Earthquake inversion evaluation method and device

Abstract

The invention discloses a seismic inversion evaluation method and device, and the method comprises the steps: obtaining the seismic data and drilling data of a target layer, and calculating the signal-to-noise ratio and dominant frequency of the seismic data of the target layer; determining a lithofacies type of the target layer and a corresponding seismic waveform feature according to the seismic data and the drilling data; calculating a correlation coefficient between each lithofacies type and the corresponding seismic waveform feature according to the lithofacies type and the corresponding seismic waveform feature; generating a seismic waveform classification plane graph according to the lithofacies type and the corresponding seismic waveform features; counting the drilling density of each lithofacies type according to the seismic waveform classification plane graph; comprehensive evaluation indexes are calculated; the comprehensive evaluation index is used for evaluating the seismic inversion credibility. According to the invention, effective pre-evaluation of seismic inversion credibility can be realized, and the risk of oil-gas exploration and development is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas exploration and development, and particularly relates to a seismic inversion evaluation method and device. BACKGROUND

[0002] This section is intended to provide background or context to the embodiments of the application recited in the claims. The description herein does not constitute admission that the prior publication, square, or subject matter described herein and / or the material contained therein are prior art to the claimed application.

[0003] Seismic inversion technology is an important means for reservoir prediction and reservoir heterogeneity evaluation in oil and gas exploration and development. Geologists look for favorable zones according to the results of seismic inversion to guide the deployment of exploration wells; development personnel generally establish geological models and predict the distribution of remaining oil based on high-precision seismic inversion results. In order to determine the uncertainty brought by the seismic inversion results in subsequent geological analysis and development modeling applications, the seismic inversion results are generally evaluated for reliability after seismic inversion.

[0004] The conventional methods for evaluating the inversion results mainly include three aspects: first, the correlation between the inversion results at the drilled well point position and the original logging curve is calculated, and the higher the correlation, the more reliable the inversion results; second, the planar graph of the seismic inversion results is compared with the seismic attributes to confirm the lateral constraint effect of the seismic data; and third, the blind well not participating in the inversion calculation is used for verification. The mainstream seismic inversion algorithm is a process of combining wells and seismic data, and the different weight proportions in the combination process will affect the inversion results, causing large changes in the inversion results. When different parameters are selected, the correlation between the inversion results and the original logging curve and the correlation with the seismic attributes can be controlled. The quality of the blind well verification result is highly related to the selection of the blind well. The above methods only consider the reliability of the final results of the inversion, which is one-sided and artificial, and cannot objectively and truly quantitatively determine the reliability of the inversion results.

[0005] When the inversion algorithm and parameter selection are appropriate and the seismic longitudinal resolution and lateral resolution are balanced, the reliability of the inversion results depends more on the quality of the input data. The prior art does not perform pre-evaluation of the reliability of the input seismic data and drilling data, and cannot effectively evaluate the reliability of the seismic inversion based on the quality of the input data, resulting in increased risk of oil and gas exploration and development. SUMMARY

[0006] The embodiments of the present application provide a seismic inversion evaluation method to effectively pre-evaluate the reliability of seismic inversion and reduce the risk of oil and gas exploration and development, which comprises the following steps:

[0007] Seismic data and drilling data of a target layer are obtained, and the signal-to-noise ratio and the main frequency of the target layer seismic data are calculated.

[0008] determine facies types of the target layer and corresponding seismic waveform features according to the seismic data and the drilling data;

[0009] calculate a correlation coefficient between each facies type and the corresponding seismic waveform feature according to the facies types and the corresponding seismic waveform features;

[0010] generate a seismic waveform classification plan according to the facies types and the corresponding seismic waveform features;

[0011] count drilling density of each facies type according to the seismic waveform classification plan;

[0012] perform normalization processing on the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency, and calculate a comprehensive evaluation index according to the normalized drilling density, the normalized correlation coefficient, the normalized signal-to-noise ratio and the normalized main frequency; the comprehensive evaluation index is used for evaluating the seismic inversion credibility.

[0013] The embodiment of the present application also provides a seismic inversion evaluation device for realizing effective pre-evaluation of the seismic inversion credibility and reducing oil and gas exploration and development risks.

[0014] The data acquisition module is used for acquiring seismic data and drilling data of the target layer, and calculating a signal-to-noise ratio and a main frequency of the seismic data of the target layer.

