A virtual well reservoir analysis method, device, equipment, medium and program

Abstract

The present invention relates to the field of geological and mineral exploration and discloses a reservoir analysis method for virtual wells, comprising: standardizing a historical well logging curve set into a standard well logging curve set, performing sedimentary subfacies analysis on the historical well logging curve set to obtain sedimentary facies results for a single well; performing comparative analysis on the standard well logging curve set to obtain well-seismic calibration results; generating seismic response characteristics of multiple types of sedimentary facies; clustering and analyzing the seismic response characteristics of the multiple types of sedimentary facies to obtain multiple single-type sedimentary facies distribution characteristics and constructing multiple virtual wells; calculating the target layer P-wave impedance logging curve to determine the reservoir P-wave impedance range; performing seismic wave impedance inversion using the target layer P-wave impedance logging curve to obtain a P-wave impedance data volume; and using the reservoir P-wave impedance range and the P-wave impedance data volume to statistically calculate distribution measurement data for the target layer. The present invention also provides a reservoir analysis device, equipment, medium, and program for virtual wells. The present invention can improve the efficiency of reservoir analysis for virtual wells.

Description

Technical Field

[0001] The present invention relates to the technical field of geological mineral exploration and development, and in particular to a reservoir analysis method, device, equipment, medium and program for a virtual well. Background Art

[0002] A reservoir refers to a rock formation with interconnected pores that allows oil and gas to be stored and seeped through it, usually referred to as a reservoir. Reservoir prediction is generally divided into two categories: qualitative prediction and quantitative prediction. Qualitative prediction refers to determining the presence or absence of a reservoir, while quantitative prediction refers to predicting the distribution area and thickness of the reservoir. Based on actual experience in drilling implementation, quantitative prediction of reservoirs is the key to breakthroughs in oil and gas exploration. It directly affects the success rate of drilling and the subsequent economic benefits of exploration. Therefore, quantitative analysis of reservoirs is necessary.

[0003] Existing reservoir analysis techniques are mostly based on fitting relationship methods. In practical applications, this approach relies on extensive drilling data to establish accurate and reliable fitting relationships between seismic attributes and reservoir parameters. However, in the early stages of exploration, this data is often scarce. Furthermore, seismic attributes are of limited applicability for reservoir prediction in complex lithologies. Relying solely on seismic attributes often fails to establish accurate and reliable fitting relationships with reservoir parameters, resulting in low efficiency in virtual well reservoir analysis. Summary of the Invention

[0004] In response to the above problems, embodiments of the present invention provide a reservoir analysis method, apparatus, device, medium, and program for a virtual well.

[0005] In a first aspect, an embodiment of the present invention provides a reservoir analysis method for a virtual well, comprising:

[0006] Acquiring a historical well logging curve set, standardizing the historical well logging curve set into a standard well logging curve set, performing feature extraction on the standard well logging curve set to obtain curve response characteristics, performing sedimentary subfacies analysis on the historical well logging curve set based on the curve response characteristics to obtain single-well sedimentary facies results;

[0007] Generate synthetic seismic records based on the standard well logging curve set, compare and analyze the synthetic seismic records using pre-acquired real seismic data to obtain well-seismic calibration results; generate seismic response characteristics of multiple types of sedimentary facies based on the single well sedimentary facies results and the well-seismic calibration results;

[0008] Performing seismic attribute feature clustering analysis on the multi-type sedimentary facies seismic response characteristics to obtain multiple single-type sedimentary facies distribution characteristics, matching the single-well sedimentary facies results with the multi-type sedimentary facies seismic response characteristics to obtain a sedimentary facies seismic response feature map, and constructing multiple virtual wells using a standard well logging curve set and the sedimentary facies seismic response feature map;

[0009] Calculating a target layer P-wave impedance logging curve using pre-acquired drilling reservoir type data and the standard logging curve set, and determining a reservoir P-wave impedance range using the target layer P-wave impedance logging curve;

[0010] An interpretation horizon is constructed using pre-acquired seismic data and the virtual well, and seismic wave impedance inversion is performed using the target layer P-wave impedance logging curve and the interpretation horizon to obtain a P-wave impedance data volume. The distribution measurement data of the target layer is statistically calculated using the reservoir P-wave impedance range and the P-wave impedance data volume, thereby completing the reservoir analysis.

[0011] According to an embodiment of the present invention, the step of standardizing the historical well logging curve set into a standard well logging curve set includes:

[0012] Selecting a standard well logging curve from the historical well logging curves;

[0013] Constructing a frequency histogram of each historical logging curve in the historical logging curve set to obtain a logging histogram set, and using the logging histogram in the logging histogram set corresponding to the standard logging curve as a standard logging histogram;

[0014] extracting a range feature and a frequency feature from the standard logging histogram in sequence, and performing frequency adjustment on the remaining logging histograms in the logging histogram set except the standard logging histogram according to the range feature and the frequency feature to obtain a standard logging histogram set;

[0015] The historical well logging curve set is numerically adjusted using the standard well logging histogram set to obtain a standard well logging curve set.

[0016] According to an embodiment of the present invention, generating synthetic seismic records based on the standard well logging curve set includes:

[0017] Extracting a longitudinal wave acoustic wave time difference logging curve and a density logging curve from the standard logging curve set;

[0018] Calculating a reflection coefficient using the longitudinal wave acoustic time difference logging curve and the density logging curve;

[0019] The reflection coefficient is used to convolve the pre-acquired seismic wavelet to obtain a seismic record.

