Reservoir property detection method and device, electronic equipment and storage medium

Abstract

The application provides a reservoir property detection method and device, electronic equipment and storage medium, and belongs to the technical field of oil exploration and development. The method comprises the following steps: acquiring real-time P-wave azimuth AVO gather data of a well area; inputting the P-wave azimuth AVO gather data into an AVO inversion model which has been constructed, and obtaining reservoir property parameters of the target well area by inversion, wherein the reservoir property parameters comprise S-wave velocity reflectivity, P-wave velocity reflectivity and density reflectivity, and a restriction condition and a weight coefficient are added to an inversion objective function of the AVO inversion model. The application starts from the most basic mathematical level, adds a restriction condition and designs a weight coefficient to the original objective function of least squares, improves the coefficient matrix, so that the sensitivity of the solution of the inversion objective function to errors is reduced, the influence of data errors on the result is determined, noise interference is coped with, and the sensitivity of the solution of the inversion objective function to errors and noise is greatly reduced.

Description

Reservoir property testing methods, devices, electronic equipment and storage media Technical Field

[0001] This invention belongs to the field of petroleum exploration and development technology, particularly the field of AVO pre-stack inversion technology, and specifically relates to a reservoir property detection method, device, electronic equipment and storage medium. Background Technology

[0002] The main challenge in oilfield exploration is determining the hydrocarbon-bearing properties and quantities of the target formation. These indicators are crucial for deciding on subsequent detailed exploration efforts, including the number of wells drilled, the scale of oilfield construction, and pipeline laying – a plan that spans several years or even longer. The difficulty in determining hydrocarbon-bearing properties and calculating hydrocarbon quantities lies in the fact that we can only obtain one or at most two reservoir parameters from seismic data.

[0003] Existing research indicates that acoustic impedance is related to lithology, porosity, fluid composition, and saturation. However, acoustic impedance alone cannot theoretically determine hydrocarbon content, let alone calculate hydrocarbon yield; only guesses and approximate values ​​can be obtained. Furthermore, a second parameter, shear wave impedance, has been introduced, which reduces the uncertainty regarding hydrocarbon content and its magnitude, but it remains an inaccurate assessment and estimation, severely impacting oilfield exploration and development.

[0004] Currently, mainstream software on the market, such as pre-stack inversion software like Hampton-Russell and Jason, has outdated pre-stack inversion modules. This is because they can only calculate two parameters, and the calculation of the second parameter is still somewhat inaccurate, failing to meet the requirements of oil and gas exploration. Furthermore, other specialized software on the market often has strong locality, meaning it is only applicable to a specific region, certain strata, or certain lithological combinations, lacking universality. In some cases, the results of pre-stack inversion can mislead interpreters' understanding of reservoir properties because they consider the problem from a rock physics perspective.

[0005] To improve the accuracy of pre-stack inversion, those skilled in the art have conducted increasing research and practice on AVO pre-stack inversion, achieving effective results. Among these, pre-stack seismic inversion, used for inverting P-wave and S-wave velocity ratios, has emerged and has become one of the important geophysical technologies for oil exploration and development. The P-wave and S-wave velocity ratio, containing S-wave information, often has stronger lithological discrimination and fluid identification capabilities than impedance, and is a commonly used parameter for lithological characterization and fluid identification. Density parameters are also effective parameters for reservoir property description. In conventional well logging for oil and gas layer identification, density and resistivity are important criteria for distinguishing oil and water layers and are key parameters for predicting the lateral distribution patterns of reservoirs and fluids, holding significant reference value in reservoir property prediction.

[0006] Among existing pre-stack seismic inversion techniques, simultaneous inversion of the three pre-stack parameters is one of the most commonly used techniques. This technique effectively utilizes the AVO information contained in pre-stack seismic data, obtaining three parameters simultaneously through multiple partially stacked data volumes, providing more effective elastic parameters or parameter combinations for lithology and fluid identification. However, simultaneous inversion of the three parameters still has some problems. For example, the AVO inversion problem is highly ill-conditioned, requiring reasonable regularization. The regularization coefficient is usually an estimate, which introduces certain errors into the inversion results of the three pre-stack parameters. The ill-conditioned equation is also highly sensitive to errors, resulting in poor accuracy of the pre-stack three-parameter inversion. At the same time, noise also has a significant impact on the inversion results of this technique, seriously affecting the acquisition of reservoir physical parameters, and thus hindering the accurate tracking and interpretation of reservoir physical properties.

