High-precision stratigraphic comparison method and device and electronic equipment

Abstract

The invention provides a high-precision stratigraphic comparison method and device and electronic equipment, and the method comprises the steps: constructing filtering factors through logging information, extracting a high-frequency seismic data volume and a low-frequency seismic data volume from seismic data volumes through employing the filtering factors, carrying out the deconvolution of the high-frequency seismic data volume, the medium-frequency seismic data volume and the low-frequency seismic data volume through time-varying seismic wavelets, and carrying out the deconvolution of the high-frequency seismic data volume, the medium-frequency seismic data volume and the low-frequency seismic data volume. According to the method, the target data volume with the visual resolution and the frequency bandwidth far higher than those of the seismic data volume can be obtained, so that the cross-well transverse characteristics can be distinguished more accurately by utilizing the profile data of the target data volume, and a sequence division scheme is adjusted based on the cross-well transverse characteristics to realize high-precision stratigraphic contrast; stratigraphic contrast is finely carried out by utilizing high resolution and transverse change characteristics of a target data profile, and guarantee is provided for fine exploration and development of oil and gas fields, especially geological research development.

Description

Technical Field

[0001] The present application relates to the field of geological technology, and in particular to a high-precision stratum comparison method, device, and electronic equipment. Background Art

[0002] Stratigraphic correlation is a fundamental task in geological research. Based on understanding the longitudinal variation patterns of strata, stratigraphic sections are divided into different types and levels of stratigraphic units according to various characteristics and attributes of the strata. In practice, especially in comprehensive petroleum geology research, the increasing understanding of various stratigraphic characteristics and attributes has led to the emergence of numerous precise and effective stratigraphic division schemes, known as multiple stratigraphic division schemes. Stratigraphic division can be based on petrological characteristics, biological characteristics, isotopic ages, and other factors. Different division criteria lead to different stratigraphic units. For example, lithostratigraphic units can be established based on lithologic characteristics, chronostratigraphic units based on temporal attributes, biostratigraphic units based on paleontological (or paleoecological) characteristics, and magnetostratigraphic units based on magnetic properties. Any characteristic or attribute can serve as the basis for stratigraphic division; that is, as many stratigraphic units as there are characteristics and attributes can be divided or established. This is the theoretical basis for multiple stratigraphic division schemes. A stratigraphic section can be divided into multiple different stratigraphic units based on different characteristics and attributes, and the boundaries of these units are often inconsistent. To understand the distribution patterns of strata, stratigraphic division alone is not enough; the lateral distribution and structural characteristics of strata must be understood through stratigraphic comparison.

[0003] Stratigraphic correlation is to compare stratigraphic units divided by different profiles to see whether their characteristics or attributes are consistent and whether their stratigraphic positions are equivalent, so as to understand their mutual relationships and distribution patterns. Broadly speaking, stratigraphic correlation includes global, large-area stratigraphic correlation between different basins, regional stratigraphic correlation within the same sedimentary basin, and oil (gas) layer comparison within an oil field. Stratigraphic division is the study of how strata change over time (vertically). In fact, stratigraphic division and stratigraphic correlation are inseparable. Stratigraphic division is the basis of stratigraphic correlation, and stratigraphic correlation promotes stratigraphic division. Through stratigraphic division and stratigraphic correlation, the formation time of strata and their position in the geological time system can be determined, thereby clarifying whether there are any missing strata in the study area and what the reasons for the missing strata are.

[0004] Existing stratigraphic correlation methods fuse well logging data with seismic data to generate well-seismic sequence stratigraphic data, which is then used for stratigraphic correlation. This approach is limited by the resolution of the seismic data and presents difficulties in correlating high-frequency sequences of the fourth and fifth magnitudes. Post-stack optimization processing of the seismic data is necessary to improve resolution and imaging accuracy, and to highlight the seismic response characteristics of different sequence structures.

[0005] However, because well logging data and seismic data represent strata in two different domains (time and depth), their resolutions differ significantly. For example, if the stratigraphic scale is too large, averaging effects are strong, making it difficult to reveal stratigraphic features. If the stratigraphic scale is too small, the seismic data resolution may not support this, making lateral comparisons difficult and making it difficult to understand lateral sedimentary patterns.

[0006] Existing technologies typically use various methods to improve the resolution of seismic data, such as compressing seismic wavelets or removing the effects of interference and tuning during underground seismic wave propagation on underground strata. This broadens the frequency bandwidth of effective seismic signals, particularly accurately broadening high-frequency components. However, these methods for improving seismic data resolution can only extend the effective bandwidth to a limited extent, resulting in a significant resolution gap between seismic data and well logging data. Summary of the Invention

[0007] This specification provides a high-precision formation comparison method, device and electronic equipment to solve the problem of difficulty in lateral comparison caused by the large difference in resolution between existing seismic data and well logging data.

[0008] To solve the above technical problems, the first aspect of the present specification provides a high-precision formation comparison method, comprising: obtaining a seismic data volume and a target data volume in a target area; determining a well-to-well formation comparison result based on seismic wave data in the seismic data volume; obtaining a target data profile in the target data volume corresponding to the well-to-well seismic profile based on the well-to-well formation comparison result, and using the target data profile to perform a fine comparison of inter-well formations; wherein the target data volume is determined by: extracting a time-varying seismic wavelet from the seismic data volume in the target area; constructing a high-frequency filter factor and a low-frequency filter factor based on well logging data in the target area; extracting high-frequency information from the seismic data volume using the high-frequency filter factor to obtain a high-frequency seismic data volume, and extracting low-frequency information from the seismic data volume using the low-frequency filter factor to obtain a low-frequency seismic data volume; performing diffusion filtering on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume respectively; and performing deconvolution on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume respectively using the time-varying seismic wavelet, and fusing the deconvolution results to obtain the target data volume.

[0009] In some embodiments, based on the well-to-well stratigraphic correlation result, a target data profile corresponding to the well-to-well seismic profile is obtained, and the target data profile is used for fine correlation of interwell strata, comprising: displaying a seismic data profile corresponding to the well-to-well seismic profile on a first display layer by a first display component; the display color depth in the seismic data profile is determined according to the value of the seismic data; displaying a target data profile corresponding to the well-to-well seismic profile on a second display layer by a second display component; the display color depth is determined according to the value of the target data; wherein the first display layer and the second display layer are presented to the user in superposition, and the seismic data profile and the target data profile are transparent backgrounds.

[0010] In some embodiments, based on the well-to-well stratigraphic correlation result, a target data profile corresponding to the well-to-well seismic profile is obtained, and the target data profile is used for fine correlation of interwell strata, comprising: obtaining lithology characteristic data volume, paleontological fossil assemblage data volume, sedimentary environment data volume, isotope dating data volume, seismic data volume, and target data volume of a target area; setting a virtual time window on the well-to-well seismic profile of the lithology characteristic data volume, the paleontological fossil assemblage data volume, the sedimentary environment data volume, the isotope dating data volume, the seismic data volume, and the target data volume, the virtual time window is synchronously slid from the same position of the data volume, and the data in each data volume in the virtual time window at the same position is taken as input data of a network model, and a stratigraphic division suggestion in the virtual time window is output by the network model; the stratigraphic division suggestions corresponding to the virtual time windows at each position are fused to obtain an overall stratigraphic division suggestion of the target area on the well-to-well seismic profile.

