Method and device for evaluating fidelity of time domain seismic processing post-stack data

Abstract

The invention relates to the technical field of oil and gas field seismic data processing, in particular to a fidelity evaluation method and device for time domain seismic processing post-stack data, and the method comprises the steps: carrying out the waveform cross-correlation analysis of a seismic synthesis record of a target well in a research area and seismic data around the target well, so as to generate a cross-correlation coefficient; matching the seismic synthesis record with a wave peak amplitude value and / or a wave trough amplitude value of the seismic data around the target well to generate an amplitude energy matching degree; and evaluating the fidelity of the time domain seismic processing post-stack data according to the cross correlation coefficient and the amplitude energy matching degree. According to the method, the objectivity of seismic data processing result data evaluation can be improved, the evaluation efficiency and precision are improved, and a scientific basis is provided for subsequent exploration decision making.

Description

Technical Field

[0001] This disclosure relates to the field of seismic exploration technology for oil and gas fields, and in particular to a method, apparatus, equipment, storage medium, and computer program for evaluating the fidelity of time-domain seismic processing post-stack data. Background Technology

[0002] Seismic exploration is the most important geophysical exploration method for mineral resource exploration such as oil, natural gas, natural gas hydrates and coal, geological engineering surveys, regional geological surveys and deep crustal research. It mainly utilizes the propagation and variation of seismic wave fields caused by artificial excitation in the underground medium to infer the distribution of underground strata, structural morphology, sedimentation, lithofacies changes and oil and gas conditions.

[0003] Post-stack time-domain seismic data is a type of processed data volume that maintains relative amplitude after pre-stack or post-stack time migration of seismic data (usually referring to pure wave data volume without post-stack filtering and amplitude equalization). It is the most widely used and important type of data for subsequent sedimentary interpretation, seismic attribute analysis, and seismic inversion. The "three highs" (high signal-to-noise ratio, high resolution, and high fidelity) of post-stack seismic data have always been the goal pursued in seismic exploration. Currently, there are corresponding estimation methods and parameter indicators for evaluating the signal-to-noise ratio and resolution of post-stack data; however, there is a lack of necessary methods and parameter indicators for quantitatively evaluating the "fidelity" of post-stack data. Summary of the Invention

[0004] This disclosure provides a method, apparatus, equipment, storage medium, and computer program for evaluating the fidelity of post-stack data in time-domain seismic processing. It offers a quantitative evaluation method for seismic fidelity, enabling quality control during seismic data processing, final acceptance of processing results, and assessment of the reliability and usability of seismic processing data before interpretation. This improves the objectivity of seismic data processing result evaluation, increases evaluation efficiency and accuracy, and provides a scientific basis for subsequent exploration decisions.

[0005] Firstly, this disclosure provides a method for evaluating the fidelity of post-stack time-domain seismic processing data, including:

[0006] Waveform cross-correlation analysis was performed on the seismic synthetic records of the target well in the study area and the seismic data around the target well to generate cross-correlation coefficients;

[0007] The peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record are matched with the seismic data around the target well to generate an amplitude energy matching degree.

[0008] The fidelity of the time-domain seismic processing post-stack data is evaluated based on the cross-correlation coefficient and the amplitude energy matching degree.

[0009] In some embodiments of this disclosure, waveform cross-correlation analysis is performed on the seismic synthetic record of the target well within the study area and the seismic data surrounding the target well to generate cross-correlation coefficients, including:

[0010] Extract wavelet from the seismic data around the target well and determine the length of the wavelet;

[0011] A sliding time window is generated based on the length of the wavelet;

[0012] Within the sliding time window, the waveform similarity between the peaks of the seismic composite record and the peaks of the seismic data around the target well is determined, and / or the waveform similarity between the troughs of the seismic composite record and the troughs of the seismic data around the target well is determined;

[0013] The cross-correlation coefficient is generated based on the waveform similarity between the peaks of the seismic composite record and the peaks of the seismic data surrounding the target well, and / or the waveform similarity between the troughs of the seismic composite record and the troughs of the seismic data surrounding the target well.

[0014] In some embodiments of this disclosure, a method for evaluating the fidelity of post-stack time-domain seismic processing data further includes:

[0015] The seismic composite record is generated based on the sonic logging data, density logging data, and seismic data of the target well.

[0016] In some embodiments of this disclosure, a method for evaluating the fidelity of post-stack time-domain seismic processing data further includes:

[0017] The peak amplitude values ​​and / or trough amplitude values ​​of the earthquake composite record are normalized;

[0018] The peak amplitude and / or trough amplitude values ​​of the seismic data around the target well are normalized.

[0019] In some embodiments of this disclosure, matching the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record with the seismic data surrounding the target well to generate an amplitude-energy matching degree includes:

[0020] The amplitude energy matching degree is generated based on the normalized peak amplitude and / or trough amplitude values ​​of the seismic synthetic record corresponding to the current earthquake time, and the normalized peak amplitude and / or trough amplitude values ​​of the seismic data around the target well corresponding to the current earthquake time.

[0021] In some embodiments of this disclosure, evaluating the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree includes:

[0022] The fidelity evaluation parameters for the time-domain seismic processing post-stack data are generated based on the cross-correlation coefficient and the amplitude energy matching degree.

[0023] The fidelity of the time-domain seismic processing post-stack data is evaluated based on the fidelity evaluation parameters and preset evaluation rules.

[0024] Secondly, this disclosure provides a fidelity evaluation device for post-stack data of time-domain seismic processing, comprising:

[0025] The cross-correlation generation module is used to perform waveform cross-correlation analysis on the seismic synthetic records of the target well in the study area and the seismic data around the target well to generate cross-correlation numbers.

