Seismic data frequency lifting parameter processing method and device, electronic equipment, storage medium and program product

Abstract

Embodiments of the present application provide a seismic data frequency-raising parameter processing method and device, electronic equipment, storage medium and program product, the method comprising: performing data analysis on logging data of multiple drillings to generate a seismic and drilling data parameter set. According to a forward model, seismic forward modeling is performed on the seismic and drilling data parameter set to generate a forward plot. According to the forward plot, a seismic reflection valley wave time difference is determined. If the seismic reflection valley wave time difference does not satisfy a preset identification condition, the main frequency of the forward wavelet is adjusted, and iterative seismic forward modeling is performed on the adjusted main frequency to generate an iterated seismic reflection valley wave time difference. According to the iterated seismic reflection valley wave time difference, an iterated frequency-raising parameter is determined, which is used for reservoir identification and characterization. Through seismic forward modeling and iterative optimization mechanism, accurate matching of the frequency-raising parameter and the target reservoir characteristics is achieved, and the accuracy of parameter frequency raising is improved.

Description

Technical Field

[0001] This application relates to the field of offshore oil and gas field development, and in particular to a method, apparatus, electronic device, storage medium, and program product for processing seismic data frequency upscaling parameters. Background Technology

[0002] The increased well spacing in offshore oil and gas field development has led to a greater reliance on 3D seismic data for thin reservoir identification and planar distribution characterization. There is an inherent trade-off between the resolution and signal-to-noise ratio (SNR) of seismic data; as the dominant frequency increases, the SNR of high-frequency signals decreases significantly, potentially causing data distortion. Therefore, balancing the dominant frequency and signal resolution has become a key technical requirement for the efficient development of offshore oil and gas fields.

[0003] In existing technologies, the dominant frequency of seismic signals is adjusted using empirical formulas to improve resolution.

[0004] However, existing methods rely on human experience, and the frequency-upgraded seismic data is insufficient to meet the data resolution requirements for thin reservoir identification and characterization. Existing methods also suffer from inaccurate parameter frequency upgrading. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, storage medium, and program product for processing earthquake data frequency upscaling parameters, in order to improve the accuracy of parameter frequency upscaling.

[0006] In a first aspect, embodiments of this application provide a method for processing seismic data frequency upscaling parameters, including:

[0007] Data analysis is performed on logging data from multiple wells to generate seismic and drilling data parameter sets;

[0008] Seismic forward modeling is performed on the earthquake and drilling data parameter sets based on the forward modeling model to generate forward modeling charts;

[0009] Determine the time difference of seismic reflection valley waves based on the aforementioned forward modeling chart;

[0010] If the earthquake reflection valley wave time difference does not meet the preset identification conditions, the dominant frequency of the forward wavelet is adjusted, and an iterative earthquake forward modeling simulation is performed on the adjusted dominant frequency to generate the iterative earthquake reflection valley wave time difference.

[0011] Based on the time difference of the seismic reflection valley wave after the iteration, the frequency-upgrading parameters after the iteration are determined, and the frequency-upgrading parameters after the iteration are used for reservoir identification and characterization.

[0012] In one possible implementation, the step of performing seismic forward modeling on the seismic and drilling data parameter set according to the forward modeling model to generate a forward modeling chart includes: setting the dominant frequency of the forward modeling wavelet according to the dominant frequency of the seismic data in the seismic and drilling data parameter set; setting the thickness parameter of the main sandstone layer in the forward modeling model to a fixed value; performing seismic forward modeling based on multiple sets of interlayer mudstone thickness parameters and the target sandstone layer thickness parameter and the dominant frequency of the forward modeling wavelet to generate forward modeling results; performing phase shift processing on the forward modeling results to obtain reservoir inversion results; and drawing a chart based on the reservoir inversion results to generate a forward modeling chart.

[0013] In one possible implementation, before performing seismic forward modeling on the seismic and drilling data parameter set based on the forward model, the method further includes: creating a layered structure of the forward model, wherein the layered structure of the forward model includes a main sandstone layer, interbedded mudstone, and a target sandstone layer; obtaining the velocity and density parameters of the sandstone and mudstone in the seismic and drilling data parameter set; and filling the layered structure of the forward model with data based on the velocity and density parameters of the sandstone and mudstone to obtain the forward model.

[0014] In one possible implementation, determining the seismic reflection valley time difference based on the forward modeling plot includes: identifying the seismic reflection valley positions corresponding to the main sandstone layer and the target sandstone layer based on the reservoir inversion results corresponding to each thickness combination in the forward modeling plot; comparing the seismic reflection valley positions corresponding to the main sandstone layer and the target sandstone layer to generate a comparison result; if the valley positions corresponding to the main layer and the target layer are separated in the comparison result, then calculating the time difference between the valleys of the main layer and the target layer to obtain the seismic reflection valley time difference.

