Reservoir mobility determination method, device and equipment and computer readable storage medium

Abstract

The present disclosure relates to the technical field of seismic exploration technology, in particular to a reservoir mobility determination method, device and equipment and a computer readable storage medium. The reservoir mobility determination method comprises the following steps: obtaining a seismic signal of a reservoir; determining a target time-frequency spectrum from the seismic signal by using a second-order synchronous squeezing wavelet transform technology; and determining the mobility of the reservoir based on the target time-frequency spectrum. The second-order synchronous squeezing wavelet transform technology can effectively improve the resolution of the time-frequency spectrum of the mobility attribute, thereby improving the accuracy of the determined mobility.

Description

Technical Field

[0001] This disclosure relates to the field of seismic exploration technology, and in particular to a method, apparatus, device, and computer-readable storage medium for determining reservoir mobility. Background Technology

[0002] Mobility is a property used to detect reservoir fluids. Utilizing mobility properties to determine the location of oil and gas reservoirs can provide a valuable basis for reservoir prediction. Existing methods mostly employ conventional time-frequency analysis techniques (such as continuous wavelet transform and S-transform) to perform time-frequency analysis on seismic data, thereby extracting the energy of effective frequency bands for mobility extraction and detection. However, mobility calculation is limited by time-frequency analysis methods, and the resolution of current technologies cannot meet the needs of fine-grained detection. Summary of the Invention

[0003] This disclosure provides a method, apparatus, and computer-readable storage medium for determining reservoir mobility. It can effectively improve the resolution of the temporal spectrum of mobility attributes by using second-order synchronous squeeze wavelet transform technology, thereby improving the accuracy of the determined mobility.

[0004] In a first aspect, this disclosure provides a method for determining reservoir mobility, including:

[0005] Obtain seismic signals from the reservoir;

[0006] The target time spectrum was determined from the seismic signal using a second-order synchronous squeezing wavelet transform technique.

[0007] The reservoir mobility is determined based on the target time-frequency spectrum.

[0008] In some embodiments, determining the reservoir mobility based on the target time-frequency spectrum includes:

[0009] The peak frequency at each moment is determined based on the target time spectrum;

[0010] The partial derivative of the spectrum with respect to frequency is calculated based on the peak frequency.

[0011] The reservoir mobility is determined based on the partial derivative.

[0012] In some embodiments, the calculation of the partial derivative of the spectrum with respect to frequency based on the peak frequency includes:

[0013] Find the partial derivative of the time spectrum with respect to the angular frequency at the peak frequency.

[0014] In some embodiments, determining the reservoir mobility based on the partial derivative includes:

[0015] The reservoir mobility is obtained by performing maximum value normalization based on the partial derivative.

[0016] In some embodiments, determining the target time spectrum from the seismic signal using a second-order synchronous squeezing wavelet transform technique includes:

[0017] The seismic signal was subjected to wavelet transform to obtain time-frequency results;

[0018] The partial derivatives of the time-frequency results with respect to time are obtained by taking the partial derivatives of the time-frequency results.

[0019] The target-determining frequency spectrum in the seismic signal is determined based on the partial derivatives.

[0020] In some embodiments, determining the target-determining frequency spectrum in the seismic signal based on the partial derivative includes:

[0021] The first-order instantaneous frequency of the seismic signal is determined based on the partial derivatives.

[0022] The second-order instantaneous frequency is determined based on the first-order instantaneous frequency;

[0023] The target time spectrum is obtained by squeezing the area around the second-order instantaneous frequency.

[0024] Secondly, this disclosure provides a reservoir mobility determination apparatus, comprising:

[0025] The acquisition module is used to acquire seismic signals from the reservoir.

[0026] The first determining module is used to determine the target time spectrum from the seismic signal using second-order synchronous squeezing wavelet transform technology;

[0027] The second determining module is used to determine the reservoir mobility based on the target time spectrum.

[0028] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.