[0011] In some embodiments, when the virtual time window slides on the data volume, it is first slid horizontally, then slid vertically once after sliding horizontally to the boundary of the virtual time window, and then slid horizontally again; the distance of each horizontal sliding is less than the width of the virtual time window, and the distance of vertical sliding is less than the height of the virtual time window.

[0012] In some embodiments, based on the well-to-well stratigraphic correlation result, a target data profile corresponding to the well-to-well seismic profile is obtained, and the target data profile is used for fine correlation of interwell strata, further comprising: obtaining a preliminary stratigraphic division result of a user; comparing the preliminary stratigraphic division result with the overall stratigraphic division suggestion to screen out inconsistent stratigraphic division data; presenting the inconsistent stratigraphic division data to the user.

[0013] In some embodiments, the network model includes: a data preprocessing module, which is used to splice the lithologic characteristic data body, paleontological fossil assemblage data body, sedimentary environment data body, isotope dating data body, seismic data body, and target data body in a virtual time window at the same location to form a multidimensional feature matrix; a convolution module, which is used to perform a convolution operation on the multidimensional feature matrix through a two-dimensional convolution kernel to extract the correlation pattern of data between different data bodies; an LSTM module, which is used to process the convolution results using an LSTM network model to capture the vertical continuity and long-distance dependency of the strata, wherein the long-distance dependency includes sedimentary cycles and strata contact relationships; and an output module, which is used to determine the stratigraphic division suggestion based on the output result of the LSTM module.

[0014] A second aspect of the present specification provides a high-precision formation comparison device, comprising: an acquisition unit for acquiring a seismic data volume and a target data volume in a target area; a determination unit for determining a well-to-well formation comparison result based on seismic wave data in the seismic data volume; a comparison unit for acquiring a target data profile corresponding to a well-to-well seismic profile based on the well-to-well formation comparison result, and performing fine inter-well formation comparison using the target data profile; the device further comprises: a first extraction unit for determining the target data volume, a construction unit, a second extraction unit, a filtering unit, and a processing unit, wherein the first extraction unit is configured to extract a time-varying seismic wavelet from the seismic data volume in the target area; The construction unit is used to construct a high-frequency filter factor and a low-frequency filter factor based on the well logging data of the target area; the second extraction unit is used to use the high-frequency filter factor to extract high-frequency information from the seismic data volume to obtain a high-frequency seismic data volume, and use the low-frequency filter factor to extract low-frequency information from the seismic data volume to obtain a low-frequency seismic data volume; the filtering unit is used to perform diffusion filtering on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume respectively; the processing unit is used to use the time-varying seismic wavelet to perform deconvolution on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume respectively, and fuse the deconvolution results to obtain the target data volume.

[0015] In some embodiments, the comparison unit includes: a first display subunit, used to display the seismic data profile corresponding to the well-connected seismic profile on the first display layer through a first display component; the depth of the display color in the seismic data profile is determined according to the value of the seismic data; a second display subunit, used to display the target data profile corresponding to the well-connected seismic profile on the second display layer through a second display component; the depth of the display color is determined according to the value of the target data; wherein the first display layer and the second display layer are superimposed and presented to the user, and the seismic data profile and the target data profile have a transparent background.

[0016] In some embodiments, the comparison unit includes: a first acquisition subunit, used to acquire the lithologic characteristic data body, paleontological fossil assemblage data body, sedimentary environment data body, isotope dating data body, seismic data body, and target data body of the target area; a processing subunit, used to set a virtual time window on the well-connected seismic profile of the lithologic characteristic data body, the paleontological fossil assemblage data body, the sedimentary environment data body, the isotope dating data body, the seismic data body, and the target data body, the virtual time window sliding synchronously starting from the same position of the data body, and taking the data in each data body in the virtual time window at the same position as the input data of the network model, and outputting the stratigraphic division suggestion within the virtual time window through the network model; a fusion subunit, used to fuse the stratigraphic division suggestions corresponding to the virtual time windows at each position to obtain the overall stratigraphic division suggestion for the target area on the well-connected seismic profile.

[0017] In some embodiments, when the virtual time window slides on the data body, it first slides horizontally, then slides vertically once after sliding horizontally to the boundary of the virtual time window, and then slides horizontally again; the distance of each horizontal sliding is less than the width of the virtual time window, and the distance of the vertical sliding is less than the height of the virtual time window.

[0018] In some embodiments, the comparison unit also includes: a second acquisition subunit, used to obtain the user's preliminary stratigraphic division results; a screening subunit, used to compare the preliminary stratigraphic division results with the overall stratigraphic division recommendations to screen out inconsistent stratigraphic division data; and a presentation subunit, used to present inconsistent stratigraphic division data to the user.

[0019] In some embodiments, the network model includes: a data preprocessing module, which is used to splice the lithologic characteristic data body, paleontological fossil assemblage data body, sedimentary environment data body, isotope dating data body, seismic data body, and target data body in a virtual time window at the same location to form a multidimensional feature matrix; a convolution module, which is used to perform a convolution operation on the multidimensional feature matrix through a two-dimensional convolution kernel to extract the correlation pattern of data between different data bodies; an LSTM module, which is used to process the convolution results using an LSTM network model to capture the vertical continuity and long-distance dependency of the strata, wherein the long-distance dependency includes sedimentary cycles and strata contact relationships; and an output module, which is used to determine the stratigraphic division suggestion based on the output result of the LSTM module.

[0020] The third aspect of this specification provides an electronic device, comprising: a memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor implements the high-precision stratigraphic comparison method described in any one of the first aspects by executing the computer instructions.

[0021] A fourth aspect of this specification provides a computer storage medium, wherein the computer storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the high-precision formation comparison method described in any one of the first aspects is implemented.

[0022] A fifth aspect of this specification provides a computer program product, comprising a computer program, which, when executed by a processor, implements the high-precision stratigraphic comparison method described in any one of the first aspects.

