Method, device and equipment for predicting reservoir physical property parameters based on post-stack seismic data

Abstract

The application discloses a method, device and equipment for predicting reservoir physical property parameters based on post-stack seismic data, wherein according to post-stack seismic data, different frequency frequency division seismic data are obtained by using a proposed robust spectral decomposition method, compared with pre-stack seismic data used by conventional technology, the robust spectral decomposition method effectively ensures the correctness of spectral decomposition data; interlayer quality factor data are determined according to different frequency frequency division seismic data, unknown abnormal conditions of single frequency frequency division seismic data can be avoided; the mean of all interlayer quality factor data can reduce the uncertainty of errors in the process of obtaining the quality factor; porosity, permeability and gas saturation parameter data are obtained by performing nonlinear synchronous inversion on final interlayer quality factor data, compared with conventional single parameter inversion, the physical connection among the three parameters of porosity, permeability and gas saturation can be better used, and the accuracy of the inversion result is improved.

Description

Technical Field

[0001] This invention relates to the field of natural gas geophysical exploration technology, and in particular to a method, apparatus and equipment for predicting reservoir physical parameters based on post-stack seismic data. Background Technology

[0002] Natural gas, as a vital global resource, presents both a hot topic and a significant challenge in its exploration and development. As natural gas exploration deepens, exploration targets become deeper and smaller, demanding increasingly higher resolution and accuracy in seismic reservoir prediction. Simultaneously, natural gas exploration places higher demands on seismic technology; seismic prediction must not only qualitatively determine the gas-bearing capacity of reservoirs but also quantitatively analyze the quality of reservoir physical properties, excluding low-porosity, low-gas-saturation, and other commercially unviable lean gas reservoirs. Therefore, continuous development of high-precision quantitative prediction technologies for gas reservoir physical properties is necessary to improve the success rate of seismic exploration of natural gas reservoirs. Natural gas reservoir physical properties, such as porosity, permeability, and gas saturation, are crucial parameters for natural gas reservoir prediction. Currently, one effective method for obtaining these parameters is pre-stack seismic inversion, which, at its core, establishes a deterministic relationship between seismic amplitude response and reservoir parameters based on a rock physics model. Summary of the Invention

[0003] The inventors discovered that due to the extremely complex formation conditions of complex gas reservoirs, there is a highly nonlinear relationship and uncertainty between seismic response characteristics and reservoir parameters, making it difficult to accurately establish rock physics inversion. Furthermore, pre-stack seismic data, limited by current seismic acquisition technology, often contains noise that is difficult to remove, especially in structurally complex areas where the signal-to-noise ratio is low, resulting in significant errors in the prediction of physical property parameters and failing to meet the requirements of quantitative exploration. In addition, conventional techniques for predicting porosity, permeability, and gas saturation often focus on single parameters, failing to fully utilize the physical relationships between these three parameters and lacking integrated synchronous prediction methods. Therefore, developing a post-stack seismic method for synchronous prediction of porosity, permeability, and gas saturation to solve the above technical problems is particularly urgent. In view of the above problems, this invention proposes a method, apparatus, and device for predicting reservoir physical property parameters based on post-stack seismic data to overcome or at least partially solve the above problems.

[0004] In a first aspect, embodiments of the present invention provide a method for predicting reservoir physical parameters based on post-stack seismic data, the method comprising:

[0005] Robust spectral decomposition was performed on the post-stack seismic data of the study area to obtain frequency-divided seismic data of different frequencies;

[0006] Inter-layer quality factors at different frequencies are determined based on the frequency-division seismic data at different frequencies.

[0007] The formation quality factor is determined based on the reservoir sensitive reference frequency and the interlayer quality factor.

[0008] The formation quality factor and the pre-constructed relationship structure between the formation quality factor and reservoir physical property parameters are used to establish the target inversion structure of the reservoir physical property parameters.

[0009] The reservoir physical parameters are obtained based on the target inversion structure of the reservoir physical parameters.