[0029] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0030] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0031] This disclosure provides a method for determining reservoir mobility by acquiring seismic signals from the reservoir; using second-order synchronous squeezing wavelet transform technology to determine the target time spectrum from the seismic signals; and determining the reservoir mobility based on the target time spectrum. The second-order synchronous squeezing wavelet transform technology can effectively improve the resolution of the time spectrum of mobility attributes, thereby improving the accuracy of the determined mobility. Attached Figure Description

[0032] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0033] Figure 1 A schematic flowchart illustrating a method for determining reservoir mobility according to an embodiment of this disclosure;

[0034] Figure 2 A schematic diagram of a modulation signal provided in an embodiment of this application;

[0035] Figure 3 A schematic diagram of the time-frequency domain provided for an embodiment of this application;

[0036] Figure 4 A schematic diagram of the time-frequency domain provided for an embodiment of this application;

[0037] Figure 5 A schematic diagram illustrating the resolution in the time-frequency domain, provided as an embodiment of this application;

[0038] Figure 6 A schematic diagram of a seismic record synthesized based on the Marmousi model, provided for an embodiment of this application;

[0039] Figure 7 This is a schematic diagram of a wavelet transform-calculated manifold profile provided in an embodiment of this application;

[0040] Figure 8 This is a schematic diagram of a flow profile calculated by synchronous extrusion wavelet transform according to an embodiment of this application;

[0041] Figure 9 This is a schematic diagram of a flow profile calculated by a second-order synchronous squeeze wavelet transform according to an embodiment of this application.

[0042] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0044] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure 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 this disclosure 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 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.

[0045] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0046] Example 1

[0047] Before introducing the embodiments of this application, a brief overview of related technologies is provided. Before introducing the fluidity theory and fluidity extraction technology process of this invention, it is assumed that the fluid flowability of the rock is within a reasonable range, and the reflection of elastic longitudinal waves at the interface between the elastic medium and the water-saturated porous medium is considered. In this case, the asymptotic expression of the reflection coefficient at a certain angular frequency derived by Silin can be used to better illustrate the background and theoretical basis of this invention.

[0048] The asymptotic expression for the reflection coefficient at a certain angular frequency derived by Silin is an important theoretical result, describing the propagation and reflection characteristics of elastic waves in porous media (as shown in Equation (1)). This expression can be used to infer the fluid flow in porous media and provides a theoretical basis for manifold extraction techniques.

[0049]

[0050] In the formula, R0 and R1 are dimensionless parameters related to fluid and rock mechanical properties such as porosity, density, and elastic modulus; i is the imaginary unit; and ρ... b η is the volume density of the reservoir fluid, ω is the angular frequency of the seismic wave, κ is the reservoir permeability, and η is the fluid viscosity coefficient. Differentiating the above equation with respect to ω yields:

[0051]

[0052] At the same time Golohubin defines the imaging properties as A(x, y):

[0053]

[0054] In the above formula, dS(ω) low ) is the seismic spectrum, which can be obtained through time-frequency analysis. Equation (3) is derived from equations (1) and (2). From equation (3), it can be seen that the mobility attribute can reflect the permeability of the reservoir. C is a complex function related to the porous rock coefficient, which can be obtained through rock physics testing.

[0055]

[0056] In petroleum engineering, mobility properties refer to the ability or characteristics of a liquid or gas to flow through rock pores. Mobility properties not only describe the mobility of fluids in pores, but also encompass their flow velocity, the interaction between the fluid and the pore walls, and the influence of the rock pore structure on fluid flow.

[0057]

[0058] In the formula, M is the reservoir fluid mobility, κ is the reservoir permeability, and η is the fluid viscosity coefficient. It can be seen from the above formula that the better the connectivity of the reservoir, the lower the viscosity coefficient of the fluid it contains, and the greater the fluid mobility in the reservoir. To intuitively reflect the fluid mobility contained in the reservoir, equation (4) can be modified using equation (5) to obtain:

[0059]

[0060] Based on the known time-frequency spectrum of seismic data and rock physical tests, we can calculate C, and then predict the mobility of fluids contained in the reservoir using seismic data. Simultaneously, parameter C also serves as a regulating factor, which, in the case of multiple wells, can constrain the mobility properties calculated from seismic data, making it more consistent with actual conditions. If rock samples and well logging data are lacking in the exploration area, we can still set the coefficient C as a constant to calculate the relative value of reservoir fluid mobility, and perform qualitative analysis of reservoir properties. The above analysis shows that mobility properties are significantly affected by the results of time-frequency analysis.