[0020] According to an embodiment of the present invention, the seismic attribute feature cluster analysis is performed on the seismic response features of the multi-type sedimentary facies to obtain multiple single-type sedimentary facies distribution features, including:

[0021] Sequentially extracting seismic attribute features and seismic waveform features from the seismic response features of the multiple types of sedimentary facies to obtain seismic attribute features of the multiple types of sedimentary facies;

[0022] Dividing the multi-type sedimentary facies seismic attribute features into a plurality of primary seismic attribute feature groups, randomly selecting a primary central seismic attribute feature of each of the primary seismic attribute feature groups, and calculating the Euclidean distance between each seismic attribute feature in the multi-type sedimentary facies seismic attribute features and each of the primary central seismic attribute features;

[0023] Grouping each seismic attribute feature in the multiple types of sedimentary facies seismic attribute features according to the proximity principle and the Euclidean distance to obtain multiple secondary seismic attribute feature groups;

[0024] Calculating the secondary central seismic attribute feature of each of the secondary seismic attribute feature groups, calculating the center distance between each of the secondary central seismic attribute features and the corresponding primary central seismic attribute feature, and taking the average of all the center distances as the average center distance;

[0025] Each of the secondary seismic attribute feature groups is updated and iterated according to the average center distance to obtain multiple single-type sedimentary phase seismic attribute feature groups, the secondary center seismic attribute features of the standard seismic attribute feature group are used as single-type sedimentary phase seismic attribute features, and each of the single-type sedimentary phase seismic attribute features is sequentially mapped to the multi-type sedimentary phase seismic response features to obtain multiple single-type sedimentary phase distribution features.

[0026] According to an embodiment of the present invention, the method of calculating the target layer's P-wave impedance logging curve using pre-acquired drilling reservoir type data and the standard logging curve set includes:

[0027] Filtering out a reservoir logging curve corresponding to the drilling reservoir type data from the standard logging curve set;

[0028] Using the longitudinal wave acoustic time difference logging curve of the reservoir logging curve as the reservoir longitudinal wave acoustic time difference logging curve, and using the density logging curve of the reservoir logging curve as the reservoir density logging curve;

[0029] A reservoir compressional wave velocity logging curve is calculated based on the reservoir compressional wave acoustic time difference logging curve, and a target layer compressional wave impedance logging curve is calculated based on the reservoir compressional wave velocity logging curve and the reservoir density logging curve.

[0030] According to an embodiment of the present invention, performing seismic wave impedance inversion using the target layer P-wave impedance logging curve and the interpreted horizon to obtain a P-wave impedance data volume includes:

[0031] constructing an initial seismic wave impedance model using the interpreted horizon;

[0032] Performing seismic forward modeling using the initial seismic wave impedance model to obtain an analytical longitudinal wave impedance logging curve;

[0033] performing impedance matching on the analysis P-wave impedance logging curve and the target layer P-wave impedance logging curve to obtain an impedance difference;

[0034] The model parameters in the initial seismic wave impedance model are iteratively modified according to the impedance difference to obtain a standard seismic wave impedance model, and the longitudinal wave impedance data volume is generated using the standard seismic wave impedance model.

[0035] In a second aspect, an embodiment of the present invention provides a reservoir analysis device for a virtual well, characterized by comprising:

[0036] A sedimentary facies analysis module is used to obtain a historical well logging curve set, standardize the historical well logging curve set into a standard well logging curve set, perform feature extraction on the standard well logging curve set to obtain curve response characteristics, perform sedimentary subfacies analysis on the historical well logging curve set based on the curve response characteristics, and obtain single-well sedimentary facies results;

[0037] A well seismic calibration module is configured to generate synthetic seismic records based on the standard well logging curve set, compare and analyze the synthetic seismic records using pre-acquired real seismic data, and obtain well seismic calibration results; and generate seismic response characteristics of multiple types of sedimentary facies based on the single well sedimentary facies results and the well seismic calibration results;

[0038] a virtual well construction module for performing seismic attribute feature clustering analysis on the multi-type sedimentary facies seismic response features to obtain a plurality of single-type sedimentary facies distribution features, matching the single-well sedimentary facies results with the multi-type sedimentary facies seismic response features to obtain a sedimentary facies seismic response feature map, and constructing a plurality of virtual wells using a standard well logging curve set and the sedimentary facies seismic response feature map;

[0039] an impedance range analysis module, configured to calculate a target layer P-wave impedance logging curve using pre-acquired drilling reservoir type data and the standard logging curve set, and determine a reservoir P-wave impedance range using the target layer P-wave impedance logging curve;

[0040] The reservoir analysis module is used to construct an interpretation layer using the pre-acquired seismic data and the virtual well, perform seismic wave impedance inversion using the P-wave impedance logging curve of the target layer and the interpretation layer to obtain a P-wave impedance data body, and use the P-wave impedance range of the reservoir and the P-wave impedance data body to statistically calculate the distribution measurement data of the target layer, thereby ending the reservoir analysis.

[0041] In a third aspect, an embodiment of the present invention provides an electronic device, comprising:

[0042] processor;

[0043] a memory for storing instructions executable by the processor;

[0044] The processor is configured to execute the instructions to implement a reservoir analysis method for a virtual well as described in the first aspect.

[0045] In the fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the reservoir analysis method of a virtual well as described in the first aspect is implemented.

[0046] In a fifth aspect, an embodiment of the present invention provides a computer program, which, when executed by a processor, implements a reservoir analysis method for a virtual well as described in the first aspect.