[0026] An amplitude energy matching degree generation module is used to match the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record with the seismic data around the target well to generate an amplitude energy matching degree.

[0027] The fidelity evaluation module is used to evaluate the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree.

[0028] In some embodiments of this disclosure, the cross-correlation coefficient generation module includes:

[0029] A wavelet extraction unit is used to extract wavelets from the seismic data around the target well and determine the length of the wavelets;

[0030] A sliding time window generation unit is used to generate a sliding time window based on the length of the wavelet;

[0031] A waveform similarity determination unit is used to determine, within the sliding time window, the waveform similarity between the peaks of the seismic synthetic record and the peaks of the seismic data around the target well, and / or the waveform similarity between the troughs of the seismic synthetic record and the troughs of the seismic data around the target well;

[0032] The cross-correlation generation unit is used to generate the cross-correlation number based on the waveform similarity between the peaks of the seismic synthetic record and the peaks of the seismic data around the target well, and / or the waveform similarity between the troughs of the seismic synthetic record and the troughs of the seismic data around the target well.

[0033] In some embodiments of this disclosure, a fidelity evaluation device for post-stack time-domain seismic processing data further includes:

[0034] The synthetic record generation module is used to generate the seismic synthetic record based on the sonic logging data, density logging data and seismic data of the target well.

[0035] In some embodiments of this disclosure, a fidelity evaluation device for post-stack time-domain seismic processing data further includes:

[0036] The synthetic amplitude normalization module is used to normalize the peak amplitude values ​​and / or trough amplitude values ​​of the synthetic seismic record.

[0037] The seismic amplitude normalization module is used to normalize the peak amplitude and / or trough amplitude values ​​of the seismic data around the target well.

[0038] In some embodiments of this disclosure, the amplitude energy matching degree generation module includes:

[0039] The amplitude energy matching degree generation unit is used to generate the amplitude energy matching degree based on the normalized peak amplitude value and / or trough amplitude value of the seismic synthetic record corresponding to the current earthquake time, and the normalized peak amplitude value and / or trough amplitude value of the seismic data around the target well corresponding to the current earthquake time.

[0040] In some embodiments of this disclosure, the fidelity evaluation module includes:

[0041] The fidelity evaluation parameter generation unit is used to generate fidelity evaluation parameters for the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree.

[0042] The fidelity evaluation unit is used to evaluate the fidelity of the time-domain seismic processing post-stack data according to the fidelity evaluation parameters and preset evaluation rules.

[0043] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.

[0044] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0045] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0046] This disclosure provides a method, apparatus, device, storage medium, and computer program for evaluating the fidelity of time-domain seismic processed post-stack data. The corresponding method for evaluating the fidelity of time-domain seismic processed post-stack data includes: first, performing waveform cross-correlation analysis on the seismic synthetic record of the target well in the study area and the seismic data around the target well to generate a cross-correlation coefficient; then, matching the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record and the seismic data around the target well to generate an amplitude-energy matching degree; finally, evaluating the fidelity of the time-domain seismic processed post-stack data based on the cross-correlation coefficient and the amplitude-energy matching degree.

[0047] This invention addresses the current situation where the fidelity evaluation of post-stack seismic data relies primarily on visual assessment and qualitative methods. It provides a quantitative evaluation method for seismic fidelity, enabling quality control during seismic data processing, final acceptance of processed results, and assessment of the reliability and usability of seismic data before interpretation. This significantly improves the objectivity of seismic data processing data evaluation, increases evaluation efficiency and accuracy, and provides a scientific basis for subsequent exploration decisions. Attached Figure Description

[0048] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0049] Figure 1 This is a flowchart illustrating a method for evaluating the fidelity of post-stack data in time-domain seismic processing, as provided in an embodiment of this disclosure.

[0050] Figure 2 This is a flowchart illustrating step 100 of a method for evaluating the fidelity of time-domain seismic processing post-stack data provided in an embodiment of this disclosure.

[0051] Figure 3 This is another flowchart illustrating a method for evaluating the fidelity of post-stack data in time-domain seismic processing, as provided in an embodiment of this disclosure.

[0052] Figure 4 This is a schematic diagram of a third method for evaluating the fidelity of time-domain seismic processing post-stack data, provided in an embodiment of this disclosure.

[0053] Figure 5 This is a flowchart illustrating step 200 of a method for evaluating the fidelity of time-domain seismic processing post-stack data provided in an embodiment of this disclosure.

[0054] Figure 6 This is a flowchart illustrating step 300 of a method for evaluating the fidelity of post-stack data in time-domain seismic processing, as provided in an embodiment of this disclosure.

[0055] Figure 7A schematic diagram of the structure of a fidelity evaluation system for time-domain seismic processing post-stack data provided as an application example of this disclosure.

[0056] Figure 8 A technical flowchart illustrating a method for evaluating the fidelity of post-stack data in time-domain seismic processing, provided as an application example of this disclosure.

[0057] Figure 9 This is a flowchart illustrating a method for evaluating the fidelity of post-stack data in time-domain seismic processing, provided as an application example of this disclosure.

[0058] Figure 10 A schematic diagram of the calculation results provided for the application example of this disclosure.

[0059] Figure 11 The image shows the results of a time-domain seismic processing post-stack data fidelity evaluation method, provided as an application example of this disclosure, being tested in a western region.

[0060] Figure 12 A block diagram of a device for evaluating the fidelity of time-domain seismic processing post-stack data provided in an embodiment of this disclosure.

