Oil and gas production method and device

Abstract

The present application discloses an oil and gas extraction method and device, belonging to the technical field of oil and gas extraction. The oil and gas extraction method includes: obtaining the reflection coefficient of the formation in the area to be exploited, the reference propagation velocity of the detection wave in the formation, and the reference density of the formation; performing basic tracking inversion processing based on the reflection coefficient, the reference propagation velocity, and the reference density to determine the target density of the formation and the target propagation velocity of the detection wave in the formation in the area to be exploited; determining the boundary of the formation in the area to be exploited based on the target density and the target propagation velocity; determining the position to be exploited for oil and gas based on the boundary of the formation in the area to be exploited, so as to extract oil and gas at the position to be exploited. The present application solves the problem of relatively low oil and gas extraction efficiency. The present application is used for oil and gas extraction.

Description

Technical Field

[0001] This application relates to the technical field of oil and gas extraction, and particularly relates to an oil and gas extraction method and device. Background Art

[0002] With the development of oil and gas extraction technologies, the requirements for oil and gas extraction efficiency are also getting higher and higher.

[0003] In related technologies, before oil and gas extraction, it is necessary to explore the geological structure of the geographical area to be exploited, determine the oil and gas-bearing formations in this geographical area based on the exploration results, and then extract the oil and gas in these formations.

[0004] However, at present, the formations in many geographical areas are relatively thin, and the accuracy of the exploration results for this geographical area in reflecting the actual geological structure is relatively low, thus resulting in low oil and gas extraction efficiency. Summary of the Invention

[0005] This application provides an oil and gas extraction method and device, which can solve the problem of low oil and gas extraction efficiency. The technical solutions are as follows:

[0006] On the one hand, an oil and gas extraction method is provided, and the method includes:

[0007] Obtain the reflection coefficient of the formation in the area to be exploited, the reference propagation speed of the detection wave in the formation, and the reference density of the formation;

[0008] Perform basic trace inversion processing based on the reflection coefficient, the reference propagation speed, and the reference density to determine the target density of the formation in the area to be exploited and the target propagation speed of the detection wave in the formation;

[0009] Based on the target density and the target propagation speed, determine the boundary of the formation in the area to be exploited;

[0010] Based on the boundary of the formation in the area to be exploited, determine the position to be exploited for oil and gas, so as to extract oil and gas at the position to be exploited.

[0011] Optionally, the reference propagation speed and the reference density are obtained based on the seismic logging data in the area to be exploited after low-pass filtering.

[0012] Optionally, the determining the boundary of the formation in the area to be exploited based on the target density and the target propagation speed includes:

[0013] Based on the target density, output the first formation characterization map of the area to be exploited, and different regions with different colors in the first formation characterization map represent different target densities of the formation;

[0014] Output a second formation characterization map of the to-be-mined area based on the target propagation speed, where areas with different colors in the second formation characterization map represent different target propagation speeds of the formations;

[0015] Determine the boundary of the formation in the to-be-mined area based on the first formation characterization map and the second formation characterization map.

[0016] Optionally, the reflection coefficient includes: reflection wave reflection coefficient, transmission wave reflection coefficient, and density reflection coefficient; the reference propagation speed includes: reference propagation speed of the reflection wave and reference propagation speed of the transmission wave, and the target propagation speed includes: target propagation speed of the reflection wave and target propagation speed of the transmission wave;

[0017] The performing basic trace inversion processing based on the reflection coefficient, the reference propagation speed, and the reference density to determine the target density of the formation in the to-be-mined area and the target propagation speed of the detection wave in the formation includes:

[0018] Based on the first formula group, respectively determine the target propagation speed of the reflection wave, the target propagation speed of the transmission wave, and the target density; the first formula group includes: and ρ(t) = ρ 0 (t)exp∫R ρ (t)dt;

[0019] where t represents time, V P (t) represents the target propagation speed of the reflection wave, V P0 (t) represents the reference propagation speed of the reflection wave, R P (t) represents the reflection wave reflection coefficient, V S (t) represents the target propagation speed of the transmission wave, represents the reference propagation speed of the transmission wave, R S (t) represents the transmission wave reflection coefficient, ρ(t) represents the target density, ρ 0 (t) represents the reference density, R ρ (t) represents the density reflection coefficient.

[0020] Optionally, the obtaining the reflection coefficient of the formation in the to-be-mined area includes:

[0021] Determine the dipole decomposition coefficient that minimizes the value of the objective function, where the objective function is: ||d - G W m W || 2 + λ||m W || 1 ; where d is the data vector, d represents the pre-stack angle gather, mW represents the dipole decomposition coefficient; G W represents the target kernel matrix, G W is the product of the wavelet kernel matrix and the reflectivity pattern matrix, G W m W represents the prestack angle gather of d; λ represents a trade-off factor, and the trade-off factor is used to control the sparsity of the dipole decomposition coefficient;

[0022] Determine the reflection coefficient based on the dipole decomposition coefficient, and the reflection coefficient is the product of the dipole decomposition coefficient and the reflectivity pattern matrix.

[0023] Optionally, the determining the reflection coefficient based on the dipole decomposition coefficient includes:

[0024] Determine the reflection coefficient based on a first formula, and the first formula is:

[0025]

[0026] wherein, represents the reflection coefficient, R P (t) represents the reflection coefficient of the reflected wave, R S (t) represents the reflection coefficient of the transmitted wave, R ρ (t) represents the density reflection coefficient, represents the reflectivity pattern matrix, represents the dipole decomposition coefficient, r e represents the even wedge reflectivity pattern, r o represents the odd wedge reflectivity pattern, a eP (t) represents the even component coefficient corresponding to the reflected wave, b oP (t) represents the odd component coefficient corresponding to the reflected wave, a eS (t) represents the even component coefficient corresponding to the transmitted wave, b oS (t) represents the odd component coefficient corresponding to the transmitted wave, a eρ represents the even component coefficient corresponding to the density, b oρ represents the odd component coefficient corresponding to the density.

[0027] Optionally, the prestack angle gather of d satisfies a second formula:

[0028]

[0029] wherein, represents the prestack angle gather of d, represents the wavelet kernel matrix, W P represents the wavelet corresponding to the incident wave, W S represents the wavelet corresponding to the transmitted wave, W ρThe wavelet corresponding to the density, θ i represents the incident angle of the detection wave, 1 ≤ θ i ≤ N.

[0030] On the other hand, an oil and gas extraction device is provided, and the oil and gas extraction device includes:

[0031] An acquisition module for acquiring the reflection coefficient of the formation in the area to be exploited, the reference propagation velocity of the detection wave in the formation, and the reference density of the formation;

[0032] A processing module for performing basic tracking inversion processing based on the reflection coefficient, the reference propagation velocity, and the reference density to determine the target density of the formation in the area to be exploited and the target propagation velocity of the detection wave in the formation;

[0033] A first determination module for determining the boundary of the formation in the area to be exploited based on the target density and the target propagation velocity;

[0034] A second determination module for determining the position to be exploited for oil and gas based on the boundary of the formation in the area to be exploited, so as to extract oil and gas at the position to be exploited.

