Sand body distribution prediction method, equipment and medium

Abstract

The invention provides a sand body distribution prediction method, equipment and a medium, and relates to the technical field of seismic exploration. The method comprises the following steps: acquiring seismic response waves and logging data beside a hoistway in a target area; based on the seismic response waves beside the hoistway and the logging data, a singular value decomposition algorithm is adopted, and the corresponding relation between the seismic response wave components of the target area and different sedimentary facies is determined; a likelihood function used for describing the sedimentary facies plane distribution probability is constructed by taking the corresponding relation between the seismic response wave component of the target area and different sedimentary facies as prior information; determining plane distribution probabilities of different sedimentary facies based on a likelihood function and the seismic response waves of the target area; based on the plane distribution probabilities of different sedimentary facies, grouping seismic response waves corresponding to different sedimentary facies to obtain a plurality of sample sets; seismic wave inversion is carried out based on each sample set, and the vertical thickness and plane distribution of the sand body in the target area are obtained. According to the sand body distribution prediction method provided by the invention, the transverse resolution of the sand body is improved.

Description

Technical Field

[0001] The present invention relates to the field of seismic exploration technology, and in particular to a sand body distribution prediction method, equipment and medium. Background Art

[0002] The characterization of sand body boundaries is crucial to oil and gas exploration. Existing sand body distribution prediction methods have low lateral resolution, and the characterization of sand body boundaries with different lateral thicknesses (i.e., changes in sand body types) is fuzzy, making it difficult to accurately identify their distribution and morphology. Especially for situations where the lateral sand body types change rapidly, the fuzzy boundary characterization will make it impossible to predict the sand body distribution. Therefore, there is an urgent need for a sand body distribution prediction method with high lateral resolution to accurately characterize sand bodies with lateral phase changes and realize in-depth exploration and development of oil and gas. Summary of the invention

[0003] The present invention provides a sand body distribution prediction method, equipment and medium, which are used to solve the defect of low lateral resolution in the prior art and achieve high-precision characterization of sand bodies with lateral phase changes.

[0004] The present invention provides a sand body distribution prediction method, comprising the following steps: Obtain seismic response waves and logging data beside the wellbore in the target area; Based on the wellbore side seismic response wave and the well logging data, a singular value decomposition algorithm is used to determine the correspondence between the seismic response wave components of the target area and different sedimentary phases; Taking the correspondence between the seismic response wave components and different sedimentary facies in the target area as prior information, a likelihood function is constructed to describe the probability of sedimentary facies plane distribution. Determining the planar distribution probability of different sedimentary phases based on the likelihood function and the seismic response wave of the target area; Based on the planar distribution probability of different sedimentary phases, the seismic response waves corresponding to different sediments are grouped to obtain multiple sample sets; Seismic wave inversion is performed based on each sample set to obtain the vertical thickness and planar distribution of the sand body in the target area.

[0005] A sand body distribution prediction method provided by the present invention also includes: Acquire original seismic waveform data of the target area; Based on a matching pursuit algorithm, the coal seam reflection wave is filtered out from the original seismic waveform data to obtain the seismic response wave.

[0006] According to a sand body distribution prediction method provided by the present invention, based on a matching pursuit algorithm, the coal seam reflection wave is filtered out from the original seismic waveform data, comprising: Performing seismic forward modeling on the target area to obtain simulated reflection waves of coal seams in the target area; Based on the matching pursuit algorithm, the simulated reflection wave is used as a matching wavelet to perform spectrum decomposition on the original seismic wave to obtain seismic wavelets at different frequencies; the seismic wavelets include a target seismic wavelet that best matches the simulated reflection wave; Based on the seismic wavelets other than the target seismic wavelet, the seismic wave is reconstructed to obtain the seismic response wave.

[0007] According to a sand body distribution prediction method provided by the present invention, the well logging data includes: well logging sand body thickness data; Based on the wellbore side seismic response wave and the logging data, a singular value decomposition algorithm is used to determine the correspondence between the seismic response wave components of the target area and different sedimentary phases, including: Based on the well-side channel seismic response wave and the well logging sand body thickness data, a singular value decomposition algorithm is used to determine the corresponding relationship between the well-side channel seismic response wave components and different sedimentary phases; Based on the correspondence between the seismic response wave components of the well bypass and the different sedimentary phases, the correspondence between the seismic response wave components of the inter-well area and the different sedimentary phases is determined.

[0008] According to a sand body distribution prediction method provided by the present invention, the likelihood function is as follows: Indicates i Earthquake waves, Indicates i The disturbance amount of the elastic parameters corresponding to the seismic wave, the elastic parameters include thickness, I is the prior information, w represents the seismic wavelet, and N represents the number of seismic waves.

[0009] According to a sand body distribution prediction method provided by the present invention, the seismic wave inversion is a seismic wave inversion based on a phase-controlled waveform.

[0010] According to a sand body distribution prediction method provided by the present invention, the seismic wave inversion comprises: Constructing a posterior probability distribution function based on the likelihood function and the prior probability distribution function; Seismic wave inversion is performed based on the posterior probability distribution.