[0061] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0062] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0063] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0064] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0065] The fidelity of post-stack seismic data depends primarily on two stages: seismic acquisition and seismic processing. High fidelity is achieved in acquisition if all valid wavefield propagation signals are received and recorded accurately and without loss during the acquisition phase. In seismic data processing, high fidelity is achieved if each processing step maintains undistorted and unlost information, and if the relative amplitude (energy) relationship of valid reflected signals and the lateral consistency of waveform characteristics remain unchanged throughout the process. Reflected wave groups on the final post-stack pure wave profile obtained after seismic acquisition and processing correspond to the subsurface strata impedance interfaces, and the relative dynamic characteristics of seismic signals between points on the final post-stack profile are well preserved. This is considered to represent relatively high-fidelity seismic data processing (Wang Xueqiu, 2002).

[0066] There has been considerable research in China regarding the consideration and evaluation of fidelity (or "maintaining relative amplitude") in seismic data processing. For example, Wang Huazhong et al. (2008), Guo Shuxiang et al. (2009), Rui Yongjun (2011), Wang Jun et al. (2012), and Jiang Mingchuan (2014) have summarized the criteria and methods for evaluating amplitude preservation before and after different processing stages in seismic data processing. These mainly include: ① subtraction method, ② amplitude curve comparison method, ③ wavelet consistency correlation analysis method, ④ time-frequency analysis method, ⑤ plane analysis of seismic attributes along layers, ⑥ slice analysis method, ⑦ synthetic record coherence method, and ⑧ pre-stack gather AVO attribute analysis method. Through testing with forward modeling data and actual seismic data, they analyzed and evaluated the amplitude preservation performance of commonly used processing modules such as amplitude compensation, pre-stack denoising, deconvolution, and migration in seismic data processing. Although some of these methods involve evaluating the relative amplitude preservation of the stacked profiles obtained at different processing stages, they are limited to comparing and qualitatively evaluating the stacked profiles obtained before and after the application of different processing modules, and do not provide a specific method for quantitatively evaluating the fidelity of post-stack seismic data.

[0067] Example One

[0068] For the reasons mentioned above, this disclosure provides a method for evaluating the fidelity of post-stack data in time-domain seismic processing. Figure 1 This is a flowchart illustrating a method for evaluating the fidelity of post-stack time-domain seismic processing data, provided in an embodiment of this disclosure. Figure 1 As shown, a method for evaluating the fidelity of post-stack data in time-domain seismic processing includes:

[0069] Step 100: Perform waveform cross-correlation analysis on the seismic synthetic record of the target well in the study area and the seismic data around the target well to generate cross-correlation coefficients;

[0070] Step 200: Match the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record with the seismic data around the target well to generate an amplitude energy matching degree;

[0071] Step 300: Evaluate the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree.

[0072] This disclosure provides a method for evaluating the fidelity of time-domain seismic processing post-stack data, comprising: first, performing waveform cross-correlation analysis on the seismic synthetic record of the target well in the study area and the seismic data around the target well to generate a cross-correlation coefficient; then, matching the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record and the seismic data around the target well to generate an amplitude-energy matching degree; and finally, evaluating the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude-energy matching degree.

[0073] This invention utilizes a comprehensive comparative analysis of waveform and amplitude (or energy) of synthetic seismic records generated by acoustic and density logging and well-side seismic traces to determine the fidelity of post-stack seismic data.

[0074] Example Two

[0075] Regarding step 100, the seismic synthetic record refers to artificial seismic waveform data obtained by simulating the propagation process of seismic waves using a computer. This method can obtain the characteristics of seismic wave propagation in complex geological media through numerical simulation without conducting actual earthquake experiments.

[0076] Additionally, the waveform cross-correlation analysis in step 100 is used to compare the similarity between two waveforms, and it can quantify the similarity between the two waveforms. Specifically:

[0077] Suppose we have two time series signals x(t) and y(t). Calculate the cross-correlation function R(τ) of x(t) and y(t), which is expressed as:

[0078] R(τ)=∫x(t)y(t+τ)dt; where τ is the time delay.

[0079] When τ = 0, R(0) is the correlation coefficient between x(t) and y(t), and its value ranges from -1 to 1. When R(0) = 1, it means that the two waveforms are completely similar; when R(0) = -1, it means that the two waveforms are completely opposite; when R(0) = 0, it means that the two waveforms are completely uncorrelated.

[0080] For step 200, preferably, firstly, the normalized energy sum of the peaks and troughs of the reflected waves of the synthetic seismic record and the actual seismic trace near the well is calculated respectively, and then the amplitude energy matching degree between the peak or trough energy of the synthetic seismic record near the same location and the peak or trough energy of the corresponding actual seismic trace near the well is calculated in turn.

[0081] Regarding step 300, it is understandable that with the increasing demand for exploration of complex mineral deposits such as underground concealed strata lithologic oil and gas reservoirs, shale oil and gas, coalbed methane, and natural gas hydrates, the requirements for seismic exploration fidelity are also becoming increasingly stringent. In particular, the judgment and evaluation of the fidelity of post-stack data obtained from seismic acquisition and processing is especially important. It can be said that it directly determines whether the acquired and processed seismic data can be used for subsequent seismic data interpretation, and whether the seismic interpretation results can support subsequent exploration drilling decisions.