[0023] The high-precision stratigraphic comparison method provided in this specification constructs filter factors through well logging data, and then uses these filter factors to extract high-frequency seismic data volumes and low-frequency seismic data volumes from the seismic data volume. By deconvolving the high-, medium-, and low-frequency seismic data volumes through time-varying seismic wavelets, a target data volume with an "apparent resolution" and a bandwidth much higher than that of the seismic data volume can be obtained. Therefore, the cross-sectional data of the target data volume can be used to more accurately distinguish the lateral characteristics between wells, and the stratigraphic division scheme can be adjusted based on the lateral characteristics between wells to achieve high-precision stratigraphic comparison; the high resolution and lateral variation characteristics of the target data profile are used to carry out detailed stratigraphic comparison, providing a guarantee for the detailed exploration and development of oil and gas fields, especially development geological research. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the implementation methods or the description of the prior art. Obviously, the drawings described below are only some implementation methods recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 A schematic diagram of a high-precision formation comparison method provided in this specification;

[0026] Figure 2 A schematic diagram of the process for determining the target data body;

[0027] Figure 3 This is a schematic diagram of the sequence division scheme for Well A in the target area DY;

[0028] Figure 4 This is a schematic diagram of the AB well-connected stratigraphic comparison section in the target area DY;

[0029] Figure 5 This is a schematic diagram of the well-connected seismic profile of DY in the target area;

[0030] Figure 6 This is a schematic diagram of the target data profile of the DY well in the target area;

[0031] Figure 7 This is a schematic diagram of the superposition of the target data section and the seismic data section of the DY well in the target area;

[0032] Figure 8 This is a comparison chart of the seismic data profile of the DY well in the target area and the target data profile;

[0033] Figure 9 A schematic flow chart of obtaining a target data section corresponding to a well-connected seismic section based on well-connected formation comparison results, and using the target data section to perform fine comparison of formations between wells;

[0034] Figure 10 It is a structural diagram of the network model;

[0035] Figure 11 Another flow chart for obtaining a target data section corresponding to a well-connected seismic section based on well-connected formation comparison results, and performing fine comparison of formations between wells using the target data section;

[0036] Figure 12 A schematic diagram of the structure of a high-precision formation comparison device provided in this specification;

[0037] Figure 13 This is a functional block diagram of an electronic device provided in this manual. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0039] Oil exploration usually includes acquisition, processing, interpretation and reservoir prediction.

[0040] Acquisition: Use seismic, well logging and other technical means to collect physical information of underground geological bodies, such as seismic wave reflection data, formation resistivity, etc., to provide basic data for subsequent analysis and processing.

[0041] Processing: The massive amount of collected data is processed through a series of processes such as denoising, filtering, and correction to improve data quality, enhance effective information, suppress interference signals, and make the data more conducive to subsequent geological interpretation.

[0042] Interpretation: Combining multidisciplinary knowledge such as geology and geophysics, the processed data is analyzed and interpreted to identify stratigraphic structure, structural characteristics, lithologic changes, etc., and infer the possible location and distribution range of underground oil and gas reservoirs.

[0043] Reservoir prediction: Using seismic attribute analysis, logging constrained inversion and other technologies, we predict reservoir parameters such as lithology, physical properties, and oil content, clarify the distribution range and quality differences of reservoirs, and provide key reservoir information for oil and gas field development.

[0044] During the "interpretation" phase, a variety of data must be integrated to analyze and understand the underground geological structure and stratigraphic characteristics. Stratigraphic correlation is a key task. By comparing stratigraphic characteristics between different wells or between seismic data and well data, such as lithology, electrical properties, and reflected seismic wave characteristics, the stratigraphic sequence relationships, sedimentary cycles, and lateral variations of the strata can be determined, thereby helping to identify stratigraphic structure, structural characteristics, and the distribution range of oil and gas reservoirs. For example, by comparing logging curves from different wells, analyzing changes in the lithology and physical properties of the strata, and combining them with the reflection characteristics of seismic data, the lateral continuity and variation patterns of the strata can be determined, providing a basis for the interpretation and evaluation of oil and gas reservoirs.

[0045] Existing technologies typically use various methods to improve the resolution of seismic data, such as compressing seismic wavelets or removing the effects of interference and tuning during underground wave propagation on the subsurface strata. This broadens the frequency bandwidth of effective seismic signals, particularly accurately broadening high-frequency components. However, these methods for improving seismic data resolution only extend the effective bandwidth to a limited extent, resulting in a significant resolution difference between seismic data and well logging data, making horizontal comparison difficult.

[0046] In contrast, this specification provides a high-precision stratigraphic comparison method that improves the accuracy of stratigraphic comparison by introducing target data.

[0047] like Figure 1 As shown, the high-precision formation comparison method includes the following S10 to S30.

[0048] S10: Acquire the seismic data volume and target data volume of the target area.

[0049] In seismic exploration, seismic waves are artificially generated, such as through explosive detonations or the use of controlled vibrators, to generate elastic waves. These waves propagate through the underground medium, experiencing reflection, refraction, and transmission when encountering interfaces between different strata. Seismic detectors located above or below the surface receive these reflected and refracted waves, convert them into electrical signals, and record them as seismic wave data.

[0050] A seismic data volume typically organizes and arranges seismic wave data collected within a specific area during seismic exploration, organized by spatial location and time. It provides a complete description of seismic wave information in three dimensions (typically two-dimensional ground position plus depth). Each data point in the data volume corresponds to seismic wave characteristics, such as amplitude and frequency, at a specific location and time underground. Think of it as a three-dimensional "data cube," containing a vast amount of seismic wave data.

[0051] The seismic data volume can also be a data volume obtained after processing the seismic wave data. These processings may include compressing the seismic wavelet, or removing the influence of interference, tuning and other effects of seismic waves on underground strata during underground propagation, broadening the frequency band range of effective seismic signals, and especially broadening the high-frequency components more accurately.

[0052] The target data volume is a three-dimensional data set formed by organizing and storing the target data at each location in the underground stratum. In a three-dimensional spatial grid, each grid point corresponds to a target data, and the target data at these grid points together constitute the target data volume.

[0053] The target data body can be obtained by Figure 2 S11 to S15 shown are determined.

[0054] S11: Extracting time-varying seismic wavelets from the seismic data volume of the target area.

[0055] Specifically, each seismic trace at the drilling location can be extracted, and then multiple seismic traces can be balanced; adjacent traces beside the well can be extracted and multi-channel balanced; the time window center of each seismic trace after the equalization process is taken at equal intervals; each time window signal is processed in succession to obtain the time window wavelet corresponding to each time window signal; after the extraction of each time window wavelet, each time window wavelet is subjected to energy normalization processing, the time window wavelet corresponding to the first time window signal is directly normalized, and the time window wavelets corresponding to the remaining time window signals are windowed and normalized with the first time window as the window function, thereby obtaining each normalized time window wavelet; the normalized time window wavelet is smoothed to obtain time-varying seismic wavelet information.

[0056] S12: Construct high-frequency filter factors and low-frequency filter factors based on the well logging data of the target area.

[0057] S12 can first pre-process the logging data of the target area, such as removing outliers, filling missing values, and standardizing the data; then perform Fourier transform on the pre-processed logging data to obtain the energy distribution of the logging data at different frequencies (spectral analysis results), and present the spectrum analysis results to the user. The user will determine and input the first frequency value and the second frequency value based on the spectrum analysis results, where the first frequency value is the boundary value between low frequency and medium frequency, and the second frequency value is the boundary value between medium frequency and high frequency. The boundaries and center frequencies of low frequency and high frequency in the logging data are determined based on the first frequency value and the second frequency value entered by the user; the low-frequency filter factor and the high-frequency filter factor are constructed based on the boundaries and center frequencies of low frequency and high frequency.