[0007] In summary, reducing the ill-conditioning of inversion problems and improving the inversion accuracy of the three pre-stack parameters are technical challenges that urgently need to be overcome by those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to provide a reservoir property detection method, device, electronic device, and storage medium to solve the technical problems in the prior art, such as large inversion errors caused by the use of estimated regularization coefficients during pre-stack three-parameter inversion, and poor accuracy of three-parameter solutions due to the high sensitivity of pre-stack three-parameter inversion to data errors and noise.

[0009] To achieve the above objectives, a first aspect of the present invention provides a reservoir property detection method, the method comprising:

[0010] Acquire real-time P-wave azimuth AVO gather data for the well area;

[0011] The P-wave azimuth AVO gather data is input into the constructed AVO inversion model to obtain the reservoir physical parameters of the well area. The reservoir physical parameters include shear wave velocity reflectivity, P-wave velocity reflectivity and density reflectivity.

[0012] Historical P-wave azimuth AVO gather data is used as the data source when constructing the AVO inversion model.

[0013] The process of determining the inversion objective function of the AVO inversion model is as follows: Based on the linear equation system constructed by the multichannel reflection coefficients, the objective function of least squares inversion, S = (AX - b), is determined. T (AX-b); By adding constraints to the objective function, the objective function is modified to S = (AX-b). T (AX-b)+λX TX, where the magnitude of the first parameter λ cannot be estimated in advance; for the first term (AX-b) of the objective function T (AX-b) includes the weighting coefficient P and the second term X. T With the addition of weight coefficient Q to X, the objective function is modified to S = (AX - b). T P(AX-b)+X T QX;

[0014] Correspondingly, the solution X = (A) of the AVO inversion model T PA+Q) -1 (A T Pb);

[0015] Wherein, the weight coefficient P represents the confidence level of the first term of the objective function, the weight coefficient Q represents the confidence level of the second term of the objective function, A represents the coefficient matrix of the linear equation system, X represents the reservoir physical property parameters to be solved, and b represents the historical P-wave azimuth AVO gather data.

[0016] Optionally, the matrix form of the linear equation system is:

[0017]

[0018] in, R PP (θ n ) indicates that the reflection angle is θ n Longitudinal wave reflection coefficient, R PP (θ m ) indicates that the reflection angle is θ m Longitudinal wave reflection coefficient, R PP (θ f ) indicates that the reflection angle is θ f The longitudinal wave reflection coefficient at time γ is the pre-estimated ratio of the transverse wave velocity to the longitudinal wave velocity, R Vp R represents the longitudinal wave velocity reflectivity. Vs R represents the transverse wave velocity reflectivity. ρ This indicates density reflectance.

[0019] Optionally, when constructing the AVO inversion model, the weighting coefficient P is the inverse matrix of the covariance matrix of the P-wave azimuth AVO gather data.

[0020] Optionally, when constructing the AVO inversion model, the weighting coefficient Q is calculated based on the pre-stack composite gather of the well area corresponding to the historical P-wave azimuth AVO gather data, and is the inverse matrix of the covariance matrix of the solution X.

[0021] A second aspect of the present invention provides a reservoir property testing device, the device comprising:

[0022] The data acquisition module is used to acquire real-time P-wave azimuth AVO gather data of the well area;

[0023] The AVO inversion module is used to input the P-wave azimuth AVO gather data into the constructed AVO inversion model to invert the reservoir physical parameters of the well area. The reservoir physical parameters include shear wave velocity reflectivity, P-wave velocity reflectivity and density reflectivity.

[0024] The AVO inversion model is constructed using historical P-wave azimuth AVO gather data as the data source.

[0025] The process for determining the inversion objective function of the AVO inversion model is as follows:

[0026] Based on a system of linear equations constructed using multichannel reflection coefficients, the objective function S = (AX - b) for least squares inversion is determined. T (AX-b);

[0027] By adding constraints to the objective function, the objective function is modified to S = (AX - b). T (AX-b)+λX T X, where the size of the first parameter λ cannot be estimated in advance;

[0028] For the first term (AX-b) of the objective function T (AX-b) includes the weighting coefficient P and the second term X. T With the addition of weight coefficient Q to X, the objective function is modified to S = (AX - b). T P(AX-b)+X T QX;

[0029] Accordingly, the solution X = (A) of the AVO inversion model T PA+Q) -1 (A T Pb);

[0030] Wherein, the weight coefficient P represents the confidence level of the first term of the objective function, the weight coefficient Q represents the confidence level of the second term of the objective function, A represents the coefficient matrix of the linear equation system, X represents the reservoir physical property parameters to be solved, and b represents the historical P-wave azimuth AVO gather data.

