A method, system, medium, and equipment for determining the quality factors of medium-deep formations.

Abstract

This invention relates to a method, system, medium, and device for determining the quality factor of intermediate-deep strata, comprising: acquiring source wavelet and receiver wavelet, and performing Fourier transforms on them respectively to obtain the corresponding amplitude spectra; calculating the probability density functions of the wavelet power spectrum before and after attenuation; calculating target parameters based on the probability density functions of the wavelet power spectrum before and after attenuation and the wavelet amplitude spectra before and after attenuation; calculating the centroid frequency difference of the power spectrum based on the probability density functions of the wavelet power spectrum before and after attenuation; and calculating the quality factor of the corresponding layer based on the target parameters and the centroid frequency difference of the power spectrum. This invention constructs a probability density function using the power spectra of the wavelet before and after attenuation, and uses this as a weight to perform a weighted integral on the logarithmic term of the amplitude spectral ratio to construct a new index with a structure similar to Jeffrey divergence, thereby suppressing spectral ratio vibrations caused by noise and achieving the goal of stably and accurately estimating the quality factor of intermediate-deep strata under different actual seismic data conditions.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology, and more specifically, to a method, system, medium, and equipment for determining the quality factors of medium-deep strata. Background Technology

[0002] The quality factor Q is a crucial physical parameter describing seismic wave absorption and attenuation. It not only characterizes the absorption and attenuation capacity of a formation but is also closely related to reservoir properties and hydrocarbon content. More developed reservoir fractures lead to stronger seismic wave absorption and attenuation, resulting in a lower quality factor. Similarly, more developed reservoir porosity leads to stronger absorption and attenuation, and a lower quality factor. Furthermore, gas-bearing reservoirs exhibit significantly stronger absorption and attenuation capabilities than non-gas-bearing reservoirs; low quality factor anomalies in formation traps often indicate high gas content. Additionally, the quality factor can be used for absorption and attenuation compensation to improve the resolution of mid-to-deep seismic data. Therefore, accurately calculating the formation quality factor Q is of significant theoretical and practical importance for identifying high-quality reservoirs, detecting hydrocarbons, and improving the resolution of mid-to-deep seismic data.

[0003] Traditional methods for estimating the quality factor Q are mostly based on changes in the amplitude spectrum morphology, with the centroid frequency shift (CFS) method being widely used due to its computational simplicity. However, the CFS method relies heavily on assumptions about wavelet morphology, and its estimation results often exhibit systematic biases and insufficient universality under the complex and variable wavelet morphology conditions of actual seismic data. To address this issue, researchers, from an information theory perspective, treat the normalized amplitude spectrum as a probability density function and utilize Jeffrey divergence to measure the difference in spectral distribution before and after attenuation, proposing the generalized centroid frequency shift (GCFS) method. This method perfectly overcomes the sensitivity of CFS to wavelet morphology, but under noise interference, it exhibits a systematic underestimation of the quality factor Q, affecting its applicability in mid-to-deep low signal-to-noise ratio seismic data. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, system, medium and equipment for determining the quality factors of medium-deep formations, addressing the problems existing in the prior art.

[0005] The technical solution adopted by this invention to solve its technical problem is: constructing a method for determining the quality factor of medium-deep strata, comprising: Step S1: Obtain the source wavelet and the received wavelet; Step S2: Perform Fourier transforms on the source wavelet and the receiving wavelet respectively to obtain the corresponding amplitude spectrum; Step S3: Calculate the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation based on the calculated amplitude spectrum; Step S4: Calculate the target parameters based on the probability density function of the wavelet power spectrum before attenuation, the probability density function of the wavelet power spectrum after attenuation, the amplitude spectrum of the wavelet before attenuation, and the amplitude spectrum of the wavelet after attenuation. Step S5: Calculate the power spectrum centroid frequency difference based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation. Step S6: Calculate the quality factor of the corresponding layer segment based on the target parameters and the power spectrum centroid frequency difference.