[0047] Compared with the prior art, the above technical solution of the present invention has the following beneficial effects:

[0048] The embodiments of the present invention construct several virtual wells based on the well logging curves of surrounding drilled wells and the seismic response characteristics of multiple sedimentary facies. The reservoir impedance range is determined using the surrounding drilled wells. Seismic impedance inversion is performed based on the virtual wells to obtain a seismic impedance data volume. Based on the reservoir impedance range and seismic impedance data volume, the distribution range and thickness of the reservoir in the study area are statistically calculated. This allows for relatively rapid and objective quantitative reservoir analysis in the early stages of exploration, when basic data is relatively scarce. Therefore, the virtual well reservoir analysis method, apparatus, equipment, and medium proposed by the present invention can address the issue of low efficiency in performing reservoir analysis on virtual wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 A flowchart showing a reservoir analysis method for a virtual well according to a first embodiment of the present invention is shown;

[0051] Figure 2 The distribution map of multiple types of sedimentary facies in the study area of ​​Example 1 of the present invention is shown;

[0052] Figure 3 It shows a schematic diagram of constructing a virtual well in the study area according to the first embodiment of the present invention;

[0053] Figure 4 The reservoir distribution map of the Sinian Dengying Formation in the study area of ​​Example 1 of the present invention is shown;

[0054] Figure 5 The thickness distribution map of the Sinian Dengying Formation reservoir in the study area of ​​Example 1 of the present invention is shown;

[0055] Figure 6 A functional module diagram of a virtual well reservoir analysis device according to a second embodiment of the present invention is shown;

[0056] Figure 7 A schematic diagram of the structure of an electronic device for implementing the reservoir analysis method of a virtual well according to the third embodiment of the present invention is shown. DETAILED DESCRIPTION

[0057] The present disclosure will be further described below with reference to the embodiments shown in the accompanying drawings.

[0058] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0059] The present invention proposes a reservoir analysis method for virtual wells based on geological exploration technology. The method is based on post-stack sparse pulse wave impedance inversion, and a number of virtual wells are constructed based on the logging curves of surrounding drilled wells and the seismic response characteristics of multiple types of sedimentary phases. The reservoir wave impedance range is determined using the surrounding drilled wells, and seismic wave impedance inversion is performed based on the virtual wells to obtain a seismic wave impedance data body. Based on the reservoir wave impedance range on the well and the seismic wave impedance data body, the distribution range and thickness of the reservoir in the study area are statistically calculated. The method can relatively quickly and accurately complete the quantitative analysis of the reservoir, and has great application value in the field of geological and mineral survey and exploration.

[0060] Example 1

[0061] like Figure 1 As shown, the present invention proposes a reservoir analysis method for a virtual well, comprising the following steps:

[0062] S1. Obtain a historical logging curve set, standardize the historical logging curve set into a standard logging curve set, perform feature extraction on the standard logging curve set to obtain curve response characteristics, perform sedimentary subfacies analysis on the historical logging curve set based on the curve response characteristics, and obtain single-well sedimentary facies results.

[0063] In an embodiment of the present invention, the historical logging curve set refers to a set of logging curves of wells drilled in the surrounding areas of the study area. In geological exploration areas, there are usually no wells drilled to the target layer in the study area, so it is necessary to refer to the logging curves of wells drilled to the target layer in the surrounding areas of the study area.

[0064] In detail, since the logging curves in the historical logging curve set are drilling and logging work carried out by different drilling companies in different periods, it is necessary to standardize these well logging curves that have been drilled so that the values ​​of the same logging curves drilled by different drilling companies in different periods are within the same value range, thereby improving the accuracy of the logging curves.

[0065] In an embodiment of the present invention, the step of standardizing the historical well logging curve set into a standard well logging curve set includes:

[0066] Selecting a standard well logging curve from the historical well logging curves;

[0067] Constructing a frequency histogram of each historical logging curve in the historical logging curve set to obtain a logging histogram set, and using the logging histogram in the logging histogram set corresponding to the standard logging curve as a standard logging histogram;

[0068] extracting a range feature and a frequency feature from the standard logging histogram in sequence, and performing frequency adjustment on the remaining logging histograms in the logging histogram set except the standard logging histogram according to the range feature and the frequency feature to obtain a standard logging histogram set;

[0069] The historical well logging curve set is numerically adjusted using the standard well logging histogram set to obtain a standard well logging curve set.

[0070] Specifically, the standard logging curve refers to the most standard and accurate logging curve among the historical logging curves, and the standard logging curve can be confirmed by the operator; the curve response characteristics include measurement value characteristics such as longitudinal wave acoustic wave time difference, density, medium induction resistivity, and compensated neutron.

[0071] Specifically, the sedimentary subfacies analysis of the historical logging curve set according to the curve response characteristics to obtain the single-well sedimentary facies results means dividing the different logging curve response characteristics of the same formation into multiple different sedimentary facies types based on the longitudinal wave acoustic wave time difference, density and other logging curves in the standard logging curve set. Under normal circumstances, the sedimentary facies types are greater than or equal to 2, that is, at least include sedimentary facies types that are conducive to reservoir development and sedimentary facies types that are not conducive to reservoir development. In the specific implementation process, the sedimentary facies types that are conducive to reservoir development can be further divided into multiple sedimentary subfacies according to the differences in logging response characteristics. Similarly, the sedimentary facies types that are not conducive to reservoir development can be further divided into multiple sedimentary subfacies according to the differences in logging response characteristics.

[0072] In one embodiment of the present invention, the test area is a certain area in the Sichuan Basin, and the obtaining of a historical logging curve set and standardizing the historical logging curve set into a standard logging curve set refers to standardizing the logging curves of three drilled wells A1, A2, and A3 in the surrounding area of ​​the test area, and the performing of sedimentary subfacies analysis on the historical logging curve set according to the curve response characteristics to obtain the single-well sedimentary facies results refers to conducting logging sedimentary facies analysis on the three drilled wells to determine the sedimentary facies of the three wells in the Sinian Dengying Formation, wherein Well A1 is a platform margin facies, Well A2 is a platform margin slope facies, and Well A3 is a trough facies, wherein the platform margin facies is a favorable facies belt for reservoir development.

[0073] In an embodiment of the present invention, by obtaining a historical logging curve set and standardizing the historical logging curve set into a standard logging curve set, the data accuracy of the historical logging curve set can be improved, thereby improving the accuracy of subsequent reservoir quantitative analysis. By extracting features from the standard logging curve set, curve response characteristics are obtained, and sedimentary subphase analysis is performed on the historical logging curve set based on the curve response characteristics to obtain single-well sedimentary phase results. This can realize sedimentary phase analysis of a single-well reservoir, thereby facilitating the subsequent inference of the sedimentary phase of the target layer based on the sedimentary phase of the single-well reservoir.