[0061] To address the problems in related technologies, this application provides a method for determining reservoir mobility. This method can be applied to electronic devices such as mobile phones, tablets, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), and data transmission devices. This application does not limit the specific type of electronic device. The electronic device can be a processor of a data transmission device.

[0062] Figure 1 This is a flowchart illustrating a method for determining reservoir mobility according to an embodiment of this disclosure. Figure 1 As shown, the method for determining reservoir mobility includes:

[0063] Step S101: Obtain the seismic signal of the reservoir.

[0064] In this embodiment, a reservoir refers to a portion of an underground rock layer capable of storing and producing oil, natural gas, or other fluid resources. In oil and gas exploration and development, accurately understanding reservoir characteristics (such as thickness, permeability, and porosity) is crucial for resource assessment and exploitation strategies. Seismic signals are reflected wave information obtained from underground rock layers using seismic exploration techniques. These signals contain rich information about subsurface structure, rock type, and fluid content. By analyzing these signals, the existence, location, and characteristics of reservoirs can be inferred.

[0065] In this embodiment, seismic exploration technology is used, including steps such as deploying seismic detectors, generating seismic waves, and receiving reflected waves. The acquired seismic signals are preprocessed and then used for subsequent time-frequency analysis.

[0066] Step S102: The target time spectrum is determined from the seismic signal using second-order synchronous squeezing wavelet transform technology.

[0067] In this embodiment, the second-order synchronous squeezing wavelet transform is a high-resolution time-frequency analysis method. This method is based on wavelet transform and can be described as squeezing the wavelet transform coefficients around the second-order instantaneous frequency, resulting in a more concentrated energy representation and thus better time-frequency resolution. This enables high-precision analysis of thin layers. Furthermore, the peak frequency can robustly characterize the main frequency features of the signal.

[0068] In this embodiment, the target time spectrum is a time-frequency distribution map reflecting reservoir characteristics extracted from seismic signals using second-order synchronous squeeze wavelet transform technology. It demonstrates the reservoir's response characteristics at different times and frequencies, serving as an important basis for further analysis of parameters such as reservoir mobility.

[0069] In this embodiment, the preprocessed seismic signal can be input into a second-order synchronous squeezing wavelet transform algorithm to calculate the target time spectrum. This process involves steps such as wavelet function selection, transform parameter adjustment, and result optimization.

[0070] Step S103: Determine the reservoir mobility based on the target time spectrum.

[0071] In this embodiment, mobility is an important parameter for hydrocarbon identification, indicating the interaction between the permeability of the pore structure in the reservoir rock framework and the viscosity of the pore fluid. The ratio of permeability to fluid viscosity defines mobility, reflecting the fluid's ability to flow through a porous medium. The magnitude of mobility directly reveals the fluid's flow capacity in the subsurface reservoir; generally, a higher mobility value indicates stronger flow capacity. As a hallmark parameter for classifying conventional and unconventional hydrocarbon reservoirs, mobility plays a crucial role in hydrocarbon exploration. In hydrocarbon detection and reservoir prediction, the application of mobility information can help determine reservoir permeability and the impact of pore structure on fluid flow, thereby guiding the formulation of exploration and development strategies. Therefore, accurate assessment and understanding of mobility are essential for effective hydrocarbon exploration and development.

[0072] In practical applications, the asymptotic expression for the reflection coefficient at a certain angular frequency derived by Silin is one of the key tools. This expression can be used to analyze the seismic wave reflection of elastic P-waves at the interface between elastic and water-saturated porous media, thus providing important geological information for oil and gas exploration. Therefore, combining the fluid flowability of rocks with the seismic wave reflection characteristics can more accurately interpret seismic data, improve the accuracy of reservoir prediction, and provide more effective technical support for the exploration and development of oil and gas resources.

[0073] This disclosure provides a method for determining reservoir mobility by acquiring seismic signals from the reservoir; using second-order synchronous squeezing wavelet transform technology to determine the target time spectrum from the seismic signals; and determining the reservoir mobility based on the target time spectrum. The second-order synchronous squeezing wavelet transform technology can effectively improve the resolution of the time spectrum of mobility attributes, thereby improving the accuracy of the determined mobility.

