Method and device for determining formation quality factor, electronic equipment and storage medium

Abstract

Embodiments of the present application disclose a method and device for determining a formation quality factor, electronic equipment and a storage medium. The method comprises: exciting a seismic wave to a target formation, determining a reference wavelet, a corresponding receiving wavelet and a target frequency band; determining a spectral ratio logarithm and a frequency value based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the receiving wavelet, and determining a spectral ratio logarithm average difference based on a plurality of spectral ratio logarithms and a frequency average difference based on a plurality of frequency values; determining the quality factor of the target formation based on the spectral ratio logarithm average difference and the frequency average difference, wherein the quality factor represents the degree of absorption and attenuation of the seismic wave in the target formation. The technical scheme of the embodiments of the present application solves the problem that the current method for determining the quality factor is easily affected by noise, resulting in inaccurate values of the quality factor, reduces noise interference, and improves the accuracy of the quality factor.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to methods, apparatus, electronic equipment and storage media for determining formation quality factors. Background Technology

[0002] During the propagation of seismic waves in the subsurface medium, the inelasticity of the strata causes absorption and attenuation, resulting in a reduction in energy and a narrowing of the frequency band. This absorption and attenuation is typically described using the quality factor Q. The quality factor Q can be used to improve the resolution of seismic data, reservoir characterization, and fluid detection; therefore, robust and reliable estimation of the quality factor Q is of great significance.

[0003] Current methods for estimating the formation quality factor Q include the spectral ratio method, centroid frequency shift method, logarithmic spectral area difference method, and peak frequency method, among which the spectral ratio method is the most widely used. The spectral ratio method involves obtaining the logarithm of the amplitude spectral ratio between the received wavelet (attenuated seismic wave) and the reference wavelet (unattenuated seismic wave) within a certain frequency band. Based on this, a least-squares linear fit is performed to obtain the slope of the fitted line, and the Q value is estimated according to the relationship between the slope and the Q value. Theoretically, this method has high estimation accuracy, but its main drawbacks are its high sensitivity to noise and poor stability.

[0004] Due to the limitations of seismic data acquisition conditions, the acquired actual seismic data often contains noise interference, causing a deviation between the calculated spectral ratio logarithm and the true value. The traditional spectral ratio method for calculating the quality factor Q value uses a least squares linear fitting method. When performing the least squares operation, squaring the fitted data amplifies the influence of large deviations, leading to unstable fitting results and making it highly sensitive to anomalous noise. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for determining formation quality factors, so as to achieve the determination of the quality factors of a target formation.

[0006] According to one aspect of the present invention, a method for determining formation quality factors is provided, comprising:

[0007] Generating seismic waves towards the target stratum, determining the reference wavelet, the corresponding received wavelet, and the target frequency band;

[0008] The spectral comparison logarithm and frequency value are determined based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet, and the average difference of the spectral comparison logarithm and the average difference of the frequency value are determined based on the multiple spectral comparison logarithms.

[0009] The quality factor of the target formation is determined based on the logarithmic mean difference of the spectral ratio and the mean difference of the frequency, wherein the quality factor represents the degree of absorption and attenuation of the seismic wave in the target formation.

[0010] According to another aspect of the present invention, an apparatus for determining formation quality factors is provided, comprising:

[0011] The wavelet receiving module is used to generate seismic waves into the target strata and determine the reference wavelet and the received wavelet;

[0012] The average difference calculation module is used to determine the spectral comparison logarithm and frequency value based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet, and to determine the spectral comparison logarithmic average difference based on multiple spectral comparison logarithms and the frequency average difference based on multiple frequency values.

[0013] The factor calculation module is used to determine the quality factor of the target formation based on the logarithmic mean difference of the spectral ratio and the mean difference of the frequency, wherein the quality factor represents the degree of absorption and attenuation of the seismic wave in the target formation.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining formation quality factors according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for determining formation quality factors according to any embodiment of the present invention.