[0074] S2. Generate synthetic seismic records based on the standard logging curve set, compare and analyze the synthetic seismic records using pre-acquired real seismic data to obtain well-seismic calibration results; generate multi-type sedimentary facies seismic response characteristics based on the single well sedimentary facies results and the well-seismic calibration results.

[0075] In an embodiment of the present invention, generating synthetic seismic records based on the standard well logging curve set includes:

[0076] Extracting a longitudinal wave acoustic wave time difference logging curve and a density logging curve from the standard logging curve set;

[0077] Calculating a reflection coefficient using the longitudinal wave acoustic time difference logging curve and the density logging curve;

[0078] The reflection coefficient is used to convolve the pre-acquired seismic wavelet to obtain a seismic record.

[0079] Specifically, the calculation of the reflection coefficient using the longitudinal wave acoustic time difference logging curve and the density logging curve refers to using a Fourier basis as a transformation basis of the reflection coefficient to further calculate the reflection coefficient.

[0080] In an embodiment of the present invention, the use of pre-acquired real seismic data to compare and analyze the synthetic seismic records to obtain well-seismic calibration results refers to performing matching calibration such as main frequency matching, wave group matching, and layer group matching on the real seismic data and the synthetic seismic records.

[0081] In detail, generating multiple types of sedimentary phase seismic response characteristics based on the single-well sedimentary phase results and the well-seismic calibration results refers to obtaining the sedimentary phase types of different drilled wells in the same formation based on the single-well sedimentary phase results, and determining the seismic response characteristics corresponding to different types of sediments in the same formation by analyzing the waveform and amplitude characteristics of the wellside seismic data of the same type of sedimentary phases in different drilled wells in the same formation in the well-seismic calibration results.

[0082] In an embodiment of the present invention, synthetic seismic records are generated based on the standard logging curve set, and the synthetic seismic records are compared and analyzed using pre-acquired real seismic data to obtain well-seismic calibration results. By generating seismic response characteristics of multiple types of sedimentary phases based on the single-well sedimentary phase results and the well-seismic calibration results, the seismic response characteristics corresponding to different types of sediments in the same formation can be determined, thereby improving the accuracy of subsequent virtual well construction.

[0083] S3. Perform seismic attribute feature clustering analysis on the multi-type sedimentary facies seismic response characteristics to obtain multiple single-type sedimentary facies distribution characteristics, match the single-well sedimentary facies results with the multi-type sedimentary facies seismic response characteristics to obtain a sedimentary facies seismic response feature map, and construct multiple virtual wells using a standard logging curve set and the sedimentary facies seismic response feature map.

[0084] In an embodiment of the present invention, the seismic attribute feature cluster analysis is performed on the seismic response features of the multi-type sedimentary facies to obtain multiple single-type sedimentary facies distribution features, including:

[0085] Sequentially extracting seismic attribute features and seismic waveform features from the seismic response features of the multiple types of sedimentary facies to obtain seismic attribute features of the multiple types of sedimentary facies;

[0086] Dividing the multi-type sedimentary facies seismic attribute features into a plurality of primary seismic attribute feature groups, randomly selecting a primary central seismic attribute feature of each of the primary seismic attribute feature groups, and calculating the Euclidean distance between each seismic attribute feature in the multi-type sedimentary facies seismic attribute features and each of the primary central seismic attribute features;

[0087] Grouping each seismic attribute feature in the multiple types of sedimentary facies seismic attribute features according to the proximity principle and the Euclidean distance to obtain multiple secondary seismic attribute feature groups;

[0088] Calculating the secondary central seismic attribute feature of each of the secondary seismic attribute feature groups, calculating the center distance between each of the secondary central seismic attribute features and the corresponding primary central seismic attribute feature, and taking the average of all the center distances as the average center distance;

[0089] Each of the secondary seismic attribute feature groups is updated and iterated according to the average center distance to obtain multiple single-type sedimentary phase seismic attribute feature groups, the secondary center seismic attribute features of the standard seismic attribute feature group are used as single-type sedimentary phase seismic attribute features, and each of the single-type sedimentary phase seismic attribute features is sequentially mapped to the multi-type sedimentary phase seismic response features to obtain multiple single-type sedimentary phase distribution features.

[0090] In detail, the calculation of the secondary center seismic attribute characteristics of each of the secondary seismic attribute characteristic groups refers to the calculation of the characteristics with equal Euclidean distances to each seismic attribute characteristic in the secondary seismic attribute characteristic group, and the updating and iteration of each of the secondary seismic attribute characteristic groups according to the average center distance refers to the step of returning the secondary seismic attribute characteristic group as the primary seismic attribute characteristic group to calculate the Euclidean distances between each seismic attribute characteristic in the multi-type sedimentary phase seismic attribute characteristics and each of the primary center seismic attribute characteristics and iterating when the average center distance is greater than a preset distance threshold.

[0091] In one embodiment of the present invention, the seismic attribute characteristic cluster analysis is performed on the seismic response characteristics of the multi-type sedimentary facies to obtain multiple single-type sedimentary facies distribution characteristics. For example, the three drilled wells are seismically calibrated respectively, and the seismic response characteristics of the three types of sedimentary facies, platform margin facies, platform margin slope facies and trough facies, are determined based on the well seismic calibration results and the seismic data near the wells. Based on the seismic response characteristics of the three types of sedimentary facies, seismic waveform cluster analysis is carried out to obtain the following: Figure 2 The planar distribution characteristics of the three types of sedimentary facies are shown. Figure 2 From left to right in the middle, there are trough facies, platform slope facies and platform margin facies, and the different sedimentary facies types are bounded by black dashed lines.

