5) are as follows:

[0072] F(x i ,ω i ,t)=f(x i ,ω i ,t)+α i g(x i ,ω i ,t) (1);

[0073] In formula (1), F(x i ,ω i ,t) is the first wave impedance body, f(x i ,ω i ,t) absolute wave impedance body, g(x i ,ωi ,t) is the relative wave impedance, α i is the superposition coefficient, i is the sampling point number, x i is the main survey line number for seismic data sampling, ω i is the earthquake channel number, t is the time;

[0074] F(u i )=α i Z(u i )+β i Y(u i ) (2);

[0075] In formula (2), F(u i ) is the target wave impedance body, Z(u i ) is the first wave impedance, Y(u i ) is the second wave impedance body; α i and β i is the difference coefficient, u i Contains x i ,ω i and t, x i is the main survey line number for seismic data sampling, ω i is the earthquake channel number, t is the time;

[0076]

[0077] In formula (5), L is the earthquake residual, y i is the actual earthquake data, f(x i ,ω i , t) is the synthetic seismic record, i is the sampling point number, when the function value L is the minimum, the first inversion data volume or the second inversion data volume is output, x i is the main survey line number for seismic data sampling, ω i is the earthquake channel number, and t is the time.

[0078] It can be understood that in this embodiment, the predicted target wave impedance inversion is compared with the well data to obtain Figure 6 The posterior well-connected profile is shown, and the profile is analyzed to verify the accuracy of the inversion prediction. The letters A and B in the figure represent well A and well B respectively.

[0079] This embodiment obtains the target logging data and wellside seismic data of the oil field, further performs layer calibration based on the target logging data and wellside seismic data, determines the mapping relationship between the target logging data and wellside seismic data, and then performs wave impedance inversion based on the target logging data and wellside seismic data based on different preset models and the mapping relationship between the target logging data and wellside seismic data to obtain the target wave impedance body. That is, the embodiment of the present invention uses the preset model to perform wave impedance iterative inversion, improves the accuracy of reservoir prediction, and reduces the risk of drilling.

[0080] Embodiment 2

[0081] Please refer to the figure, Figure 7 It is a schematic diagram of the functional modules of a fault-karst reservoir analysis system 200 provided in an embodiment of the present application.

[0082] The data acquisition module 210 is used to acquire target well logging data and wellside seismic data of the oil field;

[0083] The layer calibration module 220 is used to perform layer calibration based on the target well logging data and the wellside seismic data, and determine the mapping relationship between the target well logging data and the wellside seismic data;

[0084] The wave impedance inversion module 230 is used to perform wave impedance inversion according to the target well logging data and the well seismic data based on different preset models and the mapping relationship between the target well logging data and the well seismic data to obtain the target wave impedance body.

[0085] It should be understood that the system corresponds to the above-mentioned fault-karst reservoir analysis method embodiment and can execute the various steps involved in the above-mentioned method embodiment. The specific functions of the system can be found in the above description. To avoid repetition, the detailed description is appropriately omitted here. The system includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device.

[0086] Embodiment 3

[0087] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0088] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0089] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0090] Embodiment 4

[0091] An embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored, and the instructions are used to execute a program with steps of a method for analyzing a discontinuous solution reservoir when executed by a processor.

[0092] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0093] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0097] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0098] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0099] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A fault-karst reservoir analysis method, characterized in that: The method comprises: Obtain target logging data and wellside seismic data of oil fields; Performing layer calibration based on target well logging data and wellside seismic data to determine the mapping relationship between target well logging data and wellside seismic data; Based on different preset models and the mapping relationship between target well logging data and wellside seismic data, wave impedance inversion is performed according to the target well logging data and wellside seismic data to obtain the target wave impedance body.

2. The fault-karst reservoir analysis method according to claim 1, characterized in that: The step of obtaining target logging data comprises: Obtain initial oilfield logging data; Analyzing the initial well logging data of the oil field to determine invalid well logging data; Eliminate invalid logging data from the initial logging data of the oil field to obtain valid logging data; Based on the effective logging data, a logging curve is obtained; According to the well logging curve, judging whether the dense layer baseline in the well logging curve is unified; If they are not unified, the dense layer baseline is adjusted using the histogram method until all dense layer baselines in the logging curve are unified, and the effective logging data corresponding to the logging curve with unified dense layer baseline is used as the target logging data.