[0035] Optionally, the reference propagation velocity and the reference density are obtained based on the seismic logging data in the area to be exploited after low-pass filtering.

[0036] Optionally, the first determination module is further configured to:

[0037] Output a first formation characterization map of the area to be exploited based on the target density, where regions with different colors in the first formation characterization map represent different target densities of the formation;

[0038] Output a second formation characterization map of the area to be exploited based on the target propagation velocity, where regions with different colors in the second formation characterization map represent different target propagation velocities of the formation;

[0039] Determine the boundary of the formation in the area to be exploited based on the first formation characterization map and the second formation characterization map.

[0040] Optionally, the reflection coefficient includes: the reflection coefficient of the reflected wave, the reflection coefficient of the transmitted wave, and the density reflection coefficient; the reference propagation velocity includes: the reference propagation velocity of the reflected wave and the reference propagation velocity of the transmitted wave, and the target propagation velocity includes: the target propagation velocity of the reflected wave and the target propagation velocity of the transmitted wave;

[0041] The processing module is further configured to:

[0042] Based on the first set of formulas, respectively determine the target propagation velocity of the reflected wave, the target propagation velocity of the transmitted wave, and the target density; the first set of formulas includes: and ρ(t) = ρ 0 (t)exp∫R ρ (t)dt;

[0043] where t represents time, V P (t) represents the target propagation velocity of the reflected wave, V P0 (t) represents the reference propagation velocity of the reflected wave, R P (t) represents the reflection coefficient of the reflected wave, V S (t) represents the target propagation velocity of the transmitted wave, represents the reference propagation velocity of the transmitted wave, R S (t) represents the reflection coefficient of the transmitted wave, ρ(t) represents the target density, ρ 0 (t) represents the reference density, R ρ (t) represents the density reflection coefficient.

[0044] Optionally, the acquisition module is further configured to:

[0045] Determine the dipole decomposition coefficient that minimizes the value of the objective function, where the objective function is: ||d - G W m W || 2 + λ||m W || 1 ; where d is the data vector, d represents the pre-stack angle gather, m W represents the dipole decomposition coefficient; G W represents the target kernel matrix, G W is the product of the wavelet kernel matrix and the reflectivity pattern matrix, G W m W represents the pre-stack angle gather of d; λ represents the trade-off factor, and the trade-off factor is used to control the sparsity of the dipole decomposition coefficient;

[0046] Based on the dipole decomposition coefficient, determine the reflection coefficient, where the reflection coefficient is the product of the dipole decomposition coefficient and the reflectivity pattern matrix.

[0047] Optionally, the acquisition module is further configured to:

[0048] Based on the first formula, determine the reflection coefficient, where the first formula is:

[0049]

[0050] where, Represents the reflection coefficient, R P (t) represents the reflection coefficient of the reflected wave, R S (t) represents the reflection coefficient of the transmitted wave, R ρ (t) represents the density reflection coefficient Represents the reflectivity pattern matrix Represents the dipole decomposition coefficient, r e Represents the even wedge reflectivity pattern, r o Represents the odd wedge reflectivity pattern, a eP (t) represents the even component coefficient corresponding to the reflected wave, b oP (t) represents the odd component coefficient corresponding to the reflected wave, a eS (t) represents the even component coefficient corresponding to the transmitted wave, b oS (t) represents the odd component coefficient corresponding to the transmitted wave, a eρ Represents the even component coefficient corresponding to the density, b oρ Represents the odd component coefficient corresponding to the density

[0051] Optionally, the prestack angle gather of d satisfies the second formula:

[0052]

[0053] Wherein Represents the prestack angle gather of d Represents the wavelet kernel matrix, W P Represents the wavelet corresponding to the incident wave, W S Represents the wavelet corresponding to the transmitted wave, W ρ Represents the wavelet corresponding to the density, θ i Represents the incident angle of the detection wave, 1 ≤ θ i ≤ N

[0054] The beneficial effects brought by the technical solution provided by this application at least include:

[0055] In the oil and gas extraction method provided by this application, based on the reflectivity of the formation, the reference propagation speed of the detection wave in the formation, and the reference density of the formation, basic trace inversion processing can be carried out. Thus, the obtained target density and target propagation speed can more accurately reflect the formation structure. Therefore, based on this formation structure, the formation containing oil and gas can be more accurately determined, and further the efficiency of oil and gas extraction can be improved Brief Description of the Drawings

[0056] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0057] Figure 1 is a flowchart of an oil and gas extraction method provided by an embodiment of the present application;

[0058] Figure 2 is a characterization diagram of a reflection coefficient provided by an embodiment of the present application;

[0059] Figure 3 is a schematic diagram of a wedge model provided by an embodiment of the present application;

[0060] Figure 4 is a schematic diagram of a seismic profile provided by an embodiment of the present application;

[0061] Figure 5 is a schematic diagram of an inversion result provided by an embodiment of the present application;

[0062] Figure 6 is a second formation characterization diagram provided by an embodiment of the present application;

[0063] Figure 7 is a seismic data diagram provided by an embodiment of the present application;

[0064] Figure 8 is a schematic diagram of a wavelet provided by an embodiment of the present application;

[0065] Figure 9 is a schematic diagram of the propagation speed of a detection wave in a formation provided by an embodiment of the present application;

[0066] Figure 10 is a characterization diagram of the reflectivity of a formation provided by an embodiment of the present application;

[0067] Figure 11 is a comparison schematic diagram of a target speed provided by an embodiment of the present application;

[0068] Figure 12 is a structural schematic diagram of an oil and gas extraction device provided by an embodiment of the present application. Detailed implementation manners

[0069] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0070] With the improvement of the exploration degree of geological structures, there is an increasing need to explore more concealed target formations in geological structures. These target formations have characteristics such as small trap sizes and thin reservoir thicknesses. Especially for thin interbedded sandstone and mudstone reservoirs, the seismic response reflection signals of thin interbedded sandstone and mudstone reservoirs are weak. Therefore, it is difficult to accurately identify this formation through conventional seismic data, which restricts the fine structural interpretation of the formation, fine reservoir prediction, and trap implementation. Furthermore, it is relatively difficult to carry out oil and gas exploitation on these target formations based on the current exploration results of geological structures.

[0071] The embodiments of the present application provide an oil and gas exploitation method and device, which can improve the detection accuracy of thin layers in geological structures, make the layer boundaries of each formation in the geological structure more clearly presented, and further improve the fineness and accuracy of the reflection of the geological structure. Furthermore, based on this exploration result, the efficiency of oil and gas exploitation can be improved.