[0031] Optionally, in the AVO inversion module, the matrix form of the linear equation system is:

[0032]

[0033] in, R PP (θ n ) indicates that the reflection angle is θn Longitudinal wave reflection coefficient, R PP (θ m ) indicates that the reflection angle is θ m Longitudinal wave reflection coefficient, R PP (θ f ) indicates that the reflection angle is θ f The longitudinal wave reflection coefficient at time γ is the pre-estimated ratio of the transverse wave velocity to the longitudinal wave velocity, R Vp R represents the longitudinal wave velocity reflectivity. Vs R represents the transverse wave velocity reflectivity. ρ This indicates density reflectance.

[0034] Optionally, when constructing the AVO inversion model, the weighting coefficient P is the inverse matrix of the covariance matrix of the P-wave azimuth AVO gather data.

[0035] Optionally, when constructing the AVO inversion model, the weighting coefficient Q is calculated based on the pre-stack composite gather of the well area corresponding to the historical P-wave azimuth AVO gather data, and is the inverse matrix of the covariance matrix of the solution X.

[0036] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a reservoir property detection method as described in the first aspect of the present invention.

[0037] A fourth aspect of the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a reservoir property detection method as described in the first aspect of the present invention.

[0038] It is known that the maximum reflection angle of typical AVO gather data is approximately 30 degrees. Therefore, the coefficient matrix A of the linear equation system constructed based on multi-channel reflection coefficients is a rank-deficient matrix or an ill-conditioned matrix, with a condition number reaching 300. This linear equation system cannot be solved by the least squares method. In existing technologies, reasonable regularization is generally performed based on the degree of ill-conditioning of the inversion problem; however, the regularization coefficients are often estimated values, which are inaccurate. This invention starts from the most basic mathematical level, improving the coefficient matrix by adding constraints and designing weight coefficients to the original objective function of least squares. This reduces the sensitivity of the solution X to errors and determines the impact of data errors on the results to address noise interference, significantly reducing the sensitivity of the solution X to errors and noise. The three parameters obtained by synchronous inversion have better accuracy. Through the high-precision solution of the three parameters, the hydrocarbon content and amount in the well area can be determined more accurately, and even basic data support can be provided for quantitative estimation of porosity and fluid saturation.

[0039] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 is an exemplary flowchart of a reservoir property testing method;

[0042] Figure 2 shows the inversion results of density reflectivity when the signal-to-noise ratio is 1 / 1, where (2a) is the conventional inversion result, (2b) is the regularized inversion result, and (2c) is the weight quantification inversion result implemented in the embodiment of the present invention.

[0043] Figure 3 shows the inversion results of density reflectivity when the signal-to-noise ratio is 3 / 1, where (3a) is the conventional inversion result, (3b) is the regularized inversion result, and (3c) is the weighted quantitative inversion result implemented in the embodiment of the present invention.

[0044] Figure 4 shows the inversion results of density reflectivity without noise, where (4a) is the conventional inversion result, (4b) is the inversion result after regularization, and (4c) is the inversion result of weight quantification implemented in the embodiment of the present invention.

[0045] Figure 5 is a schematic diagram showing the consistency between wellbore inversion results and logging data interpretation.

[0046] Figure 6 is a schematic diagram of one component of a reservoir property testing device. Detailed Implementation

[0047] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0048] Method Example 1

[0049] This embodiment provides a reservoir property detection method for simultaneous three-parameter inversion in a target well area. After obtaining the reservoir property parameters, a rock physics model can be used to determine the hydrocarbon content and quantitatively calculate the hydrocarbon content in the reservoir of the target well area.

[0050] Specifically, referring to Figure 1, a reservoir property testing method includes the following implementation steps:

[0051] S100. Acquire real-time P-wave azimuth AVO gather data of the well area. This real-time P-wave azimuth AVO gather data can also be referred to as actual measured values ​​or actual observation data.