3. The fault-karst reservoir analysis method according to claim 1, characterized in that: The preset model includes a low-frequency model and a phase trend model; The method of performing wave impedance inversion based on the target well logging data and the well seismic data based on different preset models and the mapping relationship between the target well logging data and the well seismic data to obtain the target wave impedance body includes: Based on the mapping relationship between the low-frequency model and the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain a first wave impedance body; updating parameters of the phase trend model according to the first wave impedance body; Based on the updated phase trend model and the mapping relationship between the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain the target wave impedance body.

4. The fault-karst reservoir analysis method according to claim 3, characterized in that: The method of performing wave impedance inversion based on the low-frequency model and the mapping relationship between the target well logging data and the wellside seismic data to obtain a first wave impedance body includes: Based on the mapping relationship between the low-frequency model and the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain a first inversion data volume; A discrimination analysis is performed on the first inversion data volume to obtain a first wave impedance volume.

5. The fault-karst reservoir analysis method according to claim 4, characterized in that: Performing discrimination analysis on the first inversion data volume to obtain a first wave impedance volume, including: Using formula (1), discrimination analysis is performed on the first inversion data volume to obtain the first wave impedance volume: F(x i ,oh i ,t)=f(x i ,oh i ,t)+a i g(x i ,oh i ,t) (1); In formula (1), F(x i ,ω i ,t) is the first wave impedance body, f(x i ,ω i ,t) absolute wave impedance body, g(x i ,ω i ,t) is the relative wave impedance, α i is the superposition coefficient, i is the sampling point number, x i is the main survey line number for seismic data sampling, ω i is the earthquake channel number, and t is the time.

6. The fault-karst reservoir analysis method according to claim 3, characterized in that: The method of performing wave impedance inversion based on the target well logging data and the well seismic data based on the updated phase trend model and the mapping relationship between the target well logging data and the well seismic data to obtain the target wave impedance body includes: Based on the updated phase trend model and the mapping relationship between the target well logging data and the wellside seismic data, wave impedance inversion is performed according to the target well logging data and the wellside seismic data to obtain a second inversion data volume; Determining whether the difference between the second inversion data volume and the first inversion data volume reaches a preset difference value; If yes, taking the second inversion data volume as the target inversion data volume; Performing discrimination analysis on the target inversion data body to obtain a second wave impedance body; Based on the first wave impedance body and the second wave impedance body, a target wave impedance body is obtained.

7. The fault-karst reservoir analysis method according to claim 6, characterized in that: The step of obtaining a target wave impedance body based on the first wave impedance body and the second wave impedance body comprises: Based on the first wave impedance body and the second wave impedance body, using formula (2), the target wave impedance body is obtained: F(u i )=α i Z(u i )+β i Y(u i ) (2); In formula (2), F(u i ) is the target wave impedance body, Z(u i ) is the first wave impedance body, Y(u i ) is the second wave impedance body; α i and β i is the difference coefficient, u i Contains x i ,ω i and t, x i is the main survey line number for seismic data sampling, ω i is the earthquake channel number, and t is the time.

8. A fault-karst reservoir analysis system, characterized in that: The system comprises: A data acquisition module is used to acquire target well logging data and wellside seismic data of the oil field; The layer calibration module is used to perform layer calibration based on the target well logging data and the wellside seismic data, and determine the mapping relationship between the target well logging data and the wellside seismic data; The wave impedance inversion module is used to perform wave impedance inversion according to the target well logging data and wellside seismic data based on different preset models and the mapping relationship between the target well logging data and the wellside seismic data to obtain the target wave impedance body.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the fault-karst reservoir analysis method described in any one of claims 1-7 is executed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions for causing a machine to execute the fault-karst reservoir analysis method according to any one of claims 1 to 7.

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