[0015] The seismic waveform feature determination module is used for determining facies types of the target layer and corresponding seismic waveform features according to the seismic data and the drilling data.

[0016] The correlation coefficient calculation module is used for calculating a correlation coefficient between each facies type and the corresponding seismic waveform feature according to the facies types and the corresponding seismic waveform features.

[0017] The seismic waveform classification plan generation module is used for generating a seismic waveform classification plan according to the facies types and the corresponding seismic waveform features.

[0018] The drilling density determination module is used for counting drilling density of each facies type according to the seismic waveform classification plan.

[0019] The comprehensive evaluation index calculation module is used for performing normalization processing on the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency, and calculating a comprehensive evaluation index according to the normalized drilling density, the normalized correlation coefficient, the normalized signal-to-noise ratio and the normalized main frequency; the comprehensive evaluation index is used for evaluating the seismic inversion credibility.

[0020] The embodiment of the present application also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor realizes the above seismic inversion evaluation method when executing the computer program.

[0021] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the seismic inversion evaluation method.

[0022] The embodiment of the present application also provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to realize the seismic inversion evaluation method.

[0023] In the embodiment of the present application, the signal-to-noise ratio and the main frequency of the target layer seismic data are calculated by acquiring the seismic data and the drilling data of the target layer; the lithofacies type and the corresponding seismic waveform feature of the target layer are determined according to the seismic data and the drilling data; the correlation coefficient between each lithofacies type and the corresponding seismic waveform feature is calculated according to the lithofacies type and the corresponding seismic waveform feature; the seismic waveform classification plan is generated according to the lithofacies type and the corresponding seismic waveform feature; the drilling density of each lithofacies type is counted according to the seismic waveform classification plan; the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency are normalized, and the comprehensive evaluation index is calculated according to the normalized drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency; and the comprehensive evaluation index is used to evaluate the seismic inversion credibility. In the above process, the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency are determined based on the seismic data and the drilling data before the seismic inversion, the comprehensive evaluation index is calculated by multiple parameters, the credibility of the seismic inversion is quantified before the inversion, the problem of low credibility of the seismic inversion result caused by the input data defects is avoided from the source, the effective pre-evaluation of the seismic inversion credibility is realized, and the risk of oil and gas exploration and development is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort. In the drawings:

[0025] Figure 1 The flow chart of the seismic inversion evaluation method in the embodiment of the present application;

[0026] Figure 2 The signal-to-noise ratio plan of the target layer seismic data in the embodiment of the present application;

[0027] Figure 3 The main frequency plan of the target layer seismic data in the embodiment of the present application;

[0028] Figure 4A seismic waveform feature map of a lithofacies type of a target layer in an embodiment of the present application;

[0029] Figure 5 A seismic waveform classification plan view in an embodiment of the present application;

[0030] Figure 6 A well-seismic correlation coefficient statistical map of different lithofacies types in an embodiment of the present application;

[0031] Figure 7 A plan view of a pre-evaluation seismic inversion reliability in a division in an embodiment of the present application;

[0032] Figure 8 A schematic diagram of a seismic inversion evaluation device in an embodiment of the present application. DETAILED DESCRIPTION

[0033] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer and more apparent, the embodiments of the present application are further described in detail below with reference to the drawings. Herein, the schematic embodiments of the present application and the descriptions thereof are used to explain the present application, but not as a limitation on the present application.

[0034] The acquisition, storage, use, processing and the like of data in the technical scheme of the present application all comply with the relevant provisions of the national laws and regulations.

[0035] Figure 1 A flowchart of a seismic inversion evaluation method in an embodiment of the present application, the method comprising:

[0036] Step 101, acquiring seismic data and drilling data of a target layer, and calculating a signal-to-noise ratio and a main frequency of the seismic data of the target layer;

[0037] Step 102, determining a lithofacies type of the target layer and corresponding seismic waveform features according to the seismic data and the drilling data;

[0038] Step 103, calculating a correlation coefficient between each lithofacies type and corresponding seismic waveform features according to the lithofacies type and the corresponding seismic waveform features;

[0039] Step 104, generating a seismic waveform classification plan view according to the lithofacies type and the corresponding seismic waveform features;

[0040] Step 105, according to the seismic waveform classification plan view, calculating a drilling density of each lithofacies type;

[0041] Step 106, performing a normalization processing on the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency, and calculating a comprehensive evaluation index according to the normalized drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency; the comprehensive evaluation index is used to evaluate the seismic inversion reliability.