[0006] In the method for determining the quality factor of medium-deep strata according to the present invention, in step S3, the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation are calculated by the following formulas: ; ; In the formula, Let be the probability density function of the wavelet power spectrum before attenuation. Let be the probability density function of the power spectrum of the attenuated wavelet. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

[0007] In the method for determining the quality factor of medium-deep formations described in this invention, the target parameters are obtained by using the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation as weighting factors, and constructing a logarithmic selection of wavelet amplitude spectra.

[0008] In the method for determining the quality factor of intermediate-deep formations according to the present invention, in step S4, the target parameter is calculated by the following formula: ; In the formula, The objective parameter is a parameter with a Jeffrey divergence structure. Let be the probability density function of the wavelet power spectrum before attenuation. Let be the probability density function of the power spectrum of the attenuated wavelet. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

[0009] In the method for determining the quality factor of intermediate-deep formations according to the present invention, step S5, calculating the power spectrum centroid frequency difference based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation, includes: The centroid frequency of the wavelet power spectrum before attenuation is obtained by calculating based on the probability density function of the wavelet power spectrum before attenuation. The centroid frequency of the attenuated wavelet power spectrum is obtained by calculating based on the probability density function of the attenuated wavelet power spectrum. The centroid frequency difference of the power spectrum is obtained by calculating based on the centroid frequency of the wavelet power spectrum before attenuation and the centroid frequency of the wavelet power spectrum after attenuation.

[0010] In the method for determining the quality factor of intermediate-deep formations described in this invention, the centroid frequencies of the wavelet power spectrum before attenuation and the wavelet power spectrum after attenuation are calculated using the following formulas: = ; = ; In the formula, The centroid frequency of the wavelet power spectrum before attenuation; The centroid frequency of the attenuated wavelet power spectrum. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

[0011] In the method for determining the quality factor of intermediate-deep formations described in this invention, the quality factor is calculated using the following formula: ; In the formula, Q is the quality factor. For the target parameters, Due to travel time differences, The difference in the centroid frequency of the power spectrum.

[0012] The present invention also provides a system for determining the quality factors of medium-deep formations, comprising: Wavelet acquisition unit, used to acquire source wavelets and receive wavelets; An amplitude spectrum calculation unit is used to perform Fourier transforms on the source wavelet and the receiving wavelet respectively to obtain the corresponding amplitude spectrum; The probability density function calculation unit is used to calculate the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation based on the calculated amplitude spectrum. The target parameter calculation unit is used to calculate the target parameters based on the probability density function of the wavelet power spectrum before attenuation, the probability density function of the wavelet power spectrum after attenuation, the amplitude spectrum of the wavelet before attenuation, and the amplitude spectrum of the wavelet after attenuation. The centroid frequency calculation unit is used to calculate the power spectrum centroid frequency difference based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation. The quality factor calculation unit is used to calculate the quality factor of the corresponding layer segment based on the target parameters and the centroid frequency difference of the power spectrum.

[0013] The present invention also provides a storage medium storing a computer program adapted for loading by a processor to perform the steps of the method for determining the quality factor of medium-deep formations as described above.

[0014] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the method for determining the quality factor of medium-deep formations as described above by calling the computer program stored in the memory.