[0092] Specifically, matching the single-well sedimentary phase results with the multi-type sedimentary phase seismic response characteristics to obtain a sedimentary phase seismic response characteristic mapping refers to establishing a mapping relationship between drilled wells, sedimentary phase types and seismic response characteristics based on the sedimentary phase types of different drilled wells in the same formation in the single-well sedimentary phase results and the seismic response characteristics of different types of sedimentary phases in the same formation in the multi-type sedimentary phase seismic response characteristics.

[0093] In an embodiment of the present invention, constructing multiple virtual wells using the standard logging curve set and the sedimentary facies seismic response characteristic mapping means determining the sedimentary facies type and seismic response characteristics of the target layer based on the standard logging curve set, and constructing multiple virtual wells based on the sedimentary facies type and seismic response characteristics of the target layer.

[0094] In one embodiment of the present invention, the standard well logging curve set and the sedimentary facies seismic response characteristic mapping are used to construct multiple virtual wells. For example, the well logging curves of wells A1, A2, and A3 and the seismic response characteristics of the three types of sedimentary facies are used to construct the following virtual wells: Figure 3 Virtual well 1, virtual well 2 and virtual well 3 are shown.

[0095] In an embodiment of the present invention, by performing seismic attribute feature clustering analysis on the seismic response characteristics of the multiple types of sedimentary facies, a plurality of single-type sedimentary facies distribution characteristics are obtained, and the sedimentary facies results of the single well are matched with the seismic response characteristics of the multiple types of sedimentary facies to obtain a sedimentary facies seismic response feature map. A mapping relationship between the drilled wells, sedimentary facies types, and seismic response features can be established. By utilizing a standard logging curve set and the sedimentary facies seismic response feature map to construct a plurality of virtual wells, a virtual well in the test area can be constructed according to the sedimentary facies type and the standard logging curve set, thereby improving the accuracy of the virtual well.

[0096] S4. Calculate the target layer P-wave impedance logging curve using the pre-acquired drilling reservoir type data and the standard logging curve set, and determine the reservoir P-wave impedance range using the target layer P-wave impedance logging curve.

[0097] In an embodiment of the present invention, the drilling reservoir type data refers to the reservoir type data of the target layer. According to the standards of the oil and gas industry, the drilled reservoir types are usually divided into three categories according to the reservoir physical properties, namely Class I, Class II and Class III reservoirs, among which Class I reservoirs are the best and Class III reservoirs are the worst. If the physical properties of the drilled formation are worse than those of the Class III reservoir, the drilling is classified as drilling into a non-reservoir.

[0098] In an embodiment of the present invention, the method of calculating the target layer's P-wave impedance logging curve using the pre-acquired drilling reservoir type data and the standard logging curve set includes:

[0099] Filtering out a reservoir logging curve corresponding to the drilling reservoir type data from the standard logging curve set;

[0100] Using the longitudinal wave acoustic time difference logging curve of the reservoir logging curve as the reservoir longitudinal wave acoustic time difference logging curve, and using the density logging curve of the reservoir logging curve as the reservoir density logging curve;

[0101] A reservoir compressional wave velocity logging curve is calculated based on the reservoir compressional wave acoustic time difference logging curve, and a target layer compressional wave impedance logging curve is calculated based on the reservoir compressional wave velocity logging curve and the reservoir density logging curve.

[0102] In detail, the calculation of the reservoir P-wave velocity logging curve based on the reservoir P-wave acoustic time difference logging curve refers to calculating the P-wave velocity based on the P-wave time difference and P-wave length in the reservoir P-wave acoustic time difference logging curve, thereby generating the reservoir P-wave velocity logging curve, and the calculation of the target layer P-wave impedance logging curve based on the reservoir P-wave velocity logging curve and the reservoir density logging curve refers to multiplying the reservoir P-wave velocity logging curve and the reservoir density logging curve to calculate the target layer P-wave impedance logging curve.

[0103] Specifically, the use of the target layer longitudinal wave impedance logging curve to determine the reservoir longitudinal wave impedance range refers to determining the reservoir development of the target layer based on the reservoir development of the drilled well, and performing range correction on the target layer longitudinal wave impedance logging curve based on the reservoir development to obtain the reservoir longitudinal wave impedance range.

[0104] In an embodiment of the present invention, by using pre-acquired drilling reservoir type data and the standard logging curve set to calculate the target layer P-wave impedance logging curve, and using the target layer P-wave impedance logging curve to determine the reservoir P-wave impedance range, the reservoir P-wave impedance range can be analyzed in combination with the reservoir type and development conditions, thereby improving the accuracy of the reservoir P-wave impedance range.

[0105] S5. Construct an interpretation horizon using the pre-acquired seismic data and the virtual well, perform seismic wave impedance inversion using the P-wave impedance logging curve of the target layer and the interpretation horizon to obtain a P-wave impedance data volume, and calculate distribution measurement data of the target layer using the P-wave impedance range of the reservoir and the P-wave impedance data volume, thereby ending reservoir analysis.

[0106] In the embodiment of the present invention, the seismic data refers to the seismic data around the test area, and the interpretation horizon refers to data including the seismic data, target layer number data of the test area, reverse faults, etc.

[0107] In detail, the method of performing seismic wave impedance inversion using the target layer P-wave impedance logging curve and the interpreted horizon to obtain a P-wave impedance data volume includes:

[0108] constructing an initial seismic wave impedance model using the interpreted horizon;

[0109] Performing seismic forward modeling using the initial seismic wave impedance model to obtain an analytical longitudinal wave impedance logging curve;

[0110] performing impedance matching on the analysis P-wave impedance logging curve and the target layer P-wave impedance logging curve to obtain an impedance difference;

[0111] The model parameters in the initial seismic wave impedance model are iteratively modified according to the impedance difference to obtain a standard seismic wave impedance model, and the longitudinal wave impedance data volume is generated using the standard seismic wave impedance model.

[0112] In detail, the initial seismic wave impedance model can be generated by software such as BCI, Jason, Starata, etc. based on the interpretation horizon or generated by using deduction methods such as vertical seismic horizon constraints.