[0011] According to a sand body distribution prediction method provided by the present invention, the seismic wave inversion comprises: Based on the correlation analysis between the well logging data and lithology, a lithology sensitivity curve is selected; The lithology sensitivity curve is used as prior information to invert the seismic wave.

[0012] The present invention also provides a sand body distribution prediction device, comprising the following modules: The data acquisition module is used to obtain seismic response waves and logging data beside the wellbore in the target area.

[0013] The singular value decomposition module is used to determine the correspondence between the seismic response wave components of the target area and different sedimentary phases based on the seismic response wave beside the wellbore and the logging data by using a singular value decomposition algorithm.

[0014] The likelihood function construction module is used to construct a likelihood function for describing the probability of sedimentary facies plane distribution based on the correspondence between the seismic response wave components in the target area and different sedimentary facies as prior information.

[0015] The distribution probability determination module is used to determine the planar distribution probability of different sedimentary phases based on the likelihood function and the seismic response wave of the target area.

[0016] The sample grouping module is used to group the seismic response waves corresponding to different sediments based on the planar distribution probability of different sedimentary phases to obtain multiple sample sets.

[0017] The sand body distribution prediction module is used to perform seismic wave inversion based on each sample set to obtain the vertical thickness and planar distribution of the sand body in the target area.

[0018] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any of the sand body distribution prediction methods described above is implemented.

[0019] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the sand body distribution prediction method as described above is implemented.

[0020] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the sand body distribution prediction method as described above is implemented.

[0021] The sand body distribution prediction method, device and medium provided by the present invention, by means of the seismic response wave and logging data of the well bypass in the target area, adopts the singular value decomposition algorithm to determine the correspondence between the seismic response wave component and different sedimentary phases (different types of sand bodies), and based on this, determines the plane distribution probability of different sedimentary phases, and then groups the seismic response waves corresponding to different depositions on the plane, and inverts them based on each group of seismic response waves, and finally obtains the vertical thickness and plane distribution of the sand body in the target area. That is, the present invention achieves the resolution and inversion characterization of each lateral sedimentary phase by determining the plane distribution probability of different sedimentary phases, grouping the seismic response waves corresponding to different depositions, and then inverting the groups, that is, ensuring the lateral resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a schematic flow chart of the sand body distribution prediction method provided in an embodiment of the present invention.

[0024] Figure 2-a It is a geological model diagram in the two-dimensional forward modeling provided by an embodiment of the present invention.

[0025] Figure 2-b It is a forward modeling seismic profile diagram in the two-dimensional forward modeling provided by an embodiment of the present invention.

[0026] Figure 2-c An embodiment of the present invention provides a graph of amplitude and frequency at the maximum peak of a seismic waveform in a two-dimensional forward modeling.

[0027] Figure 3-a It is a 30 Hz time domain forward modeling seismic profile diagram provided by an embodiment of the present invention.

[0028] Figure 3-b It is a 60 Hz time domain forward modeling seismic profile diagram provided by an embodiment of the present invention.

[0029] Figure 3-c It is a 90 Hz time domain forward modeling seismic profile diagram provided by an embodiment of the present invention.

[0030] Figure 3-d It is a 30 Hz time-domain forward theoretical model diagram provided by an embodiment of the present invention.

[0031] Figure 3-e It is a 60 Hz time-domain forward theoretical model diagram provided by an embodiment of the present invention.

[0032] Figure 3-f It is a 90 Hz time-domain forward theoretical model diagram provided by an embodiment of the present invention.

[0033] Figure 4-a It is a through-well seismic profile diagram of Well A and Well B provided in an embodiment of the present invention.

[0034] Figure 4-b It is a seismic waveform diagram of Well A and Well B provided by an embodiment of the present invention.

[0035] Figure 5 It is a sand body thickness distribution histogram provided by an embodiment of the present invention.

[0036] Figure 6 This is a sand body lithology interpretation diagram provided by an embodiment of the present invention.

[0037] Figure 7 It is a seismic phase-logging phase characteristic diagram of the Benxi Formation in the study area provided by an embodiment of the present invention.

[0038] Figure 8 This is a quality control chart of the waveform indicating the optimal cutoff frequency provided by an embodiment of the present invention.

[0039] Fig. 9 It is a phase-controlled waveform indication inversion diagram provided by an embodiment of the present invention.

[0040] Fig.10 This is a plan view of sandstone distribution provided by an embodiment of the present invention.

[0041] Fig.11 It is a schematic diagram of the structure of a sand body distribution prediction device provided in an embodiment of the present invention.

[0042] Fig.12 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Combine the following Figure 1-Figure 10 The sand body distribution prediction method provided by an embodiment of the present invention is described.

[0045] Figure 1 It is a schematic diagram of the flow of the sand body distribution prediction method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101: Obtain seismic response waves and logging data beside the wellbore in the target area.

[0046] Step 102: Based on the wellbore side seismic response wave and the well logging data, a singular value decomposition algorithm is used to determine the correspondence between the seismic response wave components of the target area and different sedimentary phases.

[0047] Step 103: Using the correspondence between the seismic response wave components in the target area and different sedimentary facies as prior information, a likelihood function for describing the probability of planar distribution of sedimentary facies is constructed.