[0015] In one possible implementation, if the seismic reflection valley time difference does not meet the preset identification conditions, the dominant frequency of the forward wavelet is adjusted, and iterative seismic forward modeling is performed on the adjusted dominant frequency to generate the iterative seismic reflection valley time difference. This includes: determining whether the seismic reflection valley time difference is zero; if the seismic reflection valley time difference is zero, the preset identification conditions are not met, the dominant frequency of the forward wavelet is adjusted to obtain the adjusted forward wavelet; and iterative seismic forward modeling is performed based on the adjusted forward wavelet to generate the iterative seismic reflection valley time difference.

[0016] In one possible implementation, after determining the iterative frequency-upgrading parameters based on the iteratively determined seismic reflection valley time difference, the method further includes: performing frequency-upgrading processing on the original seismic data based on the iterative frequency-upgrading parameters to obtain frequency-upgraded seismic data; dividing the reservoir identifiable thickness intervals under multiple mudstone interlayer thicknesses based on the zero-value line of the seismic reflection valley time difference in the forward model; performing planar distribution characterization of the reservoir based on the frequency-upgraded seismic data and the reservoir identifiable thickness intervals to generate processing results; and adjusting the frequency-upgrading parameters based on the processing results.

[0017] Secondly, embodiments of this application provide a processing apparatus for seismic data frequency upscaling parameters, comprising:

[0018] The data parsing module is used to parse logging data from multiple wells and generate seismic and drilling data parameter sets.

[0019] The seismic forward modeling module is used to perform seismic forward modeling on the seismic and drilling data parameter sets based on the forward modeling model, and generate forward modeling charts.

[0020] The first determining module is used to determine the time difference of seismic reflection valley waves based on the forward modeling chart;

[0021] The first adjustment module is used to adjust the dominant frequency of the forward wavelet if the earthquake reflection valley wave time difference does not meet the preset identification conditions, and to perform iterative earthquake forward modeling simulation on the adjusted dominant frequency to generate the iterative earthquake reflection valley wave time difference.

[0022] The second determining module is used to determine the iterative frequency-raising parameters based on the iterative seismic reflection valley wave time difference. The iterative frequency-raising parameters are used for reservoir identification and characterization.

[0023] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0024] The memory stores computer-executed instructions;

[0025] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0026] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0027] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0028] The seismic data frequency upscaling parameter processing method, apparatus, electronic device, storage medium, and program product provided in this application construct a parameter set by parsing drilling data, generate a forward modeling chart through seismic forward modeling simulation, and iteratively adjust the dominant frequency of the forward modeling wavelet based on whether the time difference of the seismic reflection valley wave meets the identification conditions, ultimately determining the optimal frequency upscaling parameter. This solves the problems of insufficient seismic data resolution and reliance on experience in frequency upscaling parameter selection in thin reservoir identification. Through an iterative optimization mechanism, it achieves accurate matching between the frequency upscaling parameter and the target reservoir characteristics, improving the accuracy of parameter frequency upscaling. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0030] Figure 1 This is a schematic diagram of the system structure of a computer device provided in an embodiment of this application;

[0031] Figure 2 A flowchart illustrating the method for processing frequency-upgrading parameters of seismic data provided in this application. Figure 1 ;

[0032] Figure 3 A schematic diagram showing the overlay of the one-dimensional model of oilfield A and the phase shift results of the seismic forward modeling waveform provided in the embodiments of this application;

[0033] Figure 4 Schematic diagram of forward modeling provided for embodiments of this application Figure 1 ;

[0034] Figure 5 A flowchart illustrating the method for processing frequency-upgrading parameters of seismic data provided in this application. Figure 2 ;

[0035] Figure 6 Schematic diagram of forward modeling provided for embodiments of this application Figure 2 ;

[0036] Figure 7 Schematic diagram of forward modeling provided for embodiments of this application Figure 3 ;

[0037] Figure 8 A flowchart illustrating the method for processing frequency-upgrading parameters of seismic data provided in this application. Figure 3 ;

[0038] Figure 9A schematic diagram of the processing device for the frequency upscaling parameters of seismic data provided in this application;

[0039] Figure 10 A schematic diagram of the structure of the electronic device provided in this application.

[0040] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0042] First, let me explain the terms used in this application:

[0043] Forward modeling is the process of calculating the response of electrical logging through numerical simulation under known formation model parameters and instrument structure parameters.

[0044] Thin reservoir: Thin reservoir is a common term in the fields of geology and oil and gas exploration and development. It refers to a formation unit that is relatively thin but still has the ability to store oil, gas or other fluids.