[0074] In some embodiments, step S103, determining the reservoir mobility based on the target time-frequency spectrum, includes:

[0075] Step S1031: Determine the peak frequency at each moment based on the target time spectrum.

[0076] In this embodiment, when seismic waves pass through the reservoir, the reflection coefficients of the top and bottom of the reservoir and each high-permeability body can be obtained. The reflection coefficients at the low-frequency end, in particular, contain more information about the high-permeability bodies, and the reservoir fluid mobility is derived from these low-frequency reflection coefficients. When the high-permeability bodies are thin, given the limited resolution of seismic exploration, it is difficult to calculate their corresponding fluid mobility from their individual reflection coefficients. However, the overall reservoir fluid mobility can be calculated based on the reflection coefficients of the top and bottom of the medium, thereby estimating the reservoir fluid mobility. In the actual seismic record mobility calculation, the target time spectrum obtained by performing a second-order synchronous compression wavelet transform on the seismic record can be represented by A(ω,t). After obtaining the time spectrum, the peak frequency f is calculated for each time point. peak Peak frequency is the frequency at which the time-spectral energy is at its maximum. The purpose of this step is to determine the dominant frequency components of the seismic record at different time points, thereby enabling a more accurate description of fluid flow characteristics in subsurface reservoirs. Calculating the peak frequency will provide crucial information to help identify fluid mobility within the formation and provide a time-frequency resolution basis for further mobility calculations.

[0077] Step S1032: Calculate the partial derivative of the frequency spectrum with respect to the peak frequency.

[0078] In this embodiment of the application, the partial derivative of the time spectrum with respect to the angular frequency at the peak frequency is obtained.

[0079] In this embodiment, the magnitude of the mobility attribute is proportional to the partial derivative of the reflection coefficient with respect to frequency, which can be described by formula (18):

[0080]

[0081] In practical calculations, the partial derivative of the reflection coefficient with respect to the angular frequency can be approximated by the time-frequency result A(w,t) obtained from the second-order synchronous squeezed wavelet transform, i.e.:

[0082]

[0083] Furthermore, since the second-order synchronous squeezing wavelet transform has stronger sparsity and energy concentration properties compared to the wavelet transform, this invention chooses to calculate at the peak frequency f. peak The partial derivative of the time spectrum with respect to the angular frequency at that point is:

[0084]

[0085] Step S1033: Determine the reservoir mobility based on the partial derivative.

[0086] In this embodiment of the application, the reservoir mobility can be obtained by performing maximum value normalization processing based on the partial derivative.

[0087] In this embodiment of the application, the maximum value of M in equation (17) is normalized to obtain the normalized mobility equation (21), thus obtaining the relative mobility profile, i.e.

[0088]

[0089] In the formula, I is the relative mobility profile, and M max This represents the maximum calculated mobility.

[0090] In this embodiment of the application, we can obtain the relevant flow information of the seismic profile according to formula (21), thereby effectively identifying the reservoir fluid.

[0091] In some embodiments, step S102 can be implemented by the following steps:

[0092] Step S1021: Perform wavelet transform on the seismic signal to obtain time-frequency results.

[0093] In this embodiment of the application, it is assumed that the signal x(t) is a simple harmonic signal, and the time-frequency result of the wavelet transform of the signal can be expressed as Equation (7);

[0094]

[0095] In the formula This represents the time-frequency result of the wavelet transform, where 'a' is the wavelet scale. Denotes the complex conjugate of wavelets. This represents the mother wavelet.

[0096] Step S1022: Calculate the partial derivative based on the time-frequency result to obtain the partial derivative of the time-frequency result with respect to time.

[0097] In this embodiment of the application, according to Passarol's theorem, equation (7) can be rewritten in the inverse Fourier transform form:

[0098]

[0099] In the formula, X(ω) and Φ(ω) are the Fourier transform results of the simple harmonic wave and the wavelet, respectively.

[0100] The expression for a simple harmonic wave is also given: x(t)=A(t)e j2πφ(t) Substituting this into equation (8), and with ω = 2πf, where f is the frequency, the wavelet transform can be rewritten as the frequency-dependent inverse Fourier transform expression:

[0101]

[0102] Simplifying equation (9) yields the accurate expression for the wavelet transform result of the simple harmonic wave:

[0103]

[0104] Taking the partial derivative of equation (10) yields the partial derivative of the wavelet transform result with respect to time t:

[0105]

[0106] Step S1023: Determine the target-determining frequency spectrum in the seismic signal based on the partial derivative.