[0072] Figure 1 is a flowchart of an oil and gas exploitation method provided by the embodiments of the present application. This method can be used in a computer, such as Figure 1 as shown, this method may include:

[0073] Step 101, obtain the reflection coefficient of the formation in the area to be exploited, the reference propagation speed of the detection wave in the formation, and the reference density of the formation.

[0074] Step 102, perform basic trace inversion processing based on the reflection coefficient, reference propagation speed, and reference density to determine the target density of the formation in the area to be exploited and the target propagation speed of the detection wave in the formation.

[0075] Step 103, determine the boundary of the formation in the area to be exploited based on the target density and target propagation speed.

[0076] Step 104, determine the position to be exploited for oil and gas based on the boundary of the formation in the area to be exploited, so as to carry out oil and gas exploitation at the position to be exploited.

[0077] In summary, in the oil and gas exploitation method provided by the embodiments of the present application, the computer can perform basic trace inversion processing based on the reflectivity of the formation, the reference propagation speed of the detection wave in the formation, and the reference density of the formation. Thus, the obtained target density and target propagation speed can more accurately reflect the formation structure. Therefore, based on this formation structure, the formation containing oil and gas can be more accurately determined, and further the efficiency of oil and gas exploitation can be improved.

[0078] It should be noted that the exploration of geological structures is to send detection waves to the geological structures, obtain seismic data from the waves returned by the propagation of the detection waves in the geological structures, and then analyze the specific geological structures based on this seismic data. For example, the target density of the formation in the area to be mined and the target propagation speed of the detection waves in the formation can be obtained based on the seismic data analysis. Furthermore, the specific geological structure can be determined based on the target density and the target propagation speed. The detection waves emitted from the incident plane (such as the ground) can also be called plane waves. When the plane waves reach the boundary between two media (such as two formations) in the geological structure, there will be reflected waves and transmitted waves. The reflected wave can be called the P wave, and the transmitted wave can be called the S wave. The reflection coefficients of the reflected wave and the transmitted wave can satisfy a certain relationship. For example, this relationship can be the linear approximation relationship of the plane wave reflection coefficient derived by Aki and Richards in 1980. This linear approximation relationship can be used for AVA (Amplitude variation with azimuth) analysis and inversion. This linear approximation relationship is usually used to calculate the amplitude variation of the reflection coefficient at different incident angles of the plane wave. This linear approximation can effectively represent the relationship between the reflection coefficients when the incident angle reaches 40° and the change in the elastic properties of the formation boundary crossed by the plane wave is small. This linear approximation relationship can be expressed by the following formula (1), and this formula (1) is:

[0079]

[0080] where R PP (θ) represents the reflection coefficient of the plane wave related to the incident angle θ, V P represents the transmission speed of the P wave in the formation, V S represents the transmission speed of the S wave in the formation, represents the average speed of the P wave when crossing the boundary between two adjacent formations, represents the average speed of the S wave when crossing the boundary between two adjacent formations, represents the average density value of two adjacent formations, ΔV P represents the speed difference of the P wave transmitted in two adjacent formations, ΔV S represents the speed difference of the S wave transmitted in two adjacent formations, Δρ represents the density difference between two adjacent formations, represents the shear-compression wave velocity ratio of the formation, n represents the formation medium, n is a constant, and the value of n can be 2.

[0081] The relative changes in the speeds and densities of two adjacent formations are very small. For example, ΔV P and ΔV S are smaller than Δρ, and They can all take a value of 0.2. The above formula (1) can be called the Aki and Richards equation. Since the above formula (1) is a linear summation of three reflectivity terms (i.e., the terms in the brackets) with coefficients related to the incident angle, the formula (1) can be expressed as a matrix-vector multiplication at each time sample t. For a set with N incident angles, the above formula (1) can be written as the following formula (2):

[0082]

[0083] where C P , C S and C ρ are constants related to the incident angle and corresponding to the incident wave velocity, transmitted wave velocity, and density respectively, R P , R S and R ρ are three reflection coefficient time series related to the incident wave velocity, transmitted wave velocity, and density respectively; C P =(0.5 + 0.5tan 2 θ), It should be noted that the above formula (2) also reflects the forward modeling of seismic data.

[0084] In the embodiments of the present application, V P (t) is called the reflection coefficient of the reflected wave, V S (t) is called the reflection coefficient of the transmitted wave, R ρ (t) is called the density reflection coefficient, and the reflection coefficients of the formation in the area to be mined include: the reflection coefficient V P (t) of the reflected wave, the reflection coefficient V S (t) of the transmitted wave, and the density reflection coefficient R ρ (t).

[0085] Since the seismic wavelet is convolved with the reflection coefficient of the formation at a certain plane wave incident angle, a synthetic seismic record related to the incident angle can be formed, and this convolution model does not consider the complete wave propagation effect. There is almost no difference between the convolution result at an incident angle less than 25° and the simulation result of the actual wave equation, but the difference is large when the incident angle is large. In the embodiments of the present application, a given wavelet is convolved with both sides of the above formula (2) to obtain the following formula (4):

[0086]

[0087] where S PP (t, θ i ) represents the angle gather at a plane wave incident angle of θ i , and W(t, θ iIt represents the wavelet kernel matrix, \(t\) represents time, and \(\tau\) is the time parameter.

[0088] Through the above convolution, the following formula (5) can be obtained:

[0089]

[0090] Among them, \(S\) PP represents the pre-stack angle gather including the data vector \(d\), \(W\) P , \(W\) S and \(W\) ρ constitute the wavelet kernel matrix, and the wavelet kernel matrix is also the Jacobian matrix used for the inverse problem; \(W\) P represents the wavelet corresponding to the incident wave, \(W\) S represents the wavelet corresponding to the transmitted wave, \(W\) ρ represents the wavelet corresponding to the density.

[0091] In the embodiments of the present application, it is assumed that the formation model is layered because the sharp boundaries of the layered model are usually more helpful for explaining the geological structure than the smooth boundaries. Such a blocky layer model will generate a spike reflection coefficient sequence at the boundary, and this reflection coefficient sequence can be decomposed into the sum of impulse pairs. In the basis pursuit inversion (BPI) algorithm, the dipole decomposition is used to represent the reflection coefficient as the sum of even and odd impulse pairs multiplied by scalars. Each layer of the formation in the model can be represented by its top reflector and bottom reflector, for example, represented by two impulse functions \(c\delta(t - t_0)\) and \(d\delta(t - t_0 + n\Delta t)\) respectively. For example, each reflection coefficient can be represented as \(c\delta(t)+d\delta(t + n\Delta t)\). Among them, \(n\Delta t\) represents the time thickness of the formation, \(\Delta t\) represents the sampling rate, and \(c\) and \(d\) represent two coefficients. The top reflector of the formation is also the interface between this formation and another formation adjacent to its top, and the bottom reflector of the formation is also the interface between this formation and another formation adjacent to its bottom.