[0048] Step 104: Determine the planar distribution probability of different sedimentary phases based on the likelihood function and the seismic response wave of the target area.

[0049] Step 105: based on the planar distribution probabilities of different sedimentary phases, group the seismic response waves corresponding to different sediments to obtain multiple sample sets.

[0050] Step 106: Perform seismic wave inversion based on each sample set to obtain the sand body distribution in the target area, wherein the sand body distribution includes vertical thickness and planar distribution.

[0051] It should be noted that the seismic response waves in this embodiment are multiple seismic waves distributed on a plane. Grouping the seismic response waves corresponding to different deposits means dividing the seismic response waves in the area where a certain sedimentary phase is located into one group in the plane space. Different sedimentary phases refer to sedimentary phases with different thicknesses.

[0052] The inversion is performed based on each group of seismic response waves respectively, because each group of seismic impact waves has commonality, because they are response waves from the same sedimentary phase. It can be understood that each sedimentary phase has its own adaptive inversion parameters. In this embodiment, each group of seismic response waves can be inverted using its own adaptive inversion parameters during inversion, thereby achieving accurate characterization of the sedimentary phase. Compared with the prior art in which sedimentary phases are not classified and inverted, the seismic response waves corresponding to different deposits are inverted simultaneously using the same inversion parameters, thereby causing the inversion effects of different sedimentary phases to fail to achieve their own optimal problem (because the same inversion parameters cannot take into account the inversion accuracy of all sedimentary phases), the sand body distribution prediction method provided by the embodiment of the present invention has a stronger resolution and higher inversion accuracy.

[0053] Specifically, different sedimentary phases have their own optimal choices in inversion parameters such as the optimal number of samples, optimal cutoff frequency and smoothing radius.

[0054] The optimal number of samples characterizes the influence of the spatial variation of the seismic response wave on the sedimentary facies. The selection of the optimal number of samples needs to consider the influence of the sedimentary facies and the number of wells in the target area. Taking the well spacing of 300m as an example, the optimal number of samples for tidal channel microfacies is set to 5, the optimal number of samples for sand flats is set to 6, and the optimal number of samples for swamp-mud flats is set to 7.

[0055] The optimal cutoff frequency needs to consider the thickness of the sand body in the target area. The thickness distribution range of the strata in the target area is mainly between 2m and 8m, and the average velocity of the strata is 5504 m / s. When the resolution is calculated at 1 / 4 wavelength and 1 / 8 wavelength, the optimal cutoff frequency of the tidal channel microfacies is about 80 Hz, the optimal cutoff frequency of the sand flat is about 170 Hz, and the optimal cutoff frequency of the swamp-mud flat is about 200 Hz.

[0056] The smoothing radius affects the smoothness of the inversion results. A larger smoothing radius will lead to smoother inversion results, but may lose some important geological details; a smaller smoothing radius can retain more details, but may increase the randomness of the inversion results. The smoothing radius of tidal channel microfacies is 8, the smoothing radius of sand flat is 7, and the smoothing radius of swamp-mud flat is 6. Through fine adjustment of these key parameters, the inversion effect can be significantly improved, making the inversion results more consistent with the actual geological situation, and providing more accurate geological information for oil and gas exploration and development.

[0057] The embodiment of the present invention combines well logging data, singular value decomposition algorithm, likelihood function, etc. when determining the planar distribution of different sedimentary phases, which will be described in detail later.

[0058] The embodiment of the present invention can achieve accurate characterization of sand bodies in target areas with different sedimentary phases through the above method. For scenarios where the lateral sedimentary phases in the target area are of different thicknesses or even have fast lateral phase changes, the embodiment of the present invention can also accurately characterize them. For example, in Jiaxian District, located in the northeast of the Yishan Slope in the Ordos Basin, the main development strata are the Permian Shihezi Formation, Shanxi Formation, Taiyuan Formation and Carboniferous Benxi Formation. Compared with the Lower Permian Shanxi Formation and the Middle Permian Lower Shihezi Formation, the Upper Carboniferous Benxi Formation has greater exploration and development potential for tight gas, but the Upper Carboniferous Benxi Formation is a set of sandstone deposits located below the coal seam, mainly tidal waterway deposits in a tidal flat environment, and the sand bodies have the characteristics of fast lateral phase changes, thin sand bodies, and low permeability and density. The existing inversion method cannot accurately characterize the lateral distribution of the sand body, but the sand body distribution prediction method provided by the embodiment of the present invention can accurately characterize the lateral separation of the sand body by identifying and dividing its lateral sedimentary phases and then inverting them separately.

[0059] In some embodiments of the present invention, in order to improve the accurate prediction of the planar distribution of the sedimentary phase, the seismic waves used for inversion are also preprocessed. In summary, the original seismic wave data of the target area are preprocessed to remove interference to obtain the seismic response wave of the target area in step 101. It should be noted that the seismic response wave described in step 101 herein is actually the response wave remaining after removing interference from the original seismic response wave data.