[0052] S200. Input the P-wave azimuth AVO gather data into the constructed AVO inversion model to obtain the reservoir physical parameters of the target well area. The reservoir physical parameters include shear wave velocity and reflectivity R. Vs Longitudinal wave velocity reflectivity R Vp and density reflectance R ρ The AVO inversion model incorporates constraints and weighting coefficients into its inversion objective function.

[0053] The constructed AVO inversion model is a synchronous three-parameter AVO inversion model. Historical P-wave azimuth AVO gather data was used as the data source during its construction.

[0054] Furthermore, the inversion objective function of this model is determined through the following steps:

[0055] S01. Based on the linear equation system constructed using multichannel reflection coefficients, the objective function of the least squares inversion is determined. At this point, the equation system of the objective function is the original equation system, and is expressed as S=(AX-b). T (AX-b). Where A represents the coefficient matrix of the linear equation system, X represents the reservoir physical property parameters to be solved, and b represents the historical P-wave azimuth AVO gather data, which can also be called historical measurement values ​​or historical observation data.

[0056] Since the maximum reflection angle of a typical AVO gather is about 30 degrees, the coefficient matrix of the linear equation system constructed based on the multi-channel reflection coefficient is a rank-deficient matrix or an ill-conditioned matrix, with a condition number of 300. Therefore, the objective function equation system cannot be solved by the least squares method.

[0057] S02. Add constraints to the objective function. After adding constraints, the objective function is modified to S = (AX - b). T (AX-b)+λX T X.

[0058] By adding the constraint λX T After X, the coefficient matrix is ​​improved, and the sharp expansion effect of noise in the ill-conditioned matrix is ​​suppressed, making it possible to obtain a meaningful solution to the objective function equation system. This depends on the value of the first parameter λ, but the value of the first parameter λ cannot be estimated in advance. Therefore, it is necessary to improve the objective function after adding constraints.

[0059] S03. For the first term (AX-b) of the objective function T (AX-b) includes the weighting coefficient P and the second term X. TAfter adding a weight coefficient Q to X, the objective function is modified to S = (AX - b). T P(AX-b)+X T QX. Here, the weight coefficient P represents the first term (AX-b) of the objective function. T The confidence level of (AX-b) represents the contribution of the first term to the objective function, while the weight coefficient Q characterizes the second term X in the objective function. T The credibility of X is the degree to which the second term contributes to the objective function.

[0060] By objectively determining the contributions of each of the two terms to the objective function using the weight coefficients P and Q, the solution to the objective function is always within a reasonable range.

[0061] After determining the final inversion objective function through the above steps S01-S03, the objective function equations are solved using the least squares method, and the solution X = (A T PA+Q) -1 (A T Pb).

[0062] Method Example 2

[0063] Method Embodiment Two is a preferred embodiment of Method Embodiment One. Specifically, this embodiment provides a reservoir property detection method for simultaneous three-parameter inversion of a target well area. After obtaining the reservoir property parameters, a rock physics model can be used to determine the hydrocarbon content in the reservoir of the target well area and to quantitatively calculate the hydrocarbon content.

[0064] Referring to Figure 1, a reservoir property testing method specifically includes the following implementation steps:

[0065] S100. Acquire real-time P-wave azimuth AVO gather data of the well area. This real-time P-wave azimuth AVO gather data can also be referred to as actual measured values ​​or actual observation data.

[0066] S200. Input the P-wave azimuth AVO gather data into the constructed AVO inversion model to obtain the reservoir physical parameters of the target well area. The reservoir physical parameters include shear wave velocity and reflectivity R. Vs Longitudinal wave velocity reflectivity R Vp and density reflectance R ρ .

[0067] Among them, the constructed AVO inversion model is the AVO synchronous three-parameter inversion model, which uses historical P-wave azimuth AVO gather data as the data source when constructing the model.

[0068] Furthermore, the inversion objective function of this model is determined through the following steps:

[0069] S01. Based on the linear equation system constructed using multichannel reflection coefficients, the objective function of the least squares inversion is determined. At this point, the equation system of the objective function is the original equation system, and is expressed as S=(AX-b). T (AX-b).

[0070] Specifically, the matrix form of the linear equation system constructed based on the multichannel reflection coefficient is as follows:

[0071]

[0072] Where A represents the coefficient matrix of the linear equation system, X represents the reservoir physical property parameters to be solved, and b represents the historical P-wave azimuth AVO gather data, which can also be called historical measurement values ​​or historical observation data.