[0082] The "fidelity" of seismic data refers to the accuracy with which it reflects information about subsurface strata. The closer the information interpreted from seismic data is to the actual subsurface conditions, the higher the fidelity. Conversely, if the seismic data does not match the actual subsurface characteristics or exhibits significant distortion or deviation, the fidelity is lower. Essentially, the key to measuring the fidelity of seismic data lies in determining whether there is a positive correlation between the seismic reflection amplitude (or energy) and the reflection coefficient of the subsurface wave impedance interface. The higher this positive correlation, the higher the fidelity of the seismic data.

[0083] Example Three

[0084] Based on the above embodiments, see Figure 2 Step 100 includes:

[0085] Step 101: Extract the wavelet from the seismic data around the target well and determine the length of the wavelet;

[0086] A wavelet, also known as a seismic wavelet, is a signal with a definite start time, finite energy, and a certain duration. It is the basic unit in seismic records. It is generally believed that the seismic wave generated when an earthquake source is excited is only a very short-duration sharp pulse. As the sharp pulse propagates in the viscoelastic medium, the high-frequency components of the sharp pulse decay rapidly, and the waveform grows accordingly, thus forming a seismic wavelet. A seismic wavelet typically has a duration of 2 to 3 phases, about 90 ms, and then propagates underground in the form of a seismic wavelet.

[0087] A wavelet can be defined by its amplitude spectrum and phase spectrum. The phase spectrum can be of the type of zero phase, constant phase, minimum phase, mixed phase, etc. For zero-phase and constant-phase wavelets, they can be simply regarded as a collection of sine waves with different amplitudes and frequencies. All sine waves are zero-phase or constant-phase (such as 90°). In the frequency domain, the wavelet extraction problem consists of two parts: determining the amplitude spectrum and the phase spectrum. Determining the phase spectrum is more difficult and is the main source of error in the inversion.

[0088] Preferably, step 101 can be implemented by the following two methods:

[0089] 1. Use time-frequency analysis techniques (such as short-time Fourier transform, wavelet transform, etc.) to extract wavelet signals of specific frequency bands in seismic waves. This method can separate wavelets with different propagation characteristics.

[0090] 2. Decompose the waveform using characteristic parameters (such as poles and extreme points) to extract individual wavelet segments. This method does not require prior knowledge of the wavelet frequency characteristics.

[0091] Step 102: Generate a sliding time window based on the length of the wavelet;

[0092] Define a fixed-length time window that slides across the time series signal. For each time window, various analyses and processing techniques can be applied to the signal within that window, such as Fourier transform and wavelet transform. By progressively sliding the time window, the characteristics of the entire signal over time can be obtained.

[0093] Specifically, the length of the wavelet can be set to a sliding window, or the sliding window can be set to an hourly window.

[0094] Step 103: Within the sliding time window, determine the waveform similarity between the peaks of the seismic composite record and the peaks of the seismic data around the target well, and / or the waveform similarity between the troughs of the seismic composite record and the troughs of the seismic data around the target well;

[0095] Step 104: Generate the cross-correlation coefficient based on the waveform similarity between the peaks of the seismic synthetic record and the peaks of the seismic data around the target well, and / or the waveform similarity between the troughs of the seismic synthetic record and the troughs of the seismic data around the target well.

[0096] In steps 103 and 104, the waveform similarity between the peaks or troughs of the seismic composite record and the peaks or troughs of the seismic data surrounding the target well is analyzed to obtain the cross-correlation coefficient P between the post-stack wellside seismic data and the waveform of the composite seismic record. W The formula is as follows:

[0097]

[0098] In the formula, S is the synthetic seismic trace, R is the actual seismic trace near the well, and t i Let P be time, and τ be the sliding time window. To determine the waveform correlation for each peak or trough, cross-correlation analysis here uses the wavelet length or a small time window. Clearly, P... w There are positive values ​​and there are negative values.

[0099] Based on the above embodiments, see Figure 3A method for evaluating the fidelity of post-stack data in time-domain seismic processing, further comprising:

[0100] Step 400: Generate the seismic composite record based on the sonic logging data, density logging data, and seismic data of the target well.

[0101] Specifically, the formation reflection coefficient is first calculated based on sonic logging and density logging data; then, seismic wavelets are extracted from the vicinity of the seismic trace near the target well and convolved with the formation reflection coefficient to produce a synthetic seismic record.

[0102] Furthermore, the acoustic impedance of the strata is first calculated according to formula (2):

[0103] AI = ρ × Vp (2)

[0104] Where AI represents acoustic impedance, ρ represents density logging data, and Vp represents acoustic logging data.

[0105] Next, calculate the reflection coefficient R:

[0106]

[0107] AI1 and AI2 are the acoustic impedances of two adjacent strata in the current stratum.

[0108] Based on the above embodiments, see Figure 4 A method for evaluating the fidelity of post-stack data in time-domain seismic processing, further comprising:

[0109] Step 500: Normalize the peak amplitude values ​​and / or trough amplitude values ​​of the earthquake composite record;

[0110] Step 600: Normalize the peak amplitude and / or trough amplitude values ​​of the seismic data around the target well.

[0111] Understandably, steps 500 and 600 help improve the contrast of the composite seismic record and seismic data, making seismic events across different amplitude ranges clearer. Normalization involves adjusting the amplitude of the data so that it fluctuates within a certain standard range.

[0112] Here, we will use earthquake data as an example to explain steps 500 and 600: First, find the largest wave peak A in the entire dataset. max and the minimum trough value A min Then, the seismic data is normalized using formula (4):

[0113]

[0114] Where A(t) is the amplitude of the original seismic data at time or depth t, A norm (t) is the normalized amplitude.