[0092] This reflection coefficient can be further represented by the following formula (6):

[0093] \(c\delta(t)+d\delta(t + n\Delta t)=a\) e *\(r\) e +\(b\) o *\(r\) o ; (6)

[0094] Among them, \(a\) e represents the coefficient of the even component, \(b\) o represents the coefficient of the odd component, \(r\) e represents the even component, \(r\) o represents the odd component. \(r\) e =\(\delta(t)+\delta(t + n\Delta t)\), \(r\) o= δ(t) - dδ(t + nΔt). Since the formation thickness is usually unknown and can include all possible formation thicknesses, n can vary from zero to the theoretically resolvable layer time thickness. For example, n can be equal to one-fourth of the period of the seismic wavelet. For instance, for the peak frequency at 25 Hz of the wavelet, its period is 40 ms, and the maximum wedge thickness (i.e., the maximum time thickness of the formation) is 10 ms. The sampling rate Δt plays a decisive role in the accuracy of the reflection coefficient and the minimum resolvable formation time thickness. A relatively small sampling rate can improve the accuracy of the reflection coefficient and the resolution of the formation. By way of example, Figure 2 is a characterization diagram of a reflection coefficient provided by an embodiment of the present application. As Figure 2 shown, any reflection coefficients r1 and r2 can be expressed as the sum of an even component and an odd component. The two impulse functions in the even component have the same amplitude and sign, and the two impulse functions in the odd component have the same amplitude but opposite signs.

[0095] In the embodiment of the present application, a wedge model is used to represent the reflection coefficient. The wedge model includes an odd wedge and an even wedge, and the wedge model is equivalent to a dipole reflector. The odd wedge represents the above-mentioned odd component, and the even wedge represents the above-mentioned even component. Figure 3 is a schematic diagram of a wedge model provided by an embodiment of the present application, where Figure (b) represents the odd wedge and Figure (c) represents the even wedge. Figure 4 is a schematic diagram of a seismic profile provided by an embodiment of the present application. It represents the seismic record diagram after the convolution of the reflection coefficient and the seismic wavelet, and represents the seismic profile synthesized by the convolution of the Ricker wavelet with a main frequency of 30 Hz and the corresponding reflection coefficient model.

[0096] Since the sampling rate is Δt, each trace of the even wedge consists of a pair of equal impulse functions (spikes) with an interval of nΔt. In the embodiment of the present application, a reflector kernel matrix for the reflectivity pair is constructed by moving the reflectivity along the time axis by mΔt samples, where m ranges from 1 to the number of samples in the seismic trace. Thus, the reflectivity pattern of each even wedge can be written as the following formula (8):

[0097] r e (t, m, nΔt) = δ(t - mΔt) + δ(t - mΔt + nΔt). (8)

[0098] Correspondingly, the reflectivity pattern of each odd wedge can be written as the following formula (9):

[0099] r o (t, m, nΔt) = δ(t - mΔt) - δ(t - mΔt + nΔt). (9)

[0100] Furthermore, three reflection coefficients RP , R S and R ρ can be considered as the sum of the even-wedge reflectivity pattern and the odd-wedge reflectivity pattern. These three reflection coefficients can be expressed by the following system of equations (10):

[0101]

[0102]

[0103]

[0104] where a eP , b oP , a eS , b oS , a eρ and b oρ are all dipole decomposition coefficients. a eP represents the even-component coefficient corresponding to the reflected wave, b oP represents the odd-component coefficient corresponding to the reflected wave, a eS represents the even-component coefficient corresponding to the transmitted wave, b oS represents the odd-component coefficient corresponding to the transmitted wave, a eρ represents the even-component coefficient corresponding to the density, b oρ represents the odd-component coefficient corresponding to the density, N represents the maximum formation thickness, and M represents the maximum data length.

[0105] By decomposing through the above formula (10) and taking all the coefficients as a time series, the decomposition matrix form of the reflection coefficient can be obtained. This decomposition matrix form can be expressed by the first formula, which is the following formula (11):

[0106]

[0107] where, represents the reflection coefficient, represents the reflectivity pattern matrix, represents the dipole decomposition coefficient matrix.

[0108] In the embodiments of the present application, substituting the above formula (11) into the above formula (5), the second formula can be obtained, which is the following formula (12):

[0109]

[0110] where, represents the prestack angle gather of d, represents the wavelet kernel matrix. In the above formula (12), the multiplication of the wavelet kernel matrix and the reflectivity pattern matrix (which can also be called the wedge reflectivity pattern matrix) can form the target kernel matrix GW The simulated vector coefficients a eP , b oP , a eS , b oS , a eρ and b oρ can form a new model vector m W . Through this decomposition, any set of angles can be modeled by the sum of wedge seismic dictionary elements. The above formula (12) is the dipole decomposition result of the above formula (5), which can be used as a linear equation for prestack seismic inversion. The stability of the above formula (5) depends on factors such as wavelet accuracy, total ionization degree of the data state, and noise level. In the embodiments of the present application, performing dipole decomposition on the above formula (5) does not change its stability conditions.

[0111] In the embodiments of the present application, the dipole decomposition coefficients a eP , b oP , a eS , b oS , a eρ and b oρ can be calculated first; and then based on the first formula, that is, the above formula (11), the reflection coefficients R P (t), R S (t) and R ρ (t) are calculated. To achieve the acquisition of the reflection coefficient in step 101. In the embodiments of the present application, the basic tracking inversion method can be used to process the parameters obtained in step 101. Basic tracking (BP) is an L1 norm optimization method, which solves the solution of the function by minimizing the objective function, that is, the following formula (13), that is, determining the dipole decomposition coefficient that minimizes the value of the objective function. The objective function is:

[0112] ||d - G W m W || 2 + λ||m W || 1 ; (13)

[0113] where d is the data vector, d represents the prestack angle gather, m W represents the dipole decomposition coefficient; G W represents the target kernel matrix, G W is the product of the wavelet kernel matrix and the reflectivity pattern matrix, G W m WThe pre-stack angle gather representing d; λ represents a trade-off factor, which is used to control the sparsity of the dipole decomposition coefficients. A larger value of λ will result in a sparser solution, while a smaller value of λ can obtain a smoother solution. For a standardized dataset, the value of λ is usually around 1. For field data, the optimal value of λ can be determined by calibrating the local data using well logging data.

[0114] In the embodiments of the present application, formula (13) can also be solved by other methods other than the basic tracking method. For example, the other method can include the L-BFGS method. The objective function of the basic tracking method includes the L2 norm of the data error term and the L1 norm of the solution, where the L2 term is the root mean square error of the calculated data, and the L1 term is the sum of the absolute values of the solution. These two terms are optimized simultaneously to obtain the final solution.