[0073] R PP (θ n ) indicates that the reflection angle is θ n Longitudinal wave reflection coefficient, R PP (θ m ) indicates that the reflection angle is θ m Longitudinal wave reflection coefficient, R PP (θ f ) indicates that the reflection angle is θ f The longitudinal wave reflection coefficient at time γ is the pre-estimated ratio of the transverse wave velocity to the longitudinal wave velocity, R Vp R represents the longitudinal wave velocity reflectivity. Vs R represents the transverse wave velocity reflectivity. ρ This indicates density reflectance.

[0074] Since the maximum reflection angle of a typical AVO gather is about 30 degrees, the coefficient matrix of the linear equation system constructed based on the multi-channel reflection coefficient is a rank-deficient matrix or an ill-conditioned matrix, with a condition number of 300. Therefore, the objective function equation system cannot be solved by the least squares method.

[0075] S02. Add constraints to the objective function. After adding constraints, the objective function is modified to S = (AX - b). T (AX-b)+λX T X.

[0076] By adding the constraint λX T After X, the coefficient matrix is ​​improved, and the sharp expansion effect of noise in the ill-conditioned matrix is ​​suppressed, making it possible to obtain a meaningful solution to the objective function equation system. This depends on the value of the first parameter λ, but the value of the first parameter λ cannot be estimated in advance. Therefore, it is necessary to improve the objective function after adding constraints.

[0077] S03. For the first term (AX-b) of the objective function T (AX-b) includes the weighting coefficient P and the second term X. T After adding a weight coefficient Q to X, the objective function is modified to S = (AX - b). T P(AX-b)+X T QX. Here, the weight coefficient P characterizes the confidence level of the first term of the objective function, i.e., the first term (AX-b). T The contribution of (AX-b) to the objective function, and the weighting coefficient Q characterizes the second term X of the objective function. T The credibility of X is the degree to which the second term contributes to the objective function.

[0078] By objectively determining the contributions of each of the two terms to the objective function using the weight coefficients P and Q, the solution to the objective function is always within a reasonable range.

[0079] After determining the inversion objective function through the above steps S01-S03, the objective function equations are solved using the least squares method, and the solution X = (A T PA+Q) -1 (A T Pb).

[0080] Method Example 3

[0081] Method Embodiment Three is a preferred embodiment of Method Embodiment Two. Specifically, this embodiment provides a reservoir property detection method for simultaneous three-parameter inversion of a target well area. After obtaining the reservoir property parameters, a rock physics model can be used to determine the hydrocarbon content in the reservoir of the target well area and to quantitatively calculate the hydrocarbon content.

[0082] Referring to Figure 1, a reservoir property testing method specifically includes the following implementation steps:

[0083] S100. Acquire real-time P-wave azimuth AVO gather data of the well area. This real-time P-wave azimuth AVO gather data can also be referred to as actual measured values ​​or actual observation data.

[0084] S200. Input the P-wave azimuth AVO gather data into the constructed AVO inversion model to obtain the reservoir physical parameters of the target well area. The reservoir physical parameters include shear wave velocity and reflectivity R. Vs Longitudinal wave velocity reflectivity R Vp and density reflectance R ρ .

[0085] Among them, the constructed AVO inversion model is the AVO synchronous three-parameter inversion model, which uses historical P-wave azimuth AVO gather data as the data source when constructing the model.

[0086] Furthermore, the inversion objective function of this model is determined through the following steps:

[0087] S01. Based on the linear equation system constructed using multichannel reflection coefficients, the objective function of the least squares inversion is determined. At this point, the equation system of the objective function is the original equation system, and is expressed as S=(AX-b). T (AX-b).

[0088] Specifically, the linear equation system constructed based on the multichannel reflection coefficient is as follows:

[0089]

[0090] Where A represents the coefficient matrix of the linear equation system, X represents the reservoir physical property parameters to be solved, and b represents the historical P-wave azimuth AVO gather data, which can also be called historical measurement values ​​or historical observation data.

