A method for identifying igneous rocks using seismic data

Abstract

The present invention belongs to the technical field of post-stack reflection wave data processing and interpretation in seismic exploration and the technical field of igneous rock identification, and discloses a method for identifying igneous rocks using seismic data. The numerical structural characteristics of the a value on the acoustic well logging are used to interpret a large set of stratigraphic layers, and the interpreted large set of stratigraphic layers are used as the calculation time window for obtaining the a value from the ground reflection seismic data. The a value obtained on the acoustic well logging is used as the initial value, and a viscoelastic synthetic seismic record is generated using forward filtering. The a value is continuously updated so that the waveform of the viscoelastic synthetic seismic record approaches the seismic data trace near the wellbore. The updated a value on the well logging is used to calibrate the a value obtained from the seismic data to obtain the optimal a value. The optimal a value is used to perform post-stack reverse filtering processing to highlight the differences between the igneous rock and the surrounding rock in seismic waveform and amplitude, thereby achieving the purpose of identifying igneous rocks. This solves the problem of low accuracy faced in the process of identifying igneous rocks using seismic data in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of processing and interpretation of post-stack reflection wave data in seismic exploration and the technical field of igneous rock identification. The present invention relates to a method for identifying igneous rocks by using seismic data. Background Art

[0002] Igneous rocks, one of the three major types of rock, are widely developed in oil and gas basins both domestically and internationally. Igneous rocks exhibit complex lithology and lithofacies, multiple phases, significant planar heterogeneity, rapid vertical and horizontal lithologic variations, and complex rock physical characteristics. The relationship between igneous rock development and reservoir formation is complex, and the accurate prediction of sandstone reservoirs in igneous rock-developed areas has become a major bottleneck restricting oil and gas development, severely impacting the exploration and development of oil and gas fields in these areas. There is an urgent need to conduct igneous rock identification research, clarify the spatial distribution patterns of igneous rocks, and further improve drilling success rates.

[0003] In traditional seismic data processing and interpretation, seismic attributes are often used to identify igneous rocks. However, in practice, the number of seismic attribute parameters extracted from seismic data is large, and there is information redundancy between the various seismic attributes, even leading to embarrassing contradictions. In terms of seismic waveform and amplitude characteristics, igneous rocks have low dominant frequencies and chaotic reflections, which differ from the surrounding rocks. However, because conventional processing methods fail to effectively distinguish igneous rocks from the surrounding rocks during processing, the differences between the igneous rocks and the surrounding rocks on the seismic profile are weakened, making it difficult for interpreters to delineate the igneous rock boundaries through differences in seismic waveforms and amplitudes.

[0004] During the propagation of seismic waves, the viscoelastic nature of the underground medium itself causes energy absorption, attenuation, and phase stretching distortion, reducing the overall resolution and signal-to-noise ratio of the data. Since igneous rocks are weakly diagenetic and mostly aggregate in the form of clastic rocks, they absorb and attenuate seismic waves more strongly than conventional sandstones. Based on this characteristic, the different absorption and attenuation properties of igneous rocks and surrounding rocks can be exploited to highlight the differences in seismic waveforms and amplitudes between igneous rocks and surrounding rocks, thereby achieving the purpose of identifying igneous rocks. The absorption coefficient a is an important parameter that describes the absorption and attenuation of rocks. However, the underground structure is complex, and there are many factors that affect the absorption and attenuation of seismic waves. Therefore, the value of a is often difficult to determine accurately, which leads to inaccuracies in subsequent data processing. Therefore, a reasonable estimation of the absorption coefficient a is of great significance for the identification of igneous rocks.

[0005] Currently, methods for calculating the absorption coefficient a primarily include core measurement and spectral ratio methods. Core measurement requires obtaining underground core samples, which is costly and the testing environment cannot reproduce the actual underground environment, resulting in large measurement errors. Single calculation methods such as the spectral ratio method are susceptible to the influence of seismic data quality. For igneous rocks, seismic data generally have a low signal-to-noise ratio, poor imaging, and are often located in medium-to-deep layers. Using existing methods to estimate the absorption coefficient a results in large errors in actual application, failing to provide sufficient data support for interpretation. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for identifying igneous rocks using seismic data, so as to solve the problem of low accuracy in the process of identifying igneous rocks using seismic data in the prior art.