[0010] Optionally, the pre-constructed relationship between formation quality factors and reservoir physical property parameters is as follows:

[0011]

[0012] Where real and immag represent the operations of taking the real part and imaginary part, respectively;

[0013]

[0014] ρ=(1-S g )ρ w +S g ρ g Saturated fluid density, in kilograms per cubic meter;

[0015] Fast longitudinal wave energy, j = 1, 2;

[0016] K Gj =K m +α 2 M j , saturated rock bulk modulus, in megapascals, j = 1, 2;

[0017] Slow longitudinal wave impedance, in meters per second * kilograms per cubic meter, j = 1, 2;

[0018] Slow longitudinal wave complex number, j = 1, 2;

[0019] Skeletal bulk modulus, in megapascals;

[0020] μ m =K m μ s / K s Skeletal shear modulus, in megapascals;

[0021] Geismann's formula for calculating stiffness parameters is dimensionless.

[0022] When j=1, it represents the thickness of the aquifer; when j=2, it represents the thickness of the gas-bearing layer. The unit is meters.

[0023] Q represents the quality factor, which is dimensionless;

[0024] S g Indicates gas saturation, dimensionless;

[0025] Porosity is a dimensionless quantity.

[0026] κ represents permeability, measured in Darcy;

[0027] K s This represents the bulk modulus of the rock matrix, expressed in megapascals (MPa).

[0028] μ s This represents the shear modulus of the rock matrix, expressed in megapascals (MPa).

[0029] ρ g This indicates the density of a gas, expressed in kilograms per cubic meter.

[0030] ρ w This indicates the density of water, expressed in kilograms per cubic meter.

[0031] d represents the average thickness of the strata, in meters;

[0032] i represents the imaginary number symbol, which is dimensionless;

[0033] π represents the mathematical constant pi, which is dimensionless.

[0034] f0 is the reservoir sensitive reference frequency, measured in Hertz.

[0035] Optionally, the bulk modulus of the rock matrix, the shear modulus of the rock matrix, the gas density, and the water density are determined based on rock physics tests or empirical values ​​of the study area;

[0036] The reservoir sensitive reference frequency is determined based on the wellbore frequency division test results.

[0037] Optionally, the robust spectral decomposition of the post-stack seismic data of the study area is determined in the following manner:

[0038]

[0039] in, This indicates the least squares fit with error. λ represents the robust constraint term; G represents the wavelet matrix at different frequencies; m represents the seismic data matrix at different frequencies; S represents the actual post-stack seismic data; and λ is the weighting factor of the robust constraint term.

[0040] Optionally, the inter-layer quality factors for different frequencies are determined based on the frequency-divided seismic data of different frequencies, specifically through the following methods:

[0041]

[0042] Where m(t0,f) z m(t1,f) z ) represent frequencies of f z The frequency-division data are taken at the top and bottom of the seismic interpretation layer, z = 1, 2, 3…n; Δt represents the time difference between the top and bottom layers.

[0043] Optionally, the target inversion structure for establishing the reservoir physical property parameters is as follows:

[0044]

[0045] Where f(x) is a pre-constructed structural formula relating formation quality factors and reservoir physical property parameters.

[0046] Optionally, after obtaining the reservoir physical property parameters, the process may further include:

[0047] The reservoir physical parameters are corrected based on the variance of the well-side inversion data, the mean of the well-side inversion data of the reservoir physical parameters, the variance of the well logging data of the reservoir physical parameters, and the mean of the well logging data of the reservoir physical parameters.

[0048] Optionally, the formula for correcting the reservoir physical properties is as follows:

[0049]

[0050] Where y represents the corrected reservoir physical property parameters, including porosity, permeability, and gas saturation; Var(x) represents the variance of the well bypass inversion data for porosity, permeability, and gas saturation; Var(w) represents the variance of the logging data for porosity, permeability, and gas saturation. This represents the mean value of well bypass inversion data for porosity, permeability, and gas saturation. This represents the mean value of well logging data for porosity, permeability, and gas saturation.

[0051] Secondly, embodiments of the present invention provide an apparatus for predicting reservoir physical parameters based on post-stack seismic data, which may include:

[0052] The robust spectral decomposition module is used to perform robust spectral decomposition on the post-stack seismic data of the study area to obtain frequency-divided seismic data of different frequencies.

[0053] The inter-layer quality factor determination module is used to determine the inter-layer quality factor at different frequencies based on the frequency-divided seismic data at different frequencies.

[0054] The formation quality factor determination module is used to determine the formation quality factor based on the reservoir sensitive reference frequency and the interlayer quality factor.

[0055] The inversion module is used to establish a target inversion structure for the reservoir physical parameters by combining the formation quality factor and the pre-constructed relationship structure between the formation quality factor and the reservoir physical parameters; and to obtain the reservoir physical parameters based on the target inversion structure for the reservoir physical parameters.