[0115] Preferably, if it is desired to adjust the normalized data to a specific range (e.g., [-1,1] or [0,1]), further adjustments can be made:

[0116] A adjusted (t)=A norm (t)×(ba)+a (5)

[0117] Among them, A adjusted (t) is the adjusted amplitude, and a and b are the lower and upper limits of the target range, respectively.

[0118] Example Four

[0119] Based on the above embodiments, see Figure 5 Step 200 includes:

[0120] Step 201: Generate the amplitude energy matching degree based on the normalized peak amplitude and / or trough amplitude values ​​of the seismic synthetic record corresponding to the current earthquake time, and the normalized peak amplitude and / or trough amplitude values ​​of the seismic data around the target well corresponding to the current earthquake time.

[0121] Specifically, the normalized energy sums of the peaks and troughs of the reflected waves from the synthetic seismic record and the actual seismic trace near the well are calculated sequentially. Then, the amplitude energy matching degree P between the peak or trough energy of the synthetic seismic record near the same location and the corresponding peak or trough energy of the actual seismic trace near the well is calculated sequentially. e The calculation formula is as follows:

[0122]

[0123] In the formula, Es is the normalized energy of the peak or trough of the reflected wave in the synthetic seismic record, and Er is the normalized energy of the peak or trough of the reflected wave in the actual seismic trace near the corresponding time point.

[0124] Based on the above embodiments, see Figure 6 Step 300 includes:

[0125] Step 301: Generate fidelity evaluation parameters for the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree;

[0126] Specifically, calculate P W With P eThe product of these two values ​​yields the estimated fidelity of the post-stack seismic data, F. In other words, the fidelity F of the post-stack seismic data at a given time period is defined as the waveform similarity P between the synthetic seismic record and the well-side seismic trace at that time period. W Energy matching degree P between the two E The product of is given by the following formula:

[0127] F(t i ) = P w (t i )·P e (t i (6)

[0128] In the formula, P W To synthesize the well-seismic similarity between the seismic record and the waveform of the post-stack well-side seismic trace, P e This is used to determine the well-seismic matching degree between the normalized energy of the peak or trough of the synthesized seismic record and the normalized energy of the peak or trough of the corresponding well-side seismic record.

[0129] Step 302: Evaluate the fidelity of the time-domain seismic processing post-stack data according to the fidelity evaluation parameters and preset evaluation rules.

[0130] This disclosure provides a method for evaluating the fidelity of time-domain seismic processing post-stack data, comprising: first, performing waveform cross-correlation analysis on the seismic synthetic record of the target well in the study area and the seismic data around the target well to generate a cross-correlation coefficient; then, matching the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record and the seismic data around the target well to generate an amplitude-energy matching degree; and finally, evaluating the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude-energy matching degree.

[0131] This invention overcomes the limitations of previous subjective qualitative evaluations of the amplitude fidelity of post-stack seismic processing results. It not only provides quantitative parameters for evaluating seismic data processing results, but also effectively improves the scientific rigor, objectivity, and accuracy of the evaluation.

[0132] Example Five

[0133] To further illustrate the solution, based on the above embodiments, this embodiment provides an application example using a specific work area as an example to further explain a method for evaluating the fidelity of post-stack data in time-domain seismic processing.

[0134] First, this invention provides a fidelity evaluation system for post-stack data in time-domain seismic processing. See also... Figure 7This system is used to implement the aforementioned method for quantitative evaluation of the fidelity of post-stack seismic data. The system can directly generate numerical curves for evaluating the fidelity of post-stack seismic data. The device mainly consists of six components: input data, synthesis of synthetic seismic records, calculation of waveform similarity between post-stack seismic data and synthetic seismic records, calculation of peak and trough energy matching between pure wave data of post-stack seismic data and synthetic records, calculation of fidelity of pure wave data of post-stack seismic data, and output display.

[0135] Next, based on the above-mentioned fidelity evaluation system for post-stack time-domain seismic processing data, see [link to relevant documentation]. Figure 8 as well as Figure 9 The method includes the following steps:

[0136] S1: Input post-stack seismic pure wave data, seismic reference surface, and replacement velocity;

[0137] A seismic reference surface is a surface used to standardize the vertical position of seismic data. Because surface conditions during seismic data acquisition (such as elevation changes and topographic relief) can affect the propagation time of seismic waves, the seismic reference surface provides a unified reference point, enabling effective comparison and comprehensive interpretation of data from different locations.

[0138] Replacement velocity is a velocity parameter used in seismic data processing to convert seismic time data into depth data. It is used in static correction processes to compensate for time delays caused by surface irregularities and low-velocity surface layers.

[0139] S2: Input well location coordinates, surface elevation, and sonic logging and density logging data;

[0140] Well location coordinates refer to the geographical coordinates of the drilling location, usually expressed in longitude and latitude (or UTM coordinates). These coordinates are used to determine the precise location of the well on the surface and for positioning in geological and geophysical models.

[0141] Surface elevation refers to the altitude of the wellhead location. This information is crucial in seismic data processing because surface elevation affects the propagation time of seismic waves, directly impacting static correction and depth conversion.

[0142] Sonic logging data records the propagation speed of sound waves in underground formations, typically measured in microseconds per foot (μs / ft) or microseconds per meter (μs / m). Sonic logging data is used to calculate formation velocity models and plays a crucial role in identifying geological horizons and converting seismic data to depth.

[0143] Density logging data records the density of the formation, typically expressed in grams per cubic centimeter (g / cm³). 3 Density logging data is measured in units of 1 / 3. It is used for lithological analysis and porosity calculation of formations, and plays an important role in the inversion processing of seismic data.