[0007] The above-mentioned object of the present invention is achieved through the following technical solutions:

[0008] A method for identifying igneous rocks using seismic data. First, the numerical structural characteristics of the a value on the sonic logging are used to interpret a large set of stratigraphic layers. Then, the interpreted large set of stratigraphic layers is used as the calculation time window for the a value of ground reflection seismic data. The a value obtained on the sonic logging is used as the initial value. Viscoelastic synthetic seismic records are generated using forward filtering, and the a value is continuously updated so that the waveform of the viscoelastic synthetic seismic record approaches the seismic data trace near the well. Finally, the updated a value on the logging is used to calibrate the a value obtained from the seismic data to obtain the optimal a value. The optimal a value can be used to carry out post-stack reverse filtering processing. Based on the different absorption and attenuation properties of igneous rocks and surrounding rocks, the differences in seismic waveforms and amplitudes between igneous rocks and surrounding rocks are highlighted, thereby achieving the purpose of identifying igneous rocks. The technical process is as follows: Figure 1 shown.

[0009] The above method for identifying igneous rocks using seismic data is specifically as follows:

[0010] Step 1: Calculate the initial a value using the spectrum ratio method for the acoustic logging data in the work area;

[0011] Step 2: Using the numerical structural characteristics of the initial a value obtained in step 1, a large set of stratigraphic horizon interpretations are carried out, the stratigraphic horizons are used as ground reflection seismic data to obtain a calculation window, and the spectral ratio method is used to obtain the a value of the seismic data within the calculation window;

[0012] Step 3: Using the initial value a obtained in step 1 as the initial value, generate a viscoelastic synthetic seismic record, and continuously adjust the value a so that the correlation coefficient between the viscoelastic synthetic seismic record and the wellside seismic data trace reaches a preset threshold value b;

[0013] ① Perform spectrum analysis on shallow seismic data within the time window of 0.5s-1.2s, and obtain the dominant frequency of the seismic data as D. Use the known reflection coefficient sequence R(t) and the Ricker wavelet r(t) with the dominant frequency D to generate a synthetic seismic record s(t) through convolution operation, denoted as s(t) = R(t)*r(t), where * represents convolution operation;

[0014] ② For the generated synthetic seismic record s(t), use the forward filtering formula Generate viscoelastic synthetic seismic records a(t), where S(ω) is the Fourier transform of the synthetic seismic record s(t), ω is the circular frequency, t is the vertical one-way travel time, a is the absorption coefficient, Re represents the real part operation, and the extracted seismic trace near the well is recorded as w side (t);

[0015] ③s a (t) and w side (t) Let the value range of t be [0, T], t = i × Δt, T = m × Δt, where t represents the time depth, Δt represents the time sampling interval, i represents the discrete value, and m represents the discrete value corresponding to the maximum time depth. Establish s a (t) and w side (t) cross-correlation objective function

[0016] ④Continuously adjust and update the value of a and repeat steps ② and ③. When CO(a)≥b, stop the calculation and get the adjusted value of a. b is the pre-set threshold value, and the value range is [0.8, 1].

[0017] Step 4: Use the adjusted a value to calibrate the a value of the seismic data obtained in step 2 to obtain the optimal a;

[0018] Step 5: Use the best a to perform reverse filtering on the stacked seismic data volume to highlight the differences in waveform and amplitude between igneous rocks and surrounding rocks. On the processed seismic profile, use the waveform and amplitude differences to identify igneous rocks.

[0019] In the above scheme, the logarithmic spectrum ratio method is used to obtain the initial a value in step 1 and step 2, which is calculated using the formula Obtain the initial a, where f is the frequency value, τ is the time depth, π = 3.14, A1(f) is the amplitude value of the overlying stratum, and A2(f) is the amplitude value of the current stratum.