[0045] The increased well spacing in offshore oil and gas field development necessitates greater reliance on 3D seismic data for thin reservoir identification and planar characterization. There is an inherent trade-off between seismic data resolution and signal-to-noise ratio (SNR). As the dominant frequency increases, the SNR of high-frequency signals decreases significantly, potentially leading to data distortion. Therefore, balancing dominant frequency and signal resolution has become a key technical requirement for efficient offshore oil and gas field development. Current technologies adjust the dominant frequency of seismic signals using empirical formulas to improve resolution. However, these methods rely on human experience, and the frequency-increased seismic data often fails to meet the resolution requirements for thin reservoir identification and characterization. Furthermore, existing methods suffer from inaccurate parameter frequency increases.

[0046] To address the aforementioned technical problems, this application proposes the following technical concept: The inventors consider constructing a forward model based on geological features and seismic data characteristics. This model is then used to perform seismic forward modeling simulations on seismic and drilling data, generating a forward modeling chart. Based on the chart, it is determined whether the time difference of the seismic reflection valley wave meets preset identification conditions. If the conditions are not met, the dominant frequency of the forward modeling wavelet is adjusted. Iterative seismic forward modeling simulations are then performed on the adjusted dominant frequency to determine the optimal frequency-raising parameters.

[0047] Figure 1 This is a schematic diagram of the system architecture of the computer device provided in an embodiment of this application. Figure 1 As shown, the computer device includes: a receiving device 101, a processing device 102, and a display device 103.

[0048] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the processing method of seismic data frequency upscaling parameters. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0049] In the specific implementation process, the receiving device 101 can be an input / output interface or a communication interface, and can acquire logging data from multiple wells.

[0050] The processing device 102 can perform seismic forward modeling on the set of seismic and drilling data parameters, adjust the dominant frequency of the forward modeling wavelet, and generate the time difference of the iterative seismic reflection valley wave.

[0051] The display device 103 can be used to display the time difference of the earthquake reflection valley wave after the above iteration.

[0052] The display device can also be a touch screen, used to receive user commands while displaying the above content, so as to realize the operation interaction with the user.

[0053] It should be understood that the above-mentioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0054] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0055] Figure 2 A flowchart illustrating the method for processing frequency-upgrading parameters of seismic data provided in this application. Figure 1 ,like Figure 2 As shown, the method includes:

[0056] S201: Perform data analysis on logging data from multiple wells to generate a set of seismic and drilling data parameters.

[0057] Specifically, data analysis is performed on the logging curves and logging interpretations of multiple wells to generate a set of seismic and drilling data parameters.

[0058] In this embodiment, the set of seismic and drilling data parameters includes, but is not limited to, sandstone velocity, sandstone density, mudstone velocity, average mudstone density, average thickness of the target reservoir, average thickness of the main thick layer closest to the target layer, average distance between the main thick layer and the target reservoir, and the dominant frequency of the original seismic data.

[0059] For example, the average sandstone velocity in the seismic and drilling data parameter set is 2260 m / s, and the average sandstone density is 2.11 g / L. The average velocity of the mudstone is 2480 m / s, and the average density of the mudstone is 2.27 g / L. The average thickness of the target reservoir is 5 meters, the average thickness of the main thick layer closest to the target layer is 10 meters, the average thickness of the mudstone interlayer between the main thick layer and the target reservoir is 8 meters, and the main frequency of the original seismic data is 35 Hz.

[0060] S202: Perform seismic forward modeling on the seismic and drilling data parameter sets based on the forward modeling model, and generate forward modeling charts.

[0061] Specifically, the dominant frequency of the forward modeling wavelet is set, the thickness parameter of the main sandstone layer is set to a fixed value, seismic forward modeling is performed based on multiple sets of interlayer mudstone thickness parameters, the target sandstone thickness parameters, and the dominant frequency of the forward modeling wavelet, the forward modeling results are phase-shifted, and the reservoir inversion results are plotted to generate a forward modeling chart.

[0062] S203: Determine the time difference of seismic reflection valley waves based on the forward modeling chart.

[0063] Specifically, the locations of the seismic reflection valley waves corresponding to the main sandstone layer and the target sandstone layer are identified and compared. If the valley wave locations corresponding to the main layer and the target layer are separated in the comparison results, the time difference between the valley waves of the main layer and the target layer is calculated to obtain the seismic reflection valley wave time difference.

[0064] S204: If the time difference of the earthquake reflection valley wave does not meet the preset identification conditions, the dominant frequency of the forward modeling wavelet is adjusted, and the adjusted dominant frequency is subjected to iterative earthquake forward modeling simulation to generate the iterative earthquake reflection valley wave time difference.

[0065] Specifically, if the time difference of the seismic reflection valley wave is zero, the preset identification conditions are not met. The dominant frequency of the forward wavelet is adjusted, and an iterative seismic forward modeling simulation is performed based on the adjusted forward wavelet to generate the iterative seismic reflection valley wave time difference.