[0019] The technical solution of this invention involves exciting seismic waves into a target formation to determine a reference wavelet, a corresponding received wavelet, and a target frequency band. Based on the amplitude spectra of the reference wavelet and the received wavelet, the spectral comparison logarithm and frequency values ​​are determined. The average logarithmic difference of the spectral comparisons is determined based on multiple spectral comparison logarithms, and the average frequency difference is determined based on multiple frequency values. The quality factor of the target formation is determined based on the average logarithmic difference of the spectral comparisons and the average frequency difference, where the quality factor represents the degree of absorption and attenuation of seismic waves in the target formation. This technical solution solves the problem that current methods for determining the quality factor are easily affected by noise, leading to inaccurate quality factor values. It reduces noise interference and improves the accuracy of the quality factor.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for determining formation quality factors according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for determining formation quality factors according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a synthesized record of the direct wave from a zero-biased VSP.

[0025] Figure 4 This is a graph showing the Q-value estimation results using the noiseless VSP direct wave synthesis record spectral ratio method and the average difference method;

[0026] Figure 5 This is a graph showing the relative error of Q-value estimation using the noiseless VSP direct wave synthesis recording spectral ratio method and the average difference method;

[0027] Figure 6 This is a graph showing the Q-value estimation results of the noisy VSP direct wave synthesis record spectral ratio method and the average difference method;

[0028] Figure 7 Actual zero-bias VSP seismic data map;

[0029] Figure 8 Figure showing the Q-value estimation results based on the spectral ratio method and the mean difference method using actual zero-section VSP seismic data;

[0030] Figure 9 This is a schematic diagram of the structure of a formation quality factor determination device according to Embodiment 3 of the present invention.

[0031] Figure 10 This is a schematic diagram of the structure of an electronic device that implements a method for determining formation quality factors according to Embodiment 4 of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Example 1

[0035] Figure 1 This is a flowchart of a method for determining formation quality factors according to Embodiment 1 of the present invention. This embodiment is applicable to the determination of formation quality factors. The method can be executed by a formation quality factor determination device, which can be implemented in hardware and / or software and can be configured in computer equipment or oil and gas exploration equipment. Figure 1 As shown, the method includes:

[0036] S110. Generate seismic waves into the target stratum to determine the reference wavelet, the corresponding receiving wavelet, and the target frequency band.

[0037] In this embodiment, the target formation refers to the formation whose quality factor needs to be determined. For example, in oil and gas exploration, seismic waves are excited on the ground (or sea surface), and the seismic waves are received by a geophone along the vertical direction of the formation to further determine the quality factor of the formation. The target frequency band can be a pre-set frequency band. For example, the target frequency band is 5-100 Hz. The reference wavelet refers to the wavelet of the seismic wave before absorption and attenuation, and the received wavelet refers to the wavelet after absorption and attenuation that matches the reference wavelet detected by some detection devices.

[0038] Based on the above scheme, the frequency values ​​of the reference wavelet and the received wavelet are between the target frequency band.

[0039] In this embodiment, the exploration personnel can pre-set the frequency band range and set up detectors along the vertical direction of the strata. The detectors detect different reference wavelets and received wavelets. For example, 51 detectors are arranged sequentially along the vertical direction of the strata from depth 0 to 1000m, with a spacing of 20m between adjacent detectors. It should be noted that the frequencies of the reference wavelet and the received wavelet are both within the target frequency band. For example, when the target frequency band is 5-100Hz, the frequencies of the reference wavelet and the received wavelet are both within the 5-100Hz range.

[0040] Based on the above scheme, before determining the spectral comparison logarithm and frequency value based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet, the method further includes: performing Fourier transform on the reference wavelet and the received wavelet to obtain the amplitude spectrum corresponding to the reference wavelet and the amplitude spectrum corresponding to the received wavelet.

[0041] In the application scenario of this implementation, after receiving the reference wavelet and the received wavelet, a Fourier transform can be performed on the reference wavelet and the received wavelet to obtain the amplitude spectrum corresponding to the reference wavelet and the amplitude spectrum corresponding to the received wavelet. The amplitude spectrum relationship of the wavelet absorption and attenuation process can be expressed by equation (1):

[0042]

[0043] Where A0(f) is the reference wavelet amplitude spectrum, A(f) is the received wavelet amplitude spectrum, f is the frequency, Δt is the travel time, and Q is the quality factor. C represents the scattering attenuation coefficient, which is independent of frequency throughout the entire seismic frequency band and includes geometric diffusion, reflection, transmission, etc.