[0020] Step 2 in the above scheme is specifically as follows: interpolate and smooth the several initial a values ​​obtained in step 1 to obtain the a curve, and interpret a large set of stratigraphic layers based on the changes in the values ​​of the a curve in the local range. The areas with gentle changes in values ​​are interpreted as one set of strata, and the areas with drastic changes in values ​​are interpreted as another set of strata. Similarly, the a curve is interpreted as several sets of strata, and the total number of strata is not more than 6.

[0021] The fourth step in the above scheme is as follows: the adjusted a value obtained in step three is recorded as a w , the seismic data a obtained in step 2 is recorded as a s , a w a corresponding to the time depth point s Perform division operations one by one to obtain the correction coefficient right Perform spatial interpolation smoothing and compare with as Multiply to get the optimal a.

[0022] The specific step 5 in the above scheme is: using the formula The seismic data is processed by reverse filtering, where s(t) represents the amplitude of the seismic data, S(ω) is the Fourier transform result of the seismic data, ω is the angular frequency, and t is the time depth. represents the inverse Fourier transform, and a is the absorption coefficient obtained in step 4.

[0023] The beneficial effects of the present invention compared with the prior art are:

[0024] 1. The present invention utilizes acoustic logging data and ground reflection seismic data to jointly calculate the absorption coefficient a value, providing key data for obtaining higher-quality seismic profiles and having important application value for igneous rock identification.

[0025] 2. The present invention uses the obtained a to carry out forward filtering processing of seismic data, highlighting the differences between igneous rocks and surrounding rocks in waveform and amplitude, which is very beneficial for subsequent igneous rock identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the technical solution of the present invention;

[0027] Figure 2 It is a schematic diagram of interpreting a large set of stratigraphic layers using the a value obtained from sonic logging data.

[0028] Figure 3 This is the comparison of the original seismic profile before and after reverse filtering processing, (a) is the original seismic profile, and (b) is the profile after reverse filtering. DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0030] The present invention is described in detail below by specific examples, but the scope of protection of the present invention is not limited. Unless otherwise specified, the experimental methods used in the present invention are all conventional methods, and the experimental equipment, materials, reagents, etc. used can be obtained from commercial channels.

[0031] In the description of the present invention, it should be noted that the terms "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used solely for distinction and should not be construed as indicating or implying relative importance.

[0032] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0033] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0034] Example 1

[0035] Taking 3D post-stack seismic data from an oil field in eastern Taiwan as an example, the data sampling interval is 0.001s, the seismic signal recording time is 4s, and the trace spacing is 25m. This method of identifying igneous rocks using seismic data specifically includes the following steps:

[0036] The first step is to use the spectrum ratio method to obtain the initial a value for the acoustic logging data in the work area;

[0037] In the second step, the numerical structural characteristics of the initial a value obtained in step 1 are used to interpret a large set of stratigraphic layers, such as Figure 2 As shown in the figure, the a curve is divided into 5 layers according to its variation. The interpreted stratigraphic layers are used as the calculation window for the a value of ground reflection seismic data, and the logarithmic spectrum ratio method is used within the calculation window. Find the a value of the earthquake data, denoted as a s ;

[0038] The third step consists of 4 steps, namely:

[0039] ① Performing spectrum analysis on shallow seismic data within a time window of 0.5s-1.2s, we find that the dominant frequency of the seismic data is 32Hz. Using the known reflection coefficient sequence R(t) and the Ricker wavelet r(t) with a dominant frequency of 32Hz, we use the formula s(t) = R(t)*r(t), where * represents convolution operation.

[0040] ② For the generated synthetic seismic record s(t), use the forward filtering formula Generate viscoelastic synthetic seismic records a(t), where S(ω) is the Fourier transform of the synthetic seismic record s(t), ω is the circular frequency, t is the vertical one-way travel time, a is the absorption coefficient, Re represents the real part operation, and the seismic trace near the well is extracted and recorded as w side (t);

[0041] ③s a (t) and w side In (t), let the value range of t be [0,4], Δt=0.001s, m=4000, and establish s a (t) and w side (t) cross-correlation objective function

[0042] ④Continuously adjust and update the a value and repeat steps b and c. When CO(a)≥0.91, stop the calculation and get the adjusted a value, which is recorded as a w ;

[0043] Step 4: Update the a w a corresponding to the time depth point s Perform division operations one by one to obtain the correction coefficient right Perform spatial interpolation smoothing and compare with a s Multiply to get the optimal a.