[0060] Coal seam reflection waves, as strong interference information in the original seismic waveform data, seriously affect the inversion results. For example, through the analysis of the reservoir characteristics and logging phases of the target layer in the target area (Jiaxian District), the target layer sand body thickness (ΔZ) is generally 2m~15m, the coal seam thickness is 7m~14m, and the average is about 14m. The main frequency of the seismic data is only 32 Hz, and the effective bandwidth is 5 Hz~92 Hz. The upper part of the frequency-divided section is a set of 14-meter coal seams with the characteristics of strong reflection phase axes. This strong reflection shields the reflection characteristics of the lower sandstone, resulting in the distribution of the lower sandstone cannot be identified.

[0061] Therefore, the preprocessing of the original seismic wave data in this embodiment is mainly to filter out the coal seam reflection wave from the original seismic waveform data, and then use the seismic data after filtering out the coal seam reflection wave for subsequent inversion work. In this way, on the one hand, those thin sand bodies can be identified, and then the accurate sedimentary phase plane distribution can be obtained. On the other hand, since the interference is removed, the data used for inversion is more accurate, and then the inversion result is also more accurate.

[0062] When filtering out coal seam reflection waves, we mainly use the matching pursuit algorithm, and add seismic forward modeling as an aid. Specifically, we first use seismic forward modeling to theoretically simulate coal seam reflection waves, and then use the theoretical simulation results as constraints for the matching pursuit algorithm to perform spectral decomposition on the original seismic wave data, and finally remove the filtered coal seam reflection waves from the original seismic wave data.

[0063] The filtering of coal seam reflected waves is introduced in detail below.

[0064] (1) Earthquake forward modeling Seismic forward modeling is the process of obtaining the corresponding seismic response based on a known or designed geological model. Seismic forward modeling can be used to analyze the relationship between the lithology, physical properties and seismic response of the geological body, and to test the reliability of the inversion method and results. In this embodiment, we design a geological model based on the actual geological conditions, and understand the seismic reflection response of lithology changes through forward modeling. The following is an explanation with a specific example. The target area (the target area is, for example, a study area in Jiaxian District) has a target layer, the Benxi Formation sandstone, with a thickness of mainly 2m to 15m, and there is a set of 8# coal seams with stable thickness on the top. Design a geological model based on the actual geological conditions, such as Figure 2-a As shown in the figure, the model is based on mudstone as the background, with a 14 m thick coal seam on top and two sets of sandstone underneath, which are thick rectangular sand bodies and thin trapezoidal sand bodies. The parameters of each lithology are set according to the actual situation, among which the average layer velocity of sandstone is 2800 m / s, the average layer velocity of mudstone is 3300 m / s, the average layer velocity of coal rock is 2200 m / s, and the density of sandstone is 2260 kg / m 3 The density of mudstone is 2650 kg / m 3 , coal rock density is 1500 kg / m 3 The 2D forward geological model is convolved with a statistical wavelet (with a main frequency of about 32 Hz) to synthesize a 2D seismic model, such as Figure 2-b As shown. The wavelet used is a statistical wavelet extracted from the Jiaxian block, and the thickness of the river sand body and the RMS amplitude value are used to quantitatively represent the interference from the adjacent area. Based on the geological model, the reflection waveform of the coal seam in the coal-bearing stratum is obtained by forward modeling.

[0065] (2) Spectral decomposition based on matching pursuit algorithm As a wavelet decomposition method, matching pursuit can decompose the signal into a linear combination of multiple optimal matching wavelets. The reflection waveforms of coal seams and sandstones in coal-bearing strata obtained by forward modeling in the previous step can be well removed from the original seismic using the matching pursuit algorithm, as shown in Figure 3. The process is as follows: the reflection waveform of the coal seam in the coal-bearing strata obtained by forward modeling is used as the initial matching wavelet; the matching pursuit algorithm (MP) is used to iteratively search for the optimal time-frequency atom until it has a high similarity coefficient with the forward modeling result, and the optimal wavelet is used as the strong reflection of the coal seam in this channel; the seismic response of the coal seam and sandstone at different frequencies is obtained by using the matching waveform subtraction method to remove the strong reflection of the coal seam.

[0066] Specifically, this embodiment adopts seismic matching pursuit time-frequency processing technology, uses Ricker wavelet matching pursuit algorithm to analyze the seismic signal complexly to obtain three seismic parameters: instantaneous amplitude, instantaneous phase and instantaneous frequency, and the original seismic wave data is decomposed into multiple seismic wavelets at different frequencies, which contain the target seismic wavelet obtained by iteration above and best matching the forward modeling coal seam reflection wave. Finally, the seismic response wave described in this article is obtained by superposition reconstruction based on the seismic wavelets other than the target seismic wavelet. The specific parameters involved are expressed as follows: Assume that Ricker wavelet is used in seismic forward modeling, with a dominant frequency of f j The expression of the zero-phase Ricker wavelet in the time domain is: (1) Among them, f j is the main frequency of the earthquake, in Hz, t is the seismic reflection time, in s, W R (t,f j ) is the Ricker wavelet in time domain; j is the number of iterations.

[0067] The sparse expression of the seismic signal is: (2) Where S(t) is the band-limited seismic signal in the time domain, R s （m） (t) is the residual after matching pursuit, m is the number of iterative atoms determined by the set threshold to judge the iteration termination condition, and the number of atoms is determined by the time delay t j (Unit: s), earthquake main frequency f j (Unit: Hz), correlation coefficient a j and phase φ j And other 4 processing parameters to control.