[0044] S120. Based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet, determine the spectral comparison logarithm and frequency value, and determine the average difference of the spectral comparison logarithm based on multiple spectral comparison logarithms and the average difference of the frequency based on multiple frequency values.

[0045] In this embodiment, the spectral comparison logarithm refers to the logarithm of the ratio of the amplitude spectrum of the received wavelet to the amplitude spectrum of the reference wavelet. For example, if the ratio is A, the corresponding spectral comparison logarithm is lnA. The spectral comparison logarithm mean difference refers to the average of the differences between each spectral comparison logarithm and the average of the spectral comparison logarithms. The frequency mean difference can be understood as the average of the differences between each frequency value and the arithmetic mean of the frequency values.

[0046] Based on the above embodiments, determining the spectral comparison logarithm based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet includes: using the amplitude spectrum of the reference wavelet as the reference wavelet amplitude spectrum and the amplitude spectrum of the received wavelet as the received wavelet amplitude spectrum; determining the ratio of the reference wavelet amplitude spectrum to the received wavelet amplitude spectrum; and determining the spectral comparison logarithm based on the logarithm of the ratio.

[0047] Specifically, for reference wavelet and received wavelet corresponding to the same frequency in the target frequency band, the amplitude spectrum of the reference wavelet can be used as the reference wavelet amplitude spectrum, and the amplitude spectrum of the received wavelet can be used as the received wavelet amplitude spectrum. Furthermore, the ratio of the received wavelet amplitude spectrum to the reference wavelet amplitude spectrum is determined, and the logarithm of this ratio is used as the spectral comparison logarithm.

[0048] For example, dividing A(f) by A0(f) and taking the logarithm, we can obtain the result according to formula (1).

[0049]

[0050] Formula (2) is a linear function of frequency and can be rewritten as follows:

[0051] L(f)=kf+b (3)

[0052] Where L(f)=ln[A(f) / A0(f)] is the spectral logarithm; k is the slope. b is the intercept, b = ln(C).

[0053] It should also be noted that within the target frequency band, there are multiple reference wavelet amplitude spectra and received wavelet amplitude spectra corresponding to multiple frequency values, and correspondingly, there are also multiple spectral comparison logarithms, each of which is calculated according to the above formula.

[0054] Based on the above embodiments, determining the average difference of spectral logarithms based on a plurality of spectral logarithms includes: calculating the arithmetic mean of the spectral logarithms based on the plurality of spectral logarithms and the total number of spectral logarithms; and determining the average difference of spectral logarithms based on the difference between the arithmetic mean of the spectral logarithms and each spectral logarithm.

[0055] In this embodiment, the mean difference d of the spectral logarithm L(f) can be expressed as:

[0056]

[0057] Where μ represents the arithmetic mean of the spectral comparison logarithm L(f), expressed as follows:

[0058]

[0059] Substituting formula (3) into formula (5) yields

[0060]

[0061] Where μ f The arithmetic mean of frequencies is represented as follows:

[0062]

[0063] Based on the above embodiments, determining the frequency average difference based on multiple frequency values ​​includes: determining the arithmetic mean of the frequency values ​​based on the frequency values ​​corresponding to the multiple reference wavelets and the received wavelets and the total number of the frequency values; and determining the frequency average difference based on the difference between the arithmetic mean of the frequency values ​​and each frequency value.

[0064] In this embodiment, N represents the total number of frequency values, μ f The arithmetic mean of the frequency values ​​is given by formula (3) and formula (6). Further, substituting these formulas into formula (4) yields:

[0065]

[0066] Where d f The average difference in frequencies is expressed as follows:

[0067]

[0068] Furthermore, from formula (8), we can obtain

[0069]

[0070] S130. Determine the quality factor of the target formation based on the logarithmic mean difference of the spectral ratio and the mean difference of the frequency, wherein the quality factor represents the degree of absorption and attenuation of the seismic wave in the target formation.

[0071] Based on the above scheme, determining the quality factor of the target formation based on the logarithmic mean difference of the spectral comparison and the frequency mean difference includes: determining a target coefficient based on the ratio of the logarithmic mean difference of the spectral comparison to the frequency mean difference, and determining the quality factor of the target formation based on the target coefficient and the travel time. Here, the travel time is the propagation time of the seismic wave, and the target coefficient refers to the ratio of the logarithmic mean difference of the spectral comparison to the frequency mean difference, i.e., d / df. Based on the relationship between the target coefficient and the Q-value, a new Q-value estimation formula is derived as follows:

[0072]

[0073] Here, Q refers to the quality factor.