[0144] S3: Locate and obtain the seismic traces near the well based on the well location coordinates;

[0145] S4: Calculate the formation reflection coefficient based on acoustic logging and density logging data;

[0146] S5: Extract seismic wavelets from the vicinity of the well-side seismic trace and convolve them with the formation reflection coefficients calculated in step S3 to create a synthetic seismic record;

[0147] S6: Calculate the waveform similarity P between the synthesized seismic record and the corresponding well-side seismic trace within the time length using the above formula (1). W ;

[0148] S7: Calculate and normalize the energy of each peak and trough in the synthetic seismic record;

[0149] S8: Calculate and normalize the energy of each peak and trough in the seismic trace near the well.

[0150] S9: Using the above formula (5), calculate the well-seismic matching degree P between the normalized energy of the peak and trough of the reflected wave in each synthetic seismic record and the normalized energy of the peak and trough of the reflected wave in the corresponding time-seismic trace. e .

[0151] S10: Calculate the fidelity F of each post-stack seismic reflection wave group using the above formula (6);

[0152] S11: Output display fidelity curve.

[0153] To verify the effectiveness of this method, a section of synthetic seismic record and well-side seismic trace were selected for trial calculations. Figure 10 As shown, the waveform similarity between this seismic trace and the synthetic seismic record is relatively high, ranging from 0.6 to 0.9. The energy matching degree between the well and the seismic record is also average, ranging from 0.4 to 0.76. Therefore, the final calculated seismic amplitude fidelity is also average, with a maximum value of only 0.68. This demonstrates that the method accurately and objectively reflects the amplitude fidelity of this seismic record.

[0154] To further verify the practical effectiveness of the method of the present invention, the method was applied to a practical test using post-stack seismic processing pure wave data in a region of western China. For example... Figure 11As shown, in the 2500–2650 ms time period, the actual seismic traces near the well and the synthetic seismic records have a very good similarity and high correlation, with the highest similarity value reaching 1. Moreover, the energy matching degree between the seismic wave peaks and troughs in this time period is also relatively high, with the highest value reaching 0.95 (or 95%), which meets the requirement of high seismic fidelity. That is, under the premise of high waveform similarity, the relative relationship of wave group amplitude energy is also consistent. Therefore, the post-stack seismic data in the 2500–2650 ms time period has high fidelity, with the highest seismic amplitude fidelity reaching 0.89 (or 89%).

[0155] This invention, based on seismic synthetic records, proposes a parameter and method for quantitatively measuring and evaluating the amplitude fidelity of post-stack seismic data. It provides a basis for quality control in seismic data processing, acceptance of the final stacked data, and assessment of the reliability and usability of seismic data before interpretation. This method is entirely data-driven, with minimal human intervention, overcoming the limitations of previous subjective qualitative evaluations. It not only provides quantitative parameters for evaluating seismic data processing results but also effectively improves the objectivity, automation, accuracy, and efficiency of seismic data evaluation, providing a scientific basis for subsequent seismic exploration decisions.

[0156] Example Six

[0157] Based on the same inventive concept, this application also provides a fidelity evaluation device for time-domain seismic processing post-stack data, which can be used to implement the method described in the above embodiments, as shown in the following embodiments. Since the principle of the fidelity evaluation device for time-domain seismic processing post-stack data is similar to that of the fidelity evaluation method for time-domain seismic processing post-stack data, the implementation of the fidelity evaluation device for time-domain seismic processing post-stack data can refer to the implementation of the fidelity evaluation method for time-domain seismic processing post-stack data, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0158] Embodiments of the present invention provide a specific implementation of a fidelity evaluation device for time-domain seismic processing post-stack data that enables a fidelity evaluation method for time-domain seismic processing post-stack data. See also... Figure 12 A fidelity evaluation device for time-domain seismic processing post-stack data includes:

[0159] The cross-correlation generation module 10 is used to perform waveform cross-correlation analysis on the seismic synthetic record of the target well in the study area and the seismic data around the target well to generate cross-correlation numbers.

[0160] The amplitude energy matching degree generation module 20 is used to match the peak amplitude value and / or trough amplitude value of the seismic synthetic record with the seismic data around the target well to generate an amplitude energy matching degree.

[0161] The fidelity evaluation module 30 is used to evaluate the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree.

[0162] In some embodiments of this disclosure, the cross-correlation coefficient generation module includes:

[0163] A wavelet extraction unit is used to extract wavelets from the seismic data around the target well and determine the length of the wavelets;

[0164] A sliding time window generation unit is used to generate a sliding time window based on the length of the wavelet;

[0165] A waveform similarity determination unit is used to determine, within the sliding time window, the waveform similarity between the peaks of the seismic synthetic record and the peaks of the seismic data around the target well, and / or the waveform similarity between the troughs of the seismic synthetic record and the troughs of the seismic data around the target well;

[0166] The cross-correlation generation unit is used to generate the cross-correlation number based on the waveform similarity between the peaks of the seismic synthetic record and the peaks of the seismic data around the target well, and / or the waveform similarity between the troughs of the seismic synthetic record and the troughs of the seismic data around the target well.

[0167] In some embodiments of this disclosure, a fidelity evaluation device for post-stack time-domain seismic processing data further includes:

[0168] The synthetic record generation module is used to generate the seismic synthetic record based on the sonic logging data, density logging data and seismic data of the target well.

[0169] In some embodiments of this disclosure, a fidelity evaluation device for post-stack time-domain seismic processing data further includes:

[0170] The synthetic amplitude normalization module is used to normalize the peak amplitude values ​​and / or trough amplitude values ​​of the synthetic seismic record.

