A method for obtaining an attenuation factor suitable for seismic source wavelet attenuation simulation

Abstract

This invention discloses a method for determining the attenuation factor in source wavelet attenuation simulation, comprising: 1. Data preparation and offset-based spectral analysis; 2. Theoretical excitation wavelet spectral simulation; 3. Offset-based spectral ratio fitting and apparent quality factor determination; 4. Determination of the zero-offset average quality factor; 5. Determination of the zero-offset average quality factor across multiple layers; 6. Determination of the inter-layer average quality factor; 7. Establishment of a typical two-dimensional model of the target area; 8. Viscoelastic self-excitation and self-absorption attenuation simulation of the source-excited wavelet for the target layer, obtaining the target layer response wavelet. This invention fully considers the correspondence between the excitation wavelet spectrum and the target layer spectrum, and eliminates the influence of offset, resulting in a more accurate Q-value, making it more suitable for the absorption attenuation simulation of the target layer response wavelet, with a higher degree of matching with actual data, and more suitable for the evaluation of source-excited wavelets.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology, and in particular to a method for determining the attenuation factor suitable for source wavelet attenuation simulation. Background Technology

[0002] The quality factor Q is a key parameter characterizing the absorption and attenuation characteristics of seismic waves by formations, and is of great significance for high-resolution seismic imaging, reservoir prediction, and hydrocarbon detection. In seismic acquisition design, source wavelet attenuation simulation is usually performed based on a model that includes the quality factor attribute to determine the effective bandwidth of the target layer's response wavelet. The accuracy of the quality factor Q directly affects the accuracy of the source wavelet attenuation simulation results.

[0003] Currently, there are three main methods for determining the Q-value: empirical formulas, calculation from seismic data, and calculation using VSP data. Empirical formulas are derived from deep, well-consolidated rocks. For shallow, loosely structured rocks, seismic wave attenuation is more severe, and the quality factor should be smaller. For marine seismic exploration, VSP and drilling data are relatively scarce; therefore, calculation from seismic data is a better method for determining the Q-value from marine seismic data. There are numerous methods for determining the Q-value from seismic data, which can be divided into time-domain methods and frequency-domain methods. Time-domain methods have relatively higher and more stringent requirements for data quality, often making it impossible to achieve the time synchronization required by the algorithm. When the source is reproducible and true amplitude records are available, using analytical signals (maximum value method) is superior. When true amplitude data is unavailable, the spectral ratio method and spectral simulation method are more suitable, as they are more sensitive to attenuation in different frequency bands in the frequency domain. Considering the accuracy and timeliness of various Q-value extraction methods, and taking into account that marine seismic data has a relatively high signal-to-noise ratio and good quality, the spectral ratio method is generally selected for Q-value extraction calculation. The principle is to obtain the Q-value by calculating the slope of the linear relationship between the logarithmic ratio of the deep and shallow seismic wave spectra and the frequency.

[0004] Traditional spectral comparison methods have two significant drawbacks: 1) Reference signal distortion: Traditional methods typically use shallow reflection signals as an approximation of the excitation wavelet. However, shallow sediments are usually quite porous and have a strong absorption and attenuation effect on seismic waves, resulting in severe distortion of the shallow signal relative to the original excitation wavelet. Using this as a reference introduces significant systematic errors. 2) Offset effect interference: Traditional methods do not fully consider the influence of different offsets in common center point gathers. First, the existence of amplitude variation with offset means that the reflection amplitude and phase of gathers at different offsets differ. Second, increasing the offset leads to an increase in the seismic wave propagation path and a change in the incident angle, causing the obtained Q value to deviate from the true inherent attenuation properties of the formation. Theoretically, the larger the offset, the smaller the obtained Q value. This is not a reflection of the true absorption characteristics of the formation, but rather a combined result of propagation path effects, geometric diffusion, and AVO effects. The Q value corresponding to zero offset is least affected by these interference factors and best represents the true Q value of the formation. Therefore, how to overcome the effects of shallow signal distortion and offset effect to obtain accurate and reliable zero-offset formation Q values ​​is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a method for obtaining the formation quality factor Q value, in order to solve the problem of inaccurate Q value estimation caused by shallow reference signal distortion and offset effect interference in the traditional spectral ratio method. Ultimately, it obtains zero-offset Q value and inter-layer Q value that are closer to the true attenuation characteristics of the formation. This method is applicable to source wavelet attenuation simulation, making the simulated target layer response wavelet more similar to the frequency band characteristics of actual data, and providing a better technical method for evaluating the effective frequency band of source-excited wavelets.