[0058] Both the low-frequency filter factor and the high-frequency filter factor are numerical values ​​that vary with frequency. The high-frequency filter factor is used to enhance high-frequency data and attenuate low-frequency and medium-frequency data, while the low-frequency filter factor enhances low-frequency data and attenuates high-frequency and medium-frequency data. For example, if the frequency is high, the high-frequency filter factor is greater than 1, and the low-frequency filter factor is less than 1; if the frequency is low, the high-frequency filter factor is less than 1, and the low-frequency filter factor is greater than 1; if the frequency is medium, the high-frequency filter factor and the low-frequency filter factor are both less than 1.

[0059] There are many forms of filter factors, such as Gaussian filter factors and Butterworth filter factors. Taking the Gaussian filter factor as an example, for high-frequency filtering, its expression can be: Among them, H high (f) is the high-frequency filter factor, f is the frequency variable, f c Is the center frequency of the high-frequency filter, generally taking a value near the boundary frequency, σ is the parameter that controls the filter width. The smaller the σ value, the sharper the filtering effect and the more severe the high-frequency components are attenuated. For low-frequency filtering, the expression of the Gaussian filter factor is similar: Among them, H low (f) is the low-frequency filter factor, f c It is the center frequency of the low-frequency filter, which is generally a low-frequency value that can reflect large-scale structures. σ is a parameter that controls the filter width. The smaller the σ value, the sharper the filtering effect and the more the low-frequency components are attenuated.

[0060] S13: extracting high-frequency information from the seismic data volume using the high-frequency filter factor to obtain a high-frequency seismic data volume, and extracting low-frequency information from the seismic data volume using the low-frequency filter factor to obtain a low-frequency seismic data volume.

[0061] S13 can multiply the frequency domain data of the seismic data volume with the high-frequency filter factor, that is, multiply the frequency domain values ​​of the seismic data corresponding to the same frequency value with the high-frequency filter factor, and then transform the result into the time domain, thereby obtaining a high-frequency seismic data volume. Similarly, the frequency domain data of the seismic data volume can be multiplied with the low-frequency filter factor, that is, multiply the frequency domain values ​​of the seismic data corresponding to the same frequency value with the low-frequency filter factor, and then transform the result into the time domain, thereby obtaining a low-frequency seismic data volume.

[0062] S14: performing diffusion filtering on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume respectively.

[0063] In seismic data processing, diffusion filtering can redistribute the energy of seismic waves in time and space. Diffusion filtering has many effects on high-, medium-, and low-frequency seismic data, and its performance at different frequencies has its own characteristics, as follows:

[0064] 1. High-frequency seismic data

[0065] Suppressing random noise: High-frequency seismic data often contains a lot of random noise. Diffusion filtering can effectively suppress this noise by smoothing the data, thereby improving the signal-to-noise ratio. For example, in seismic exploration, raw high-frequency seismic records may contain some tiny interfering signals. After diffusion filtering, this noise is significantly suppressed, making the details of the seismic signal clearer.

[0066] Highlighting stratigraphic details: Although diffusion filtering can smooth high-frequency signals, with appropriate parameters, it can suppress noise while preserving subtle stratigraphic features, such as reflections from thin layers and small faults. This helps more accurately identify and describe the details of underground geological structures, providing a better data foundation for detailed geological interpretation.

[0067] 2. Medium-frequency seismic data

[0068] Enhanced signal continuity: Medium-frequency seismic data typically contains primary reflection information from subsurface strata. Diffusion filtering can enhance the continuity of these reflection signals, improving the continuity and traceability of events. For example, in a seismic profile, diffusion-filtered medium-frequency data will produce smoother and more continuous reflection events, making it easier for geologists to track and compare horizons.

[0069] Balancing amplitude differences: Different reflection coefficients in different strata result in varying amplitudes of individual reflection signals in medium-frequency seismic data. Diffusion filtering can balance these amplitude differences to a certain extent, making the overall amplitude distribution of the seismic data more uniform. This facilitates subsequent analysis and interpretation of the seismic data, avoiding interpretation bias caused by excessive amplitude differences.

[0070] Since seismic data are mainly concentrated in the intermediate frequency, the processing effect of diffusion filtering on seismic data is also the processing effect of diffusion filtering on intermediate frequency seismic data.

[0071] 3. Low-frequency seismic data

[0072] Suppressing Low-Frequency Interference: Low-frequency seismic data may contain some low-frequency interference signals, such as instrument noise and ground vibration. Diffusion filtering can effectively suppress these low-frequency interference signals and improve the quality of low-frequency signals. For example, in some seismic acquisition environments, low-frequency mechanical vibration interference is present. Diffusion filtering can reduce this interference, allowing low-frequency seismic data to more accurately reflect the low-frequency characteristics of underground geological structures.

[0073] Highlighting Large-Scale Tectonic Information: Low-frequency seismic data primarily reflects large-scale geological structures in the subsurface. Diffusion filtering, while suppressing interference, can further highlight the characteristics of these large-scale structures, such as large folds and fault zones. This is crucial for understanding the overall morphology and structural pattern of the subsurface geological structure at a macroscopic level, providing strong data support for regional geological structural analysis.

[0074] S15: Deconvolve the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume respectively using the time-varying seismic wavelet, and fuse the deconvolution results to obtain a target data volume.

[0075] In S15, high-, medium-, and low-frequency seismic data volumes contain geological information at different scales. High-frequency data volumes help identify subtle stratigraphic changes and thin strata, while low-frequency data volumes can reflect large-scale geological structures. Deconvolution using time-varying seismic wavelets optimizes processing for different frequency components, compressing the length of the wavelets and improving the resolution of seismic records. This makes it easier to identify stratigraphic boundaries and geological volumes that are previously difficult to distinguish in conventional seismic records, facilitating more accurate geological interpretation and reservoir characterization.

[0076] Seismic waves attenuate and disperse during underground propagation, causing changes in the shape and frequency characteristics of the seismic wavelets. Time-varying seismic wavelet deconvolution can compensate for these changes based on the physical laws of seismic wave propagation and the characteristics of actual seismic data, eliminating the influence of propagation effects and ensuring that the deconvolved seismic data more accurately reflects the underground geology. For example, by appropriately enhancing high-frequency components, it can compensate for the attenuation of high-frequency seismic waves during propagation, thereby better highlighting detailed information about the underground strata.

[0077] The time-varying seismic wavelet is used to deconvolve the high, medium and low frequency data bodies, so that the responses of different geological bodies at different frequencies can be separated, which helps to identify and distinguish different types of geological bodies, such as faults, fractures, lithologic sudden change zones, etc., and provides more abundant information for stratigraphic correlation.