[0107] In this embodiment of the application, determining the target time spectrum in the seismic signal based on the partial derivative includes: determining the first-order instantaneous frequency of the seismic signal based on the partial derivative; determining the second-order instantaneous frequency based on the first-order instantaneous frequency; and performing a compression operation on the area surrounding the second-order instantaneous frequency to obtain the target time spectrum.

[0108] In this embodiment of the application, rewriting equation (11) yields the first-order instantaneous frequency estimate of the simple harmonic wave:

[0109]

[0110] in For first-order instantaneous frequency estimation, φ ′ (t) represents the actual frequency.

[0111] Meanwhile, the second-order instantaneous frequency estimate can be expressed as:

[0112]

[0113] In the formula The local delay and chirp rate estimator is represented by equations (14) and (15).

[0114]

[0115] Substituting equations (14) and (15) into equation (13) yields the expression for the second-order instantaneous frequency estimation:

[0116]

[0117] In the formula Let x(t) represent x(t) respectively. The continuous wavelet transform of the mother wavelet.

[0118] Based on the concept of synchronous squeezing, the second-order synchronous squeezing wavelet transform can be described as squeezing the wavelet transform coefficients around the second-order instantaneous frequency. Wavelet coefficients At discrete scale a k The synchronous squeezed wavelet transform value at f l With the center frequency, in the interval The value of the frequency band within is obtained by compression, and the formula is as follows:

[0119]

[0120] In the formula WT s (f l b) is a second-order synchronous squeezing wavelet transform, a k -a k-1 =(Δa) k For discrete scale intervals; Δf = f l -f l-1 This refers to the frequency range.

[0121] Example 2

[0122] Based on the above embodiments, this application further provides a method for determining reservoir mobility. This invention proposes to use a second-order synchronous squeezing wavelet transform to obtain the time-frequency spectrum of seismic data, and then calculate the fluid flowability of the reservoir. This method extends the existing synchronous squeezing method to wavelet transform and introduces second-order instantaneous frequency estimation to improve its ability to process non-steady-state signals. The second-order synchronous squeezing wavelet transform provides concentrated time-frequency representations with multiple resolutions. The second-order synchronous squeezing wavelet transform squeezes the time-frequency spectrum of the wavelet transform around the instantaneous frequency, making its energy more concentrated, thereby obtaining a high-resolution time-frequency spectrum. The second-order synchronous squeezing wavelet transform can be divided into several steps, the specific steps of which are described in detail below:

[0123] First, we introduce the relevant theory of wavelet transform. Assuming that the signal x(t) is a simple harmonic signal, the time-frequency result of the wavelet transform of the signal can be expressed as equation (7).

[0124]

[0125] In the formula This represents the time-frequency result of the wavelet transform, where 'a' is the wavelet scale. Denotes the complex conjugate of wavelets. Let represent the mother wavelet. According to Passarol's theorem, equation (7) can be rewritten in inverse Fourier transform form:

[0126]

[0127] In the formula, X(ω) and Φ(ω) are the Fourier transform results of the simple harmonic wave and the wavelet, respectively.

[0128] The expression for a simple harmonic wave is also given: x(t)=A(t)e j2πφ(t) Substituting this into equation (8), and with ω = 2πf, where f is the frequency, the wavelet transform can be rewritten as the frequency-dependent inverse Fourier transform expression:

[0129]

[0130] Simplifying equation (9) yields the accurate expression for the wavelet transform result of the simple harmonic wave:

[0131]

[0132] Taking the partial derivative of equation (10) yields the partial derivative of the wavelet transform result with respect to time t:

[0133]

[0134] Rewriting equation (11) yields the first-order instantaneous frequency estimate of the simple harmonic wave:

[0135]

[0136] in For first-order instantaneous frequency estimation, φ ′ (t) represents the actual frequency.

[0137] Meanwhile, the second-order instantaneous frequency estimate can be expressed as:

[0138]

[0139] In the formula The local delay and chirp rate estimator is represented by equations (14) and (15).