[0015] The method, system, medium, and equipment for determining the quality factor of intermediate-deep strata according to the present invention have the following beneficial effects: They include: acquiring source wavelet and receiver wavelet, and performing Fourier transforms on them to obtain the corresponding amplitude spectra; calculating the probability density functions of the wavelet power spectrum before and after attenuation; calculating the target parameters based on the probability density functions of the wavelet power spectrum before and after attenuation and the wavelet amplitude spectra before and after attenuation; calculating the centroid frequency difference of the power spectrum based on the probability density functions of the wavelet power spectrum before and after attenuation; and calculating the quality factor of the corresponding layer based on the target parameters and the centroid frequency difference of the power spectrum. The present invention constructs a probability density function using the power spectra of the wavelets before and after attenuation, and uses this as a weight to perform a weighted integral on the logarithmic term of the amplitude spectral ratio to construct a new index with a structure similar to Jeffrey divergence, thereby suppressing spectral ratio vibrations caused by noise and achieving the goal of stably and accurately estimating the quality factor of intermediate-deep strata under different actual seismic data conditions. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart illustrating the method for determining the quality factor of medium-deep formations provided in an embodiment of the present invention. Figure 2 This is a logic block diagram of the method for determining the quality factor of medium-deep strata provided in the embodiments of the present invention; Figure 3 Generalized seismic wavelet amplitude spectra of different orders u (0.4, 2, 3); Figure 4 Synthetic seismic records containing absorption and attenuation; Figure 5 Synthesized seismic records with noise absorption and attenuation; Figure 6 Probability distribution of Q-value estimation results for three methods under different noise levels: (a) 1% random noise, (b) 3% random noise, (c) 5% random noise; Figure 7Actual zero-bias VSP data; Figure 8 Q-value estimation results for actual zero-biased VSP data using three methods; Figure 9 Actual formation velocity. Detailed Implementation

[0017] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Existing technologies use the Generalized Centroid Frequency Shift (GCFS) method to estimate quality factors. However, the Jeffrey divergence obtained using the standard Jeffrey divergence definition is systematically overestimated by noise interference, leading to a systematically underestimated Q-value. To avoid excessive systematic underestimation by the GCFS method due to noise, it is necessary to reduce the influence of spectral ratio logarithmic perturbation in the JD divergence calculation formula. The JD divergence calculation process is equivalent to weighted integration and summation of the logarithmic term using the amplitude spectral probability density function as a weighting factor. If the weighting factor can effectively suppress the influence of high- and low-frequency perturbations in the logarithmic term during integration, the systematic underestimation problem of the GCFS method will be improved. Based on this principle, this invention uses the power spectral probability density function of the wavelet before and after attenuation as a weighting factor, and constructs parameters similar to the Jeffrey divergence structure using the wavelet amplitude spectrum for the logarithmic term. Specifically, this invention provides a high-precision and stable method for determining the quality factor of medium-deep formations—a Jeffrey divergence-like quality factor estimation method based on power spectral probability density function weighting. This method constructs a probability density function using the power spectra of the wavelet before and after attenuation, and then uses this as a weight to perform a weighted integral on the logarithmic term of the amplitude spectral ratio, constructing a new index with a structure similar to Jeffrey divergence—Power Spectral Density Function-Weighted Jeffrey-like Divergence (PJD). The GCFS method adopts the standard definition of Jeffrey divergence, constructing a probability density function using the amplitude spectra of the wavelet before and after attenuation, and then using this as a weight to perform a weighted integral on the logarithmic term of the amplitude spectral probability density ratio to obtain the JD divergence value. PJD, through the logarithmic form of the amplitude ratio, not only inherits the theoretical advantage of GCFS's insensitivity to wavelet morphology, but also, through the weighting mechanism of the power spectral probability density function, can theoretically suppress noise-induced spectral ratio perturbations, improving the accuracy and robustness of GCFS in environments with strong noise influence, and enhancing the estimation accuracy and stability of mid-to-deep formation quality factors.

[0019] refer to Figure 1 In a preferred embodiment, the method for determining the quality factor of medium-deep formations includes steps S1, S2, S3, S4, S5, and S6.

[0020] Step S1: Obtain the source wavelet and the receiver wavelet.

[0021] In this embodiment of the invention, the source wavelet is the wavelet before attenuation, and the receiver wavelet is the wavelet after attenuation. The source wavelet and receiver wavelet can be extracted from the seismic data to be applied.

[0022] Step S2: Perform Fourier transforms on the source wavelet and the receiver wavelet respectively to obtain the corresponding amplitude spectrum.

[0023] In this embodiment of the invention, the amplitude spectra of the source wavelet and the receiver wavelet are obtained by performing Fourier transforms on the source wavelet and the receiver wavelet, respectively. Calculating these amplitude spectra separately provides a data basis for the subsequent power spectral probability density function.