[0068] Through a matching pursuit optimization algorithm, the matching sub-wave W corresponding to the target waveform (the reflection waveform of the coal seam and sandstone in the coal-bearing stratum obtained by seismic forward modeling) can be calculated. R (t,f j ) and the sub-wave amplitude A, thereby obtaining the sub-wave W that characterizes the reflection waveform matching R (the target seismic wavelet mentioned above), that is (3) From the original seismic data S(t0, f j ) minus W R , and obtain the new seismic record S (t, f j ) (that is, the seismic response wave described in this article) can highlight the weak reflection characteristics.

[0069] (4) Where λ is the separation coefficient. By identifying and separating strong reflections, the weak reflection information can be better restored and the reservoir prediction accuracy can be improved.

[0070] Through the seismic matching pursuit waveform subtraction technology under the constraints of seismic forward modeling, strong reflections are removed and weak signals of sandstone are enhanced, providing favorable data for the prediction of subsequent seismic sand bodies. The effect of seismic matching pursuit waveform subtraction technology in removing strong reflections is closely related to the accuracy of the strong reflection matching wavelet. The Ricker wavelet or Morlet wavelet used in matching pursuit cannot match the actual complex strong reflection waveform well. Therefore, the reflection waveforms of coal seams and sandstones obtained by seismic forward modeling can be used as the initial matching wavelet, and continuous iterations are performed to finally obtain the reconstruction of seismic signals at different frequencies. In a certain study area in Jiaxian District mentioned above, it can be seen from the seismic profile in which the coal seam reflection is removed by matching pursuit controlled by forward modeling, see Figure 3-a to Figure 3-f , the dominant frequency corresponding to the 12m sand body is about 60Hz, and the tuning response at this frequency is the strongest, which can reduce the interference of coal rock on sandstone. Spectral decomposition can keep the low-frequency information of seismic data unchanged, while the main frequency is expanded from the original 32 Hz to 60 Hz, which can make the seismic data have a higher resolution. 30Hz matching pursuit can only vaguely distinguish part of the sand body reflection wave under the removal of coal seam reflection. When it is above 60Hz, it can better reduce the interference of coal rock on sandstone, the lateral change of the phase axis is reasonable, the waveform is natural and without distortion, and the sand body reflection is effectively highlighted.

[0071] After the above preprocessing, the seismic wave data with the coal seam reflection wave filtered out can effectively highlight the sand body reflection, improve the accuracy of the inversion basic data, and increase the vertical resolution.

[0072] The following is a detailed introduction to the combination of logging data, the application of the singular value decomposition algorithm, and the construction of the likelihood function involved in improving the lateral resolution.

[0073] (1) In combination with well logging data, the singular value decomposition algorithm is used to determine the correspondence between the seismic response wave components and different sedimentary facies through the well logging data and the seismic response waves near the wellbore.

[0074] First, an isochronous stratigraphic framework is established through seismic horizon interpretation, and the seismic response waveforms of the wellside channels of two adjacent wells are extracted. After top surface alignment and thickness correction, the seismic response waveforms are compared. Figure 4-a and Figure 4-b It can be seen that the seismic waveforms of the two wells are 93% similar. It can be concluded that the seismic waveforms of the two wells with similar sedimentary environments are also similar.

[0075] Then, the seismic waveform is dynamically clustered according to singular value decomposition to establish the initial model of seismic wave impedance.

[0076] Based on the above, after removing the strong reflection of the coal seam, the seismic response wave is obtained. For the seismic data in a given window, it can be represented by an M×N-order matrix A (where M is the number of seismic waves, that is, the number of seismic waves, and N is the number of sample points). According to the principle of singular value decomposition (SVD), it can be expressed as: (5) Where: U is an M×N matrix, whose columns are i AA T Orthogonal eigenvectors of i is an N×N matrix, whose columns V i A T The orthogonal eigenvectors of A; Σ is a singular value matrix whose elements are the eigenvalues ​​λ of matrix A. i Where U and V are seismic waveform data and sand body thickness data, which are n×n and m×m orthogonal matrices respectively, and V T is the conjugate transpose of V, which is an n×m-order non-negative real diagonal matrix, representing the correlation between seismic waveform data and sand body thickness. The above formula can be expanded to: (6) Right now: (7) In terms of seismic profile, the first component of the decomposition is Represents the seismic waveform with the most commonality with sand body thickness, the second component Represents the removal of the first component The seismic waveform with the most commonalities is shown below. By analogy, different components represent different sedimentary environments and geological characteristics. The same component represents the seismic waveforms corresponding to different lithological combinations.

[0077] After the singular value decomposition of matrix A is performed by formula (6), the main features of matrix A can be represented by the singular vectors corresponding to the first three non-zero singular values, that is, the dynamic clustering analysis of seismic waveforms is realized, and the corresponding relationship between the seismic response wave components of the well bypass channel and different sedimentary phases is obtained.

[0078] The corresponding relationship between the seismic response wave components of the well bypass and different sedimentary phases is expanded to obtain the corresponding relationship between the seismic response wave components of the inter-well area and different sedimentary phases. So far, the corresponding relationship between the seismic response wave components of each location in the target area and different sedimentary phases has been obtained.