[0044] Step 5: Use the formula The seismic data is processed by reverse filtering, where s(t) represents the amplitude of the seismic data, S(ω) is the Fourier transform result of the seismic data, ω is the angular frequency, and t is the time depth. Stands for inverse Fourier transform. The processed post-stack seismic data volume is displayed as an image using display software. Figure 3 (a) is the post-stack seismic section before reverse filtering. Figure 3 (b) is the post-stack seismic profile after reverse filtering. After applying the method of the present invention, the chaotic reflection and strong absorption characteristics of igneous rocks on the seismic profile are more prominent, and the difference from the surrounding rock is greater, which is very helpful for interpreters to use waveform and amplitude differences to identify igneous rocks. Figure 3 (b) The area surrounded by the two dotted lines is the igneous rock development area, and the volcanic channel characteristics are very obvious. On the cross-section without using the method of the present invention, the igneous rock characteristics are not obvious, and the igneous rock identification is very difficult.

[0045] The embodiments described above are only preferred embodiments of the present invention, and are not all feasible embodiments of the present invention. For those skilled in the art, any obvious changes made thereto without departing from the principles and spirit of the present invention should be considered to be included in the scope of protection of the claims of the present invention. Although the present invention has been described above with reference to the embodiments, various improvements can be made thereto and components thereof can be replaced by equivalents without departing from the scope of the present invention. In particular, as long as there is no technical conflict, the various features in the embodiments disclosed in the present invention can be used in combination with each other in any way, and the fact that these combinations are not exhaustively described in this specification is simply for the purpose of omitting space and saving resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A method for identifying igneous rocks using seismic data, characterized in that the method Specifically: Step 1: Calculate the initial a value using the spectrum ratio method for the acoustic logging data in the work area; Step 2: Using the numerical structural characteristics of the initial a value obtained in step 1, a large set of stratigraphic horizon interpretations are carried out, the stratigraphic horizons are used as ground reflection seismic data to obtain a calculation window, and the spectral ratio method is used to obtain the a value of the seismic data within the calculation window; Step 3: Using the initial value a obtained in step 1 as the initial value, generate a viscoelastic synthetic seismic record, and continuously adjust the value a so that the correlation coefficient between the viscoelastic synthetic seismic record and the wellside seismic data trace reaches a preset threshold value b; Step 4: Use the adjusted a value to calibrate the a value of the seismic data obtained in step 2 to obtain the optimal a; Step 5: Use the best a to perform reverse filtering on the stacked seismic data volume to highlight the differences in waveform and amplitude between igneous rocks and surrounding rocks. On the processed seismic profile, use the differences in waveform and amplitude to identify igneous rocks. In steps 1 and 2, the logarithmic spectrum ratio method is used to obtain the initial a value, which is obtained by using the formula Obtain the initial a, where f is the frequency value, τ is the time depth, π = 3.14, A1(f) is the amplitude value of the overlying layer, and A2(f) is the amplitude value of the current layer; Step 4 is as follows: the adjusted a value obtained in step 3 is recorded as a w , the seismic data a obtained in step 2 is recorded as a s , a w a corresponding to the time depth point s Perform division operations one by one to obtain the correction coefficient right Perform spatial interpolation smoothing and compare with a s Multiply to get the optimal a.

2. The method for identifying igneous rocks using seismic data according to claim 1, wherein: Step 2 is as follows: the initial a values ​​obtained in step 1 are interpolated and smoothed to obtain the a curve, and a large set of stratigraphic layers are interpreted based on the change in the local value of the a curve. The area with gentle value change is interpreted as one set of stratigraphic layers, and the area with drastic value change is interpreted as another set of stratigraphic layers. Similarly, the a curve is interpreted as several sets of stratigraphic layers, and the total number of stratigraphic layers is not more than 6.

3. The method for identifying igneous rocks using seismic data according to claim 1, wherein: Step 5 is as follows: Use the formula The seismic data is processed by reverse filtering, where s(t) represents the amplitude of the seismic data, S(ω) is the Fourier transform result of the seismic data, ω is the angular frequency, and t is the time depth. represents the inverse Fourier transform, and a is the absorption coefficient obtained in step 4.