[0074] The technical solution of this invention involves exciting seismic waves into a target formation to determine a reference wavelet, a corresponding received wavelet, and a target frequency band. Based on the amplitude spectra of the reference wavelet and the received wavelet, the spectral comparison logarithm and frequency values ​​are determined. The average logarithmic difference of the spectral comparisons is determined based on multiple spectral comparison logarithms, and the average frequency difference is determined based on multiple frequency values. The quality factor of the target formation is determined based on the average logarithmic difference of the spectral comparisons and the average frequency difference, where the quality factor represents the degree of absorption and attenuation of seismic waves in the target formation. This technical solution solves the problem that current methods for determining the quality factor are easily affected by noise, leading to inaccurate quality factor values. It reduces noise interference and improves the accuracy of the quality factor.

[0075] Example 2

[0076] Figure 2 This is a flowchart of a method for determining formation quality factors according to Embodiment 2 of the present invention. This embodiment is a preferred embodiment of the above embodiments. Before introducing this embodiment, the spectral ratio method will be introduced first:

[0077] Spectral Comparison

[0078] When seismic waves propagate through the Earth's strata, they attenuate due to the strata's non-perfect elasticity. This process can be represented by the following model:

[0079]

[0080] Where A0(f) is the reference wavelet amplitude spectrum, A(f) is the received wavelet amplitude spectrum, f is the frequency, Δt is the travel time, and Q is the absorption attenuation parameter. C represents the scattering attenuation coefficient, which is independent of frequency throughout the entire seismic frequency band and includes geometric diffusion, reflection, transmission, etc. Dividing A(f) by A0(f) and taking the logarithm, we can obtain the result according to formula (12).

[0081]

[0082] Formula (13) is a linear function of frequency and can be rewritten as follows:

[0083] L(f)=kf+b (14)

[0084] Where L(f) = ln[A(f) / A0(f)] is the spectral logarithm; k is the slope. b is the intercept, b = ln(C).

[0085] Based on formula (14), least squares linear fitting is performed within a certain frequency band, and the objective function is as follows:

[0086]

[0087] A straight line and its slope k can be obtained. Based on the relationship between the slope k and the Q value, the formula for estimating the Q value is derived.

[0088]

[0089] The method described above for estimating the Q value is called the spectral ratio method.

[0090] Mean difference method

[0091] Due to limitations in acquisition and reception conditions, actual seismic data often contains noise interference, leading to discrepancies between the calculated logarithm of the spectral ratio and the true value. Traditional spectral ratio methods for Q-value estimation employ least-squares linear fitting. However, squaring the fitted data during least-squares calculations amplifies the influence of significant deviations, resulting in unstable fitting results and high sensitivity to noise. To improve the stability of Q-value estimation, the mean difference method is proposed. The mean difference is the arithmetic mean of the absolute values ​​of the deviations of each variable from its arithmetic mean. The mean difference d of the logarithmic spectral ratio L(f) within a certain frequency band can be expressed as…

[0092]

[0093] Where μ represents the arithmetic mean of the logarithmic spectral ratio L(f), expressed as follows:

[0094]

[0095] Substituting formula (14) into formula (18) yields

[0096]

[0097] Where μ f The arithmetic mean of frequencies is represented as follows:

[0098]

[0099] Substituting formulas (14) and (19) into formula (17) yields...

[0100]

[0101] Where d f The average difference in frequencies is expressed as follows:

[0102]

[0103] From formula (21), we can obtain

[0104]

[0105] Based on the relationship between slope k and Q value, a new formula for estimating Q value is derived as follows:

[0106]

[0107] The method for estimating Q-values ​​described above is called the mean difference method. In the process of obtaining Q-values ​​using the mean difference method, the absolute value of the deviation between each variable and its arithmetic mean will not amplify the influence of large deviation data. Theoretically, its sensitivity to noise is weaker than that of the spectral ratio method.