[0078] The above S11 to S15 construct filter factors from the logging data, and then use the filter factors to extract high frequency seismic data bodies and low frequency seismic data bodies from the seismic data bodies, so that the high frequency signals and low frequency signals in the seismic data can be enhanced respectively, thereby widening the frequency band range of the target data body; after deconvolution of the high, medium and low frequency seismic data bodies by the time-varying seismic wavelet, the time-varying seismic wavelet in the seismic data can be removed, and the target data body reflecting the stratigraphic interface and geological body can be obtained. Therefore, the "apparent resolution" of the profile data of the target data body is much higher than that of the conventional seismic data profile, which can clearly reflect the stratigraphic transformation characteristics and highlight the vertical and horizontal variation characteristics of the strata, and can be used to carry out high-precision sequence stratigraphic correlation.

[0079] S20: determining a well tie stratigraphic correlation result according to the seismic wave data in the seismic data body.

[0080] Before well tie correlation is performed, single well sequence division can be performed first. Then, on the basis of the single well sequence division, a well tie stratigraphic correlation profile is established, so that the division schemes of the same set of strata in different wells have consistency and comparability. Then, a well tie seismic correlation framework is established by using the seismic wave data, and the stratigraphic and sand body correlation between wells is carried out by using the amplitude, frequency and other variation characteristics of the seismic events on the basis of the well tie stratigraphic correlation.

[0081] S30: based on the well tie stratigraphic correlation result, obtaining a target data profile in a target data body corresponding to a well tie seismic profile, and using the target data profile to perform fine stratigraphic correlation between wells.

[0082] Due to the difference between the logging and seismic data, there is often a problem of well-seismic mismatch in stratigraphic correlation. This requires further adjustment of the sequence division scheme in combination with the variation characteristics of the seismic data, but due to the difference in resolution between the well and the seismic (i.e. the resolution of the logging data is high and the resolution of the seismic data is low), it is insufficient to directly use the seismic data for stratigraphic correlation (especially in some small-scale stratigraphic correlation). The resolution of the target data body is much higher than that of the seismic data body, and the corresponding profile data in the target data body can be used to further adjust the sequence division scheme.

[0083] The high-precision stratigraphic comparison method provided in this specification constructs filter factors through well logging data, and then uses these filter factors to extract high-frequency seismic data volumes and low-frequency seismic data volumes from the seismic data volume. By deconvolving the high-, medium-, and low-frequency seismic data volumes through time-varying seismic wavelets, a target data volume with an "apparent resolution" and a bandwidth much higher than that of the seismic data volume can be obtained. Therefore, the cross-sectional data of the target data volume can be used to more accurately distinguish the lateral characteristics between wells, and the stratigraphic division scheme can be adjusted based on the lateral characteristics between wells to achieve high-precision stratigraphic comparison; the high resolution and lateral variation characteristics of the target data profile are used to carry out detailed stratigraphic comparison, providing a guarantee for the detailed exploration and development of oil and gas fields, especially development geological research.

[0084] Some existing stratigraphic correlation methods involve first calibrating well logs with cores, imbuing them with sedimentological significance, identifying stratigraphic stacking patterns, demarcating datum cycles of varying orders within a single well, and performing multi-well correlations. These methods then combine reflection characteristics such as amplitude, frequency, phase, continuity, and geometry of seismic waves reflected at different stratigraphic interfaces to ultimately establish a high-resolution sequence stratigraphic framework for the study area. However, this method requires separating reflected waves from seismic records. Due to the influence of reflected waves, refracted waves, circumvented waves, and interfering noise from different stratigraphic interfaces, the separation process is complex and suffers from poor accuracy. The frequency band is narrow, and the stratigraphic correlation accuracy is far lower than the high-precision stratigraphic correlation method presented in this specification.

[0085] The following uses the target area DY in a bay basin as an example to illustrate the high-precision stratigraphic correlation method provided in this specification. The target DY formation features thin sand bodies, poor continuity, and rapid inter-well variations, making lateral correlation challenging. Stratigraphic and sand body correlation results directly impact the analysis of reservoir distribution patterns and development geology research. Using the high-precision stratigraphic correlation method provided in this specification to perform stratigraphic correlation in this target area DY includes the following steps: 1 through 3.

[0086] Step 1: Single well sequence division

[0087] First, the single-well sequence division was carried out: taking Well A as an example, according to the logging curve, lithology and cycle characteristics, the target area DY was divided into three sections and eight oil groups: the first section of the East is a third-order sequence, which is an anti-cycle as a whole, with multiple high-frequency cycles inside, and is subdivided into three oil groups according to the cycle conversion surface; the second section of the East is a third-order sequence, with one positive and one negative complete cycle. The largest lake flooding surface of the second-order sequence is developed in the upper part, which can be used as a comparative marker layer for the entire area. It is subdivided into three oil groups according to the cycle conversion surface; the third section of the East is a positive cycle as a whole, and is subdivided into upper and lower oil groups. Figure 3 Schematic diagram of the sequence division scheme for Well A in the target area DY.

[0088] Step 2: Well-to-well formation comparison

[0089] Based on the sequence divisions of individual wells, stratigraphic correlation profiles were established across connected wells. This ensures consistency and comparability between the division schemes for the same strata across different wells. This well comparison reveals similar overall stratigraphic development characteristics across the DY interfaces and lithologic assemblages in the target area, demonstrating good consistency. However, several differences exist: Well A in the upper Ed3 subsection exhibits a positive cycle, while Well B exhibits a complete cycle. In the lower Ed3 subsection, Well A exhibits a complete positive and negative cycle, while Well B exhibits a superposition of two positive cycles. Furthermore, stratigraphic variations between different wells require further verification using seismic data. Figure 4 This is a schematic diagram of the comparative stratigraphic section of the AB wells in the target area DY.

[0090] Step 3: Well-seismic stratigraphic comparison

[0091] Conventional seismic data are used to establish a well-to-well seismic comparison framework. On the basis of well-to-well stratigraphic comparison, the amplitude, frequency and other variation characteristics of the seismic phase axis are used to further carry out detailed comparison of stratigraphic and sand bodies between wells. Figure 5 The following is a schematic diagram of the well-connected seismic profile of the target area DY. From the seismic well-connected profile, it can be seen that the eight oil formations in the target area DY are fully developed and have good overall consistency.

[0092] Due to the differences between well logging and seismic data, there is often a mismatch between well logging and seismic data in stratigraphic comparison. Figure 5 The stratigraphic division schemes for the two wells based on well logging information do not completely match those on the seismic profile. This requires further adjustment of the stratigraphic division scheme based on the changing characteristics of the earthquake. However, due to the difference in well-seismic resolution (high logging resolution, low seismic resolution), direct use of seismic data for stratigraphic comparison is insufficient, especially in some small-scale stratigraphic comparisons. Therefore, higher-resolution data is needed to assist in conducting detailed stratigraphic comparisons.