[0024] Step S3: Calculate the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation based on the calculated amplitude spectrum.

[0025] In this embodiment of the invention, in step S3, the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation are calculated by the following formulas: (1); (2); In the formula, Let be the probability density function of the wavelet power spectrum before attenuation. Let be the probability density function of the power spectrum of the attenuated wavelet. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

[0026] This invention abandons the amplitude spectrum probability density function used in the traditional GCFS method as a weight, and instead uses the power spectrum probability density functions of the wavelets before and after attenuation as weighting factors. The power spectrum is the square of the amplitude spectrum, which is more sensitive to signal energy and can more effectively highlight the dominant frequency energy region, thus naturally suppressing high and low frequency spectral disturbances caused by noise during the integration process.

[0027] Step S4: Calculate the target parameters based on the probability density function of the wavelet power spectrum before attenuation, the probability density function of the wavelet power spectrum after attenuation, the amplitude spectrum of the wavelet before attenuation, and the amplitude spectrum of the wavelet after attenuation.

[0028] In this embodiment of the invention, the target parameters are obtained by constructing a logarithmic term using the wavelet amplitude spectrum, with the power spectral probability density function before and after attenuation as weighting factors. Specifically, a metric similar to Jeffrey divergence but with a novel structure—Power Spectral Density Weighted Jeffrey Divergence (PJD)—is constructed using the sum of the power spectral probability density functions of the wavelets before and after attenuation as weighting factors. This metric integrates the logarithmic term of the amplitude spectrum with the weights of the power spectral probability density function, rather than directly using the normalized amplitude spectral probability density function. This structural change is the theoretical basis for improving noise immunity.

[0029] The target parameter is calculated using the following formula: (3); In the formula, The objective parameter is a parameter with a Jeffrey divergence structure. Let be the probability density function of the wavelet power spectrum before attenuation. Let be the probability density function of the power spectrum of the attenuated wavelet. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

[0030] Step S5: Calculate the centroid frequency difference of the power spectrum based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation.

[0031] In this embodiment of the invention, step S5, calculating the centroid frequency difference of the power spectrum based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation, includes: calculating the centroid frequency of the wavelet power spectrum before attenuation based on the probability density function of the wavelet power spectrum before attenuation; calculating the centroid frequency of the wavelet power spectrum after attenuation based on the probability density function of the wavelet power spectrum after attenuation; and calculating the centroid frequency difference of the power spectrum based on the centroid frequencies of the wavelet power spectrum before and after attenuation.

[0032] The centroid frequencies of the wavelet power spectrum before attenuation and after attenuation are calculated using the following formulas: = (4); = (5); In the formula, The centroid frequency of the wavelet power spectrum before attenuation; The centroid frequency of the attenuated wavelet power spectrum. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

[0033] Step S6: Calculate the quality factor of the corresponding segment based on the target parameters and the centroid frequency difference of the power spectrum.

[0034] In this embodiment of the invention, the seismic wave attenuation model is substituted into the newly constructed... The definition (i.e., the aforementioned formula (3)) is derived mathematically, ultimately yielding the following: The new analytical estimation formula for the Q value, with the power spectrum centroid frequency difference as the core and the power spectrum centroid frequency difference as the variable, is as follows: (6); In the formula, Q is the quality factor. For the target parameters, Due to travel time differences, The difference in the centroid frequency of the power spectrum.

[0035] As can be seen from formula (6), although the new analytical estimation formula for Q value is similar in form to the GCFS method, its inherent physical meaning and mathematical foundation have been fundamentally changed, thus giving the method a completely new performance.

[0036] The method of this invention (PJD) inherits the two major advantages of being insensitive to wavelet morphology (universality) and having strong resistance to noise interference (robustness) under the theory of the present invention. It solves the shortcomings of the existing CFS method (dependent on wavelet morphology) and GCFS method (sensitive to noise), and achieves the goal of stable and accurate estimation of Q value of medium and deep strata under different actual seismic data conditions.