[0056] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements the method for predicting reservoir physical parameters based on post-stack seismic data as described in the first aspect.

[0057] Fourthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the method for predicting reservoir physical parameters based on post-stack seismic data as described in the first aspect.

[0058] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0059] This invention provides a method, apparatus, and device for predicting reservoir physical parameters based on post-stack seismic data. The method implements predictions from quality factors to porosity, permeability, and gas saturation by pre-establishing a quantitative relationship structure between formation quality factors and porosity, permeability, and gas saturation parameters. This overcomes the dependence of conventional techniques on rock physics models and is suitable for study areas where rock physics data is scarce and accurate models are difficult to establish. Based on post-stack seismic data, the proposed robust spectral decomposition method obtains frequency-divided seismic data at different frequencies. Compared to pre-stack seismic data used in conventional techniques, this method has a higher signal-to-noise ratio, and the robust spectral decomposition method effectively ensures the quality of the spectral decomposition data. The accuracy of the data is ensured by: interpreting the stratigraphic information and frequency-division seismic data of the target layer, selecting different frequency-division seismic data to calculate inter-layer quality factor data, which avoids the situation where there are unknown anomalies in the single-frequency frequency-division seismic data; taking the mean of all inter-layer quality factor data as the final inter-layer quality factor data (stratigraphic quality factor), which can reduce the uncertainty of errors in the quality factor calculation process; and performing nonlinear synchronous inversion on the final inter-layer quality factor data to obtain porosity, permeability, and gas saturation parameter data. Compared with conventional single-parameter inversion, this method can better utilize the physical relationship between the three parameters of porosity, permeability, and gas saturation, thus improving the accuracy of the inversion results.

[0060] Furthermore, amplitude correction is performed on the porosity, permeability, and gas saturation data obtained from the inversion to obtain the final porosity, permeability, and gas saturation parameters, which can effectively solve the problem of scale deviation between the inversion results and the true values.

[0061] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0062] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0063] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0064] Figure 1 This is a flowchart of a method for predicting reservoir physical parameters based on post-stack seismic data provided in an embodiment of the present invention;

[0065] Figure 2 This is a specific example of a post-stack seismic profile provided in an embodiment of the present invention;

[0066] Figure 3 for Figure 2 Frequency-divided seismic data at 25Hz;

[0067] Figure 4 for Figure 2 Frequency-divided seismic data at 35Hz;

[0068] Figure 5 for Figure 2 Frequency-divided seismic data at 40Hz;

[0069] Figure 6 for Figures 3-5 Inter-layer quality factors determined from frequency-division seismic data;

[0070] Figure 7 For based on Figure 6 The interlayer quality factor determined from the frequency-division seismic data is the stratigraphic quality factor.

[0071] Figure 8 These are the porosity (a), permeability (b), and gas saturation (c) curves retrieved in this embodiment of the invention.

[0072] Figure 9 for Figure 8Porosity (a), permeability (b), and gas saturation (c) curves after medium amplitude correction;

[0073] Figure 10 This is a schematic diagram of the device for predicting reservoir physical parameters based on post-stack seismic data provided in an embodiment of the present invention. Detailed Implementation

[0074] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0075] This invention provides a method for predicting reservoir physical parameters based on post-stack seismic data. The reservoir physical parameters in this invention include porosity, permeability, and gas saturation. Addressing the shortcomings of existing technologies that use conventional techniques to predict individual parameters without fully considering the physical relationships between them, the inventors innovatively propose a method for simultaneously predicting these reservoir physical parameters. (Refer to...) Figure 1 As shown, the method may include the following steps:

[0076] Step S11: Perform robust spectral decomposition on the post-stack seismic data of the study area to obtain frequency-divided seismic data of different frequencies.

[0077] The post-stack seismic data in the embodiments of the present invention includes post-stack seismic profiles and seismic interpretation horizons, etc. For example, refer to... Figure 2 In a specific example of a study area provided, the white dashed line represents the top of the seismic interpretation layer and the black dashed line represents the bottom of the seismic interpretation layer. The research target is located between the top and bottom of the reservoir.