[0093] Step 4:

[0094] By utilizing the high-resolution vertical and horizontal features of the target data volume profile, we can further carry out detailed comparison of inter-well strata and sand bodies on the basis of well-to-well stratum comparison and implement the stratum comparison plan. By combining the well logging curve and lithologic characteristics with the lateral changes of the target data, we can infer the inter-well strata and lithologic change characteristics. The target data volume profile highlights the lateral change characteristics of the sand body, which can accurately find the annihilation point of the sand body. However, conventional seismic profiles are affected by resolution, and the phase axis cannot effectively reflect the changes in the sand body. Figure 5 and Figure 6 As shown, Figure 6 This is a schematic diagram of the target data profile of the DY well in the target area.

[0095] Judging from the schematic diagram of the superposition of the target data profile and the seismic data profile, the target data profile removes the sidelobe interference of the seismic wavelet and can better reflect the changing characteristics of the lithologic interface. The vertical resolution and lateral changes of the target data are more accurate than those of the seismic profile, and can be used as auxiliary data for fine stratigraphic comparison of wells. Figure 7 This is a schematic diagram of the superposition of the target data section and the seismic data section of the DY well in the target area.

[0096] According to the logging curves and lithologic characteristics, and their corresponding changes in conventional seismic profiles and target data profiles, it can be seen that one set of seismic reflections in the conventional seismic profile corresponds to two sets of reflections in the target data profile, and the waveform characteristics of the seismic reflections correspond well to the logging GR curves. Figure 8 This is a comparison chart of the seismic data profile of the DY well in the target area and the target data profile. Figure 8 (a) is a conventional seismic profile. Figure 8 (b) in Figure 1 is the target data profile. The green box in (a) shows one set of peak reflections (black), while (b) corresponds to two sets of reflection interfaces (black). The well logging GR curve (blue curve) and lithologic data (yellow for sandstone and dark gray for mudstone) indicate the presence of multiple thin sandstone layers. Changes in the GR curve (lithologic interface) correspond to stratigraphic boundaries, but due to the limited resolution of conventional seismic profiles, the correspondence is poor. However, the correspondence with the target data profile is better.

[0097] In some embodiments, S30 includes: displaying the seismic data profile corresponding to the well-connected seismic profile on the first display layer through the first display component; displaying the target data profile corresponding to the well-connected seismic profile on the second display layer through the second display component; wherein the first display layer and the second display layer are superimposed and presented to the user.

[0098] Since the seismic data section and target data section use transparent background, they can be clearly seen when they are superimposed. Seismic data section and target data section can use different colors to visualize seismic data and target data, so that seismic data and target data can be clearly distinguished from each other in the superimposed display interface. Figure 7 As shown in the figure, a detailed comparison of the stratigraphic layers between wells can be performed based on the superposition display results of the seismic data profile and the target data profile, for example, to adjust the stratigraphic division scheme.

[0099] In some embodiments, as Figure 9 As shown, S30 includes the following S31 to S33.

[0100] S31: Acquire the lithologic characteristic data volume, paleontological fossil assemblage data volume, sedimentary environment data volume, isotope dating data volume, seismic data volume, and target data volume of the target area.

[0101] Lithologic data records the physical properties, mineral composition, and structural characteristics of rocks within the target area. Paleontological fossil assemblage data records the types, quantities, preservation states, and assemblage characteristics of fossils within the target area's strata, reflecting information about biological communities during geological history. Sedimentary environment data records the geographical environment, hydrodynamic conditions, and provenance during the deposition of strata within the target area. Isotope dating data records the formation age of rocks or minerals, determined by measuring the decay patterns of radioactive isotopes within the strata.

[0102] S32: A virtual time window is set on the well-connected seismic profile of the lithologic characteristic data body, the paleontological fossil assemblage data body, the sedimentary environment data body, the isotope dating data body, the seismic data body, and the target data body. The virtual time window slides synchronously starting from the same position of the data body, and the data in each data body in the virtual time window at the same position is used as input data of the network model, and the stratigraphic division suggestion within the virtual time window is output through the network model.

[0103] The virtual time window can be slid along a "bow" path or a document line break path on the well-connected seismic section.

[0104] In some embodiments, as Figure 10 As shown in Figure 3, the network model includes a data preprocessing module, a convolution module, an LSTM module, and an output module.

[0105] The data preprocessing module is used to splice the lithologic characteristic data body, paleontological fossil assemblage data body, sedimentary environment data body, isotope dating data body, seismic data body, and target data body in the virtual time window of the same location to form a multidimensional feature matrix.

[0106] The data volume in the virtual time window can be treated as a two-dimensional data volume, and the two-dimensional data of various data volumes can be spliced ​​together to obtain a three-dimensional data volume. For example, if the size of the virtual time window is M (length) × N (height), then the multidimensional feature matrix formed by splicing is M × N × 6, where M and N are positive integers.

[0107] The convolution module is used to perform a convolution operation on the multidimensional feature matrix through a two-dimensional convolution kernel to extract the correlation pattern of data between different data bodies.

[0108] The size of the two-dimensional convolution kernel can be N×6. The convolution operation is performed by the convolution kernel and the M×N×6 multi-dimensional feature matrix. The convolution result is the correlation pattern between different data bodies.

[0109] The LSTM module is used to process the convolution results using the LSTM network model to capture the vertical continuity and long-distance dependencies of the strata, including sedimentary cycles and stratum contact relationships.

[0110] The output module is configured to determine the stratigraphic division suggestion according to the output result of the LSTM network model.

[0111] The output module can output the probability that a position in the profile belongs to each reference stratum respectively, and finally output the identification of a stratum with the maximum probability value. The reference stratum can be divided by a user according to the logging data, that is, a single-well sequence division result. The single-well sequence division result can be included on the well-to-well data profile of the seismic data volume, so as to facilitate extraction of each reference stratum therefrom.

[0112] S33: The stratigraphic division suggestions corresponding to the virtual time windows at each position are fused to obtain an overall stratigraphic division suggestion of the target area on the well-to-well seismic profile.

[0113] When the virtual time window slides on the data volume, it is first slid horizontally, then slid vertically once after the horizontal sliding reaches the boundary of the virtual time window, and then slid horizontally again; the distance of each horizontal sliding is less than the width of the virtual time window, and the distance of the vertical sliding is less than the height of the virtual time window.

[0114] The same position can determine different stratigraphic division suggestions when the virtual time window is at different positions. When the stratigraphic division suggestions corresponding to the virtual time windows at each position are fused, the different stratigraphic division suggestions can be averaged or the maximum value thereof can be taken.

[0115] The above S31 to S33 learn the correlation between the collection of multi-modal data (i.e. lithological feature data, paleontological fossil assemblage data, sedimentary environment data, isotope dating data, seismic data volume, target data) on the same profile and the stratigraphic division result through the network model. The profile data of these data volumes can be directly input into the network model to quickly and accurately obtain the overall stratigraphic division suggestion. Stratigraphic correlation based on the overall stratigraphic division suggestion can greatly reduce the workload of stratigraphic correlation of the user.