[0115] In the embodiments of the present application, a set of initial models also needs to be combined with the reflection coefficient model (i.e., the wedge model) to determine the final V P (t), V S (t) and ρ(t), that is, the target propagation velocity of the reflected wave, the target propagation velocity of the transmitted wave, and the target density of the formation in the formation of the area to be mined. The initial model includes the reference propagation velocity of the detection wave in the formation of the area to be mined and the reference density ρ 0 (t), and the reference propagation velocity includes: the reference propagation velocity V P0 (t) of the reflected wave and the reference propagation velocity V S0 (t) of the transmitted wave.

[0116] The initial model is constructed in the same way as in post-stack seismic inversion, and the reflected wave propagation velocity, the transmitted wave propagation velocity, and the density value derived from the interpreted horizons are obtained by interpolating and analyzing well logging data. Different from the smooth results output by traditional AVA inversion, the BPI inversion results in the embodiments of the present application are shown as blocky layers without wavelet imprints. The main difference between AVA inversion and BPI inversion lies in the assumption that the earth model is a smooth transition or a blocky transition. The different objective functions obtained by AVA inversion are conventional inversions, using the minimized L2 error norm and the solution term; while BPI inversion uses the above formula (13), which is analyzed and assumed based on the differences between different distributions of blocky and smooth inversions, and can be explained by an idealized inversion principle consisting of two layers with different seismic properties. Figure 5 is a schematic diagram of an inversion result provided by the embodiments of the present application. The potential result of the idealized formation can be represented by Figure 5 the broken line z1 in, and the potential result of AVA inversion can be represented by Figure 5The broken line z2 in [ ] indicates that the potential results of AVA inversion make it very difficult to interpret the geological structure because of strong side lobes and rather blurred boundaries between two layers. The potential results of BPI inversion can be indicated by the broken line z3 in [ ] Figure 5 From this result, it can be seen that the BPI inversion results are almost constant within the same formation and more closely follow the interface of the formation.

[0117] In the embodiments of the present application, the Earth is assumed to be a stepped Earth model with constant velocity and density within each layer. When the velocity change amount when crossing the boundary is small compared with the absolute velocity, and the density change amount is also small compared with the absolute density value, the above three reflection coefficients R P , R S and R ρ can be approximately expressed by the following formula (14):

[0118]

[0119] Since most of the original logging data are high-frequency data, while the inversion results need to include information of the full frequency band, the missing low-frequency components need to be added during inversion. In the embodiments of the present application, this initial model is added to add the low-frequency components. The reference propagation velocity and reference density in this initial model can be obtained based on the seismic logging data in the to-be-mined area after low-pass filtering. After adding the initial model on the basis of formula (14), and then integrating with respect to the time variable t, the following formula (15) representing the absolute velocity and density can be obtained:

[0120] ln V P (t)=∫R P (t)dt + ln V P0 (t),

[0121] ln V S (t)=∫R S (t)dt + ln V S0 (t),

[0122] ln ρ(t)=∫R S ρ(t)dt + ln V ρ0 (t). (15)

[0123] Based on the derivation of the above formula (15), the following first formula group, that is, formula (16), that is, the expressions of the target propagation velocity V P (t) of the reflected wave in the formation, the target propagation velocity V S (t) of the transmitted wave in the formation and the target density ρ(t) of the formation can be obtained. This first formula group includes:

[0124]

[0125]

[0126] ρ(t) = ρ 0 (t) exp ∫R ρ (t) dt. (16)

[0127] In the embodiments of the present application, after obtaining the dipole decomposition coefficients based on the above formula (13), the obtained dipole decomposition coefficients can be substituted into the above formula (11) to obtain three reflection coefficients R P , R S and R ρ , thus realizing the acquisition of the reflection coefficients in step 101. After that, the obtained three reflection coefficients and and ρ 0 (t) in the obtained initial model can be substituted into the above formula (16) to implement the basic tracking inversion process, and obtain the target propagation velocity V P (t) of the reflected wave, the target propagation velocity V S (t) of the transmitted wave in the formation, and the target density ρ(t) of the formation in the to-be-exploited area. Then, the above step 103 can be executed to determine the boundary of the formation in the to-be-exploited area based on V P (t), V S (t) and ρ(t) of the formation, and then execute the above step 104 to determine the to-be-exploited position of the oil and gas based on the boundary of the formation in the to-be-exploited area, so as to carry out oil and gas exploitation at the to-be-exploited position.

[0128] Optionally, in the above step 103, a first formation characterization map of the to-be-exploited area can be output based on the target density, and different regions with different colors in the first formation characterization map represent different target densities of the formation. A second formation characterization map of the to-be-exploited area can also be output based on the target propagation velocity, and different regions with different colors in the second formation characterization map represent different target propagation velocities of the formation. Then, based on the first formation characterization map and the second formation characterization map, the boundary of the formation in the to-be-exploited area is determined. The second formation characterization map can include a formation characterization map corresponding to the reflected wave velocity and a formation characterization map corresponding to the transmitted wave velocity. Exemplarily, Figure 6 is a second formation characterization map provided by the embodiments of the present application, Figure 6 in which FIG. a is the formation characterization map corresponding to the reflected wave velocity V P , and FIG. b is the formation characterization map corresponding to the transmitted wave velocity V S . It can be known from Figure 6 that regions of the same color belong to the same formation, and the boundary of the formation can be more clearly determined from Figure 6 .

[0129] It should be noted that pre-stack seismic data contains more information on subsurface elastic properties than seismic data because the Amplitude Variation with Offset (AVO) is related to V P , V S and ρ. The original pre-stack seismic data is usually sorted by offset and can be converted to the angle domain as AVA data. V P , V S and ρ inversion based on AVA data will lead to AVA inversion problems, that is, the AVA inversion results are contaminated by noise, band-limited and non-unique. Therefore, it is necessary to use prior information to select models with specific properties to solve these problems. Pre-stack seismic data usually has a low signal-to-noise ratio (S / N), and this S / N is non-stable. Therefore, it is crucial to use prior information to determine the stability of the AVA inversion algorithm. AVA inversion prior information usually comes from existing rock physics models. For example, the model includes AVA parameters, such as the relationship between V P , V S and ρ. In addition to these relationships, some available in-situ relationships between V P , V S and ρ, which usually originate from log measurements, can also be used. The prior information can be represented by the AVA version in the Bayesian framework, which assumes a Gaussian prior probability distribution of model parameters. However, the solution through this prior distribution is too smooth and follows the band limitation of seismic data.