[0078] This invention, through robust spectral decomposition of the aforementioned seismic data, obtains frequency-divided seismic data at different frequencies, referring to... Figures 3-5 As shown, Figure 2 The seismic profiles in the image were robustly decomposed to obtain frequency-divided seismic data at 25Hz, 35Hz, and 40Hz. As can be seen from the figure, there are significant differences in the magnitude and distribution of values ​​in the profiles at different frequencies. It should be noted that... Figures 3-5 The vertical stripes are traces of seismic data calculated channel by channel, and this embodiment of the invention does not impose any other limitations on them.

[0079] In an optional embodiment, this invention provides a specific method for robust spectral decomposition of post-stack seismic data, as shown in the following formula:

[0080]

[0081] in, This indicates the least squares fit with error. λ represents the robust constraint term; G represents the wavelet matrix at different frequencies; m represents the seismic data matrix at different frequencies; S represents the actual post-stack seismic data; and λ is the weighting factor of the robust constraint term.

[0082] In this embodiment of the invention, robust constraints are used. This can make the spectral decomposition process more stable and the results more accurate.

[0083] Step S12: Determine the inter-layer quality factor for different frequencies based on frequency-division seismic data of different frequencies.

[0084] This step is based on the frequency-division seismic data obtained in step S11 above, to calculate and determine the inter-layer quality factor for different frequencies. For example, in this embodiment of the invention, there are a total of n frequency-division seismic data, which can be determined in the following way in specific implementation:

[0085]

[0086] Where m(t0,f) z m(t1,f) z ) represent frequencies of f z The frequency-division data are taken at the top and bottom of the seismic interpretation layer, z = 1, 2, 3…n; Δt represents the time difference between the top and bottom layers.

[0087] It should be noted that, in this embodiment of the invention, a reservoir sensitive reference frequency f0 can be predetermined to calculate the inter-layer quality factor of the frequency-division seismic data within a preset frequency range near the reservoir sensitive reference frequency f0. (Refer to...) Figure 6 As shown, Figures 3-5 Inter-layer quality factors calculated from frequency-divided seismic profiles at 25Hz, 35Hz, and 40Hz. Figure 6 It can be seen that the inter-layer quality factors calculated from seismic profiles of different frequencies have small differences.

[0088] Step S13: Determine the formation quality factor based on the reservoir sensitive reference frequency and interlayer quality factor.

[0089] In this embodiment of the invention, the formation quality factor is the mean of the inter-layer quality factors of frequency-division seismic data within a preset frequency range near the reservoir sensitive reference frequency f0. In this embodiment, the arithmetic mean of the inter-layer quality factors of frequency-division seismic data at different frequencies is used as the formation quality factor. Specifically, refer to the following formula:

[0090]

[0091] In this embodiment of the invention, the reservoir sensitive reference frequency f0 is 30Hz. Figure 7 The figure shown is the mean value of the inter-layer quality factor calculated from all frequency-division seismic data within a preset frequency range near the sensitive reference frequency of 30Hz in this embodiment of the invention. In this embodiment, using the arithmetic mean of all frequency-division seismic data within a preset frequency range near the reservoir sensitive reference frequency f0 as the formation quality factor helps to avoid calculation errors caused by seismic data noise.

[0092] Step S14: Establish the target inversion structure of reservoir physical parameters by combining the formation quality factor and the pre-constructed relationship structure between formation quality factor and reservoir physical parameters; and obtain the reservoir physical parameters based on the target inversion structure of reservoir physical parameters.

[0093] Based on the quantitative relationship equation between formation quality factor and porosity, permeability, and gas saturation parameters, the final inter-layer quality factor data is used as input data, while porosity, permeability, and gas saturation parameters are used as parameters to be predicted, denoted as […]. The established reservoir physical property parameters (porosity, permeability, gas saturation) target inversion structure (target inversion function) is as follows:

[0094]

[0095] Where f(x) is a pre-constructed structural formula relating formation quality factors and reservoir physical property parameters.

[0096] In this embodiment of the invention, the target inversion functions for porosity, permeability, and gas saturation can be solved using a conventional differential evolution algorithm, thus simultaneously obtaining the inversion data for porosity, permeability, and gas saturation. The advantage of simultaneous inversion is that it can fully utilize the physical relationships between the parameters to be inverted, improving inversion accuracy. (Refer to...) Figure 8 As shown, the inverted porosity (a), permeability (b), and gas saturation (c) curves are presented.