[0171] The seismic amplitude normalization module is used to normalize the peak amplitude and / or trough amplitude values ​​of the seismic data around the target well.

[0172] In some embodiments of this disclosure, the amplitude energy matching degree generation module includes:

[0173] The amplitude energy matching degree generation unit is used to generate the amplitude energy matching degree based on the normalized peak amplitude value and / or trough amplitude value of the seismic synthetic record corresponding to the current earthquake time, and the normalized peak amplitude value and / or trough amplitude value of the seismic data around the target well corresponding to the current earthquake time.

[0174] In some embodiments of this disclosure, the fidelity evaluation module includes:

[0175] The fidelity evaluation parameter generation unit is used to generate fidelity evaluation parameters for the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree.

[0176] The fidelity evaluation unit is used to evaluate the fidelity of the time-domain seismic processing post-stack data according to the fidelity evaluation parameters and preset evaluation rules.

[0177] This disclosure provides a fidelity evaluation device for time-domain seismic processing post-stack data, comprising: a cross-correlation generation module for performing waveform cross-correlation analysis on the seismic synthetic record of the target well in the study area and the seismic data around the target well to generate a cross-correlation number; an amplitude-energy matching degree generation module for matching the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record and the seismic data around the target well to generate an amplitude-energy matching degree; and a fidelity evaluation module for evaluating the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation number and the amplitude-energy matching degree.

[0178] This invention addresses the current situation where the fidelity evaluation of post-stack seismic data relies primarily on visual assessment and qualitative methods. It provides a quantitative evaluation device for seismic fidelity, enabling quality control during seismic data processing, final acceptance of processed results, and assessment of the reliability and usability of seismic data before interpretation. This significantly improves the objectivity of seismic data processing data evaluation, increases evaluation efficiency and accuracy, and provides a scientific basis for subsequent exploration decisions.

[0179] Example Seven

[0180] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0181] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that, when executed by a processor, the computer program implements the steps of the method described in the above embodiments, specifically including the following:

[0182] Waveform cross-correlation analysis was performed on the seismic synthetic records of the target well in the study area and the seismic data around the target well to generate cross-correlation coefficients;

[0183] The peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record are matched with the seismic data around the target well to generate an amplitude energy matching degree.

[0184] The fidelity of the time-domain seismic processing post-stack data is evaluated based on the cross-correlation coefficient and the amplitude energy matching degree.

[0185] In some embodiments of this disclosure, waveform cross-correlation analysis is performed on the seismic synthetic record of the target well within the study area and the seismic data surrounding the target well to generate cross-correlation coefficients, including:

[0186] Extract wavelet from the seismic data around the target well and determine the length of the wavelet;

[0187] A sliding time window is generated based on the length of the wavelet;

[0188] Within the sliding time window, the waveform similarity between the peaks of the seismic composite record and the peaks of the seismic data around the target well is determined, and / or the waveform similarity between the troughs of the seismic composite record and the troughs of the seismic data around the target well is determined;

[0189] The cross-correlation coefficient is generated based on the waveform similarity between the peaks of the seismic composite record and the peaks of the seismic data surrounding the target well, and / or the waveform similarity between the troughs of the seismic composite record and the troughs of the seismic data surrounding the target well.

[0190] In some embodiments of this disclosure, a method for evaluating the fidelity of post-stack time-domain seismic processing data further includes:

[0191] The seismic composite record is generated based on the sonic logging data, density logging data, and seismic data of the target well.

[0192] In some embodiments of this disclosure, a method for evaluating the fidelity of post-stack time-domain seismic processing data further includes:

[0193] The peak amplitude values ​​and / or trough amplitude values ​​of the earthquake composite record are normalized;

[0194] The peak amplitude and / or trough amplitude values ​​of the seismic data around the target well are normalized.

[0195] In some embodiments of this disclosure, matching the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record with the seismic data surrounding the target well to generate an amplitude-energy matching degree includes:

[0196] The amplitude energy matching degree is generated based on the normalized peak amplitude and / or trough amplitude values ​​of the seismic synthetic record corresponding to the current earthquake time, and the normalized peak amplitude and / or trough amplitude values ​​of the seismic data around the target well corresponding to the current earthquake time.

[0197] In some embodiments of this disclosure, evaluating the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree includes:

[0198] The fidelity evaluation parameters for the time-domain seismic processing post-stack data are generated based on the cross-correlation coefficient and the amplitude energy matching degree.

[0199] The fidelity of the time-domain seismic processing post-stack data is evaluated based on the fidelity evaluation parameters and preset evaluation rules.

[0200] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, characterized in that, when the computer program is executed by a processor, it implements the steps of the method described in the above embodiments, specifically including the following:

[0201] Waveform cross-correlation analysis was performed on the seismic synthetic records of the target well in the study area and the seismic data around the target well to generate cross-correlation coefficients;

[0202] The peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record are matched with the seismic data around the target well to generate an amplitude energy matching degree.

[0203] The fidelity of the time-domain seismic processing post-stack data is evaluated based on the cross-correlation coefficient and the amplitude energy matching degree.

[0204] In some embodiments of this disclosure, waveform cross-correlation analysis is performed on the seismic synthetic record of the target well within the study area and the seismic data surrounding the target well to generate cross-correlation coefficients, including:

[0205] Extract wavelet from the seismic data around the target well and determine the length of the wavelet;

[0206] A sliding time window is generated based on the length of the wavelet;

[0207] Within the sliding time window, the waveform similarity between the peaks of the seismic composite record and the peaks of the seismic data around the target well is determined, and / or the waveform similarity between the troughs of the seismic composite record and the troughs of the seismic data around the target well is determined;

[0208] The cross-correlation coefficient is generated based on the waveform similarity between the peaks of the seismic composite record and the peaks of the seismic data surrounding the target well, and / or the waveform similarity between the troughs of the seismic composite record and the troughs of the seismic data surrounding the target well.