[0066] S205: Based on the time difference of the seismic reflection valley wave after iteration, determine the frequency-upgrading parameters after iteration. The frequency-upgrading parameters after iteration are used for reservoir identification and characterization.

[0067] Specifically, when the iterative simulation effectively separates the reflection valley wave of the target sandstone layer from the reflection of the main layer, the dominant wavelet frequency value is recorded. The recorded dominant wavelet frequency value is the optimal frequency-raising parameter, and the optimal frequency-raising parameter is used to identify and characterize the thin reservoir.

[0068] As can be seen from the above embodiments, by analyzing drilling data to construct a parameter set, performing seismic forward modeling to generate a forward modeling chart, and iteratively adjusting the dominant frequency of the forward modeling wavelet based on whether the time difference of the seismic reflection valley wave meets the identification conditions, the optimal frequency-raising parameters are finally determined. This solves the problems of insufficient seismic data resolution and reliance on experience in the selection of frequency-raising parameters in thin reservoir identification. Through the iterative optimization mechanism, the accurate matching of frequency-raising parameters with the target reservoir characteristics is achieved, and the accuracy of parameter frequency enhancement is improved.

[0069] In one embodiment of this application, step S202 includes:

[0070] S2021: Set the dominant frequency of the forward wavelet based on the dominant frequency of the seismic data in the seismic and drilling data parameter set.

[0071] In this embodiment, the Rick wavelet is used as the forward wavelet, and the dominant frequency of the forward wavelet is set to be consistent with the dominant frequency of the original seismic data.

[0072] For example, the dominant frequency of the forward wavelet is set to 35Hz, the dominant frequency of the original seismic data.

[0073] S2022: Set the thickness parameter of the main sandstone layer in the forward model to a fixed value, perform seismic forward modeling based on multiple sets of interlayer mudstone thickness parameters, the target sandstone layer thickness parameters, and the dominant frequency of the forward modeling wavelet, and generate forward modeling results.

[0074] Specifically, parameters from the seismic and drilling data parameter sets are used to fill the velocity and density parameters of sandstone and mudstone. The thickness of the main sandstone layer is fixed, while the thickness of the interlayer mudstone and the target sandstone layer are changed respectively. Seismic forward modeling is performed using the Rike wavelet, which is consistent with the dominant frequency of the original seismic data, based on the convolution model to generate forward modeling results.

[0075] For example, the average thickness of the target reservoir is denoted as h1, the average thickness of the main thick layer closest to the target layer is denoted as h2, and the average distance between the main thick layer and the target reservoir is denoted as h3. During seismic forward modeling, the thickness of the main layer is fixed at h2, the thickness of the target layer varies from [1 to 2×h1], and the thickness of the mudstone interlayer between the main layer and the target layer varies from [1 to 2×h3].

[0076] S2023: Perform phase shift processing on the forward modeling results to obtain the reservoir inversion results.

[0077] Figure 3 This is a schematic diagram showing the overlay of the one-dimensional model of oilfield A and the phase shift results of the seismic forward modeling waveform provided in the embodiments of this application.

[0078] like Figure 3 As shown, with the thickness of the main sandstone layer fixed at 10 meters, the thicknesses of the interbedded mudstone and the target sandstone layer were varied. Seismic forward modeling was performed using a 35Hz dominant frequency Ricker wavelet based on a convolution model, and the forward modeling results were processed with a seismic -90° phase shift as the reservoir inversion results. The diagram shows a one-dimensional model with a main sandstone layer thickness of 10 meters, an interbedded mudstone thickness of 10 meters, and a target sandstone layer thickness of 5 meters, along with its seismic forward modeling at -90°.

[0079] S2024: Based on the reservoir inversion results, plot the map and generate the forward modeling map.

[0080] Specifically, the time difference of seismic reflection valley waves corresponding to the main layer and the target layer in the reservoir inversion results is statistically analyzed. The larger the time difference of seismic reflection valley waves, the easier it is to identify the target layer sandstone.

[0081] For example, if the main layer and the target layer correspond to the same valley wave, then the time difference of the seismic reflection valley wave is recorded as 0. When the time difference of the seismic reflection valley wave decreases to 0, it means that the target layer sandstone cannot be identified.

[0082] Specifically, using the thickness of the interlayered mudstone as the vertical axis and the thickness of the target layer sandstone as the horizontal axis, color values ​​are used to reflect the changes in the time difference of seismic reflection valley waves to create a forward modeling chart.

[0083] In this embodiment, the color value of any point in the chart represents the seismic reflection valley time difference of the reservoir inversion result corresponding to the combination of the current interlayer mudstone thickness and the target layer sandstone thickness. The zero line of the seismic reflection valley time difference in the chart represents the seismically identifiable reservoir thickness limit corresponding to different mudstone interlayer thicknesses under the background of the current main layer thickness.