[0006] The present invention solves its problems through the following technical solution: A method for determining the attenuation factor suitable for source wavelet attenuation simulation includes the following steps: S1, Data preparation and offset spectrum analysis, to obtain several offset segments; and the amplitude spectrum of the target layer reflection signal of the seismic data in each offset segment; S2, theoretical excitation wavelet spectrum simulation, to obtain the amplitude spectrum of the theoretical excitation wavelet corresponding to each offset segment in step S1; S3, based on the amplitude spectrum of the target layer reflection signal in step S1 and the amplitude spectrum of the theoretical excitation wavelet in step S2, fit the offset spectrum ratio and obtain the apparent quality factor value. S4. Calculate the zero-offset average quality factor value based on the apparent quality factor value in step S3. S5, Calculate the average quality factor of zero offset in multiple layers. Repeat steps S1 to S4 to obtain the average quality factor of zero offset from the source to the depth of each target layer. S6. Calculate the interlayer average quality factor based on the zero offset average quality factors in step S5. S7. Establish a typical two-dimensional model of the target area and fill each layer with the average quality factor attribute between layers obtained in step S6. S8. Based on the model established in step S7, perform viscoelastic self-excitation and self-absorption attenuation simulation of the source-excited wavelet for the target layer to obtain the target layer response wavelet.

[0007] Optionally, in step S1, a preprocessed high signal-to-noise ratio common center point gather is selected and divided into several offset segments according to the offset range; the amplitude spectrum of the target layer reflection signal of the seismic data in each offset segment is calculated respectively.

[0008] Optionally, in step S2, the amplitude spectrum of the theoretical excited wavelet corresponding to each offset segment in step S1 is simulated and calculated using air gun source simulation software.

[0009] Optionally, in step S3, for each offset segment, the theoretical excitation wavelet spectrum corresponding to the offset segment is used as the reference spectrum, and the actual reflected signal spectrum of the target layer corresponding to the offset segment is used as the target spectrum. The spectral ratio logarithmic curve of the offset segment is calculated, and the curve is linearly fitted within the effective frequency band. The apparent quality factor value under the offset path is calculated based on the slope of the fitted line.

[0010] Optionally, in step S4, a mapping relationship is established between the apparent quality factor values ​​of each offset segment obtained in step S3 and the midpoint offset; a linear regression analysis is performed on the mapping relationship, and the regression line is extrapolated to the offset of zero to obtain the spectral ratio slope value corresponding to zero offset; the average quality factor of zero offset from the source to the target layer is calculated using the slope value.

[0011] Optionally, in step S5, steps S1 to S4 are repeated for target layers at different depths to obtain the zero-offset average quality factor Q_n from the source to each target layer depth t_n.

[0012] Optionally, in step S6, using the zero offset average quality factors Q_{n-1} and Q_n at different depths t_{n-1} and t_n obtained in step S5, the interlayer average quality factor Q_i between depth segments t_{n-1} and t_n is calculated by inversion or interpolation.

[0013] Optionally, the simulation of the theoretically excited wavelet spectrum described in step S2 is used as the reference spectrum in step S3. It comprehensively considers the source wavelet morphology and wavefront geometric diffusion factors corresponding to different offset distances, thus avoiding the influence of near-surface absorption effects on the wavelet morphology.

[0014] Optionally, in step S4, the linear regression analysis employs the least squares method.