[0097] In another alternative embodiment, reference is also made to Figure 1 As shown, after obtaining the reservoir physical property parameters, it may also include:

[0098] Step S15: Correct the reservoir physical parameters based on the variance of the well-side inversion data, the mean of the reservoir physical parameter well-side inversion data, the variance of the reservoir physical parameter logging data, and the mean of the reservoir physical parameter logging data.

[0099] In another optional embodiment, the formula for correcting the reservoir physical properties is as follows:

[0100]

[0101] Where y represents the corrected reservoir physical property parameters, including porosity, permeability, and gas saturation; Var(x) represents the variance of the well bypass inversion data for porosity, permeability, and gas saturation; Var(w) represents the variance of the well logging data for porosity, permeability, and gas saturation. This represents the mean value of well bypass inversion data for porosity, permeability, and gas saturation. This represents the mean value of well logging data for porosity, permeability, and gas saturation.

[0102] In this embodiment of the invention, the purpose of amplitude correction is to use the value distribution characteristics (mean and variance) of well logging data of known porosity, permeability and gas saturation in the study area to correct the inversion data, so that the two have consistency in amplitude and solve the amplitude difference caused by inversion error.

[0103] Figure 9 for Figure 8 The data includes porosity, permeability, and gas saturation after amplitude correction. A comparison reveals... Figure 9 Comparison Figure 8 The curves are basically the same in shape, but differ in amplitude.

[0104] In this embodiment of the invention, a quantitative relationship equation between formation quality factors and porosity, permeability, and gas saturation parameters is pre-established to predict porosity, permeability, and gas saturation from quality factors. This overcomes the dependence of conventional techniques on rock physics models and is suitable for study areas where rock physics data is scarce and accurate models are difficult to establish. Based on post-stack seismic data, the proposed robust spectral decomposition method is used to obtain frequency-divided seismic data of different frequencies. Compared with pre-stack seismic data used in conventional techniques, the signal-to-noise ratio is higher, and the robust spectral decomposition method effectively ensures the accuracy of the spectral decomposition data. Based on the seismic interpretation of stratigraphic information and frequency-divided seismic data of the target layer, frequency-divided seismic data of different frequencies are selected to calculate inter-layer quality factor data, which can avoid... This method avoids the presence of unknown anomalies in individual frequency-division seismic data; it uses the average of all inter-layer quality factor data as the final inter-layer quality factor data (stratigraphic quality factor), reducing the uncertainty caused by errors in the quality factor calculation process; it performs nonlinear synchronous inversion on the final inter-layer quality factor data to obtain porosity, permeability, and gas saturation parameter data, which, compared with conventional single-parameter inversion, better utilizes the physical relationship between the three parameters, improving the accuracy of the inversion results; and it performs amplitude correction on the obtained porosity, permeability, and gas saturation data to obtain the final porosity, permeability, and gas saturation parameters, effectively solving the problem of scale deviation between the inversion results and the true values.

[0105] In an optional embodiment, the pre-constructed relationship between formation quality factors and reservoir physical property parameters is a key aspect of this invention. The established structural formula provides a theoretical basis for subsequent predictions, and its structural formula is as follows:

[0106]

[0107] Where real and immag represent the operations of taking the real part and imaginary part, respectively;

[0108]

[0109] ρ=(1-S g )ρ w +S g ρ g Saturated fluid density, in kilograms per cubic meter;

[0110] Fast longitudinal wave energy, j = 1, 2;

[0111] Saturated rock bulk modulus, in megapascals, j = 1, 2;

[0112] Slow longitudinal wave impedance, in meters per second * kilograms per cubic meter, j = 1, 2;

[0113] Slow longitudinal wave complex number, j = 1, 2;

[0114] Skeletal bulk modulus, in megapascals;

[0115] μ m =K m μ s / K s Skeletal shear modulus, in megapascals;

[0116] Geismann's formula for calculating stiffness parameters is dimensionless.

[0117] When j=1, it represents the thickness of the aquifer; when j=2, it represents the thickness of the gas-bearing layer. The unit is meters.

[0118] Q represents the quality factor, which is dimensionless;

[0119] S g Indicates gas saturation, dimensionless;

[0120] Porosity is a dimensionless quantity.

[0121] κ represents permeability, measured in Darcy;

[0122] K s This represents the bulk modulus of the rock matrix, expressed in megapascals (MPa).

[0123] μ s This represents the shear modulus of the rock matrix, expressed in megapascals (MPa).

[0124] ρ g This indicates the density of a gas, expressed in kilograms per cubic meter.