[0108] In the application scenario of this invention embodiment, the reliability of the new method is first verified using forward modeling synthesis records. A horizontally layered medium is set, and its model parameters are shown in Table 1. The seismic source is a Ricker wavelet with a dominant frequency of 45Hz; 51 geophones are arranged vertically along the strata from depth 0 to 1000m, with a spacing of 20m between adjacent geophones. A zero-biased VSP seismic record containing only the first arrival wave is simulated under this model, as shown in the attached figure. Figure 3 As shown. Vertical seismic profile (VSP). For attached... Figure 3 The quality factor Q was estimated using noise-free, zero-biased VSP data synthesized in the image, employing both the spectral ratio method and the mean difference method. A flowchart illustrating the Q estimation using the mean difference method is attached. Figure 2 As shown in the figure. The frequency band was uniformly selected from 5-100Hz during the estimation process, and the estimation results are attached. Figure 4 As shown in the figure. The results show that both methods can estimate the Q value relatively accurately, and their relative errors are shown in the appendix. Figure 5 As shown, in the absence of noise, the relative error of the spectral ratio method is between 0.5% and 0.6%, while the relative error of the mean difference method is between 0.3% and 0.4%. The relative error of the mean difference method is smaller than that of the spectral ratio method, and the Q-value estimation accuracy is higher than that of the spectral ratio method.

[0109] Table 1

[0110]

[0111] Because the VSP channel spacing is very small, estimating the Q value using the direct wave between adjacent detectors is highly sensitive to random noise. (See attached...) Figure 3 Weak random noise with a mean of 0 and a standard deviation of 0.0005 (0.05% of the maximum value of the reference wavelet) was added to each channel of the synthesized zero-biased VSP data. The Q-value was estimated using both the spectral ratio method and the mean difference method, with a frequency band of 5-100 Hz selected. The estimation results are shown in the attached figure. Figure 6 As shown in the figure, the estimation results indicate that the spectral ratio method is significantly affected by noise, resulting in a large deviation from the theoretical value. The average difference method also exhibits disturbances, but its deviation from the theoretical value is significantly smaller compared to the spectral ratio method. These test results demonstrate that, under noise interference, the stability of the Q-value estimation using the average difference method is significantly higher than that using the spectral ratio method, indicating enhanced noise resistance.

[0112] Next, we will select actual data to verify the reliability of the new method. (See attached data.) Figure 7 This is zero-biased VSP data from a certain well. Downward waves were obtained from the VSP data through wavefield separation, and then first arrival waves were extracted. The formation quality factor Q was estimated from the acquired first arrival waves. (Attached) Figure 8 The figures show the Q-values ​​estimated by the spectral ratio method and the average difference method within a selected calculation frequency band of 5-100Hz. As can be seen from the figures, the overall trends of the Q-values ​​estimated by the conventional spectral ratio method and the average difference method are relatively consistent. However, the spectral ratio method exhibits significant local perturbations, making it less stable than the average difference method. The application of actual VSP data further demonstrates that the average difference method can stably estimate the formation quality factor Q-value, providing more reliable results than the spectral ratio method and demonstrating good practicality. This invention proposes a formation quality factor estimation method based on the average difference. Within the selected frequency band, the logarithm of the spectral ratio and the average difference of the frequency are calculated, deriving a quantitative relationship between the quality factor Q-value and the average difference. In the process of obtaining the quality factor Q-value using the average difference method, the absolute value of the deviation between each variable and its arithmetic mean does not amplify the influence of large deviations in the data. This effectively reduces noise interference and improves the stability of the seismic wave absorption attenuation parameter estimation.

[0113] The technical solution of this invention involves exciting seismic waves into a target formation to determine the target frequency band, reference wavelet, and received wavelet; determining the spectral comparison logarithm and frequency values ​​based on the amplitude spectra of the reference wavelet and the received wavelet; determining the average logarithmic difference of the spectral comparison based on multiple spectral comparison logarithms and the average frequency difference based on multiple frequency values; and determining the quality factor of the target formation based on the average logarithmic difference of the spectral comparison and the average frequency difference, where the quality factor represents the degree of absorption and attenuation of seismic waves in the target formation. This technical solution solves the problem that current methods for determining the quality factor are easily affected by noise, leading to inaccurate quality factor values, thus reducing noise interference and improving the accuracy of the quality factor.