[0130] In the embodiments of the present application, a high-resolution model is obtained based on BPI inversion. Specifically, a set of spike reflectivity solutions for AVA inversion problems is used to obtain V P , V S and ρ to determine the sharp boundaries at the formation interfaces. Specifically, the model space of the formation is constructed as a wedge model similar to the post-stack BPI algorithm, and all elements of the wedge model are formation reflectivities. According to the wedge model, the L1 norm optimization framework is used to derive three reflectivities, and by incorporating the reflectivities determined by the initial model into the BPI algorithm, the absolute layer velocity and layer density, that is, V P , V S and ρ, are obtained. Thus, the formation boundaries, especially the boundaries of thin reservoirs, can be determined according to the layer velocity, and the BPI algorithm can effectively eliminate the interference of seismic wavelets to detect and enhance the formation boundaries.

[0131] The implementation effects of the method provided by the embodiments of the present application are introduced below with reference to the accompanying drawings:

[0132] Exemplarily, the actual seismic data of area A in the north of China where thin beds are particularly developed is selected as the research object. Multiple sedimentary systems are developed in area A, and the main sedimentary facies types are fluvial facies, delta facies, and beach-bar facies. Multiple thin sandstones with a thickness of 3 - 5 meters are mainly developed in the study area. Due to the finer lithology of the thin sand layers, the acoustic impedance difference from the upper and lower strata is small, and coupled with the interference effect between the thin sand layers, the seismic reflection signals thereof are weak. First, testing the pre-stack BPI method of the embodiment of the present application on the angle gather with a sampling rate of 2 mm (sampling interval is about 2 ms) from area A can obtain Figure 7 the results shown below. Figure 7 contains the logging data of V P , V S and ρ. Figure 7 The waveform diagrams (a), (b), and (c) in P respectively show the logging data of V S , V Figure 7 also superimposes the logging data on the corresponding numerical and actual angle gathers. The cross-correlation coefficient between the synthetic seismic data and the extracted seismic data is 0.72, which is sufficient to prove that the time-depth relationship is reliable, and this relationship is reflected well in the post-stack seismic data.

[0133] By calibrating the logging data, a set of wavelets related to the incident angle of the plane wave can be extracted. Figure 8 is a schematic diagram of a wavelet provided by the embodiment of the present application. Figure 8 In Figure 2 each wavelet with a length of 200 milliseconds can be used to generate a wavelet kernel matrix for the corresponding angle data.

[0134] Figure 9 is a schematic diagram of the propagation speed of the detection wave in the formation provided by the embodiment of the present application. Among them, the waveform x1 represents the reflection wave velocity V P and the transmission wave velocity V S at the well log position in the inversion result, the waveform x2 represents the V P and V S of the original well (i.e., the true logging data), and the waveform x3 represents the V P0 and V S0According to the time-depth relationship, the original log curves obtained at a depth interval of 0.5 feet are converted into the time domain with a sampling rate of 2 mm. A low-pass filter with a high cut-off frequency of 10 - 15 Hz is applied using the inverse distance weighting method (for spatial interpolation and extrapolation) to construct the initial low-frequency model. By comparing the inversion results with the real log data and the initial model, it can be concluded that the BPI inversion results clearly capture the formation characteristics reflected by the well data, and the initial model characteristics are not visible in the inversion results.

[0135] Figure 10 It is a characterization diagram of the reflectivity of a formation provided by an embodiment of the present application. Figure 10 In FIG. (a), it represents a 2D post-stack seismic profile, and in FIG. (b), it represents the reflectivity R of the reflected wave obtained by inversion P , and in FIG. (c), it represents the reflectivity R of the transmitted wave obtained by inversion S . Figure 10 In FIGS. (b) and (c) of 10, V P and V S log data are also embedded. As can be seen from Figure 10 , the inverted R P and R S are shown to have a structure similar to the post-stack seismic and have a good fit with the log data at the layer boundaries. Figure 10 It can qualitatively reflect the reflectivity of the formation. For example, through colors, each color block can represent a reflection signal. If the colors are continuous and the area occupied is large, it indicates a large reflectivity in that area, and vice versa.

[0136] Please continue to refer to Figure 6 , the V P and V S log data (such as the black vertical broken lines in Figure 6 ) are band-pass filtered and then inserted into the seismic trace position where the well is located (that is, the blank position in Figure 6 ). Furthermore, the velocity can be derived from the reflection coefficient obtained by inversion, and the color of each position in the area where the black vertical broken line is located can represent the velocity at that position. Optionally, well log curves with any passband band-pass filtering that appear smooth at all boundaries can also be used to verify the conventional pre-stack inversion results. Figure 6 The area of the good fit between the BPI result and the log velocity is pointed out by the black solid arrow in

[0137] Figure 11 It is a comparison schematic diagram of the target velocity provided by an embodiment of the present application. Figure 11 In it, waveform diagram (a) represents the V P and V S, which also includes the waveform of block filtering; the waveform diagram (b) represents the result diagram of conventional inversion; the waveform diagram (c) represents the result diagram of BPI inversion. In the waveform diagram (a), the original logging data and their block-filtered data overlap. Since the original logging data contains very high-frequency components, the logging data is not directly comparable to the inversion result, that is, the waveform diagram (a) is not directly comparable to the waveform diagram (c). The block-filtered recorded data in the waveform diagram (a) can still keep the layer boundaries in the correct positions and can be compared with the well logs. The waveform diagram (c) shows that the block result of BPI can highlight the formations, and the displayed formation boundaries are clearer. For example, the boundaries are clearer in the area marked by the ellipse. The block inversion and the smooth inversion come from the same pre-stack data. As Figure 11 shown, the block inversion result (i.e., waveform diagram c) and the smooth inversion result (i.e., waveform diagram b) follow the same trend. The block result with improved resolution from BPI can be additional subsurface information available for elastic property interpretation.

[0138] In summary, in the oil and gas extraction method provided by the embodiments of the present application, the computer can perform basic trace inversion processing based on the reflectivity of the formation, the reference propagation velocity of the detection wave in the formation, and the reference density of the formation. Thus, the obtained target density and target propagation velocity can more accurately reflect the formation structure. Therefore, based on this formation structure, the formation containing oil and gas can be more accurately determined, and further, the efficiency of oil and gas extraction can be improved.

[0139] Figure 12 is a schematic structural diagram of an oil and gas extraction device provided by the embodiments of the present application. This device can be used for a computer. As Figure 12 shown, the oil and gas extraction device 120 may include:

[0140] An acquisition module 1201, configured to acquire the reflection coefficient of the formation in the area to be exploited, the reference propagation velocity of the detection wave in the formation, and the reference density of the formation.

[0141] A processing module 1202, configured to perform basic trace inversion processing based on the reflection coefficient, the reference propagation velocity, and the reference density to determine the target density of the formation in the area to be exploited and the target propagation velocity of the detection wave in the formation.

[0142] A first determination module 1203, configured to determine the boundary of the formation in the area to be exploited based on the target density and the target propagation velocity.