[0042] The following will be described in detail for each step.

[0043] In step 101, seismic data and drilling data of the target layer are acquired, and the signal-to-noise ratio and the main frequency of the seismic data of the target layer are calculated.

[0044] In specific embodiments, seismic data and drilling data in the study area are collected, including drilling location, well trajectory data, conventional logging curves, geological layering data and lithofacies interpretation data. Based on the seismic data and the drilling data, synthetic seismograms of each well are produced, well-to-seismic time depth calibration and horizon interpretation tracking are carried out. According to the top and bottom interfaces of the target layer, the signal-to-noise ratio and the main frequency of the seismic data of the target layer are calculated, and corresponding plane maps are formed.

[0045] Figure 2 The signal-to-noise ratio plane map of the seismic data of the target layer in the embodiment of the present application, the signal-to-noise ratio is used to measure the quality of the seismic data of the target layer in the study area. The signal-to-noise ratio SNR of a certain point in the study area is 10log 10 (Es / En), wherein Es is the energy of the effective signal of the target layer, and En is the energy of the noise of the target layer.

[0046] Figure 3 The main frequency plane map of the seismic data of the target layer in the embodiment of the present application, the main frequency is used to measure the resolution capability of the seismic data of the target layer in the study area. The main frequency of a certain point in the study area is wherein W i is a discrete frequency point, is the corresponding energy of the frequency W i .

[0047] In step 102, the lithofacies type of the target layer and the corresponding seismic waveform characteristics are determined according to the seismic data and the drilling data.

[0048] Figure 4 The lithofacies type and the seismic waveform characteristics of the target layer in the embodiment of the present application, in specific embodiments, on the basis of well-to-seismic calibration and horizon interpretation, the lithofacies type of each well in the target layer is analyzed and counted, classified and analyzed for the corresponding seismic waveform characteristics of different types of lithofacies. As Figure 4 shown, the target layer of the study area is carbonate ramp beach facies deposition, mainly developing granular limestone, bioclastic limestone and inter-beach mudstone deposition, when thick-layer high-energy beach bodies are developed, the lithofacies on the well shows thick-layer granular limestone development; when low-energy beach bodies are developed, the lithofacies on the well shows bioclastic limestone with thin-layer mudstone deposition; when the beach bodies are not developed, the lithofacies is inter-beach mudstone deposition with thin-layer bioclastic limestone. According to the above lithofacies classification, the corresponding seismic waveform characteristics are summarized, the seismic waveform of the I-type lithofacies combination is strong peak and strong valley characteristics, the seismic waveform of the II-type lithofacies combination is medium-strong peak and medium-strong valley characteristics, and the seismic waveform of the III-type lithofacies combination is weak valley characteristics.

[0049] In step 103, the correlation coefficient between each lithofacies type and its corresponding seismic waveform characteristics is calculated based on the lithofacies type and its corresponding seismic waveform characteristics; in step 104, a seismic waveform classification plan is generated based on the lithofacies type and its corresponding seismic waveform characteristics.

[0050] Figure 5 This is a seismic waveform classification planar diagram in an embodiment of the present invention. In a specific embodiment, based on the top and bottom interfaces of the target layer, the correlation coefficient between the synthetic records within the target layer and the well-circumferential seismic waveforms is calculated. Combined with the seismic waveform classification planar diagram, the average correlation coefficient between the synthetic records of different lithofacies types and the characteristics of the well-circumferential seismic waveforms is calculated to indicate the correlation between well-seismic data of different lithofacies within the study area. Figure 6 This is a statistical chart showing the well-seismic correlation coefficients for different lithofacies categories in this invention. The average correlation coefficient is... n represents the number of wells drilled.

[0051] In step 105, the drilling density for each lithofacies type is calculated based on the seismic waveform classification plan.