4. The method for identifying igneous rocks using seismic data according to claim 1, wherein: Step three is as follows: ① Perform spectrum analysis on shallow seismic data within the time window of 0.5s-1.2s, and obtain the dominant frequency of the seismic data as D. Use the known reflection coefficient sequence R(t) and the Ricker wavelet r(t) with the dominant frequency D to generate a synthetic seismic record s(t) through convolution operation, denoted as s(t) = R(t)*r(t), where * represents convolution operation; ② For the generated synthetic seismic record s(t), use the forward filtering formula Generate viscoelastic synthetic seismic records a (t); where S(ω) is the Fourier transform of the synthetic seismic record s(t), ω is the circular frequency, t is the vertical one-way travel time, a is the absorption coefficient, Re represents the real part operation, and the extracted seismic trace near the well is recorded as w side (t); ③s a (t) and w side (t) Let the value range of t be [0, T], t = i × Δt, T = m × Δt, where t represents the time depth, Δt represents the time sampling interval, i represents the discrete value, and m represents the discrete value corresponding to the maximum time depth. Establish s a (t) and w side (t) cross-correlation objective function ④Continuously adjust and update the value of a and repeat steps ② and ③. When CO(a)≥b, stop the calculation and get the adjusted value of a. b is the pre-set threshold value, and its value range is [0.8,1].

5. The method for identifying igneous rocks using seismic data as claimed in claim 1 can be specifically applied in oil fields, and is characterized in that: Specific applications are: The oilfield 3D post-stack seismic data is sampled at a sampling interval of 0.001s, the seismic signal recording time is 4s, and the trace spacing is 25m. The method for identifying igneous rocks using seismic data specifically includes the following steps: The first step is to use the spectrum ratio method to obtain the initial a value for the acoustic logging data in the work area; In the second step, the numerical structural characteristics of the initial a value obtained in step 1 were used to interpret a large set of stratigraphic layers, which were divided into five layers according to the changes in the a curve. The interpreted stratigraphic layers were used as the calculation window for the a value of the ground reflection seismic data, and the logarithmic spectrum ratio method was used within the calculation window. Find the a value of the earthquake data, denoted as a s ; The third step consists of 4 steps: Step 4: Update the a w a corresponding to the time depth point s Perform division operations one by one to obtain the correction coefficient right Perform spatial interpolation smoothing and compare with a s Multiply to get the best a; Step 5: Use the formula The seismic data is processed by reverse filtering, where s(t) represents the amplitude of the seismic data, S(ω) is the Fourier transform result of the seismic data, ω is the angular frequency, and t is the time depth. Stands for inverse Fourier transform.

6. The method for identifying igneous rocks using seismic data as claimed in claim 5 can be specifically applied in oil fields, wherein the third step comprises four sub-steps, namely: ① Performing spectrum analysis on shallow seismic data within a time window of 0.5s-1.2s, we find that the dominant frequency of the seismic data is 32Hz. Using the known reflection coefficient sequence R(t) and the Ricker wavelet r(t) with a dominant frequency of 32Hz, we use the formula s(t) = R(t)*r(t), where * represents convolution operation. ② For the generated synthetic seismic record s(t), use the forward filtering formula Generate viscoelastic synthetic seismic records a (t); where S(ω) is the Fourier transform of the synthetic seismic record s(t), ω is the circular frequency, t is the vertical one-way travel time, a is the absorption coefficient, Re represents the real part operation, and the extracted seismic trace near the well is recorded as w side (t); ③s a (t) and w side In (t), let the value range of t be [0,4], Δt=0.001s, m=4000, and establish s a (t) and w side (t) cross-correlation objective function ④Continuously adjust and update the a value and repeat steps b and c. When CO(a)≥0.91, stop the calculation and get the adjusted a value, which is recorded as a w ; Step 4: Update the a w a corresponding to the time depth point s Perform division operations one by one to obtain the correction coefficient right Perform spatial interpolation smoothing and compare with a s Multiply to get the optimal a.

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