[0079] Specifically, by comparing the seismic response wave components of the well bypass with the lithology, structure and structural characteristics of the known sedimentary phases, a template for the correspondence between the seismic response wave and the sedimentary phase can be established. Subsequently, this template is applied to the inter-well area, and by analyzing the seismic data, the change pattern of the seismic response wave components is identified and matched with the sedimentary phase characteristics, thereby obtaining the correspondence between the seismic response wave components in the inter-well area and different sedimentary phases. Finally, through a comprehensive analysis of the seismic response wave components of the well locations and wells in the target area, we can build a comprehensive correspondence network to accurately grasp the correspondence between the seismic response wave components at each location in the target area and different sedimentary phases. This not only enhances our understanding of the underground geological structure, but also provides valuable geological basis for oil and gas exploration and development.

[0080] (2) Using the correspondence between the seismic response wave components at each location in the target area and different sedimentary facies as prior information, a likelihood function is constructed to describe the probability of planar distribution of sedimentary facies.

[0081] The initial model of seismic wave impedance is established according to the correspondence between the seismic response wave components at each location in the target area and different sedimentary phases, and it is used as prior information to complete the construction of the likelihood function.

[0082] The likelihood function is calculated using matched filtering between the initial impedance model and the seismic waveform.

[0083] (8) Among them, L (θ|x) is the likelihood function, θ is the seismic waveform, and x is the elastic parameter sandstone model to be solved. For a convolution model: (9) Among them, w is the seismic wavelet, n is the noise, and it is assumed that the noise n is Gaussian distributed: (10) Estimating the posterior probability distribution of the elastic parameter model x from known seismic data θ can be regarded as a Bayesian inversion problem. I is the prior information. Substituting (9) into (10), the likelihood function of the seismic data is: (11) Indicates i Earthquake waves, Indicates i The disturbance amount of the elastic parameters corresponding to the seismic wave, the elastic parameters include thickness, I is the prior information, w represents the seismic wavelet, and N represents the number of seismic waves.

[0084] Based on the likelihood function and the seismic response waves in the target area, the planar distribution probability of different sedimentary phases can be determined.

[0085] (3) Based on the planar distribution probability of different sedimentary phases, the seismic response waves corresponding to different sedimentary phases are grouped.

[0086] Each type of sedimentary phase has its own thickness range. We determine the sedimentary phase type of each area on the plane based on the thickness. The plane (lateral) distribution probability of different sedimentary phases is also the distribution probability of different sand body thickness ranges. Based on this distribution probability, the seismic waves corresponding to different sediments are grouped.

[0087] For example, the first sedimentary phase (tidal channel), the second sedimentary phase (sand flat) and the third sedimentary phase (swamp-mud flat) are distributed in the lateral direction, wherein the first sedimentary phase, the second sedimentary phase and the third sedimentary phase are divided according to their respective thickness intervals. The seismic response waves collected on the plane area corresponding to the first deposition constitute the first sample set, the seismic response waves collected on the plane area corresponding to the second deposition constitute the second sample set, and the seismic response waves collected on the plane area corresponding to the third deposition constitute the third sample set. In addition, the first sample set, the second sample set and the third sample set can also include well logging data of their corresponding areas.

[0088] (4) Perform seismic wave inversion based on each sample set.

[0089] In the inversion process of this embodiment, the lithology sensitivity curve is selected, and the histogram is established through the logging curve data to select the sensitivity curve. With reference to a specific example, the thickness of the sandstone of the Benxi Formation in the target area (a study area in Jiaxian District mentioned above) is statistically analyzed, and it can be seen that the thickness of the single sand body of the Benxi Formation is 2m to 15m, and the thickness of 89% of the sand bodies is 2m to 8m, see Figure 5 To establish various lithological interpretation diagrams of natural gamma ray curves, see Figure 6 . from Figure 5 and Figure 6 As can be seen from the figure, the natural gamma curve can well identify sandstone, mudstone and coal seams. Therefore, the natural gamma curve is added as prior information in the inversion to better predict sandstone.

[0090] This embodiment adopts the seismic wave inversion technology based on phase-controlled waveform. After obtaining the above sample set, Figure 7 As shown in the figure, based on the Bayesian theory, the posterior probability distribution is obtained using the sand body probability distribution and likelihood function.

[0091] First, through the single well sedimentary phase analysis, combined with the logging curve and coring data, the logging characteristics corresponding to different sediments are clarified. Secondly, the seismic waveform shape is effectively identified and accurately classified through the lateral changes of the seismic waveform, and then the geological significance of the seismic phase is clarified, see Figure 2.

[0092] In Bayesian inversion, assuming that the elastic parameter model x to be solved is also Gaussian distributed, the prior distribution of the model can be calculated as: (12) The product of the data conditional probability distribution and the model prior probability distribution is used as the posterior probability distribution function of the model. Its specific expression is: (13) In the formula, Δx is the disturbance of the model parameters, is the variance of the model parameter disturbance. Finally, the parameters are adjusted, and the model disturbance is iterated continuously to obtain the waveform indication inversion results under the phase control condition, and finally the sand body distribution under the influence of the coal seam is predicted.