[0015] Optionally, in step S6, the formula for calculating the interlayer average quality factor Q_i is based on the cumulative attenuation assumption and is approximated using the following formula: Where t_{n-1} and t_n are the two-way travel times of the upper and lower target layers, respectively, and Q_{n-1} and Q_n are the corresponding average quality factors of zero offset from the source to the layer.

[0016] In summary, the technical effects and advantages of this invention are as follows: 1) Improved fidelity of the reference signal: By using the simulated theoretical excitation of the wavelet spectrum to replace the actual distorted shallow signal as the reference spectrum, the contamination of the reference signal by the strong absorption of the shallow layer is fundamentally eliminated, providing a more accurate and stable benchmark for the spectral ratio method.

[0017] 2) The systematic bias of offset effect is eliminated: by joint analysis of multiple offsets and extrapolation to zero offset, the influence of propagation path difference, AVO effect and non-perpendicular incidence on amplitude spectrum is effectively removed, so that the final Q value reflects the inherent absorption properties of the formation to the greatest extent, rather than a function of propagation path.

[0018] 3) The results are more reliable and have higher application value: The final zero-offset average Q value and interlayer Q value have clear physical meaning and higher accuracy. They are used to simulate wavelet attenuation with higher accuracy and are more consistent with actual data.

[0019] 4) The method is systematic and robust: The steps of this method are clear, forming a complete workflow from raw gather processing to final inter-layer Q-value output, which has good adaptability and robustness to actual seismic data. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the present invention; Figure 2 This is a schematic diagram of the actual data gather and the spectrum of the target layer at different offsets according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the simulated theoretical excitation wavelet spectrum for each offset segment according to an embodiment of the present invention; Figure 4This is a schematic diagram illustrating the variation of apparent Q value / slope with offset distance and linear extrapolation according to an embodiment of the present invention; Figure 5 This is a target layer response wavelet spectrum diagram of a target region absorption attenuation simulation according to an embodiment of the present invention; Figure 6 This is a target layer spectrum diagram of actual data for a certain target region according to an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0024] To better understand the purpose, structure, and function of this invention, the following is combined with... Figure 1 The overall flowchart and other accompanying drawings provide a more detailed description of the attenuation factor calculation method applicable to source wavelet attenuation simulation of the present invention.

[0025] Taking marine seismic exploration data as an example, the target area is an oil and gas basin. The aim is to obtain the inter-layer Q value of the strata in the area with high accuracy and use it to establish a viscoelastic attenuation model for forward modeling analysis.

[0026] S101: Data Preparation and Offset-Based Spectral Analysis This step aims to provide high-quality, offset-separated target layer spectral data for subsequent analysis.

[0027] First, from the seismic data processing system, high signal-to-noise ratio common-center gathers along the target seismic line that have undergone conventional preprocessing (including but not limited to bad path removal, amplitude recovery, deconvolution, and multiple suppression) are selected. To ensure the stability of the spectral analysis, CMP gathers with continuous reflection phase axes and clear waveforms are preferred.

[0028] Secondly, such as Figure 2 As shown, based on the distribution range of shot-receiver offsets within the gather, it is divided into K offset segments. The division principle can be equal intervals or non-uniform division based on the gather coverage frequency, ensuring that there are enough seismic traces in each offset segment for stacking or statistical averaging, thereby improving the signal-to-noise ratio of the spectrum. For example, the offset range [0, 6000m] can be divided into 10 segments, each 600m long.

[0029] Then, for each offset segment, perform the following operations: Time window extraction: On each seismic trace, a suitable time window (e.g., t0 ± Δt) is drawn, centered on the two-way travel time t0 of the reflected wave from the target layer. The length of the time window should be able to completely encompass the main lobe of the reflected wave from the target layer, while avoiding including other adjacent reflected waves as much as possible.