[0084] Figure 4 Schematic diagram of forward modeling provided for embodiments of this application Figure 1 .

[0085] like Figure 4 The image shows the seismic forward modeling results under the geological conditions of Oilfield A (interlayer mudstone thickness of 8 meters and target sandstone thickness of 5 meters). The corresponding seismic reflection valley wave time difference is 0, indicating that the target sandstone layer cannot be identified based on the current seismic data.

[0086] As can be seen from the above embodiments, by fixing the thickness of the main layer, changing the parameters of the interlayer and the target layer, performing multiple sets of forward modeling, and performing phase shift processing and plotting on the results, a standardized and visualized analytical basis is provided for forward modeling simulation and valley time difference analysis. This makes subsequent valley identification and parameter adjustment based on evidence, and improves the operability of the method and the interpretability of the results.

[0087] Figure 5 A flowchart illustrating the method for processing frequency-upgrading parameters of seismic data provided in this application. Figure 2 ,like Figure 5 As shown, before step S202, the following steps are also included:

[0088] S301: Create the layered structure of the forward model, which includes the main layer sandstone, interbedded mudstone, and target layer sandstone.

[0089] In this embodiment, the forward model consists of the following layers from top to bottom: the main sandstone layer, the interbedded mudstone layer, and the target sandstone layer. Each layer is assigned four variable parameters: thickness, P-wave velocity, S-wave velocity, and density.

[0090] S302: Obtain velocity and density parameters of sandstone and mudstone from the seismic and drilling data parameter set.

[0091] Specifically, elastic parameters of sandstone and mudstone are extracted from seismic and drilling data parameter sets. By statistically analyzing the logging interpretation results of multiple wells, the average P-wave velocity and average density of sandstone and mudstone are determined. The determined parameters are used as the basic rock physics parameters of the forward model.

[0092] S303: Data filling is performed on the layered structure of the forward model based on the velocity and density parameters of sandstone and mudstone to obtain the forward model.

[0093] Specifically, the sandstone parameters are assigned to the main sandstone layer and the target sandstone layer, and the mudstone parameters are assigned to the interlayer mudstone to obtain the forward model.

[0094] As can be seen from the above embodiments, by creating a layered structure that includes the main sandstone layer, interlayered mudstone and target sandstone layer, and filling it with velocity and density parameters from actual drilling data, the forward modeling results can be ensured to truly reflect the actual seismic response characteristics of the underground reservoir, avoiding parameter deviations caused by model distortion.

[0095] In one embodiment of this application, step S203 includes:

[0096] S2031: Identify the seismic reflection valley positions of the main sandstone layer and the target sandstone layer based on the reservoir inversion results corresponding to each thickness combination in the forward modeling plot.

[0097] Specifically, the time difference of seismic reflection valley waves corresponding to the main layer and the target layer in the reservoir inversion results is statistically analyzed. If the main layer and the target layer correspond to the same valley wave, the time difference of seismic reflection valley waves is recorded as 0.

[0098] S2032: Compare the seismic reflection valley positions corresponding to the main sandstone layer with the seismic reflection valley positions corresponding to the target sandstone layer to generate comparison results.

[0099] In this embodiment, a larger time difference between seismic reflection valley waves indicates that the target sandstone layer is easier to identify, while a time difference between seismic reflection valley waves decreasing to 0 indicates that the target sandstone layer cannot be identified.

[0100] S2033: If the valley wave positions corresponding to the main layer and the target layer are separated in the comparison results, calculate the time difference between the valley waves of the main layer and the target layer to obtain the earthquake reflection valley wave time difference.

[0101] Specifically, based on the forward modeling chart, the horizontal axis is determined to be the thickness of the target sandstone layer, and the vertical axis is the location of the point corresponding to the thickness of the interlayer mudstone, and the valley wave time difference is calculated.

[0102] As can be seen from the above embodiments, by identifying and comparing the valley positions corresponding to the main layer and the target layer, and calculating the time difference when the two are separated, the key parameter extraction of the seismic response characteristics of thin reservoirs is realized. The valley time difference is used as a quantitative indicator reflecting the vertical stacking relationship of the reservoir, providing a data basis for judging whether the current seismic data frequency is sufficient to distinguish thin reservoirs.

[0103] In one embodiment of this application, step S204 includes:

[0104] S2041: Determine whether the time difference of the earthquake reflection valley wave is zero.

[0105] In this embodiment, when the seismic reflection valley wave time difference of the inversion result is 0, it indicates that the target layer of the current seismic data is unidentifiable.

[0106] S2042: If the time difference of the earthquake reflection valley wave is zero, the preset identification condition is not met. Adjust the dominant frequency of the forward wavelet to obtain the adjusted forward wavelet.