[0093] In addition, key parameters need to be selected and optimized during phase-controlled waveform indication inversion, including: optimal number of samples, optimal cutoff frequency, and smoothing radius. Taking the optimal cutoff frequency as an example, when the seismic frequency band remains unchanged, supplementing reliable high-frequency information can improve the seismic resolution. The more high-frequency information is supplemented, the greater the randomness of the seismic inversion results. Therefore, we need to statistically set the optimal cutoff frequency of the waveform indication inversion sample wells. By statistically analyzing the correlation coefficient of the longitudinal wave impedance frequency of the Benxi Formation in 46 wells in the target area (Jiaxian District), see Figure 8 , when the frequency is 170 Hz, an inflection point appears, and when it is greater than the inflection point, the correlation coefficient tends to be stable. When the frequency is greater than 200 Hz, the trend of the correlation index decreases slowly, indicating that the randomness of the inversion becomes greater, so 200 Hz can be selected as the high cutoff frequency of the inversion.

[0094] Inversion effect analysis is a key indicator for evaluating the feasibility and rationality of inversion methods, and directly affects the promotion of inversion methods. The wells in the study area that did not participate in the inversion were used as post-test wells to calculate the consistency between their inversion effects and the sandstones of the actual drilling. Through verification, it can be seen that the coal-bearing stratum thin sand prediction method based on seismic frequency division and phase-controlled waveform indication inversion can better reduce the impact of coal seams on sand prediction, and while being faithful to the well point data, it also has good predictability for inter-well sand bodies.

[0095] Phase-controlled seismic waveform indication inversion better reflects the characteristics of rapid lateral phase change of tidal channel sand bodies on the plane, and the description of sand body boundaries is clearer, such as Fig. 9 and Fig.10 As shown in the inversion plan, it can be seen that the tidal channel is distributed in the northeast-southwest direction as a whole, in the shape of a strip. The inversion effect is clear and reliable.

[0096] The embodiment of the present invention can identify and characterize the distribution of sandstone under the influence of Benxi Formation coal-bearing strata through a method for predicting thin sand bodies in coal-bearing strata based on seismic frequency division and phase-controlled waveform indication inversion. The vertical resolution of seismic data is improved by performing spectral decomposition, analyzing the sedimentary phase-seismic phase, optimizing the key inversion parameters, and improving the lateral resolution of seismic data.

[0097] The sand body distribution prediction device provided by the present invention is described below. The sand body distribution prediction device described below and the sand body distribution prediction method described above can be referred to each other. The sand body distribution prediction device includes the following modules: The data acquisition module 1101 is used to acquire seismic response waves and logging data beside the wellbore in the target area.

[0098] The singular value decomposition module 1102 is used to determine the correspondence between the seismic response wave components of the target area and different sedimentary phases based on the seismic response wave beside the wellbore and the logging data by using a singular value decomposition algorithm.

[0099] The likelihood function construction module 1103 is used to construct a likelihood function for describing the probability of planar distribution of sedimentary phases, taking the correspondence between the seismic response wave components in the target area and different sedimentary phases as prior information.

[0100] The distribution probability determination module 1104 is used to determine the planar distribution probability of different sedimentary phases based on the likelihood function and the seismic response wave of the target area.

[0101] The sample grouping module 1105 is used to group the seismic response waves corresponding to different sediments based on the planar distribution probabilities of different sedimentary phases to obtain multiple sample sets.

[0102] The sand body distribution prediction module 1106 is used to perform seismic wave inversion based on each sample set to obtain the vertical thickness and planar distribution of the sand body in the target area.

[0103] The specific working of each module in the device can be cross-referenced with the sand body distribution prediction method described above, and will not be described in detail here.

[0104] Fig.12 An example of a physical structure diagram of an electronic device is shown in FIG. Fig.12As shown, the electronic device may include: a processor (processor) 810, a communication interface (Communications Interface) 820, a memory (memory) 830 and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call the logic instructions in the memory 830 to execute the sand body distribution prediction method, which includes: obtaining the seismic response wave and logging data beside the wellbore in the target area; based on the seismic response wave beside the wellbore and the logging data, using the singular value decomposition algorithm to determine the correspondence between the seismic response wave components and different sedimentary phases in the target area; using the correspondence between the seismic response wave components and different sedimentary phases in the target area as prior information, constructing a likelihood function for describing the probability of plane distribution of sedimentary phases; based on the likelihood function and the seismic response wave in the target area, determining the plane distribution probability of different sedimentary phases; based on the plane distribution probability of different sedimentary phases, grouping the seismic response waves corresponding to different depositions to obtain multiple sample sets; performing seismic wave inversion based on each sample set, respectively, to obtain the vertical thickness and plane distribution of the sand body in the target area. As for a more specific method, reference can be made to the sand body distribution prediction method described above, which will not be repeated here.