[0125] ρ w This indicates the density of water, expressed in kilograms per cubic meter.

[0126] d represents the average thickness of the strata, in meters;

[0127] i represents the imaginary number symbol, which is dimensionless;

[0128] π represents the mathematical constant pi, which is dimensionless.

[0129] f0 is the reservoir sensitive reference frequency, measured in Hertz.

[0130] In another alternative embodiment, the bulk modulus of the rock matrix, the shear modulus of the rock matrix, the gas density, and the water density are determined based on rock physics tests or empirical values ​​of the study area;

[0131] The reservoir-sensitive reference frequency is determined based on the results of wellbore bypass frequency division tests.

[0132] The average formation thickness in this embodiment of the invention is derived from the time difference between the top and bottom of the seismic interpretation horizon;

[0133] Based on the same inventive concept, this invention also provides an apparatus for predicting reservoir physical parameters based on post-stack seismic data, referring to... Figure 10 As shown, the device may include: a robust spectral decomposition module 11, an interlayer quality factor determination module 12, a formation quality factor determination module 13, and an inversion module 14. Its working principle is as follows:

[0134] The robust spectral decomposition module 11 is used to perform robust spectral decomposition on the post-stack seismic data of the study area to obtain frequency-divided seismic data of different frequencies.

[0135] The inter-layer quality factor determination module 12 is used to determine the inter-layer quality factor at different frequencies based on frequency-division seismic data of different frequencies.

[0136] Formation quality factor determination module 13 is used to determine formation quality factors based on reservoir sensitive reference frequency and interlayer quality factor;

[0137] The inversion module 14 is used to establish a target inversion structure for reservoir physical parameters by combining the formation quality factor and the pre-constructed relationship structure between the formation quality factor and reservoir physical parameters; and to obtain the reservoir physical parameters based on the target inversion structure for reservoir physical parameters.

[0138] In an optional embodiment, refer to Figure 10 As shown, it may also include a construction module 10, which is used to pre-construct the relationship between formation quality factors and reservoir physical property parameters.

[0139]

[0140] Where real and immag represent the operations of taking the real part and imaginary part, respectively;

[0141]

[0142] ρ=(1-S g )ρ w +S g ρ g Saturated fluid density, in kilograms per cubic meter;

[0143] Fast longitudinal wave energy, j = 1, 2;

[0144] Saturated rock bulk modulus, in megapascals, j = 1, 2;

[0145] Slow longitudinal wave impedance, in meters per second * kilograms per cubic meter, j = 1, 2;

[0146] Slow longitudinal wave complex number, j = 1, 2;

[0147] Skeletal bulk modulus, in megapascals;

[0148] μ m =K m μ s / K s Skeletal shear modulus, in megapascals;

[0149] Geismann's formula for calculating stiffness parameters is dimensionless.

[0150] When j=1, it represents the thickness of the aquifer; when j=2, it represents the thickness of the gas-bearing layer. The unit is meters.

[0151] Q represents the quality factor, which is dimensionless;

[0152] S gIndicates gas saturation, dimensionless;

[0153] Porosity is a dimensionless quantity.

[0154] κ represents permeability, measured in Darcy;

[0155] K s This represents the bulk modulus of the rock matrix, expressed in megapascals (MPa).

[0156] μ s This represents the shear modulus of the rock matrix, expressed in megapascals (MPa).

[0157] ρ g This indicates the density of a gas, expressed in kilograms per cubic meter.

[0158] ρ w This indicates the density of water, expressed in kilograms per cubic meter.

[0159] d represents the average thickness of the strata, in meters;

[0160] i represents the imaginary number symbol, which is dimensionless;

[0161] π represents the mathematical constant pi, which is dimensionless.

[0162] f0 is the reservoir sensitive reference frequency, measured in Hertz.

[0163] In an optional embodiment, the bulk modulus of the rock matrix, the shear modulus of the rock matrix, the gas density, and the water density are determined based on rock physics tests or empirical values ​​of the study area;

[0164] The reservoir sensitive reference frequency is determined based on the wellbore frequency division test results.

[0165] In another alternative embodiment, robust spectral decomposition of the post-stack seismic data for the study area is determined in the following manner:

[0166]

[0167] in, This indicates the least squares fit with error. λ represents the robust constraint term; G represents the wavelet matrix at different frequencies; m represents the seismic data matrix at different frequencies; S represents the actual post-stack seismic data; and λ is the weighting factor of the robust constraint term.