[0107] Specifically, if the preset identification conditions are not met, a higher dominant frequency Rick wavelet is used for seismic forward modeling until the time difference of the seismic reflection valley wave in the reservoir inversion result at that point is not zero.

[0108] S2043: Perform iterative seismic forward modeling based on the adjusted forward wavelet to generate the iterative seismic reflection valley wave time difference.

[0109] Figure 6 Schematic diagram of forward modeling provided for embodiments of this application Figure 2 .

[0110] Figure 7 Schematic diagram of forward modeling provided for embodiments of this application Figure 3 .

[0111] like Figure 6 As shown, seismic forward modeling was performed using the 50Hz main frequency Rick wavelet and the forward modeling chart was updated. Under the geological conditions of Oilfield A (the thickness of the interlayer mudstone is 8 meters and the thickness of the target sandstone layer is 5 meters), the time difference of the seismic reflection valley wave is 4 milliseconds.

[0112] like Figure 7 As shown, seismic forward modeling was performed using the Rick wavelet with a main frequency of 60Hz. The forward modeling chart was updated. Under the geological conditions of Oilfield A (interlayer mudstone thickness of 8 meters and target sandstone thickness of 5 meters), the time difference of seismic reflection valley waves was 11 milliseconds.

[0113] As can be seen from the above embodiments, the identification condition is determined by judging whether the valley time difference is zero, and the main frequency is adjusted and re-simulated when it is not met. This ensures that the adjustment direction of the frequency-boosting parameters is to improve the vertical resolution of thin reservoirs. By gradually approaching the optimal parameters through closed-loop iteration, the accuracy of the frequency-boosting parameters is improved.

[0114] Figure 8 A flowchart illustrating the method for processing frequency-upgrading parameters of seismic data provided in this application. Figure 3 ,like Figure 8 As shown, after step S205, the following steps are also included:

[0115] S401: The original seismic data is frequency-upgraded based on the frequency-upgraded parameters after iteration to obtain the frequency-upgraded seismic data.

[0116] Specifically, the determined frequency upsampling parameters are used as the target dominant frequency. The original three-dimensional seismic data of the study area are deconvolved to improve the resolution of the data, so that the dominant frequency of the processed seismic data is close to 50Hz, thus obtaining the frequency-upgraded seismic data.

[0117] S402: Based on the zero-value line of the seismic reflection valley wave time difference in the forward modeling chart, divide the reservoir identifiable thickness range under multiple mudstone interlayer thicknesses.

[0118] Specifically, the critical curve (i.e., the zero-value line) where the time difference between the valley wave of the main layer and the target layer is zero is obtained on the forward modeling chart of the final iteration. Based on the zero-value line, the reservoir identifiable thickness ranges corresponding to different interlayer mudstone thickness ranges are divided.

[0119] In this embodiment, the zero-value line represents the minimum thickness threshold at which the target layer of sandstone can be distinguished under different interlayer mudstone thicknesses.

[0120] S403: Based on the frequency-upgraded seismic data and the identifiable thickness range of the reservoir, the planar distribution of the reservoir is characterized, and the processing results are generated.

[0121] Specifically, on the frequency-upgraded seismic data volume, seismic attributes are extracted along the target layer. Based on the defined identifiable thickness intervals, the thickness of the target layer sandstone is quantitatively predicted, and its planar distribution map is drawn to generate the processing results.

[0122] S404: Adjust the frequency boosting parameters based on the processing results.

[0123] Specifically, the predicted reservoir thickness is compared with the measured thickness at known well points. If the error is large, it indicates that the frequency-boosting parameters may be too high or too low, and the target frequency for frequency boosting needs to be fine-tuned based on the comparison results.

[0124] As can be seen from the above embodiments, by applying frequency-upgrading parameters to process seismic data, combining the zero-value lines in the forward model to divide the identifiable thickness range, thin reservoir planar distribution is characterized, and parameters are adjusted in reverse according to the results, thus realizing a closed loop from "parameter optimization" to "practical application" and then to "feedback optimization," providing data support for continuous parameter improvement.

[0125] Figure 9 A schematic diagram of the processing device for seismic data frequency upscaling parameters provided in this application is shown below. Figure 9 As shown, the seismic data frequency upscaling parameter processing device 90 provided in this embodiment includes: a data parsing module 901, a seismic forward modeling module 902, a first determination module 903, a first adjustment module 904, and a second determination module 905.

[0126] The data parsing module 901 is used to parse logging data from multiple wells and generate seismic and drilling data parameter sets.

[0127] The seismic forward modeling module 902 is used to perform seismic forward modeling simulations on the parameter sets of seismic and drilling data based on the forward modeling model, and generate forward modeling charts.

[0128] The first determining module 903 is used to determine the time difference of seismic reflection valley waves based on the forward modeling plot.