[0116] Among the above lithological feature data volume, paleontological fossil assemblage data volume, sedimentary environment data volume, isotope dating data volume, seismic data volume, and target data volume, the target data volume has a wider frequency band and higher resolution, which can significantly improve the accuracy of the overall stratigraphic division suggestion output by the network model.

[0117] It should be noted that the resolution of the lithological feature data volume, the paleontological fossil assemblage data volume, the sedimentary environment data volume, the isotope dating data volume, and the seismic data volume is usually low, and there may be data missing. Therefore, it is usually impossible to use the network model to process these data volumes to obtain a more accurate stratigraphic division result, and thus there is no technical solution of S31 to S33 in the prior art.

[0118] Further, as Figure 11 As shown, S30 may further include the following S34 to S36.

[0119] S34: Obtain the user's preliminary stratum division result.

[0120] S35: Compare the preliminary stratigraphic division result with the overall stratigraphic division suggestion to filter out inconsistent stratigraphic division data.

[0121] S36: Presenting inconsistent stratigraphic division data to the user.

[0122] The user may first determine a preliminary stratigraphic division result based on single well sequence division, well-connected stratigraphic comparison, and well-seismic stratigraphic comparison, such as Figure 4 This preliminary stratigraphic division is the result of manual intervention, and single-well sequence division is generally quite accurate. However, the inter-well sequence division in the preliminary stratigraphic division is determined based on seismic data, which has a low resolution and therefore inevitably leads to inaccurate inter-well sequence division.

[0123] The preliminary stratigraphic division results can be compared with the overall stratigraphic division recommendations received by the network model to screen out inconsistent stratigraphic division data and present the inconsistent stratigraphic division data to the user. A user confirmation component can also be provided in the interactive interface to allow the user to confirm their knowledge of the inconsistent stratigraphic division data and determine whether the preliminary stratigraphic division results or the overall stratigraphic division recommendations will prevail. The user can also choose to ignore the inconsistent stratigraphic division data, that is, to use the preliminary stratigraphic division results as the basis.

[0124] Through the above S34 and S36, the manual division results and the division results of the network model can be integrated to reduce the workload of manual division and improve the accuracy of the final stratigraphic comparison.

[0125] This specification also provides a high-precision formation comparison device that can be used to implement the above-mentioned high-precision formation comparison method. Figure 12 As shown, it includes an acquisition unit 10, a determination unit 20, a comparison unit 30, a first extraction unit 40, a construction unit 50, a second extraction unit 60, a filtering unit 70 and a processing unit 80.

[0126] The acquisition unit 10 is used to acquire the seismic data volume and the target data volume of the target area.

[0127] The determination unit 20 is used to determine the well-to-well formation comparison result based on the seismic wave data in the seismic data volume.

[0128] The comparison unit 30 is used to obtain a target data section in a target data volume corresponding to the well-connected seismic section based on the well-connected formation comparison result, and use the target data section to perform fine comparison of the formations between wells.

[0129] The first extraction unit 40 is configured to extract a time-varying seismic wavelet from a seismic data volume of a target area.

[0130] The construction unit 50 is configured to construct a high-frequency filter factor and a low-frequency filter factor according to well logging data of the target area.

[0131] The second extraction unit 60 is configured to extract high-frequency information from the seismic data volume by using the high-frequency filter factor to obtain a high-frequency seismic data volume, and extract low-frequency information from the seismic data volume by using the low-frequency filter factor to obtain a low-frequency seismic data volume.

[0132] The filter unit 70 is configured to perform diffusion filtering on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume, respectively.

[0133] The processing unit 80 is configured to perform deconvolution on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume, respectively, by using the time-varying seismic wavelet, and fuse the deconvolution results to obtain a target data volume.

[0134] In some embodiments, the comparison unit includes a first display subunit and a second display subunit.

[0135] The first display subunit is configured to display a seismic data profile corresponding to a well-tie seismic profile on a first display layer by a first display component; the display color in the seismic data profile is determined according to the value of the seismic data.

[0136] The second display subunit is configured to display a target data profile corresponding to the well-tie seismic profile on a second display layer by a second display component; the display color is determined according to the value of the target data.

[0137] The first display layer and the second display layer are superimposed to present to a user, and the seismic data profile and the target data profile have a transparent background.

[0138] In some embodiments, the comparison unit includes a first acquisition subunit, a processing subunit, and a fusion subunit.

[0139] The first acquisition subunit is configured to acquire a lithology feature data volume, a paleontological fossil assemblage data volume, a sedimentary environment data volume, an isotope dating data volume, a seismic data volume, and a target data volume of a target area.

[0140] The processing subunit is used to set a virtual time window on the well-connected seismic profile of the lithologic characteristic data body, the paleontological fossil assemblage data body, the sedimentary environment data body, the isotope dating data body, the seismic data body, and the target data body. The virtual time window starts to slide synchronously from the same position of the data body, and the data in each data body in the virtual time window at the same position is used as the input data of the network model, and the stratigraphic division suggestion within the virtual time window is output through the network model.

[0141] The fusion subunit is used to fuse the stratigraphic division suggestions corresponding to the virtual time windows at various locations to obtain the overall stratigraphic division suggestions for the target area on the well-connected seismic profile.

[0142] In some embodiments, when the virtual time window slides on the data body, it first slides horizontally, then slides vertically once after sliding horizontally to the boundary of the virtual time window, and then slides horizontally again; the distance of each horizontal sliding is less than the width of the virtual time window, and the distance of the vertical sliding is less than the height of the virtual time window.

[0143] In some embodiments, the comparison unit further includes a second acquisition subunit, a screening subunit, and a presentation subunit.

[0144] The second acquisition subunit is used to obtain the user's preliminary stratum division result.

[0145] The screening subunit is used to compare the preliminary stratigraphic division result with the overall stratigraphic division suggestion, and screen out inconsistent stratigraphic division data.

[0146] The presentation subunit is used to present inconsistent stratigraphic division data to the user.

[0147] In some embodiments, the network model includes a data preprocessing module, a convolution module, an LSTM module and an output module.

[0148] The data preprocessing module is used to splice the lithologic characteristic data body, paleontological fossil assemblage data body, sedimentary environment data body, isotope dating data body, seismic data body, and target data body in the virtual time window of the same location to form a multidimensional feature matrix.

[0149] The convolution module is used to perform a convolution operation on the multidimensional feature matrix through a two-dimensional convolution kernel to extract the correlation pattern of data between different data bodies.

[0150] The LSTM module is used to process the convolution results using the LSTM network model to capture the vertical continuity and long-distance dependencies of the strata, including sedimentary cycles and stratum contact relationships.

[0151] The output module is used to determine the stratum division recommendations based on the output results of the LSTM module.

[0152] The embodiment of the present invention further provides an electronic device, such as Figure 13 As shown, the electronic device may include a processor 1301 and a memory 1302, wherein the processor 1301 and the memory 1302 may be connected via a bus or other means. Figure 13 The bus connection is taken as an example.