[0052] In a specific embodiment, based on the aforementioned seismic waveform classification planar diagram and the location of wells in the target layer, the planar area and number of wells for each lithofacies type are statistically analyzed. The quotient of the planar area of ​​a certain lithofacies type distribution and the number of wells is defined as the number of wells per square kilometer, which is the well density D for that lithofacies type. For example... Figure 6 As shown, there are 58 wells drilled within the Class I lithofacies assemblages, covering an area of ​​81 square kilometers, resulting in a well density of 0.72 wells per square kilometer. There are 63 wells drilled within the Class II lithofacies assemblages, covering an area of ​​210 square kilometers, resulting in a well density of 0.30 wells per square kilometer. There are 2 wells drilled within the Class III lithofacies assemblages, covering an area of ​​32 square kilometers, resulting in a well density of 0.06 wells per square kilometer. The Class I lithofacies assemblages exhibit the highest well density, indicating a higher degree of well control.

[0053] In one embodiment, based on the seismic waveform classification plan, the drilling density for each lithofacies type is calculated, including:

[0054] Based on the seismic waveform classification plan, determine the area and number of wells for each lithofacies type;

[0055] Calculate the drilling density for each lithofacies type based on its area and the number of wells drilled.

[0056] In step 106, the drilling density, correlation coefficient, signal-to-noise ratio, and dominant frequency are normalized. Based on the normalized drilling density, correlation coefficient, signal-to-noise ratio, and dominant frequency, a comprehensive evaluation index is calculated. The comprehensive evaluation index is used to evaluate the reliability of seismic inversion.

[0057] In one embodiment, drilling density, correlation coefficient, signal-to-noise ratio, and dominant frequency are normalized. Based on the normalized drilling density, correlation coefficient, signal-to-noise ratio, and dominant frequency, a comprehensive evaluation index is calculated, including:

[0058] Calculate the average correlation coefficient between different lithofacies types and their corresponding seismic waveform characteristics;

[0059] The drilling density, average correlation coefficient, signal-to-noise ratio, and dominant frequency are normalized. Based on the normalized drilling density, average correlation coefficient, signal-to-noise ratio, and dominant frequency, a comprehensive evaluation index is calculated.

[0060] In one embodiment, a comprehensive evaluation index is calculated based on the normalized drilling density, average correlation coefficient, signal-to-noise ratio, and dominant frequency, including:

[0061] Calculate the comprehensive evaluation index using the following formula:

[0062]

[0063] Where R represents the comprehensive evaluation index, SNR represents the signal-to-noise ratio, and f min D represents the main frequency, C represents the drilling density, and D represents the drilling density. avg This represents the average correlation coefficient. Using the comprehensive evaluation index calculation formula described above, a pre-assessment planar map of earthquake inversion reliability can be obtained, allowing for the regional prediction of earthquake inversion reliability.

[0064] In a specific embodiment, the reliability of the zonal pre-assessment seismic inversion includes:

[0065] a) Using lithofacies type as the basic zoning unit, independent assessment areas are divided based on seismic waveform classification plane maps;

[0066] b) Calculate three parameters independently within each lithofacies zone:

[0067] Drilling density parameter: Statistically counts the number of wells drilled per unit area in the current lithofacies zone;

[0068] Well-seismic correlation parameters: Calculate the average correlation coefficient between the composite records of all drilling points in the current lithofacies zone and the seismic waveform;

[0069] Seismic quality parameters: Extract the signal-to-noise ratio and dominant frequency values ​​corresponding to the current lithofacies zone;

[0070] c) Perform parameter normalization and comprehensive evaluation index calculation for each partition, and generate a partition reliability evaluation map, such as... Figure 7 As shown, Figure 7 This is a plan view for pre-evaluating the reliability of seismic inversion in this embodiment of the invention; wherein, different lithofacies zones use the same parameter calculation rules and weighting coefficients.

[0071] By partitioning to pre-evaluate the credibility of seismic inversion, the problem that local data quality defects are covered due to "overall evaluation" in the prior art is solved. Through the planar map of pre-evaluation of the credibility of seismic inversion, the low credibility area is directly guided to be avoided and the data acquisition target area is optimized, thereby reducing the exploration and development risk from the source.

[0072] The embodiment of the present application also provides a seismic inversion evaluation device, as described in the following embodiment. Since the principle of solving the problem of the device is similar to that of the seismic inversion evaluation method, the implementation of the device can be referred to the implementation of the seismic inversion evaluation method, and the repeated parts will not be described again.