[0113] In the embodiment of the present invention, the use of the P-wave impedance range of the reservoir and the P-wave impedance data volume to calculate the distribution measurement data of the target layer refers to calculating the distribution range and thickness of the reservoir in the study area within the target layer section of the test area based on the P-wave impedance range of the reservoir and the P-wave impedance data volume, thereby achieving quantitative prediction of the reservoir. For example, based on the P-wave impedance range of a single well reservoir, the reservoir plane distribution characteristics and reservoir thickness distribution characteristics of the Sinian Dengying Formation in the study area are determined, such as Figure 4 The reservoir distribution map of the Sinian Dengying Formation in the study area is shown in the figure. Figure 5 The reservoir thickness distribution map of the Sinian Dengying Formation in the study area is shown.

[0114] In an embodiment of the present invention, an interpretation horizon is constructed using pre-acquired seismic data and the virtual well, seismic wave impedance inversion is performed using the P-wave impedance logging curve of the target layer and the interpretation horizon to obtain a P-wave impedance data volume, and distribution measurement data of the target layer is statistically calculated using the P-wave impedance range of the reservoir and the P-wave impedance data volume. This can achieve quantitative prediction of the reservoir thickness in the target study area when basic data is relatively scarce in the early stages of exploration, thereby improving the efficiency of quantitative reservoir analysis.

[0115] The embodiment of the present invention obtains a historical logging curve set and standardizes the historical logging curve set into a standard logging curve set, thereby improving the data accuracy of the historical logging curve set and further improving the accuracy of subsequent quantitative analysis of the reservoir. By extracting features from the standard logging curve set, curve response features are obtained, and sedimentary subphase analysis is performed on the historical logging curve set based on the curve response features to obtain single-well sedimentary phase results, sedimentary phase analysis of a single-well reservoir can be achieved, thereby facilitating the subsequent inference of the sedimentary phase of the target layer based on the sedimentary phase of the single-well reservoir. Synthetic seismic records are generated based on the standard logging curve set, and the synthetic seismic records are compared and analyzed using pre-acquired real seismic data to obtain well-seismic calibration results. The results and the well-seismic calibration results generate multi-type sedimentary facies seismic response characteristics, which can determine the seismic response characteristics corresponding to different types of sediments in the same formation, thereby improving the accuracy of subsequent virtual well construction. By performing seismic attribute feature clustering analysis on the multi-type sedimentary facies seismic response characteristics, multiple single-type sedimentary facies distribution characteristics are obtained. The single-well sedimentary facies results are matched with the multi-type sedimentary facies seismic response characteristics to obtain a sedimentary facies seismic response feature map. A mapping relationship between drilled wells, sedimentary facies types and seismic response features can be established. By using a standard logging curve set and the sedimentary facies seismic response feature map to construct multiple virtual wells, virtual wells in the test area can be constructed according to the sedimentary facies type and the standard logging curve set, thereby improving the accuracy of the virtual wells.

[0116] By using pre-acquired drilling reservoir type data and the standard logging curve set to calculate the target layer P-wave impedance logging curve, and using the target layer P-wave impedance logging curve to determine the reservoir P-wave impedance range, the reservoir P-wave impedance range can be analyzed in combination with the reservoir type and development conditions, thereby improving the accuracy of the reservoir P-wave impedance range. By using pre-acquired seismic data and the virtual well to construct an interpretation horizon, seismic wave impedance inversion is performed using the target layer P-wave impedance logging curve and the interpretation horizon to obtain a P-wave impedance data volume. The reservoir P-wave impedance range and the P-wave impedance data volume are used to statistically calculate the distribution measurement data of the target layer. In the case of a lack of basic data in the early stage of exploration, quantitative prediction of the reservoir thickness of the target study area can be achieved, thereby improving the efficiency of reservoir quantitative analysis. Therefore, the reservoir analysis method of the virtual well proposed in the present invention can solve the problem of low efficiency of reservoir analysis of the virtual well.

[0117] Example 2

[0118] like Figure 6 As shown, this embodiment also provides a functional module diagram of a reservoir analysis device for a virtual well.

[0119] The virtual well reservoir analysis device 100 described in this embodiment can be installed in an electronic device. Depending on the functionality implemented, the virtual well reservoir analysis device 100 can include a sedimentary phase analysis module 101, a well seismic calibration module 102, a virtual well construction module 103, an impedance range analysis module 104, and a reservoir analysis module 105. A module, also referred to as a unit, is a series of computer program segments that can be executed by a processor in an electronic device and perform a fixed function. These modules are stored in the memory of the electronic device.

[0120] In this embodiment, the functions of each module / unit are as follows:

[0121] The sedimentary facies analysis module 101 is used to obtain a historical well logging curve set, standardize the historical well logging curve set into a standard well logging curve set, perform feature extraction on the standard well logging curve set to obtain curve response characteristics, and perform sedimentary subfacies analysis on the historical well logging curve set based on the curve response characteristics to obtain single-well sedimentary facies results;

[0122] The well seismic calibration module 102 is configured to generate synthetic seismic records based on the standard well logging curve set, compare and analyze the synthetic seismic records using pre-acquired real seismic data, and obtain well seismic calibration results; and generate seismic response characteristics of multiple types of sedimentary facies based on the single well sedimentary facies results and the well seismic calibration results;

[0123] The virtual well construction module 103 is configured to perform seismic attribute feature clustering analysis on the multi-type sedimentary facies seismic response features to obtain a plurality of single-type sedimentary facies distribution features, match the single-well sedimentary facies results with the multi-type sedimentary facies seismic response features to obtain a sedimentary facies seismic response feature map, and construct a plurality of virtual wells using a standard well logging curve set and the sedimentary facies seismic response feature map;

[0124] The impedance range analysis module 104 is configured to calculate a target layer P-wave impedance logging curve using pre-acquired drilling reservoir type data and the standard logging curve set, and determine a reservoir P-wave impedance range using the target layer P-wave impedance logging curve;

[0125] The reservoir analysis module 105 is used to construct an interpretation layer using the pre-acquired seismic data and the virtual well, perform seismic wave impedance inversion using the target layer P-wave impedance logging curve and the interpretation layer to obtain a P-wave impedance data volume, and use the reservoir P-wave impedance range and the P-wave impedance data volume to statistically calculate the distribution measurement data of the target layer, thereby ending the reservoir analysis.