[0105] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0106] On the other hand, the present invention also provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transient computer-readable storage medium, and when the computer program is executed by a processor, the computer can execute the sand body distribution prediction method provided by the above methods, the method comprising: obtaining the seismic response wave and logging data beside the wellbore in the target area; based on the seismic response wave beside the wellbore and the logging data, using the singular value decomposition algorithm to determine the correspondence between the seismic response wave components in the target area and different sedimentary phases; using the correspondence between the seismic response wave components in the target area and different sedimentary phases as prior information, constructing a likelihood function for describing the probability of plane distribution of sedimentary phases; based on the likelihood function and the seismic response wave in the target area, determining the plane distribution probability of different sedimentary phases; based on the plane distribution probability of different sedimentary phases, grouping the seismic response waves corresponding to different depositions to obtain multiple sample sets; performing seismic wave inversion based on each sample set, respectively, to obtain the vertical thickness and plane distribution of the sand body in the target area. As for a more specific method, reference can be made to the sand body distribution prediction method described above, which will not be repeated here.

[0107] On the other hand, the present invention also provides a non-transient computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it is implemented to execute the sand body distribution prediction method provided by the above methods, the method comprising: obtaining the seismic response wave and logging data beside the wellbore in the target area; based on the seismic response wave beside the wellbore and the logging data, using the singular value decomposition algorithm to determine the correspondence between the seismic response wave components and different sedimentary phases in the target area; using the correspondence between the seismic response wave components and different sedimentary phases in the target area as prior information, constructing a likelihood function for describing the probability of plane distribution of sedimentary phases; based on the likelihood function and the seismic response wave in the target area, determining the plane distribution probability of different sedimentary phases; based on the plane distribution probability of different sedimentary phases, grouping the seismic response waves corresponding to different depositions to obtain multiple sample sets; performing seismic wave inversion based on each sample set, respectively, to obtain the vertical thickness and plane distribution of the sand body in the target area. As for a more specific method, reference can be made to the sand body distribution prediction method described above, which will not be repeated here.

[0108] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sand body distribution prediction method, characterized in that: include: Obtain seismic response waves and logging data beside the wellbore in the target area; Based on the wellbore side seismic response wave and the well logging data, a singular value decomposition algorithm is used to determine the correspondence between the seismic response wave components of the target area and different sedimentary phases; Taking the correspondence between the seismic response wave components and different sedimentary facies in the target area as prior information, a likelihood function is constructed to describe the probability of sedimentary facies plane distribution. Determining the planar distribution probability of different sedimentary phases based on the likelihood function and the seismic response wave of the target area; Based on the planar distribution probability of different sedimentary phases, the seismic response waves corresponding to different sediments are grouped to obtain multiple sample sets; Seismic wave inversion is performed based on each sample set to obtain the vertical thickness and planar distribution of the sand body in the target area.

2. The sand body distribution prediction method according to claim 1, characterized in that: Also includes: Acquire original seismic waveform data of the target area; Based on a matching pursuit algorithm, the coal seam reflection wave is filtered out from the original seismic waveform data to obtain the seismic response wave.

3. The sand body distribution prediction method according to claim 2, characterized in that: Based on a matching pursuit algorithm, the coal seam reflection wave is filtered out from the original seismic waveform data, including: Performing seismic forward modeling on the target area to obtain simulated reflection waves of coal seams in the target area; Based on the matching pursuit algorithm, the simulated reflection wave is used as a matching wavelet to perform spectrum decomposition on the original seismic wave to obtain seismic wavelets at different frequencies; the seismic wavelets include a target seismic wavelet that best matches the simulated reflection wave; Based on the seismic wavelets other than the target seismic wavelet, the seismic wave is reconstructed to obtain the seismic response wave.

4. The sand body distribution prediction method according to claim 1, characterized in that: The logging data include: logging sand body thickness data; Based on the wellbore side seismic response wave and the logging data, a singular value decomposition algorithm is used to determine the correspondence between the seismic response wave components of the target area and different sedimentary phases, including: Based on the well-side channel seismic response wave and the well logging sand body thickness data, a singular value decomposition algorithm is used to determine the corresponding relationship between the well-side channel seismic response wave components and different sedimentary phases; Based on the correspondence between the seismic response wave components of the well bypass and the different sedimentary phases, the correspondence between the seismic response wave components of the inter-well area and the different sedimentary phases is determined.

5. The sand body distribution prediction method according to claim 1, characterized in that: The likelihood function is as follows: Indicates i Earthquake waves, Indicates i The disturbance amount of the elastic parameters corresponding to the seismic wave, the elastic parameters include thickness, I is the prior information, w represents the seismic wavelet, and N represents the number of seismic waves.

6. The sand body distribution prediction method according to claim 1, characterized in that: The seismic wave inversion is based on phase-controlled waveform.

7. The sand body distribution prediction method according to claim 6, characterized in that: The seismic wave inversion comprises: Constructing a posterior probability distribution function based on the likelihood function and the prior probability distribution function; Seismic wave inversion is performed based on the posterior probability distribution.

8. The sand body distribution prediction method according to claim 1, characterized in that: The seismic wave inversion comprises: Based on the correlation analysis between the well logging data and lithology, a lithology sensitivity curve is selected; The lithology sensitivity curve is used as prior information to invert the seismic wave.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the sand body distribution prediction method according to any one of claims 1 to 8 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the sand body thickness prediction method according to any one of claims 1 to 8 is implemented.

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