[0129] The first adjustment module 904 is used to adjust the dominant frequency of the forward wavelet if the time difference of the seismic reflection valley wave does not meet the preset identification conditions, and to perform iterative seismic forward modeling on the adjusted dominant frequency to generate the iterative seismic reflection valley wave time difference.

[0130] The second determining module 905 is used to determine the frequency-raising parameters after iteration based on the time difference of the seismic reflection valley wave after iteration. The frequency-raising parameters after iteration are used for reservoir identification and characterization.

[0131] In one embodiment of this application, the seismic forward modeling module 902 includes:

[0132] The first setting unit is used to set the dominant frequency of the forward wavelet based on the dominant frequency of the seismic data in the seismic and drilling data parameter set.

[0133] The second setting unit is used to set the thickness parameters of the main sandstone layer in the forward model to a fixed value, and to perform seismic forward modeling based on multiple sets of interlayer mudstone thickness parameters, the target sandstone layer thickness parameters, and the dominant frequency of the forward modeling wavelet to generate forward modeling results.

[0134] Phase-shifting units are used to perform phase-shifting processing on forward modeling results to obtain reservoir inversion results.

[0135] The drawing unit is used to draw plots based on the reservoir inversion results and generate forward modeling plots.

[0136] In one embodiment of this application, the seismic data frequency upscaling parameter processing apparatus 90 further includes:

[0137] The module is used to create the layered structure of the forward model, which includes the main layer sandstone, the interlayered mudstone, and the target layer sandstone.

[0138] The acquisition module is used to acquire the velocity and density parameters of sandstone and mudstone from the seismic and drilling data parameter sets.

[0139] The data filling module is used to fill the layered structure of the forward model with data based on the velocity and density parameters of sandstone and mudstone, so as to obtain the forward model.

[0140] In one embodiment of this application, the first determining module 903 includes:

[0141] The identification unit is used to identify the seismic reflection valley positions of the main sandstone layer and the target sandstone layer based on the reservoir inversion results corresponding to each thickness combination in the forward modeling chart.

[0142] The comparison unit is used to compare the seismic reflection valley wave positions corresponding to the main sandstone layer with the seismic reflection valley wave positions corresponding to the target sandstone layer, and generate comparison results.

[0143] The calculation unit is used to calculate the time difference between the valley waves of the main layer and the target layer if the valley wave positions of the main layer and the target layer are separated in the comparison results, so as to obtain the time difference of the seismic reflection valley wave.

[0144] In one embodiment of this application, the first adjustment module 904 includes:

[0145] The judgment unit is used to determine whether the time difference of the earthquake reflection valley wave is zero.

[0146] The adjustment unit is used to adjust the dominant frequency of the forward wavelet if the time difference of the earthquake reflection valley wave is zero, which does not meet the preset identification conditions, so as to obtain the adjusted forward wavelet.

[0147] The forward modeling unit is used to perform iterative seismic forward modeling based on the adjusted forward wavelet, generating the iterative seismic reflection valley wave time difference.

[0148] In one embodiment of this application, the seismic data frequency upscaling parameter processing apparatus 90 further includes:

[0149] The frequency upscaling module is used to upscale the original seismic data based on the iterative frequency upscaling parameters to obtain upscaled seismic data.

[0150] The segmentation module is used to segment the reservoir identifiable thickness ranges under multiple mudstone interlayer thicknesses based on the zero-value line of the seismic reflection valley wave time difference in the forward modeling plot.

[0151] The distribution characterization module is used to perform planar distribution characterization of the reservoir based on the frequency-upgraded seismic data and the identifiable thickness range of the reservoir, and generate the processing results.

[0152] The second adjustment module is used to adjust the frequency boosting parameters based on the processing results.

[0153] The seismic data frequency upscaling parameter processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0154] Figure 10 A schematic diagram of the structure of the electronic device provided in this application. Figure 10 As shown, the electronic device 100 provided in this embodiment includes at least one processor 1001 and a memory 1002. Optionally, the electronic device 100 further includes a communication component 1003. The processor 1001, memory 1002, and communication component 1003 are connected via a bus.

[0155] In the specific implementation process, at least one processor 1001 executes computer execution instructions stored in memory 1002, causing at least one processor 1001 to execute the above-mentioned method for processing earthquake data frequency upscaling parameters.

[0156] The specific implementation process of processor 1001 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0157] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0158] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0159] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0160] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for processing earthquake data frequency upscaling parameters.

[0161] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for processing earthquake data frequency upscaling parameters.