[0030] Spectrum calculation: Time window data extracted from all seismic traces within the offset segment are superimposed or averaged to further enhance the effective signal. A window function (such as the Hanning window) is applied to the averaged time window data to reduce the truncation effect, and then a fast Fourier transform is performed to obtain the actual amplitude spectrum A_obs(f, x) of the target layer reflection signal in the offset segment, where f is the frequency and x is the center offset of the offset segment.

[0031] S102: Theoretical Excitation Wavelet Spectrum Simulation. This step aims to obtain a pure reference wavelet spectrum, unaffected by near-surface attenuation, to replace the distorted shallow signals in traditional methods. This invention uses the industry-recognized airgun source simulation software Nucleus (or similar specialized software) for simulation.

[0032] In Nucleus software, accurately configure the air gun source parameters to match the actual data collected, including air gun array configuration, immersion depth, volume, pressure, etc. For example... Figure 3 As shown, for each offset segment divided in step S101, a far-field wavelet simulation is performed with its center offset x as the receiving point location. The software calculates the waveform of the seismic wave propagating from the source to the receiving point at that offset. This waveform mainly includes the source wavelet shape and the wavefront geometric diffusion effect corresponding to that offset, while essentially eliminating the absorption and attenuation by the strata. Fourier transforms are performed on the simulated wavelet waveforms at each offset to obtain the corresponding theoretically excited wavelet amplitude spectrum. This spectrum serves as the pure reference spectrum for subsequent spectral ratio analysis.

[0033] S103: Fitting of Spectral Offset Ratio and Determination of Apparent Quality Factor Q Value This step uses the data obtained in the first two steps to calculate the apparent attenuation (apparent Q value) for each offset path.

[0034] For the k-th offset segment (center offset is x_k): Calculate the logarithmic curve of the spectral ratio: Based on the basic principle of the spectral ratio method, calculate the logarithmic spectral ratio L(f, x_k): L(f, x_k) = ln[A_obs(f, x_k) / A_theo(f, x_k)] This operation is equivalent to ln(A_obs) - ln(A_theo). Since A_theo(f, x_k) does not contain information about stratum absorption, L(f, x_k) mainly reflects the total attenuation of the seismic wave along the path from the source through the stratum to the target layer and then reflected back to the receiving point.

[0035] Linear Fitting: Within the effective frequency band [f_low, f_high] (typically the frequency range where signal energy is higher than noise level), L(f, x_k) theoretically exhibits a linear relationship with the angular frequency ω=2πf: L(f, x_k) ≈ -π * t * f / Q_app(x_k) + C, where t is the two-way travel time from the source to the target layer, Q_app(x_k) is the apparent quality factor at that offset, and C is a constant term. The least squares method is used to fit a straight line to the discrete point (f, L(f, x_k)), yielding the slope value Slope(x_k) of the fitted line.

[0036] Calculate the visual Q-value: Based on the relationship between the fitted slope and the Q-value, calculate the visual quality factor at this offset distance. Q_app(x_k) = -π * t / Slope(x_k) Repeat the above process for K offset segments to obtain a set of view Q values ​​{Q_app(x_1), Q_app(x_2),..., Q_app(x_K)} and their corresponding center offsets {x_1, x_2, ..., x_K}.

[0037] S104: Calculation of the average Q value at zero offset This step aims to eliminate the influence of offset effect on Q value and obtain the zero offset Q value that best represents the inherent attenuation properties of the formation.

[0038] like Figure 4 As shown, a mapping relationship is established: the apparent Q-value Q_app(x_k) obtained in step S103 (or its corresponding original fitting slope Slope(x_k)) is used as the ordinate Y, and the corresponding center offset x_k is used as the abscissa X, forming K data points (X_k, Y_k). Theoretical analysis shows that due to the systematic influence of propagation path differences and AVO effects, Y and X are approximately linearly related.

[0039] Linear Regression and Extrapolation: Perform linear regression analysis (preferably least squares method) on the above K data points to obtain the regression line equation: Y = a * X + b, where a is the slope and b is the intercept.