[0114] Example 3

[0115] Figure 9 This is a schematic diagram of a device for determining formation quality factors according to Embodiment 3 of the present invention. Figure 9 As shown, the device includes:

[0116] The wavelet receiving module 310 is used to generate seismic waves into the target stratum and determine the reference wavelet, the corresponding received wavelet, and the target frequency band.

[0117] The average difference calculation module 320 is used to determine the spectral comparison logarithm and frequency value based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet, and to determine the spectral comparison logarithmic average difference based on multiple spectral comparison logarithms and the frequency average difference based on multiple frequency values.

[0118] The factor calculation module 330 is used to determine the quality factor of the target formation based on the logarithmic mean difference of the spectral ratio and the mean difference of the frequency, wherein the quality factor represents the degree of absorption and attenuation of the seismic wave in the target formation.

[0119] The technical solution of this invention involves exciting seismic waves into a target formation to determine a reference wavelet, a corresponding received wavelet, and a target frequency band. Based on the amplitude spectra of the reference wavelet and the received wavelet, the spectral comparison logarithm and frequency values ​​are determined. The average logarithmic difference of the spectral comparisons is determined based on multiple spectral comparison logarithms, and the average frequency difference is determined based on multiple frequency values. The quality factor of the target formation is determined based on the average logarithmic difference of the spectral comparisons and the average frequency difference, where the quality factor represents the degree of absorption and attenuation of seismic waves in the target formation. This technical solution solves the problem that current methods for determining the quality factor are easily affected by noise, leading to inaccurate quality factor values. It reduces noise interference and improves the accuracy of the quality factor.

[0120] Optionally, the average difference calculation module 320 includes:

[0121] An amplitude spectrum determination module is used to use the amplitude spectrum of the reference wavelet as the reference wavelet amplitude spectrum and the amplitude spectrum of the received wavelet as the received wavelet amplitude spectrum.

[0122] The spectral comparison logarithm determination module determines the ratio of the received wavelet amplitude spectrum to the reference wavelet amplitude spectrum, and determines the spectral comparison logarithm based on the logarithm of the ratio.

[0123] Optionally, the average difference calculation module 320 includes:

[0124] The spectral logarithm arithmetic mean determination module is used to calculate the arithmetic mean of the spectral logarithms based on multiple spectral logarithms and the total number of spectral logarithms;

[0125] The spectral comparison logarithmic mean difference determination module is used to determine the spectral comparison logarithmic mean difference based on the difference between the arithmetic mean of the spectral comparison logarithms and each of the spectral comparison logarithms.

[0126] Optionally, the average difference calculation module 320 includes:

[0127] The frequency value arithmetic mean determination module is used to determine the arithmetic mean of the frequency values ​​based on the frequency values ​​corresponding to the multiple reference wavelets and the received wavelets, as well as the total number of the frequency values.

[0128] The frequency average difference determination module is used to determine the frequency average difference based on the difference between the arithmetic mean of the frequency values ​​and each frequency value.

[0129] Optionally, the factor calculation module 330 is specifically used for:

[0130] The target coefficient is determined based on the ratio of the logarithmic mean difference of the spectral ratio to the frequency mean difference, and the quality factor of the target formation is determined based on the target coefficient and the travel time.

[0131] Wherein, the travel time is the transmission time of the seismic wave.

[0132] Optionally, the wavelet receiving module 310 includes:

[0133] Wavelet receiving unit, used to determine the reference wavelet and the received wavelet;

[0134] Optional, also includes:

[0135] The transformation module is used to perform Fourier transform on the reference wavelet and the received wavelet to obtain the amplitude spectrum corresponding to the reference wavelet and the amplitude spectrum corresponding to the received wavelet.

[0136] The formation quality factor determination device provided in the embodiments of the present invention can execute the formation quality factor determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0137] Example 4

[0138] Figure 10 This is a schematic diagram of an electronic device implementing a method for determining formation quality factors according to Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0139] like Figure 10As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0140] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0141] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining formation quality factors.

[0142] In some embodiments, the method for determining formation quality factors may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining formation quality factors described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for determining formation quality factors by any other suitable means (e.g., by means of firmware).