[0073] Figure 8 The schematic diagram of the seismic inversion evaluation device in the embodiment of the present application, the device comprises:

[0074] The data acquisition module 801 is configured to acquire seismic data and drilling data of a target layer, and calculate a signal-to-noise ratio and a main frequency of the seismic data of the target layer.

[0075] The seismic waveform feature determination module 802 is configured to determine a lithofacies type and corresponding seismic waveform features of the target layer according to the seismic data and the drilling data.

[0076] The correlation coefficient calculation module 803 is configured to calculate a correlation coefficient between each lithofacies type and corresponding seismic waveform features according to the lithofacies type and the corresponding seismic waveform features.

[0077] The seismic waveform classification planar map generation module 804 is configured to generate a seismic waveform classification planar map according to the lithofacies type and the corresponding seismic waveform features.

[0078] The drilling density determination module 805 is configured to count a drilling density of each lithofacies type according to the seismic waveform classification planar map.

[0079] The comprehensive evaluation index calculation module 806 is configured to perform normalization processing on the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency, and calculate a comprehensive evaluation index according to the normalized drilling density, the normalized correlation coefficient, the normalized signal-to-noise ratio and the normalized main frequency. The comprehensive evaluation index is used to evaluate the credibility of seismic inversion.

[0080] In an embodiment, the drilling density determination module 805 is specifically configured to:

[0081] According to the seismic waveform classification planar map, the area and the number of drillings of each lithofacies type are determined.

[0082] According to the area and the number of drillings of each lithofacies type, the drilling density of each lithofacies type is calculated.

[0083] In an embodiment, the comprehensive evaluation index calculation module 806 is specifically used for:

[0084] calculating the average correlation coefficient between different lithofacies types and corresponding seismic waveform features;

[0085] normalizing the drilling density, the average correlation coefficient, the signal-to-noise ratio and the main frequency, and calculating the comprehensive evaluation index according to the normalized drilling density, the average correlation coefficient, the signal-to-noise ratio and the main frequency.

[0086] In an embodiment, the comprehensive evaluation index calculation module 806 is specifically used for:

[0087] calculating the comprehensive evaluation index according to the normalized drilling density, the average correlation coefficient, the signal-to-noise ratio and the main frequency according to the following formula:

[0088]

[0089] wherein R represents the comprehensive evaluation index, SNR represents the signal-to-noise ratio, f represents the main frequency, D represents the drilling density, and C represents the average correlation coefficient. min avg

[0090] The embodiment of the present application further provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the above-mentioned seismic inversion evaluation method when executing the computer program.

[0091] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned seismic inversion evaluation method.

[0092] The embodiment of the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the above-mentioned seismic inversion evaluation method.

[0093] ​​In the embodiment of the present application, the signal-to-noise ratio and the main frequency of the seismic data of the target layer are calculated by acquiring the seismic data and the drilling data of the target layer; the lithofacies type and the corresponding seismic waveform feature of the target layer are determined according to the seismic data and the drilling data; the correlation coefficient between each lithofacies type and the corresponding seismic waveform feature is calculated according to the lithofacies type and the corresponding seismic waveform feature; the seismic waveform classification plan is generated according to the lithofacies type and the corresponding seismic waveform feature; the drilling density of each lithofacies type is counted according to the seismic waveform classification plan; the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency are normalized; the comprehensive evaluation index is calculated according to the normalized drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency; and the comprehensive evaluation index is used for evaluating the seismic inversion credibility. In the above process, before the seismic inversion, the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency are determined based on the seismic data and the drilling data, the comprehensive evaluation index is calculated through multiple parameters, the credibility of the seismic inversion is quantified before the inversion, the problem of low credibility of the seismic inversion result caused by input data defects is avoided from the source, the effective pre-evaluation of the seismic inversion credibility is realized, and the oil and gas exploration and development risk is reduced.

[0094] Those skilled in the art will understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0095] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams 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 apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the flowcharts and / or block diagrams. Figure 1 Figure 1 The device that implements the functions specified in one flow or multiple flows and / or one block or multiple blocks.