[0209] In some embodiments of this disclosure, a method for evaluating the fidelity of post-stack time-domain seismic processing data further includes:

[0210] The seismic composite record is generated based on the sonic logging data, density logging data, and seismic data of the target well.

[0211] In some embodiments of this disclosure, a method for evaluating the fidelity of post-stack time-domain seismic processing data further includes:

[0212] The peak amplitude values ​​and / or trough amplitude values ​​of the earthquake composite record are normalized;

[0213] The peak amplitude and / or trough amplitude values ​​of the seismic data around the target well are normalized.

[0214] In some embodiments of this disclosure, matching the peak amplitude values ​​and / or trough amplitude values ​​of the seismic synthetic record with the seismic data surrounding the target well to generate an amplitude-energy matching degree includes:

[0215] The amplitude energy matching degree is generated based on the normalized peak amplitude and / or trough amplitude values ​​of the seismic synthetic record corresponding to the current earthquake time, and the normalized peak amplitude and / or trough amplitude values ​​of the seismic data around the target well corresponding to the current earthquake time.

[0216] In some embodiments of this disclosure, evaluating the fidelity of the time-domain seismic processing post-stack data based on the cross-correlation coefficient and the amplitude energy matching degree includes:

[0217] The fidelity evaluation parameters for the time-domain seismic processing post-stack data are generated based on the cross-correlation coefficient and the amplitude energy matching degree.

[0218] The fidelity of the time-domain seismic processing post-stack data is evaluated based on the fidelity evaluation parameters and preset evaluation rules.

[0219] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0220] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0221] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0222] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0223] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0224] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0225] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0226] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0227] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A fidelity evaluation method for time-domain seismic processing poststack data, characterized in that, The method comprises the following steps: waveform cross-correlation analysis is performed on a seismic synthetic record of a target well in a study area and seismic data around the target well to generate a cross-correlation coefficient; peak amplitude values and / or trough amplitude values of the seismic synthetic record and the seismic data around the target well are matched to generate an amplitude energy matching degree; fidelity of time-domain seismic processing stacked data is evaluated according to the cross-correlation coefficient and the amplitude energy matching degree.

2. The fidelity evaluation method of claim 1, wherein, The waveform cross-correlation analysis performed on the seismic synthetic record of the target well in the study area and the seismic data around the target well to generate the cross-correlation coefficient comprises the following steps: extracting a wavelet of the seismic data around the target well and determining a length of the wavelet; generating a sliding time window according to the length of the wavelet; determining waveform similarity between a peak of the seismic synthetic record and a peak of the seismic data around the target well and / or waveform similarity between a trough of the seismic synthetic record and a trough of the seismic data around the target well in the sliding time window; generating the cross-correlation coefficient according to the waveform similarity between the peak of the seismic synthetic record and the peak of the seismic data around the target well and / or the waveform similarity between the trough of the seismic synthetic record and the trough of the seismic data around the target well.

3. The fidelity evaluation method of claim 1, wherein, The method further comprises the following steps: generating the seismic synthetic record according to acoustic logging data, density logging data of the target well and the seismic data.

4. The fidelity evaluation method of claim 1, wherein The method further comprises the following steps: normalizing peak amplitude values and / or trough amplitude values of the seismic synthetic record; normalizing peak amplitude values and / or trough amplitude values of the seismic data around the target well.

5. The fidelity evaluation method of claim 4, wherein, The matching of the peak amplitude values and / or the trough amplitude values of the seismic synthetic record and the seismic data around the target well to generate the amplitude energy matching degree comprises the following steps: generating the amplitude energy matching degree according to normalized peak amplitude values and / or trough amplitude values of the seismic synthetic record corresponding to a current seismic time and normalized peak amplitude values and / or trough amplitude values of the seismic data around the target well corresponding to the current seismic time.

6. The fidelity evaluation method according to any one of claims 1 to 5, characterized by, The evaluation of the fidelity of the time-domain seismic processing stacked data according to the cross-correlation coefficient and the amplitude energy matching degree comprises the following steps: generating a fidelity evaluation parameter of the time-domain seismic processing stacked data according to the cross-correlation coefficient and the amplitude energy matching degree; evaluating the fidelity of the time-domain seismic processing stacked data according to the fidelity evaluation parameter and a preset evaluation rule.

7. An apparatus for fidelity evaluation of time domain seismic processed stacked data, characterized in that, The method comprises the following steps: a cross-correlation coefficient generation module is configured to perform waveform cross-correlation analysis on a seismic synthetic record of a target well in a study area and seismic data around the target well to generate a cross-correlation coefficient; an amplitude energy matching degree generation module is configured to match peak amplitude values and / or trough amplitude values of the seismic synthetic record and the seismic data around the target well to generate an amplitude energy matching degree; a fidelity evaluation module is configured to evaluate fidelity of time-domain seismic processing stacked data according to the cross-correlation coefficient and the amplitude energy matching degree.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-7. The processor executes the computer program to implement the steps of the fidelity evaluation method of time-domain seismic processing stacked data according to any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the fidelity evaluation method of time-domain seismic processing stacked data according to any one of claims 1 to 6.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program is executed by the processor to implement the steps of the fidelity evaluation method of time-domain seismic processing stacked data according to any one of claims 1 to 6.

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