[0143] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0146] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0147] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0148] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0149] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0150] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining formation quality factors, characterized in that, include: Generating seismic waves towards the target stratum, determining the reference wavelet, the corresponding received wavelet, and the target frequency band; The spectral comparison logarithm and frequency value are determined based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet, and the average difference of the spectral comparison logarithm and the average difference of the frequency value are determined based on the multiple spectral comparison logarithms. The quality factor of the target formation is determined based on the logarithmic mean difference of the spectral ratio and the mean difference of the frequency, wherein the quality factor represents the degree of absorption and attenuation of the seismic wave in the target formation; The determination of the average logarithmic difference of the spectral comparisons based on multiple spectral comparison logarithms includes: The arithmetic mean of the spectral logarithms is calculated based on the plurality of spectral logarithms and the total number of spectral logarithms. The average difference of the spectral comparison logarithms is determined based on the difference between the arithmetic mean of the spectral comparison logarithms and each of the spectral comparison logarithms. Formula for calculating the average frequency difference: ; in, This represents the average difference in frequency. The arithmetic mean of frequency values; Slope calculation formula: ; in, d This indicates the difference between the spectral and logarithmic mean values; Formula for calculating quality factor: ; in, Q Indicates formation quality factor. This indicates the propagation time of seismic waves.

2. The method according to claim 1, characterized in that, The determination of the spectral comparison logarithm based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet includes: The amplitude spectrum of the reference wavelet is used as the reference wavelet amplitude spectrum, and the amplitude spectrum of the received wavelet is used as the received wavelet amplitude spectrum. The ratio of the received wavelet amplitude spectrum to the reference wavelet amplitude spectrum is determined, and the logarithm of the spectrum ratio is determined based on the logarithm of the ratio.

3. The method according to claim 1, characterized in that, The determination of the average frequency difference based on multiple frequency values ​​includes: The arithmetic mean of the frequency values ​​is determined based on the frequency values ​​corresponding to the multiple reference wavelets and the received wavelets, as well as the total number of the frequency values. The frequency mean difference is determined based on the difference between the arithmetic mean of the frequency values ​​and each frequency value.

4. The method according to claim 1, characterized in that, The determination of the quality factor of the target formation based on the logarithmic mean difference of the spectral comparison and the frequency mean difference includes: The target coefficient is determined based on the ratio of the logarithmic mean difference of the spectral ratio to the frequency mean difference, and the quality factor of the target formation is determined based on the target coefficient and the travel time. Wherein, the travel time is the transmission time of the seismic wave.

5. The method according to claim 1, characterized in that, The frequency values ​​of the reference wavelet and the received wavelet are within the target frequency band.

6. The method according to claim 1, characterized in that, Before determining the spectral comparison logarithm and frequency value based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet, the method further includes: Perform Fourier transform on the reference wavelet and the received wavelet to obtain the amplitude spectrum corresponding to the reference wavelet and the amplitude spectrum corresponding to the received wavelet.

7. A device for determining formation quality factors, characterized in that, include: The wavelet receiving module is used to generate seismic waves into the target strata and determine the reference wavelet, the corresponding received wavelet, and the target frequency band. The average difference calculation module is used to determine the spectral comparison logarithm and frequency value based on the amplitude spectrum of the reference wavelet and the amplitude spectrum of the received wavelet, and to determine the spectral comparison logarithmic average difference based on multiple spectral comparison logarithms and the frequency average difference based on multiple frequency values. The factor calculation module is used to determine the quality factor of the target formation based on the logarithmic mean difference of the spectral ratio and the mean difference of the frequency, wherein the quality factor represents the degree of absorption and attenuation of the seismic wave in the target formation; The average difference calculation module includes: The spectral logarithm arithmetic mean determination module is used to calculate the arithmetic mean of the spectral logarithms based on multiple spectral logarithms and the total number of spectral logarithms; The spectral comparison logarithmic mean difference determination module is used to determine the spectral comparison logarithmic mean difference based on the difference between the arithmetic mean of the spectral comparison logarithms and each of the spectral comparison logarithms; Formula for calculating the average frequency difference: ; in, This represents the average difference in frequency. The arithmetic mean of frequency values; Slope calculation formula: ; in, d This indicates the difference between the spectral and logarithmic mean values; Formula for calculating quality factor: ; in, Q Indicates formation quality factor. This indicates the propagation time of seismic waves.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the formation quality factor according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for determining the formation quality factor according to any one of claims 1-6.

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