[0140]

[0141] Substituting equations (14) and (15) into equation (13) yields the expression for the second-order instantaneous frequency estimation:

[0142]

[0143] In the formula Let x(t) represent x(t) respectively. The continuous wavelet transform of the mother wavelet.

[0144] Based on the concept of synchronous squeezing, the second-order synchronous squeezing wavelet transform can be described as squeezing the wavelet transform coefficients around the second-order instantaneous frequency. Wavelet coefficients At discrete scale a k The synchronous squeezed wavelet transform value at f l With the center frequency, in the interval The value of the frequency band within is obtained by compression, and the formula is:

[0145]

[0146] In the formula WT s (f l b) is a second-order synchronous squeezing wavelet transform, a k -a k-1 =(Δa) k For discrete scale intervals; Δf = f l -f l-1 This refers to the frequency range.

[0147] The principles of the second-order synchronous squeeze wavelet transform have been introduced above; their application in reservoir mobility calculation will follow. When seismic waves pass through a reservoir, we can obtain the reflection coefficients of the top and bottom of the reservoir and each high-permeability body. The reflection coefficients at the low-frequency end, in particular, contain more information about the high-permeability bodies, and reservoir fluid mobility is derived from these low-frequency reflection coefficients. When high-permeability bodies are thin, given the limited resolution of seismic exploration, it is difficult to calculate their corresponding fluid mobility from their individual reflection coefficients. However, we can calculate the overall reservoir fluid mobility based on the reflection coefficients of the top and bottom of the medium, thus estimating the reservoir fluid mobility. In actual seismic record mobility calculations, the time-frequency spectrum obtained by performing a second-order synchronous squeeze wavelet transform on the seismic record can be represented by A(ω,t). After obtaining the time-frequency spectrum, the peak frequency f is calculated for each time point. peak Peak frequency is the frequency at which the time-spectral energy is at its maximum. The purpose of this step is to determine the dominant frequency components of the seismic record at different time points, thereby enabling a more accurate description of fluid flow characteristics in subsurface reservoirs. Calculating the peak frequency will provide crucial information to help identify fluid mobility within the formation and provide a time-frequency resolution basis for further mobility calculations.

[0148] The magnitude of the mobility property is directly proportional to the partial derivative of the reflection coefficient with respect to frequency, which can be described by formula (18):

[0149]

[0150] In practical calculations, the partial derivative of the reflection coefficient with respect to the angular frequency can be approximated by the time-frequency result A(w,t) obtained from the second-order synchronous squeezed wavelet transform, i.e.:

[0151]

[0152] Furthermore, since the second-order synchronous squeezing wavelet transform has stronger sparsity and energy concentration properties compared to the wavelet transform, this invention chooses to calculate at the peak frequency f. peak The partial derivative of the time spectrum with respect to the angular frequency at that point is:

[0153]

[0154] Normalizing M in equation (17) to its maximum value yields the normalized mobility equation (21), which in turn gives the relative mobility profile.

[0155]

[0156] In the formula, I is the relative mobility profile, and M max This represents the maximum calculated mobility.

[0157] According to equation (21), we can obtain the relevant flow information of the seismic profile, thereby effectively identifying the reservoir fluid.

[0158] Example 3

[0159] Based on the above embodiments, two examples are provided here to verify the feasibility of the present invention and its improved resolution. Example 1 verifies the feasibility of the second-order synchronous squeeze wavelet transform and its higher resolution. Example 2 verifies that the mobility calculated by the second-order synchronous squeeze wavelet transform accurately reflects the fluid position.

[0160] Example 1: Implementation of second-order synchronous squeeze wavelet transform of a custom modulation signal.

[0161] First, a simple example is used to implement the second-order synchronous squeeze wavelet transform, with a custom frequency cross-modulation signal x(t). Figure 2 This is a schematic diagram of a modulation signal provided in an embodiment of this application, as shown below. Figure 2 As shown, wavelet transform is used to transform it to the time-frequency domain. Figure 3 A schematic diagram of the time-frequency domain provided in an embodiment of this application, as shown below. Figure 3 As shown. Time-frequency domain of synchronous squeezed wavelet transform. Figure 4 A schematic diagram of the time-frequency domain provided in an embodiment of this application, as shown below. Figure 4 As shown. Time-frequency domain of synchronous squeezed wavelet transform. Figure 5A schematic diagram of time-frequency domain resolution provided for an embodiment of this application is shown below. Figure 5 As shown.