[0126] In detail, each module described in the virtual well reservoir analysis device 100 in the embodiment of the present invention adopts the same technical means as the virtual well reservoir analysis method described in Example 1 when in use, and can produce the same technical effects, which will not be repeated here.

[0127] Example 3

[0128] like Figure 7 As shown, this embodiment also provides a computer electronic device, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a reservoir analysis program for a virtual well.

[0129] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits, and executing programs or modules stored in the memory 11 (such as executing a reservoir analysis program for a virtual well) and calling data stored in the memory 11 to perform various functions of the electronic device and process data.

[0130] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. equipped on the electronic device. Furthermore, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the reservoir analysis program of the virtual well, but can also be used to temporarily store data that has been output or is to be output.

[0131] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0132] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0133] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0134] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0135] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0136] The reservoir analysis program for the virtual well stored in the memory 11 of the electronic device is a combination of multiple instructions. When executed in the processor 10, the following can be achieved:

[0137] Acquiring a historical well logging curve set, standardizing the historical well logging curve set into a standard well logging curve set, performing feature extraction on the standard well logging curve set to obtain curve response characteristics, performing sedimentary subfacies analysis on the historical well logging curve set based on the curve response characteristics to obtain single-well sedimentary facies results;

[0138] Generate synthetic seismic records based on the standard well logging curve set, compare and analyze the synthetic seismic records using pre-acquired real seismic data to obtain well-seismic calibration results; generate seismic response characteristics of multiple types of sedimentary facies based on the single well sedimentary facies results and the well-seismic calibration results;

[0139] Performing seismic attribute feature clustering analysis on the multi-type sedimentary facies seismic response characteristics to obtain multiple single-type sedimentary facies distribution characteristics, matching the single-well sedimentary facies results with the multi-type sedimentary facies seismic response characteristics to obtain a sedimentary facies seismic response feature map, and constructing multiple virtual wells using a standard well logging curve set and the sedimentary facies seismic response feature map;

[0140] Calculating a target layer P-wave impedance logging curve using pre-acquired drilling reservoir type data and the standard logging curve set, and determining a reservoir P-wave impedance range using the target layer P-wave impedance logging curve;

[0141] An interpretation horizon is constructed using pre-acquired seismic data and the virtual well, and seismic wave impedance inversion is performed using the target layer P-wave impedance logging curve and the interpretation horizon to obtain a P-wave impedance data volume. The distribution measurement data of the target layer is statistically calculated using the reservoir P-wave impedance range and the P-wave impedance data volume, thereby completing the reservoir analysis.

[0142] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0143] Furthermore, if the module / unit integrated into the electronic device 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. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0144] Example 4

[0145] This embodiment provides a storage medium storing a computer program. When the computer program is executed by a processor, the steps of the reservoir analysis method for a virtual well as described above are implemented.

[0146] These program codes can also be loaded onto a computer or other programmable data processing device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 The steps of a specified function in a process or multiple processes.

[0147] Storage media include permanent and non-permanent, removable and non-removable media, and 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 storage media can 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 disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0148] Example 5

[0149] An embodiment of the present invention provides a computer program, which, when executed by a processor, implements the steps of the reservoir analysis method for a virtual well as described in the first aspect.

[0150] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer programs can also be stored in a computer-readable storage medium. These computer programs cause the computer, programmable data processing device, and / or other equipment to operate in a specific manner. Thus, the computer-readable medium storing the computer program comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0151] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. When the terms "include" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0152] It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or described herein.

[0153] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0154] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0155] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0156] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0157] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0158] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0159] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A reservoir analysis method for a virtual well, characterized in that: The method comprises: Acquiring a historical well logging curve set, standardizing the historical well logging curve set into a standard well logging curve set, performing feature extraction on the standard well logging curve set to obtain curve response characteristics, performing sedimentary subfacies analysis on the historical well logging curve set based on the curve response characteristics to obtain single-well sedimentary facies results; Generate synthetic seismic records based on the standard well logging curve set, compare and analyze the synthetic seismic records using pre-acquired real seismic data to obtain well-seismic calibration results; generate seismic response characteristics of multiple types of sedimentary facies based on the single well sedimentary facies results and the well-seismic calibration results; Performing seismic attribute feature clustering analysis on the multi-type sedimentary facies seismic response characteristics to obtain multiple single-type sedimentary facies distribution characteristics, matching the single-well sedimentary facies results with the multi-type sedimentary facies seismic response characteristics to obtain a sedimentary facies seismic response feature map, and constructing multiple virtual wells using a standard well logging curve set and the sedimentary facies seismic response feature map; Calculating a target layer P-wave impedance logging curve using pre-acquired drilling reservoir type data and the standard logging curve set, and determining a reservoir P-wave impedance range using the target layer P-wave impedance logging curve; An interpretation horizon is constructed using pre-acquired seismic data and the virtual well, and seismic wave impedance inversion is performed using the target layer P-wave impedance logging curve and the interpretation horizon to obtain a P-wave impedance data volume. The distribution measurement data of the target layer is statistically calculated using the reservoir P-wave impedance range and the P-wave impedance data volume, thereby completing the reservoir analysis.

2. The reservoir analysis method of a virtual well according to claim 1, wherein: The step of standardizing the historical well logging curve set into a standard well logging curve set includes: Selecting a standard well logging curve from the historical well logging curves; Constructing a frequency histogram of each historical logging curve in the historical logging curve set to obtain a logging histogram set, and using the logging histogram in the logging histogram set corresponding to the standard logging curve as a standard logging histogram; extracting a range feature and a frequency feature from the standard logging histogram in sequence, and performing frequency adjustment on the remaining logging histograms in the logging histogram set except the standard logging histogram according to the range feature and the frequency feature to obtain a standard logging histogram set; The historical well logging curve set is numerically adjusted using the standard well logging histogram set to obtain a standard well logging curve set.