[0037] refer to Figure 2 The present invention also provides a system for determining the quality factors of medium-deep formations.

[0038] like Figure 2 As shown, the system for determining the quality factors of medium-deep formations includes: Wavelet acquisition unit 201 is used to acquire source wavelets and receive wavelets.

[0039] The amplitude spectrum calculation unit 202 is used to perform Fourier transforms on the source wavelet and the receiver wavelet respectively to obtain the corresponding amplitude spectrum.

[0040] The probability density function calculation unit 203 is used to calculate the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation based on the calculated amplitude spectrum.

[0041] The target parameter calculation unit 204 is used to calculate the target parameters based on the probability density function of the wavelet power spectrum before attenuation, the probability density function of the wavelet power spectrum after attenuation, the amplitude spectrum of the wavelet before attenuation, and the amplitude spectrum of the wavelet after attenuation.

[0042] The centroid frequency calculation unit 205 is used to calculate the centroid frequency difference of the power spectrum based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation.

[0043] The quality factor calculation unit 206 is used to calculate the quality factor of the corresponding layer segment based on the target parameters and the power spectrum centroid frequency difference.

[0044] Specifically, the specific coordination process between the units in the intermediate-deep strata quality factor determination system can be referred to the above-mentioned intermediate-deep strata quality factor determination method, and will not be repeated here.

[0045] The three methods will be compared and explained below with specific examples.

[0046] First, a single homogeneous medium model is constructed, and the Q-value estimation accuracy of the CFS method, GCFS method, and PJD method (i.e., the method of this invention) under noise-free conditions is compared and analyzed by forward modeling seismic records with absorption attenuation.

[0047] The model parameters were set as follows: Q values ​​of 25, 50, 100, and 150, and the source wavelet consisted of three different orders of generalized seismic wavelets u (0.4, 2, and 3) with a main frequency of 45 Hz (their amplitude spectra are shown in the figure). Figure 3 As shown), the travel time difference is 300 ms, and the seismic record including absorption attenuation is as follows. Figure 4 As shown.

[0048] Secondly, the calculation frequency band was selected as 5-110Hz. The results of Q-value estimation by the three Q-value estimation methods under different generalized wavelet parameters (i.e., different orders (u=0.4, 2, 3)) are shown in Table 1.

[0049] Table 1 shows significant differences. Neither the GCFS nor the PJD method exhibited good Q-value estimation accuracy under all test conditions. Regardless of whether the u value increased from 0.4 to 3, or the Q value increased from 30 to 150, the deviation between the estimated result and the true value consistently approached zero. This indicates that the newly proposed PJD method is insensitive to changes in wavelet morphology and attenuation intensity and possesses universality. However, the CFS method's estimation results were generally higher than the true Q value, with the largest deviation at u=0.4 (e.g., an estimate of 34.01 when the true Q=25). As u increased to 2 and 3, the CFS estimate gradually approached the true value, but still showed an overestimation trend, indicating that this method is significantly affected by the wavelet morphology, and the more the wavelet amplitude spectrum deviates from Gaussian, the greater the systematic error introduced. The comparative test results show that the PJD and GCFS methods have comparable performance; changes in the wavelet do not cause systematic errors in Q-value estimation, while the CFS method exhibits complex parameter dependencies and systematic biases.

[0050] Table 1. Q-value estimation results of the three methods under different generalized wavelet parameters (u=0.4, 2, 3). Next, a stability analysis of the impact of noise was conducted.

[0051] Taking Q=100 as an example, select Figure 4 In section (b), for the third test, zero-mean Gaussian white noise was added, and 1000 independent tests were conducted. The frequency band was calculated to be 5-110Hz. Three sets of tests were performed, with noise levels of 1%, 3%, and 5% of the maximum amplitude of the source wavelet, respectively. Figure 5 As shown.