[0143] A second determination module 1204, configured to determine the position to be exploited for oil and gas based on the boundary of the formation in the area to be exploited, so as to extract oil and gas at the position to be exploited.

[0144] In summary, the oil and gas extraction device provided by the embodiments of the present application can perform basic tracking inversion processing based on the reflectivity of the formation, the reference propagation speed of the detection wave in the formation, and the reference density of the formation. Thus, the obtained target density and target propagation speed can more accurately reflect the formation structure. Therefore, based on this formation structure, the formation containing oil and gas can be more accurately determined, thereby improving the efficiency of oil and gas extraction.

[0145] Optionally, the reference propagation speed and the reference density are obtained based on the seismic logging data in the area to be exploited after low-pass filtering.

[0146] Optionally, the first determination module 1203 may further be configured to:

[0147] Based on the target density, output a first formation characterization map of the area to be exploited, where areas with different colors in the first formation characterization map represent different target densities of the formation;

[0148] Based on the target propagation speed, output a second formation characterization map of the area to be exploited, where areas with different colors in the second formation characterization map represent different target propagation speeds of the formation;

[0149] Based on the first formation characterization map and the second formation characterization map, determine the boundary of the formation in the area to be exploited.

[0150] Optionally, the reflection coefficient includes: the reflection coefficient of the reflected wave, the reflection coefficient of the transmitted wave, and the density reflection coefficient; the reference propagation speed includes: the reference propagation speed of the reflected wave and the reference propagation speed of the transmitted wave, and the target propagation speed includes: the target propagation speed of the reflected wave and the target propagation speed of the transmitted wave;

[0151] The processing module 1202 may further be configured to:

[0152] Based on the first formula set, respectively determine the target propagation speed of the reflected wave, the target propagation speed of the transmitted wave, and the target density; the first formula set includes: and ρ(t) = ρ 0 (t)exp∫R ρ (t)dt;

[0153] where t represents time, V P (t) represents the target propagation speed of the reflected wave, V P0 (t) represents the reference propagation speed of the reflected wave, R P (t) represents the reflection coefficient of the reflected wave, V S (t) represents the target propagation speed of the transmitted wave, represents the reference propagation speed of the transmitted wave, R S (t) represents the reflection coefficient of the transmitted wave, ρ(t) represents the target density, ρ 0 (t) represents the reference density, Rρ (t) represents the density reflection coefficient.

[0154] Optionally, the obtaining module 1201 can also be used for:

[0155] Determining the dipole decomposition coefficients that minimize the value of the objective function, where the objective function is: ||d - G W m W || 2 + λ||m W || 1 ; where d is the data vector, d represents the pre-stack angle gather, and m W represents the dipole decomposition coefficients; G W represents the target kernel matrix, and G W is the product of the wavelet kernel matrix and the reflectivity pattern matrix, and G W m W represents the pre-stack angle gather of d; λ represents the trade-off factor, and the trade-off factor is used to control the sparsity of the dipole decomposition coefficients;

[0156] Determining the reflection coefficient based on the dipole decomposition coefficients, where the reflection coefficient is the product of the dipole decomposition coefficients and the reflectivity pattern matrix.

[0157] Optionally, the obtaining module 1201 can also be used for:

[0158] Determining the reflection coefficient based on the first formula, where the first formula is:

[0159]

[0160] where, represents the reflection coefficient, and R P (t) represents the reflection coefficient of the reflected wave, and R S (t) represents the reflection coefficient of the transmitted wave, and R ρ (t) represents the density reflection coefficient, represents the reflectivity pattern matrix, represents the dipole decomposition coefficients, and r e represents the even wedge reflectivity pattern, and r o represents the odd wedge reflectivity pattern, and a eP (t) represents the even component coefficient corresponding to the reflected wave, and b oP (t) represents the odd component coefficient corresponding to the reflected wave, and a eS (t) represents the even component coefficient corresponding to the transmitted wave, and b oS (t) represents the odd component coefficient corresponding to the transmitted wave, and a eρ represents the even component coefficient corresponding to the density, and b oρ represents the odd component coefficient corresponding to the density.

[0161] Optionally, the pre-stack angle gather of d satisfies the second formula:

[0162]

[0163] Among them, represents the pre-stack angle gather of d, represents the wavelet kernel matrix, W P represents the wavelet corresponding to the incident wave, W S represents the wavelet corresponding to the transmitted wave, W ρ represents the wavelet corresponding to the density, θ i represents the incident angle of the detection wave, 1 ≤ θ i ≤ N.

[0164] In summary, the oil and gas extraction device provided by the embodiments of the present application can perform basic tracking inversion processing based on the reflectivity of the formation, the reference propagation speed of the detection wave in the formation, and the reference density of the formation. Thus, the obtained target density and target propagation speed can more accurately reflect the formation structure. Therefore, based on this formation structure, the formation containing oil and gas can be more accurately determined, thereby improving the efficiency of oil and gas extraction.

[0165] The embodiments of the present application also provide a computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is made to execute the oil and gas collection method provided by the above embodiments, such as Figure 1 the method shown.

[0166] The embodiments of the present application also provide a computer program product containing instructions. When the computer program product runs on a computer, the computer is made to execute the oil and gas collection method provided by the above method embodiments, such as Figure 1 the method shown.

[0167] It should be noted that the method embodiments provided by the embodiments of the present application can be mutually referred to with the corresponding device embodiments, and the present application embodiments do not make any limitations in this regard. The order of the steps of the method embodiments provided by the embodiments of the present application can be appropriately adjusted, and the steps can also be increased or decreased accordingly. Any method that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered within the protection scope of the present application, so it will not be elaborated here.

[0168] It should be noted that, in the embodiments of the present application, in the case of involving mathematical calculations, the character " / " represents the operator "divided by". The terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. The term "a plurality of" means two or more, unless otherwise clearly defined. "Substantially" means within an acceptable error range, and those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect. In the drawings, the dimensions of layers and regions may be exaggerated for clarity of illustration. Moreover, it can be understood that when an element or layer is referred to as being "on" another element or layer, it can be directly on the other element or there may be an intermediate layer. Like reference numerals throughout the specification indicate like elements.