[0091] R PP (θ n ) indicates that the reflection angle is θ n Longitudinal wave reflection coefficient, R PP (θ m ) indicates that the reflection angle is θ m Longitudinal wave reflection coefficient, R PP (θ f ) indicates that the reflection angle is θ f The longitudinal wave reflection coefficient at time γ is the pre-estimated ratio of the transverse wave velocity to the longitudinal wave velocity, R Vp R represents the longitudinal wave velocity reflectivity. Vs R represents the transverse wave velocity reflectivity. ρ This indicates density reflectance.

[0092] Since the maximum reflection angle of a typical AVO gather is about 30 degrees, the coefficient matrix of the linear equation system constructed based on the multi-channel reflection coefficient is a rank-deficient matrix or an ill-conditioned matrix, with a condition number of 300. Therefore, the objective function equation system cannot be solved by the least squares method.

[0093] S02. Add constraints to the objective function. After adding constraints, the objective function is modified to S = (AX - b). T (AX-b)+λX T X.

[0094] By adding the constraint λX TAfter X, the coefficient matrix is ​​improved, and the sharp expansion effect of noise in the ill-conditioned matrix is ​​suppressed, making it possible to obtain a meaningful solution to the objective function equation system. This depends on the value of the first parameter λ, but the value of the first parameter λ cannot be estimated in advance. Therefore, it is necessary to improve the objective function after adding constraints.

[0095] S03. For the first term (AX-b) of the objective function T (AX-b) includes the weighting coefficient P and the second term X. T After adding a weight coefficient Q to X, the objective function is modified to S = (AX - b). T P(AX-b)+X T QX. Here, the weight coefficient P characterizes the confidence level of the first term of the objective function, i.e., the first term (AX-b). T The contribution of (AX-b) to the objective function, and the weighting coefficient Q characterizes the second term X of the objective function. T The credibility of X is the degree to which the second term contributes to the objective function.

[0096] Specifically, the weighting coefficient P is the inverse of the covariance matrix of the P-wave azimuth AVO gather data, which is also the inverse of the covariance matrix of the measured value b. The weighting coefficient Q is the inverse of the covariance matrix of the solution X and can be calculated from the pre-stack gather data of the well area corresponding to the historical P-wave azimuth AVO gather data.

[0097] The principle of defining the weight coefficient P as the inverse of the covariance matrix of the P-wave azimuth AVO gather data and the weight coefficient Q as the inverse of the covariance matrix of the solution X is explained below.

[0098] First, consider the Mahalanobis distance between a point X in n-dimensional space and a distribution:

[0099] D M =[(X-μ) T S -1 (X-μ)] 1 / 2 ;

[0100] Where μ is the mean of the distribution and S is the covariance matrix of the distribution;

[0101] Based on the above principles, the objective function can be further modified to: S = (AX - b) T P(AX-b)+(X-Xavg) TQ(X-Xavg), where Xavg represents the average value of the solution X, is 0 in the reflectivity signal. Taking a one-dimensional variable as an example, if the noise of the measured value b is large, i.e., the noise of the well area observation data is large, the variance of the observation data is large, and the reciprocal of the variance is small, i.e., the weight coefficient P is small. Therefore, the weight coefficient P indicates that the first term of the objective function has low reliability, and correspondingly, its contribution to the objective function is small. Similarly, if the solution X does not change much, i.e., the reservoir physical parameters in the well area do not change much, the variance of the reservoir physical parameters is small, and the reciprocal of the variance is large, i.e., the weight coefficient Q is large. Therefore, the weight coefficient Q indicates that the second term of the objective function has high reliability, and correspondingly, its contribution to the objective function is large. It can be seen that the weight coefficients P and Q objectively determine the contributions of each of the two terms to the objective function, ensuring that the solution of the objective function remains within a reasonable range.

[0102] After determining the inversion objective function through the above steps S01-S03, the objective function equations are solved using the least squares method, and the solution X = (A T PA+Q) -1 (A T Pb).

[0103] The following content uses geophysical inversion data as an example to illustrate the feasibility of the reservoir property detection method provided by the above method embodiments.

[0104] Matrix form of linear equations The reflection angle θ n Reflection angle θ m Reflection angle θ f And the ratio γ of the transverse wave velocity to the longitudinal wave velocity should be appropriately selected, for example, the reflection angle θ. n The value is 10 degrees, and the reflection angle θ m The value is 20 degrees, and the reflection angle θ f With a value of 30 degrees and γ set to 1 / 2, the coefficient matrix at this point is:

[0105]

[0106] Furthermore, the coefficient matrix is ​​decomposed using SVD, and the decomposition result is:

[0107]

[0108] The decomposition results show that the middle triangular matrix has a value close to zero, indicating that the rank of the matrix is ​​close to 2. The linear equation system is close to linearly dependent, or the determinant of the matrix must be very small, making it an ill-conditioned matrix or a rank-deficient matrix.