[0040] Calculate the zero offset Q value: Substitute X=0 into the regression equation to obtain the Y value at the zero offset, i.e., Y(0) = b. This b value is the extrapolated zero offset spectral slope Slope(0). Using this slope, calculate the average quality factor Q_avg of the zero offset from the source to the current target layer: Q_avg = -π * t / Slope(0) This Q_avg value is the key parameter characterizing the average absorption characteristics of the strata between the source and the target layer after eliminating most of the interference from propagation paths and angle effects.

[0041] S105: Calculation of multi-level average Q value To obtain a continuous Q-value distribution in the vertical direction, it is necessary to analyze multiple layers.

[0042] For the other N-1 target layers with obvious reflection characteristics within the work area (whose two-way travel times are t_2, t_3, ..., t_N, respectively), repeat steps S101 to S104. Finally, a dataset is obtained: {(t_1, Q_avg1), (t_2,Q_avg2), ..., (t_N, Q_avgN)}, where t_n and Q_avgn represent the two-way travel time of the nth target layer and the corresponding average quality factor from the source to that layer at zero offset, respectively.

[0043] S106: Determining the interlayer Q-value This step converts the cumulative average Q-value into a more useful inter-layer Q-value.

[0044] For two adjacent target layers n-1 and n (corresponding to times t_{n-1} and t_n, and average Q values ​​Q_{n-1} and Q_n), assuming that the attenuation of seismic waves in each layer follows a constant Q model, and the total attenuation is the sum of the attenuations of each layer, then the inter-layer average quality factor Q_i of the i-th layer (located between t_{n-1} and t_n) can be approximately obtained by the following formula: 1 / Q_i = (t_n / Q_n - t_{n-1} / Q_{n-1}) / (t_n - t_{n-1}) By calculating all adjacent layer pairs, a set of interlayer Q-value sequences {Q_1, Q_2, ..., Q_{N-1}} can be obtained, corresponding to time intervals [t_0, t_1], [t_1, t_2], ..., [t_{N-1}, t_N] respectively (where t_0 can be regarded as the time at the surface or source depth, and its Q value is theoretically infinite or a maximum value).

[0045] S107: Establishing a viscoelastic geological model The above geophysical analysis results are converted into geological model attributes.

[0046] Based on the existing tectonic interpretation results and velocity models of the work area, a two-dimensional geological structure framework model of the target area is established.

[0047] The interlayer Q_i values ​​calculated in step S106 are used as the absorption properties of the corresponding geological strata (or time slices) and filled into the model framework. Thus, a viscoelastic geological model that includes not only velocity (acoustic) information but also attenuation (viscoelastic) information is constructed.

[0048] S108: Forward Simulation and Analysis Using the established viscoelastic model, a goal-oriented forward modeling simulation was performed.

[0049] Set simulation parameters: In the viscoelastic wave forward modeling software, load the viscoelastic geological model established in step S107. Set the source wavelet (i.e., the theoretically excited wavelet) to be consistent with the Nucleus simulation in S102.

[0050] Viscoelastic simulation: Self-excited and self-absorbed (i.e., zero offset) viscoelastic wave field simulation is performed in the model. The simulation process will strictly consider the amplitude attenuation and phase distortion effects of seismic waves propagating in the strata.

[0051] Obtain the response: Extract the reflected wavelet of the target layer (e.g., a thin reservoir or gas-bearing sandstone) from the simulation results, and denote it as the target layer response wavelet.

[0052] Analysis and Application: By comparing the original "theoretical excitation wavelet" with the "target layer response wavelet" after viscoelastic attenuation, the actual alteration effect of the target layer on seismic waves can be quantitatively assessed. This response wavelet can be used to: verify the rationality of the Q-value calculation results and guide the design of excitation sources. Figure 5 and Figure 6 As shown, the Q-value obtained by this invention was used in absorption attenuation simulations. The target layer response wavelet bandwidth was 7~43@-10dB, while the actual target layer bandwidth was 6~40@-10dB. The simulation results showed a high degree of agreement with the actual data, indicating that the Q-value was reasonably obtained. This model can be used as a reference for subsequent optimization and verification of the excitation source based on the target layer bandwidth requirements.