[0169] The above are only optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An oil and gas extraction method, characterized in that, the method includes: Obtain the reflection coefficient of the formation in the area to be mined, the reference propagation velocity of the detection wave in the formation, and the reference density of the formation, where the reference propagation velocity and the reference density are obtained based on the seismic logging data in the area to be mined after low-pass filtering; the obtaining of the reflection coefficient of the formation in the area to be mined includes: determining the dipole decomposition coefficient that minimizes the value of the objective function, where the objective function is: ||d - G W m W || 2 + λ||m W || 1 ; where d is the data vector, d represents the pre-stack angle gather, and m W represents the dipole decomposition coefficient; G W represents the target kernel matrix, and G W is the product of the wavelet kernel matrix and the reflectivity pattern matrix, and G W m W represents the pre-stack angle gather of d; λ represents the trade-off factor, and the trade-off factor is used to control the sparsity of the dipole decomposition coefficient; determine the reflection coefficient based on the dipole decomposition coefficient, where the reflection coefficient is the product of the dipole decomposition coefficient and the reflectivity pattern matrix; performing basic trace inversion processing based on the reflection coefficient, the reference propagation velocity, and the reference density to determine the target density of the formation in the area to be exploited and the target propagation velocity of the detection wave in the formation; determining the boundary of the formation in the area to be exploited based on the target density and the target propagation velocity; determining the position to be exploited for oil and gas based on the boundary of the formation in the area to be exploited, so as to extract oil and gas at the position to be exploited; wherein the reflection coefficient includes: reflection wave reflection coefficient, transmission wave reflection coefficient, and density reflection coefficient; the reference propagation velocity includes: reference propagation velocity of the reflection wave and reference propagation velocity of the transmission wave, and the target propagation velocity includes: target propagation velocity of the reflection wave and target propagation velocity of the transmission wave; the performing basic trace inversion processing based on the reflection coefficient, the reference propagation velocity, and the reference density to determine the target density of the formation in the area to be exploited and the target propagation velocity of the detection wave in the formation includes: Based on the first set of formulas, respectively determine the target propagation velocity of the reflected wave, the target propagation velocity of the transmitted wave, and the target density; the first set of formulas includes: and ρ(t) = ρ 0 (t)exp∫R ρ (t)dt; Among them, t represents time, V P (t) represents the target propagation speed of the reflected wave, V P0 (t) represents the reference propagation speed of the reflected wave, R P (t) represents the reflection coefficient of the reflected wave, V S (t) represents the target propagation speed of the transmitted wave, V S0 (t) represents the reference propagation speed of the transmitted wave, R S (t) represents the reflection coefficient of the transmitted wave, ρ(t) represents the target density, ρ 0 (t) represents the reference density, R ρ (t) represents the density reflection coefficient.

2. The method according to claim 1, characterized in that, the determining the boundary of the formation in the area to be exploited based on the target density and the target propagation velocity includes: outputting a first formation characterization map of the area to be exploited based on the target density, and different regions with different colors in the first formation characterization map represent different target densities of the formation; outputting a second formation characterization map of the area to be exploited based on the target propagation velocity, and different regions with different colors in the second formation characterization map represent different target propagation velocities of the formation; determining the boundary of the formation in the area to be exploited based on the first formation characterization map and the second formation characterization map.

3. The method according to claim 1, characterized in that, the determining the reflection coefficient based on the dipole decomposition coefficient includes: determining the reflection coefficient based on a first formula, and the first formula is: Among them, represents the reflection coefficient, R P (t) represents the reflection coefficient of the reflected wave, R S (t) represents the reflection coefficient of the transmitted wave, R ρ (t) represents the density reflection coefficient represents the reflectivity pattern matrix represents the dipole decomposition coefficient, r e represents the even component, r o represents the odd component, a eP (t) represents the even component coefficient corresponding to the reflected wave, b oP (t) represents the odd component coefficient corresponding to the reflected wave, a eS (t) represents the even component coefficient corresponding to the transmitted wave, b oS (t) represents the odd component coefficient corresponding to the transmitted wave, a eρ (t) represents the even component coefficient corresponding to the density, b oρ (t) represents the odd component coefficient corresponding to the density 4. The method according to claim 3, characterized in that, the pre-stack angle gather of d satisfies a second formula: Among them, represents the pre-stack angle gather of the said d, represents the wavelet kernel matrix, W P represents the wavelet corresponding to the incident wave, W S represents the wavelet corresponding to the transmitted wave, W ρ represents the wavelet corresponding to the density, θ i represents the incident angle of the detection wave, 1 ≤ θ i ≤ N.

5. An oil and gas extraction device, characterized in that, the oil and gas extraction device includes: An acquisition module, configured to acquire the reflection coefficient of a formation in an area to be mined, the reference propagation velocity of a detection wave in the formation, and the reference density of the formation, where the reference propagation velocity and the reference density are obtained based on the seismic logging data in the area to be mined after low-pass filtering; the acquisition module is further configured to: determine the dipole decomposition coefficient that minimizes the value of an objective function, where the objective function is: ||d - G W m W || 2 + λ||m W || 1 ; where d is a data vector, d represents the pre-stack angle gather, and m W represents the dipole decomposition coefficient; G W represents the target kernel matrix, and G W is the product of the wavelet kernel matrix and the reflectivity pattern matrix, and G W m W represents the pre-stack angle gather of d; λ represents a trade-off factor, and the trade-off factor is used to control the sparsity of the dipole decomposition coefficient; determine the reflection coefficient based on the dipole decomposition coefficient, where the reflection coefficient is the product of the dipole decomposition coefficient and the reflectivity pattern matrix; a processing module for performing basic trace inversion processing based on the reflection coefficient, the reference propagation velocity, and the reference density to determine the target density of the formation in the area to be exploited and the target propagation velocity of the detection wave in the formation; a first determination module for determining the boundary of the formation in the area to be exploited based on the target density and the target propagation velocity; a second determination module for determining the position to be exploited for oil and gas based on the boundary of the formation in the area to be exploited, so as to extract oil and gas at the position to be exploited; wherein the reflection coefficient includes: reflection wave reflection coefficient, transmission wave reflection coefficient, and density reflection coefficient; the reference propagation velocity includes: reference propagation velocity of the reflection wave and reference propagation velocity of the transmission wave, and the target propagation velocity includes: target propagation velocity of the reflection wave and target propagation velocity of the transmission wave; The processing module is further configured to: respectively determine a target propagation speed of the reflected wave, a target propagation speed of the transmitted wave, and the target density based on a first set of formulas; the first set of formulas includes: and ρ(t) = ρ 0 (t)exp∫R ρ (t)dt; where t represents time, V P (t) represents the target propagation speed of the reflected wave, represents the reference propagation speed of the reflected wave, R P (t) represents the reflection coefficient of the reflected wave, V S (t) represents the target propagation speed of the transmitted wave, represents the reference propagation speed of the transmitted wave, R S (t) represents the reflection coefficient of the transmitted wave, ρ(t) represents the target density, ρ 0 (t) represents the reference density, R ρ (t) represents the density reflection coefficient.

6. The oil and gas extraction device according to claim 5, wherein, the first determination module is further configured to: output a first formation characterization map of the to-be-exploited area based on the target density, where areas with different colors in the first formation characterization map represent formations with different target densities; output a second formation characterization map of the to-be-exploited area based on the target propagation speed, where areas with different colors in the second formation characterization map represent formations with different target propagation speeds; determine the boundary of the formation in the to-be-exploited area based on the first formation characterization map and the second formation characterization map.

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