[0168] In another optional embodiment, the robust spectral decomposition module 11 determines the inter-layer quality factor at different frequencies based on the frequency-divided seismic data at different frequencies, specifically in the following manner:

[0169]

[0170] Where m(t0,f) z m(t1,f) z ) represent frequencies of f z The frequency-division data are taken at the top and bottom of the seismic interpretation layer, z = 1, 2, 3…n; Δt represents the time difference between the top and bottom layers.

[0171] In another optional embodiment, the inversion module 14 is used to establish the target inversion structure of the reservoir physical property parameters as follows:

[0172]

[0173] Where f(x) is a pre-constructed structural formula relating formation quality factors and reservoir physical property parameters.

[0174] In another alternative embodiment, reference is also made to Figure 10 As shown, it may also include a correction module 15, which is used to correct the reservoir physical parameters based on the variance of the well-side inversion data, the mean of the well-side inversion data of the reservoir physical parameters, the variance of the well logging data of the reservoir physical parameters, and the mean of the well logging data of the reservoir physical parameters after obtaining the reservoir physical parameters.

[0175] In another optional embodiment, the formula used by the correction module 15 to correct the reservoir physical property parameters is as follows:

[0176]

[0177] Where y represents the corrected reservoir physical property parameters, including porosity, permeability, and gas saturation; Var(x) represents the variance of the well bypass inversion data for porosity, permeability, and gas saturation; Var(w) represents the variance of the logging data for porosity, permeability, and gas saturation. This represents the mean value of well bypass inversion data for porosity, permeability, and gas saturation. This represents the mean value of well logging data for porosity, permeability, and gas saturation.

[0178] Based on the same inventive concept, this embodiment of the invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for predicting reservoir physical parameters based on post-stack seismic data.

[0179] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the above-mentioned method for predicting reservoir physical parameters based on post-stack seismic data.

[0180] The principles by which the above-described apparatus, medium, and related equipment in this embodiment solve the problem are similar to those of the aforementioned method. Therefore, their implementation can refer to the implementation of the aforementioned method, and repeated details will not be repeated.

[0181] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0182] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0183] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0184] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0185] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for predicting reservoir physical parameters based on post-stack seismic data, characterized in that, include: Robust spectral decomposition was performed on the post-stack seismic data of the study area to obtain frequency-divided seismic data of different frequencies; Inter-layer quality factors at different frequencies are determined based on the frequency-division seismic data at different frequencies. The formation quality factor is determined based on the reservoir sensitive reference frequency and the interlayer quality factor. The formation quality factor and the pre-constructed relationship structure between the formation quality factor and reservoir physical property parameters are used to establish the target inversion structure of the reservoir physical property parameters. The reservoir physical parameters are obtained based on the target inversion structure of the reservoir physical parameters; The pre-constructed relationship between formation quality factors and reservoir physical property parameters is expressed as follows: ； Where real and immag represent the operations of taking the real part and imaginary part, respectively; ； Saturated fluid density, in kilograms per cubic meter; Fast longitudinal wave energy, j=1, 2; , saturated rock bulk modulus, in megapascals, j=1,2; Slow longitudinal wave impedance, in meters per second * kilograms per cubic meter, j=1, 2; Slow longitudinal wave complex number, j=1, 2; , bulk modulus of the skeleton, in megapascals; Skeletal shear modulus, in megapascals; Geismann's formula for calculating rigidity parameters is dimensionless. When j=1, it represents the thickness of the aquifer; when j=2, it represents the thickness of the gas-bearing layer. The unit is meters. This represents the quality factor, which is dimensionless. Indicates gas saturation, dimensionless; Porosity is a dimensionless quantity. This indicates penetration rate, measured in Darcy. This represents the bulk modulus of the rock matrix, expressed in megapascals (MPa). This represents the shear modulus of the rock matrix, expressed in megapascals (MPa). This indicates the density of a gas, expressed in kilograms per cubic meter. This indicates the density of water, expressed in kilograms per cubic meter. d represents the average thickness of the strata, in meters; i represents the imaginary number symbol, which is dimensionless; π represents the mathematical constant pi, which is dimensionless. f0 is the reservoir sensitive reference frequency, measured in Hertz.