[0052] The probability distributions of the three methods for estimating Q-values ​​in noisy environments are as follows: Figure 6 As shown. From Figure 6 As can be seen, there are significant differences in the performance of the three methods. Under different noise levels, the PJD method exhibits the strongest probability distribution focus, and the peak value's corresponding x-axis (mean) is closest to the true value. The GCFS method has the second strongest probability distribution focus, but the peak value deviates from (underestimates) the true value, and the degree of deviation increases with increasing noise level. The CFS method has the worst probability distribution focus, with the peak value generally exhibiting a right-skewed characteristic, and similarly, the deviation increases with increasing noise. These results indicate that the PDJ method not only mitigates the underestimation of the Q-value in Jeffrey divergence estimation due to noise interference but also further enhances the stability of the Q-value estimation.

[0053] The statistical analysis results of the three methods for estimating Q values ​​under 5% random noise interference are shown in Table 2.

[0054] Table 2. Statistical analysis results of Q-value estimation using three methods under 5% random noise interference. As shown in Table 2, although the CFS method has the highest mean (139.12), it suffers from severe systematic overestimation (bias +39.12%) and the worst stability, with a standard deviation of 42.35 and a coefficient of variation of 0.304, both the highest among the three methods. More seriously, the CFS method exhibits a significant right-skewed distribution (skewness 2.87) and leptokurtic thick-tailed characteristics (kurtosis 18.45), making it extremely sensitive to outliers, with a maximum measured value of 1498.67. This indicates that the method is unreliable in noisy environments and prone to extreme errors. In contrast, the GCFS method shows better distribution characteristics, approaching a normal distribution (skewness 0.68), but its systematic underestimation problem is prominent, with a mean of 61.45 deviating from the true value by -38.55%. Although its stability is moderate (standard deviation 18.23), this level of systematic bias severely affects its practical value. The PJD method performs best in all three aspects: in terms of accuracy, its mean of 91.84 is closest to the true value, with a deviation of only -8.16%; in terms of stability, its standard deviation of 16.85 and coefficient of variation of 0.183 are the lowest; and in terms of distribution characteristics, it exhibits only a slight right skewness (skewness 1.25) and the fewest outliers. This indicates that the PJD method has good robustness and reliability in noisy environments. In summary, the PJD method comprehensively outperforms the CFS and GCFS methods in terms of accuracy, stability, and anti-interference capability, making it the preferred measurement scheme in noisy environments.

[0055] This invention was applied to measured zero-bias VSP data in a certain region, which contained a total of 82 channels, with the detectors between each channel spaced 20m apart. Figure 7As shown in the figure, it can be seen that the seismic waves received by the detector exhibit significant attenuation with increasing depth. Downlink direct waves from this VSP data were obtained through data processing, and the formation Q-values ​​were estimated using the CFS, GCFS, and PJD methods, respectively, with the calculation frequency band selected as 5–100 Hz. The Q-value estimation results of the three methods are shown below. Figure 8 As shown, the results estimated by the three methods generally follow the same trend: as the burial depth increases, the Q value increases, the absorption and attenuation effect weakens, and it is related to the formation velocity (such as...). Figure 9 The results (shown) show a strong correlation, confirming the reliability of PJD in practical applications. Compared to the PJD method, the GCFS method estimates the values ​​generally lower, while the CFS method estimates the values ​​generally higher, similar to the model test results, further validating the stability of the PJD method in estimating Q-values.

[0056] Furthermore, an electronic device of the present invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the method for determining the quality factor of medium-deep formations as described above. Specifically, according to embodiments of the present invention, the processes described above with reference to the flowchart can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, when the computer program is downloaded, installed, and executed by an electronic device, it performs the functions defined above in the methods of the embodiments of the present invention. The electronic device in the present invention can be a terminal such as a laptop, desktop computer, tablet computer, or smartphone, or it can be a server.