[0053] Through the above eight steps, this invention not only obtains the interlayer Q value of the strata with high accuracy, but also successfully applies this property to construct a viscoelastic model that is more in line with the actual geophysics, and serves the optimization design of the seismic source, forming a complete technical closed loop from "Q value estimation" to "model application".

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the attenuation factor suitable for source wavelet attenuation simulation, characterized in that, Includes the following steps: S1, Data preparation and offset spectrum analysis, to obtain several offset segments; and the amplitude spectrum of the target layer reflection signal of the seismic data in each offset segment; S2, theoretical excitation wavelet spectrum simulation, to obtain the amplitude spectrum of the theoretical excitation wavelet corresponding to each offset segment in step S1; S3, based on the amplitude spectrum of the target layer reflection signal in step S1 and the amplitude spectrum of the theoretical excitation wavelet in step S2, fit the offset spectrum ratio and obtain the apparent quality factor value. S4. Calculate the zero-offset average quality factor value based on the apparent quality factor value in step S3. S5, calculate the average quality factor of zero offset in multiple layers. Repeat steps S1 to S4 to obtain the average quality factor of zero offset from the source to the depth of each target layer. S6. Calculate the interlayer average quality factor based on the zero offset average quality factors in step S5. S7. Establish a typical two-dimensional model of the target area and fill each layer with the average quality factor attribute between layers obtained in step S6. S8. Based on the model established in step S7, perform viscoelastic self-excitation and self-absorption attenuation simulation of the source-excited wavelet for the target layer to obtain the target layer response wavelet.

2. The method for determining the attenuation factor suitable for source wavelet attenuation simulation according to claim 1, characterized in that, In step S1, the preprocessed high signal-to-noise ratio common center point gather is selected and divided into several offset segments according to the offset range; the amplitude spectrum of the target layer reflection signal of the seismic data in each offset segment is calculated respectively.

3. The method for determining the attenuation factor suitable for source wavelet attenuation simulation according to claim 1, characterized in that, In step S2, the amplitude spectrum of the theoretical excited wavelet corresponding to each offset segment in step S1 is simulated and calculated using air gun source simulation software.

4. The method for determining the attenuation factor suitable for source wavelet attenuation simulation according to claim 1, characterized in that, In step S3, for each offset segment, the theoretical excitation wavelet spectrum corresponding to the offset segment is used as the reference spectrum, and the actual reflected signal spectrum of the target layer corresponding to the offset segment is used as the target spectrum. The spectral ratio logarithmic curve of the offset segment is calculated, and the curve is linearly fitted within the effective frequency band. The apparent quality factor value under the offset path is calculated based on the slope of the fitted line.

5. The method for determining the attenuation factor suitable for source wavelet attenuation simulation according to claim 1, characterized in that, In step S4, a mapping relationship is established between the apparent quality factor values ​​of each offset segment obtained in step S3 and the midpoint offset; a linear regression analysis is performed on the mapping relationship, and the regression line is extrapolated to the offset of zero to obtain the spectral ratio slope value corresponding to zero offset; the average quality factor of zero offset from the source to the target layer is calculated using the slope value.

6. The method for determining the attenuation factor suitable for source wavelet attenuation simulation according to claim 1, characterized in that, In step S5, steps S1 to S4 are repeated for target layers at different depths to obtain the zero-offset average quality factor Q_n from the source to each target layer depth t_n.

7. The method for determining the attenuation factor suitable for source wavelet attenuation simulation according to claim 1, characterized in that, In step S6, using the zero offset average quality factors Q_{n-1} and Q_n at different depths t_{n-1} and t_n obtained in step S5, the interlayer average quality factor Q_i between depth segments t_{n-1} and t_n is calculated by inversion or interpolation.

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