2. The method according to claim 1, characterized in that, The bulk modulus of the rock matrix, the shear modulus of the rock matrix, the gas density, and the water density are determined based on rock physics tests or empirical values ​​of the study area. The reservoir sensitive reference frequency is determined based on the wellbore frequency division test results.

3. The method according to claim 1, characterized in that, The robust spectral decomposition of the post-stack seismic data for the study area was determined in the following manner: ； in, This indicates the least squares fit with error. Represents robust constraint terms; Represents wavelet matrices of different frequencies; A matrix representing seismic data at different frequencies; This represents actual post-stack seismic data; , which is the weighting factor for the robust constraint term.

4. The method according to claim 1, characterized in that, The inter-layer quality factors for different frequencies are determined based on the frequency-divided seismic data at different frequencies, specifically through the following methods: ； in, , These represent frequencies f z The frequency-division data are taken at the top and bottom of the seismic interpretation layer, z=1,2,3…n; This indicates the time difference between the top and bottom layers.

5. The method according to claim 1, characterized in that, The target inversion structure for establishing the reservoir physical property parameters is as follows: ； in, This is a pre-constructed structural formula relating formation quality factors to reservoir physical property parameters.

6. The method according to any one of claims 1 to 5, characterized in that, After obtaining the reservoir physical property parameters, the process further includes: The reservoir physical parameters are corrected based on the variance of the well-side inversion data, the mean of the well-side inversion data of the reservoir physical parameters, the variance of the well logging data of the reservoir physical parameters, and the mean of the well logging data of the reservoir physical parameters.

7. The method according to claim 6, characterized in that, The formula for correcting the reservoir physical properties is as follows: ； Where y represents the corrected reservoir physical property parameters, which include: porosity, permeability, and gas saturation. This represents the variance of well bypass inversion data for porosity, permeability, and gas saturation. This represents the variance of well logging data for porosity, permeability, and gas saturation. This represents the mean value of well bypass inversion data for porosity, permeability, and gas saturation. This represents the mean value of well logging data for porosity, permeability, and gas saturation.

8. A device for predicting reservoir physical parameters based on post-stack seismic data, characterized in that, include: The robust spectral decomposition module is used to perform robust spectral decomposition on the post-stack seismic data of the study area to obtain frequency-divided seismic data of different frequencies. The inter-layer quality factor determination module is used to determine the inter-layer quality factor at different frequencies based on the frequency-divided seismic data at different frequencies. The formation quality factor determination module is used to determine the formation quality factor based on the reservoir sensitive reference frequency and the interlayer quality factor. The inversion module is used to establish a target inversion structure for the reservoir physical parameters by combining the formation quality factor and the pre-constructed relationship structure between the formation quality factor and the reservoir physical parameters; and to obtain the reservoir physical parameters based on the target inversion structure for the reservoir physical parameters. The pre-constructed relationship between formation quality factors and reservoir physical property parameters is expressed as follows: ； Where real and immag represent the operations of taking the real part and imaginary part, respectively; ； Saturated fluid density, in kilograms per cubic meter; Fast longitudinal wave energy, j=1, 2; , saturated rock bulk modulus, in megapascals, j=1,2; Slow longitudinal wave impedance, in meters per second * kilograms per cubic meter, j=1, 2; Slow longitudinal wave complex number, j=1, 2; , bulk modulus of the skeleton, in megapascals; Skeletal shear modulus, in megapascals; Geismann's formula for calculating rigidity parameters is dimensionless. When j=1, it represents the thickness of the aquifer; when j=2, it represents the thickness of the gas-bearing layer. The unit is meters. This represents the quality factor, which is dimensionless. Indicates gas saturation, dimensionless; Porosity is a dimensionless quantity. This indicates penetration rate, measured in Darcy. This represents the bulk modulus of the rock matrix, expressed in megapascals (MPa). This represents the shear modulus of the rock matrix, expressed in megapascals (MPa). This indicates the density of a gas, expressed in kilograms per cubic meter. This indicates the density of water, expressed in kilograms per cubic meter. d represents the average thickness of the strata, in meters; i represents the imaginary number symbol, which is dimensionless; π represents the mathematical constant pi, which is dimensionless. f0 is the reservoir sensitive reference frequency, measured in Hertz.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for predicting reservoir physical parameters based on post-stack seismic data as described in any one of claims 1 to 7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for predicting reservoir physical parameters based on post-stack seismic data as described in any one of claims 1 to 7.

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