[0153] The processor 1301 may be a central processing unit (CPU). The processor 1301 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0154] The memory 1302 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the high-precision formation correlation method in the embodiment of the present invention (for example, Figure 12 The processor 1301 executes the non-transitory software programs, instructions, and modules stored in the memory 1302 to perform various functional applications and data processing of the processor, thereby implementing the high-precision stratigraphic correlation method in the above-mentioned method embodiment.

[0155] The memory 1302 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created by the processor 1301, etc. In addition, the memory 1302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 1302 may optionally include a memory remotely located relative to the processor 1301, and these remote memories may be connected to the processor 1301 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0156] The one or more modules are stored in the memory 1302 and, when executed by the processor 1301 , perform the above-mentioned high-precision formation comparison method.

[0157] The specific details of the above electronic device can be understood by referring to the corresponding descriptions and effects in the method embodiment, and will not be repeated here.

[0158] This specification also provides a computer storage medium, which stores computer program instructions. When the computer program instructions are executed by a processor, the steps of the above-mentioned high-precision formation comparison method are implemented.

[0159] This specification also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the above-mentioned high-precision formation comparison method are implemented.

[0160] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). The storage medium can also include a combination of the above-mentioned types of memory.

[0161] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0162] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0163] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0164] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute certain parts of the methods of each embodiment of the present application.

[0165] The present application can be used in a wide variety of general-purpose or specialized computer system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above.

[0166] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0167] Although the present application has been described through embodiments, those skilled in the art will appreciate that there are many modifications and variations to the present application without departing from the spirit of the present application. It is intended that the appended claims include these modifications and variations without departing from the spirit of the present application.

Claims

1. A high-precision formation comparison method, characterized in that: include: Acquire seismic data volume and target data volume of target area; Determine the well-to-well formation comparison result based on the seismic wave data in the seismic data volume; Based on the well-connected formation comparison results, a target data section in the target data volume corresponding to the well-connected seismic section is obtained, and the target data section is used to perform a detailed comparison of the formations between the wells; The target data body is determined by: Extracting time-varying seismic wavelets from the seismic data volume of the target area; Construct high-frequency filter factors and low-frequency filter factors based on the well logging data of the target area; extracting high-frequency information from the seismic data volume using the high-frequency filter factor to obtain a high-frequency seismic data volume, and extracting low-frequency information from the seismic data volume using the low-frequency filter factor to obtain a low-frequency seismic data volume; performing diffusion filtering on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume respectively; The time-varying seismic wavelet is used to perform deconvolution on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume, respectively, and the deconvolution results are fused to obtain a target data volume.

2. The method according to claim 1, characterized in that Based on the well-connected stratigraphic comparison results, a target data section corresponding to the well-connected seismic section is obtained, and the target data section is used to perform a detailed stratigraphic comparison between the wells, including: The seismic data section corresponding to the well-connected seismic section is displayed on the first display layer by the first display component; the depth of the color displayed in the seismic data section is determined according to the value of the seismic data; The target data section corresponding to the well-connected seismic section is displayed on the second display layer through the second display component; the depth of the display color is determined according to the value of the target data; The first display layer and the second display layer are superimposed and presented to the user, and the seismic data profile and the target data profile are transparent backgrounds.

3. The method according to claim 1, characterized in that Based on the well-connected stratigraphic comparison results, a target data section corresponding to the well-connected seismic section is obtained, and the target data section is used to perform a detailed stratigraphic comparison between the wells, including: Obtain the lithologic characteristics data volume, paleontological fossil assemblage data volume, sedimentary environment data volume, isotope dating data volume, seismic data volume, and target data volume of the target area; Setting a virtual time window on the well-connected seismic profile of the lithologic feature data volume, the paleontological fossil assemblage data volume, the sedimentary environment data volume, the isotope dating data volume, the seismic data volume, and the target data volume, wherein the virtual time window slides synchronously from the same position of the data volume, and the data in each data volume in the virtual time window at the same position is used as input data of the network model, and the network model outputs a stratigraphic division suggestion within the virtual time window; The stratigraphic division suggestions corresponding to the virtual time windows at each location are integrated to obtain the overall stratigraphic division suggestion for the target area on the well-connected seismic profile.

4. The method according to claim 3, characterized in that When the virtual time window slides on the data body, it first slides horizontally, slides vertically once after sliding horizontally to the boundary of the virtual time window, and then slides horizontally again; the distance of each horizontal sliding is less than the width of the virtual time window, and the distance of the vertical sliding is less than the height of the virtual time window.

5. The method according to claim 3, characterized in that Based on the well-connected formation comparison results, a target data section corresponding to the well-connected seismic section is obtained, and the target data section is used to perform a detailed comparison of the formations between the wells, further comprising: Obtain preliminary stratigraphic division results from the user; Comparing the preliminary stratigraphic division result with the overall stratigraphic division suggestion to screen out inconsistent stratigraphic division data; Inconsistent stratigraphic division data is presented to the user.

6. The method according to claim 3, characterized in that The network model includes: The data preprocessing module is used to splice the lithologic feature data volume, paleontological fossil assemblage data volume, sedimentary environment data volume, isotope dating data volume, seismic data volume, and target data volume in the virtual time window of the same location to form a multi-dimensional feature matrix; A convolution module, configured to perform a convolution operation on the multidimensional feature matrix using a two-dimensional convolution kernel to extract correlation patterns between data of different data volumes; An LSTM module is used to process the convolution results using an LSTM network model to capture the vertical continuity and long-range dependencies of the strata, such as sedimentary cycles and stratum contacts; The output module is used to determine the stratum division recommendation based on the output results of the LSTM module.

7. A high-precision formation comparison device, characterized in that: include: An acquisition unit, used for acquiring a seismic data volume and a target data volume of a target area; a determination unit, configured to determine a well-to-well formation comparison result based on seismic wave data in a seismic data volume; A comparison unit is used to obtain a target data section in a target data volume corresponding to the well-connected seismic section based on the well-connected formation comparison result, and use the target data section to perform a fine comparison of the formations between the wells; The device further comprises a first extraction unit, a construction unit, a second extraction unit, a filtering unit and a processing unit for determining the target data volume; wherein, The first extraction unit is used to extract time-varying seismic wavelets from the seismic data volume of the target area; The construction unit is used to construct high-frequency filter factors and low-frequency filter factors according to the well logging data of the target area; The second extraction unit is configured to extract high-frequency information from the seismic data volume using the high-frequency filter factor to obtain a high-frequency seismic data volume, and to extract low-frequency information from the seismic data volume using the low-frequency filter factor to obtain a low-frequency seismic data volume; The filtering unit is used to perform diffusion filtering on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume respectively; The processing unit is used to respectively perform deconvolution on the seismic data volume, the high-frequency seismic data volume, and the low-frequency seismic data volume using the time-varying seismic wavelet, and fuse the deconvolution results to obtain a target data volume.

8. An electronic device, characterized in that: include: A memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor implements the high-precision formation comparison method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the high-precision formation comparison method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the high-precision formation comparison method according to any one of claims 1 to 6.

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