3. The reservoir analysis method of a virtual well according to claim 1, wherein: Generating synthetic seismic records according to the standard well logging curve set includes: Extracting a longitudinal wave acoustic wave time difference logging curve and a density logging curve from the standard logging curve set; Calculating a reflection coefficient using the longitudinal wave acoustic time difference logging curve and the density logging curve; The reflection coefficient is used to convolve the pre-acquired seismic wavelet to obtain a seismic record.

4. The reservoir analysis method of a virtual well according to claim 1, wherein: The seismic attribute feature cluster analysis is performed on the seismic response features of the multi-type sedimentary facies to obtain multiple single-type sedimentary facies distribution features, including: Sequentially extracting seismic attribute features and seismic waveform features from the seismic response features of the multiple types of sedimentary facies to obtain seismic attribute features of the multiple types of sedimentary facies; Dividing the multi-type sedimentary facies seismic attribute features into a plurality of primary seismic attribute feature groups, randomly selecting a primary central seismic attribute feature of each of the primary seismic attribute feature groups, and calculating the Euclidean distance between each seismic attribute feature in the multi-type sedimentary facies seismic attribute features and each of the primary central seismic attribute features; Grouping each seismic attribute feature in the multiple types of sedimentary facies seismic attribute features according to the proximity principle and the Euclidean distance to obtain multiple secondary seismic attribute feature groups; Calculating the secondary central seismic attribute feature of each of the secondary seismic attribute feature groups, calculating the center distance between each of the secondary central seismic attribute features and the corresponding primary central seismic attribute feature, and taking the average of all the center distances as the average center distance; Each of the secondary seismic attribute feature groups is updated and iterated according to the average center distance to obtain multiple single-type sedimentary phase seismic attribute feature groups, the secondary center seismic attribute features of the standard seismic attribute feature group are used as single-type sedimentary phase seismic attribute features, and each of the single-type sedimentary phase seismic attribute features is sequentially mapped to the multi-type sedimentary phase seismic response features to obtain multiple single-type sedimentary phase distribution features.

5. The reservoir analysis method of a virtual well according to claim 1, wherein: The method of calculating the target layer longitudinal wave impedance logging curve using the pre-acquired drilling reservoir type data and the standard logging curve set includes: Filtering out a reservoir logging curve corresponding to the drilling reservoir type data from the standard logging curve set; Using the longitudinal wave acoustic time difference logging curve of the reservoir logging curve as the reservoir longitudinal wave acoustic time difference logging curve, and using the density logging curve of the reservoir logging curve as the reservoir density logging curve; A reservoir compressional wave velocity logging curve is calculated based on the reservoir compressional wave acoustic time difference logging curve, and a target layer compressional wave impedance logging curve is calculated based on the reservoir compressional wave velocity logging curve and the reservoir density logging curve.

6. The reservoir analysis method of a virtual well according to claim 1, wherein: The method of performing seismic wave impedance inversion using the target layer P-wave impedance logging curve and the interpreted horizon to obtain a P-wave impedance data volume includes: constructing an initial seismic wave impedance model using the interpreted horizon; Performing seismic forward modeling using the initial seismic wave impedance model to obtain an analytical longitudinal wave impedance logging curve; performing impedance matching on the analysis P-wave impedance logging curve and the target layer P-wave impedance logging curve to obtain an impedance difference; The model parameters in the initial seismic wave impedance model are iteratively modified according to the impedance difference to obtain a standard seismic wave impedance model, and the longitudinal wave impedance data volume is generated using the standard seismic wave impedance model.

7. A reservoir analysis device for a virtual well, characterized in that: The device comprises: A sedimentary facies analysis module is used to obtain a historical well logging curve set, standardize the historical well logging curve set into a standard well logging curve set, perform feature extraction on the standard well logging curve set to obtain curve response characteristics, perform sedimentary subfacies analysis on the historical well logging curve set based on the curve response characteristics, and obtain single-well sedimentary facies results; A well seismic calibration module is configured to generate synthetic seismic records based on the standard well logging curve set, compare and analyze the synthetic seismic records using pre-acquired real seismic data, and obtain well seismic calibration results; and generate seismic response characteristics of multiple types of sedimentary facies based on the single well sedimentary facies results and the well seismic calibration results; a virtual well construction module for performing seismic attribute feature clustering analysis on the multi-type sedimentary facies seismic response features to obtain a plurality of single-type sedimentary facies distribution features, matching the single-well sedimentary facies results with the multi-type sedimentary facies seismic response features to obtain a sedimentary facies seismic response feature map, and constructing a plurality of virtual wells using a standard well logging curve set and the sedimentary facies seismic response feature map; an impedance range analysis module, configured to calculate a target layer P-wave impedance logging curve using pre-acquired drilling reservoir type data and the standard logging curve set, and determine a reservoir P-wave impedance range using the target layer P-wave impedance logging curve; The reservoir analysis module is used to construct an interpretation layer using the pre-acquired seismic data and the virtual well, perform seismic wave impedance inversion using the P-wave impedance logging curve of the target layer and the interpretation layer to obtain a P-wave impedance data body, and use the P-wave impedance range of the reservoir and the P-wave impedance data body to statistically calculate the distribution measurement data of the target layer, thereby ending the reservoir analysis.

8. An electronic device comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the reservoir analysis method for a virtual well according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, the reservoir analysis method of a virtual well as claimed in any one of claims 1 to 6 is implemented.

10. A computer program, characterized in that When the program is executed by a processor, the reservoir analysis method for a virtual well according to any one of claims 1 to 6 is implemented.

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