[0162] By comparing the results of wavelet transform and synchronous squeezing wavelet transform, it can be seen that the time spectrum obtained by using second-order synchronous squeezing wavelet transform has higher time-frequency resolution, especially at low frequency positions.

[0163] Example 2: Comparison of mobility processing results using the Marmousi model.

[0164] Taking the calculation of the mobility properties of the Marmousi model as an example, this paper illustrates that the resolution of fluid mobility calculated by the second-order synchronous extrusion wavelet transform is significantly higher than that calculated by the continuous wavelet transform and the synchronous extrusion wavelet transform, and can better reflect the location of the reservoir.

[0165] Figure 6 A schematic diagram of a seismic record synthesized based on the Marmousi model is provided for an embodiment of this application, as shown below. Figure 6 As shown, the lens-shaped structures between 1.0 and 1.5 s in the profile represent the locations of oil and gas reservoirs. Figure 7 This is a schematic diagram of a flow profile calculated by wavelet transform according to an embodiment of this application. Figure 8 This is a schematic diagram of a mobility profile calculated by synchronous squeeze wavelet transform according to an embodiment of this application. Figure 9 This is a schematic diagram of a mobility profile calculated by a second-order synchronous squeeze wavelet transform according to an embodiment of this application. From the mobility results obtained by the three methods, the second-order synchronous squeeze wavelet transform shows significantly higher vertical resolution of mobility, enabling a more precise characterization of the reservoir top and bottom interface positions. This represents a significant improvement in resolution compared to the synchronous squeeze wavelet transform and wavelet transform.

[0166] Example 4

[0167] This application provides a reservoir mobility determination device, including:

[0168] The acquisition module is used to acquire seismic signals from the reservoir.

[0169] The first determining module is used to determine the target time spectrum from the seismic signal using second-order synchronous squeezing wavelet transform technology;

[0170] The second determining module is used to determine the reservoir mobility based on the target time spectrum.

[0171] Example 5

[0172] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0173] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0174] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0175] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0176] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0177] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0178] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0179] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0180] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0181] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0182] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0183] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method for determining reservoir mobility, characterized in that, include: Obtain seismic signals from the reservoir; The target time spectrum was determined from the seismic signal using a second-order synchronous squeezing wavelet transform technique. The reservoir mobility is determined based on the target time-frequency spectrum.

2. The method according to claim 1, characterized in that, Determining the reservoir mobility based on the target time-frequency spectrum includes: The peak frequency at each moment is determined based on the target time spectrum; The partial derivative of the spectrum with respect to frequency is calculated based on the peak frequency. The reservoir mobility is determined based on the partial derivative.

3. The method according to claim 2, characterized in that, The partial derivative of the spectrum with respect to frequency calculated based on the peak frequency includes: Find the partial derivative of the time spectrum with respect to the angular frequency at the peak frequency.

4. The method according to claim 2, characterized in that, Determining the reservoir mobility based on the partial derivative includes: The reservoir mobility is obtained by performing maximum value normalization based on the partial derivative.

5. The method according to claim 1, characterized in that, The method of determining the target time spectrum from the seismic signal using second-order synchronous squeezing wavelet transform includes: The seismic signal was subjected to wavelet transform to obtain time-frequency results; Based on the time-frequency results, the partial derivatives of the time-frequency results with respect to time are obtained; The target-determining frequency spectrum in the seismic signal is determined based on the partial derivatives.

6. The method according to claim 5, characterized in that, The step of determining the target-time spectrum in the seismic signal based on the partial derivative includes: The first-order instantaneous frequency of the seismic signal is determined based on the partial derivatives. The second-order instantaneous frequency is determined based on the first-order instantaneous frequency; The target time spectrum is obtained by squeezing the area around the second-order instantaneous frequency.

7. A reservoir mobility determination device, characterized in that, include: The acquisition module is used to acquire seismic signals from the reservoir. The first determining module is used to determine the target time spectrum from the seismic signal using second-order synchronous squeezing wavelet transform technology; The second determining module is used to determine the reservoir mobility based on the target time spectrum.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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