[0096] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the flowcharts and / or block diagrams. Figure 1 ​​​​​​​​​one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0097] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, so that the instructions executed on the computer or other programmable data processing devices provide processes for implementing the flow Figure 1 one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0098] The above-described specific embodiments further illustrate the purposes, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are merely specific embodiments of the present application and are not used to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of seismic inversion assessment, characterized in that, The method comprises the following steps: acquiring seismic data and drilling data of a target layer, and calculating signal-to-noise ratio and main frequency of the seismic data of the target layer; determining lithofacies types of the target layer and corresponding seismic waveform features according to the seismic data and the drilling data; calculating a correlation coefficient between each lithofacies type and the corresponding seismic waveform feature according to the lithofacies types and the corresponding seismic waveform features; generating a seismic waveform classification plan according to the lithofacies types and the corresponding seismic waveform features; statistically determining drilling density of each lithofacies type according to the seismic waveform classification plan; normalizing the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency, and calculating a comprehensive evaluation index according to the normalized drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency; the comprehensive evaluation index is used for evaluating seismic inversion credibility.

2. The method of claim 1, wherein, The statistical determination of the drilling density of each lithofacies type according to the seismic waveform classification plan comprises the following steps: determining an area and a drilling number of each lithofacies type according to the seismic waveform classification plan; calculating the drilling density of each lithofacies type according to the area and the drilling number of each lithofacies type.

3. The method of claim 1, wherein, The normalization of the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency, and the calculation of the comprehensive evaluation index according to the normalized drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency comprise the following steps: calculating an average value of the correlation coefficient between different lithofacies types and corresponding seismic waveform features; normalizing the drilling density, the average value of the correlation coefficient, the signal-to-noise ratio and the main frequency, and calculating the comprehensive evaluation index according to the normalized drilling density, the average value of the correlation coefficient, the signal-to-noise ratio and the main frequency.

4. The method of claim 3, wherein, The calculation of the comprehensive evaluation index according to the normalized drilling density, the average value of the correlation coefficient, the signal-to-noise ratio and the main frequency comprises the following step: calculating the comprehensive evaluation index according to the following formula: Wherein, R represents the comprehensive evaluation index, SNR represents the signal-to-noise ratio, f main represents the main frequency, D represents the drilling density, C avg represents the correlation coefficient average.

5. A seismic inversion evaluation device, characterized by, The method comprises the following steps: a data acquisition module is configured to acquire seismic data and drilling data of a target layer, and calculate signal-to-noise ratio and main frequency of the seismic data of the target layer; a seismic waveform feature determination module is configured to determine lithofacies types of the target layer and corresponding seismic waveform features according to the seismic data and the drilling data; a correlation coefficient calculation module is configured to calculate a correlation coefficient between each lithofacies type and the corresponding seismic waveform feature according to the lithofacies types and the corresponding seismic waveform features; a seismic waveform classification plan generation module is configured to generate a seismic waveform classification plan according to the lithofacies types and the corresponding seismic waveform features; a drilling density determination module is configured to statistically determine drilling density of each lithofacies type according to the seismic waveform classification plan; a comprehensive evaluation index calculation module is configured to normalize the drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency, and calculate a comprehensive evaluation index according to the normalized drilling density, the correlation coefficient, the signal-to-noise ratio and the main frequency; the comprehensive evaluation index is used for evaluating seismic inversion credibility.

6. The apparatus of claim 5, wherein, The drilling density determination module is specifically configured to: determine an area and a drilling number of each lithofacies type according to the seismic waveform classification plan; calculate the drilling density of each lithofacies type according to the area and the drilling number of each lithofacies type.

7. The apparatus of claim 5, wherein, The comprehensive evaluation index calculation module is specifically configured to: calculate an average value of the correlation coefficient between different lithofacies types and corresponding seismic waveform features; The drilling density, the average correlation coefficient, the signal-to-noise ratio and the main frequency are normalized, and according to the normalized drilling density, the average correlation coefficient, the signal-to-noise ratio and the main frequency, a comprehensive evaluation index is calculated.

8. The apparatus of claim 7, wherein, The comprehensive evaluation index calculation module is specifically configured to: According to the normalized drilling density, the average correlation coefficient, the signal-to-noise ratio and the main frequency, the comprehensive evaluation index is calculated according to the following formula: Wherein, R represents the comprehensive evaluation index, SNR represents the signal-to-noise ratio, f main represents the main frequency, D represents the drilling density, C avg represents the correlation coefficient average.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method in any one of claims 1 to 4.

11. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to realize the method in any one of claims 1 to 4.

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