[0162] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0163] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0164] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0167] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0168] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0169] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for processing frequency upscaling parameters of seismic data, characterized in that, include: Data analysis is performed on logging data from multiple wells to generate seismic and drilling data parameter sets; Seismic forward modeling is performed on the earthquake and drilling data parameter sets based on the forward modeling model to generate forward modeling charts; Determine the time difference of seismic reflection valley waves based on the aforementioned forward modeling chart; If the earthquake reflection valley wave time difference does not meet the preset identification conditions, the dominant frequency of the forward wavelet is adjusted, and an iterative earthquake forward modeling simulation is performed on the adjusted dominant frequency to generate the iterative earthquake reflection valley wave time difference. Based on the time difference of the seismic reflection valley wave after the iteration, the frequency-upgrading parameters after the iteration are determined, and the frequency-upgrading parameters after the iteration are used for reservoir identification and characterization.

2. The method according to claim 1, characterized in that, The step of performing seismic forward modeling on the seismic and drilling data parameter sets based on the forward modeling model to generate forward modeling charts includes: The dominant frequency of the forward-modeling wavelet is set based on the dominant frequency of the seismic data in the aforementioned seismic and drilling data parameter set; The thickness parameter of the main sandstone layer in the forward model is set to a fixed value. Seismic forward modeling is performed based on multiple sets of interlayer mudstone thickness parameters, the target sandstone layer thickness parameters, and the dominant frequency of the forward modeling wavelet to generate forward modeling results. The forward modeling results are phase-shifted to obtain the reservoir inversion results; Based on the reservoir inversion results, plots are drawn to generate forward modeling plots.

3. The method according to claim 1, characterized in that, Before performing seismic forward modeling on the seismic and drilling data parameter sets based on the forward model, the method further includes: Create a layered structure for the forward model, wherein the layered structure of the forward model includes the main layer sandstone, the interlayered mudstone and the target layer sandstone; Obtain velocity and density parameters of sandstone and mudstone from seismic and drilling data parameter sets; The forward model is obtained by filling in the layered structure of the forward model with data based on the velocity and density parameters of the sandstone and mudstone.

4. The method according to claim 1, characterized in that, The determination of the seismic reflection valley wave time difference based on the forward modeling chart includes: Based on the reservoir inversion results corresponding to each thickness combination in the forward modeling chart, identify the seismic reflection valley positions corresponding to the main sandstone layer and the target sandstone layer. The seismic reflection valley wave positions corresponding to the main sandstone layer are compared with the seismic reflection valley wave positions corresponding to the target sandstone layer to generate comparison results. If the valley wave positions corresponding to the main layer and the target layer are separated in the comparison results, the time difference between the valley waves of the main layer and the target layer is calculated to obtain the earthquake reflection valley wave time difference.

5. The method according to claim 1, characterized in that, If the seismic reflection valley time difference does not meet the preset identification conditions, the dominant frequency of the forward modeling wavelet is adjusted, and iterative seismic forward modeling is performed on the adjusted dominant frequency to generate the iterative seismic reflection valley time difference, including: Determine if the time difference of earthquake reflection valley waves is zero; If the time difference of the earthquake reflection valley wave is zero, the preset identification condition is not met. The dominant frequency of the forward wavelet is adjusted to obtain the adjusted forward wavelet. Iterative seismic forward modeling is performed based on the adjusted forward wavelet to generate the iterative seismic reflection valley wave time difference.

6. The method according to any one of claims 1 to 5, characterized in that, After determining the iterative frequency-raising parameters based on the iteratively determined seismic reflection valley wave time difference, the method further includes: The original seismic data is frequency-upgraded based on the iterated frequency-upgraded parameters to obtain the frequency-upgraded seismic data. Based on the zero-value line of seismic reflection valley wave time difference in the forward modeling plate, the reservoir identifiable thickness ranges under multiple mudstone interlayer thicknesses are divided. Based on the frequency-upgraded seismic data and the identifiable thickness range of the reservoir, the planar distribution of the reservoir is characterized, and the processing result is generated. Adjust the frequency boosting parameters based on the processing results.

7. A processing device for seismic data frequency upscaling parameters, characterized in that, include: The data parsing module is used to parse logging data from multiple wells and generate seismic and drilling data parameter sets. The seismic forward modeling module is used to perform seismic forward modeling on the seismic and drilling data parameter sets based on the forward modeling model, and generate forward modeling charts. The first determining module is used to determine the time difference of seismic reflection valley waves based on the forward modeling chart; The first adjustment module is used to adjust the dominant frequency of the forward wavelet if the earthquake reflection valley wave time difference does not meet the preset identification conditions, and to perform iterative earthquake forward modeling simulation on the adjusted dominant frequency to generate the iterative earthquake reflection valley wave time difference. The second determining module is used to determine the iterative frequency-raising parameters based on the iterative seismic reflection valley wave time difference. The iterative frequency-raising parameters are used for reservoir identification and characterization.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the processing method for seismic data frequency upscaling parameters as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for processing seismic data frequency upscaling parameters as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The method includes a computer program that, when executed by a processor, implements the method for processing seismic data frequency upscaling parameters as described in any one of claims 1 to 6.

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