[0057] Furthermore, one type of storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the method for determining the quality factor of medium-deep formations as described above. Specifically, it should be noted that the storage medium described above in the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0058] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0059] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0060] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0061] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0062] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They do not limit the scope of protection of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A method for determining the quality factors of medium-deep formations, characterized in that, include: Step S1: Obtain the source wavelet and the received wavelet; Step S2: Perform Fourier transforms on the source wavelet and the receiving wavelet respectively to obtain the corresponding amplitude spectrum; Step S3: Calculate the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation based on the calculated amplitude spectrum; Step S4: Calculate the target parameters based on the probability density function of the wavelet power spectrum before attenuation, the probability density function of the wavelet power spectrum after attenuation, the amplitude spectrum of the wavelet before attenuation, and the amplitude spectrum of the wavelet after attenuation. Step S5: Calculate the power spectrum centroid frequency difference based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation. Step S6: Calculate the quality factor of the corresponding layer segment based on the target parameters and the power spectrum centroid frequency difference.

2. The method for determining the quality factor of medium-deep formations according to claim 1, characterized in that, In step S3, the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation are calculated by the following formulas: ； ； In the formula, Let be the probability density function of the wavelet power spectrum before attenuation. Let be the probability density function of the power spectrum of the attenuated wavelet. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

3. The method for determining the quality factor of medium-deep formations according to claim 1, characterized in that, The target parameters are obtained by using the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation as weighting factors, and constructing the logarithmic wavelet amplitude spectrum.

4. The method for determining the quality factor of medium-deep formations according to claim 3, characterized in that, In step S4, the target parameter is calculated using the following formula: ； In the formula, The objective parameter is a parameter with a Jeffrey divergence structure. Let be the probability density function of the wavelet power spectrum before attenuation. Let be the probability density function of the power spectrum of the attenuated wavelet. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

5. The method for determining the quality factor of medium-deep formations according to claim 1, characterized in that, In step S5, the power spectrum centroid frequency difference is calculated based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation, including: The centroid frequency of the wavelet power spectrum before attenuation is obtained by calculating based on the probability density function of the wavelet power spectrum before attenuation. The centroid frequency of the attenuated wavelet power spectrum is obtained by calculating based on the probability density function of the attenuated wavelet power spectrum. The centroid frequency difference of the power spectrum is obtained by calculating based on the centroid frequency of the wavelet power spectrum before attenuation and the centroid frequency of the wavelet power spectrum after attenuation.

6. The method for determining the quality factor of medium-deep formations according to claim 5, characterized in that, The centroid frequencies of the wavelet power spectrum before attenuation and the wavelet power spectrum after attenuation are calculated using the following formulas: = ； = ； In the formula, The centroid frequency of the wavelet power spectrum before attenuation; The centroid frequency of the attenuated wavelet power spectrum. The amplitude spectrum of the source wavelet; To receive the amplitude spectrum of the wavelet.

7. The method for determining the quality factor of medium-deep formations according to claim 1, characterized in that, The quality factor is calculated using the following formula: ； In the formula, Q is the quality factor. For the target parameters, Due to travel time differences, The difference in the centroid frequency of the power spectrum.

8. A system for determining the quality factors of medium-deep formations, characterized in that, include: Wavelet acquisition unit, used to acquire source wavelets and receive wavelets; An amplitude spectrum calculation unit is used to perform Fourier transforms on the source wavelet and the receiving wavelet respectively to obtain the corresponding amplitude spectrum; The probability density function calculation unit is used to calculate the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation based on the calculated amplitude spectrum. The target parameter calculation unit is used to calculate the target parameters based on the probability density function of the wavelet power spectrum before attenuation, the probability density function of the wavelet power spectrum after attenuation, the amplitude spectrum of the wavelet before attenuation, and the amplitude spectrum of the wavelet after attenuation. The centroid frequency calculation unit is used to calculate the power spectrum centroid frequency difference based on the probability density function of the wavelet power spectrum before attenuation and the probability density function of the wavelet power spectrum after attenuation. The quality factor calculation unit is used to calculate the quality factor of the corresponding layer segment based on the target parameters and the centroid frequency difference of the power spectrum.

9. A storage medium, characterized in that, The storage medium stores a computer program adapted for loading by a processor to perform the steps of the method for determining the quality factor of intermediate-deep formations as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the method for determining the quality factor of medium-deep formations as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

Images