[0109] To illustrate the feasibility of the reservoir property detection method provided in the above method embodiments, the verification approach is as follows: using the forward modeling method, the measured value b is calculated from the determined solution X based on the above linear equation system. Then, noise is added to the measured value b and used as the input for pre-stack synchronous three-parameter inversion to invert the solution X. Finally, the feasibility and accuracy of the method embodiments are judged by statistical analysis of the results.

[0110] The following description provides a specific application example of the aforementioned verification approach. Assume a normal distribution with a mean of 0 and a standard deviation of 0.1. Randomly sample the longitudinal wave velocity reflectivity R from this distribution. Vp Transverse wave velocity reflectivity R Vs and density reflectance R ρ (Assuming the solution X is known and represents the true value), substituting it into the above linear equations allows for the forward calculation of a series of measured values ​​b. Noise is added to these measured values ​​b to obtain the measured values ​​to be inverted. Then, the solution X is inverted using three pre-stack synchronous three-parameter inversion methods. The first method is a direct solution without any processing. The second method is a regularized inversion, but the regularization coefficient λ' is roughly estimated; for example, in this application example, it is set to 0.001. The third method uses the implementation method described in the above examples, where both terms of the objective function have reasonable weight coefficients, meaning the weights are quantified. We use the most common inversion parameter, density reflectance R... ρ Taking this as an example, we examine the feasibility of this algorithm, and the verification results are shown in Figures 2 to 4.

[0111] Figure 2 shows the inversion results of density reflectance when the signal-to-noise ratio is 1 / 1 (i.e., 50% of the measured value is noise). Among them, (2a) is the inversion result using the first pre-stack synchronous three-parameter inversion method. The inversion result is almost unrelated to the true value, with a correlation coefficient of 0.0279 and a very large value range, which is far beyond the reasonable range, and the inversion fails completely; (2b) is the inversion result using the second pre-stack synchronous three-parameter inversion method. The inversion result has a very weak linear relationship with the true value, with a correlation coefficient of 0.1475, and the inversion is also unsuccessful; (2c) is the inversion result using the reservoir property detection method implemented by the method embodiment. The inversion result obtained after weight quantification has a significant linear relationship with the true value, with a correlation coefficient of 0.5268. It can be seen that the inversion result obtained by the method embodiment under this noise condition is the best.

[0112] Figure 3 shows the inversion results of density reflectance when the signal-to-noise ratio is 3 / 1 (i.e., 25% of the measured value is noise). Among them, (3a) is the inversion result using the first pre-stack synchronous three-parameter inversion method, which is still invalid with a correlation coefficient of only 0.0467; (3b) is the inversion result using the second pre-stack synchronous three-parameter inversion method, which improves the linear relationship between the inversion result and the true value with a correlation coefficient of 0.3503, but the inversion result still needs improvement; (3c) is the inversion result using the reservoir property detection method implemented in the method embodiment. After weight quantification, the linear relationship between the inversion result and the true value is further enhanced with a correlation coefficient of 0.6859. The inversion result obtained at this time can be used for signal interpretation.

[0113] Figure 4 shows the inversion results of density reflectance in the absence of noise. Among them, due to the absence of noise, the inversion result using the first pre-stack synchronous three-parameter inversion method is correct, as shown in part (4a), and the inversion result is the same as the true value. The inversion result using the second pre-stack synchronous three-parameter inversion method is shown in part (4b). Since the regularization parameter λ' is relatively large in the absence of noise, the result is not accurately inverted. Due to the quantitative weighting, that is, the weights are automatically adjusted according to the noise level, the inversion result of the reservoir property detection method implemented by the method embodiment has a good linearity with the true value, as shown in part (4c).

[0114] Finally, real-time P-wave azimuth AVO gather data of the target well area was used for testing, and the test results are shown in Figure 5. As can be seen from the figure, when the real-time P-wave azimuth AVO gather data is input into the AVO inversion model constructed in the method embodiment, three parameters are obtained through inversion. The reservoir's hydrocarbon content and hydrocarbon quantity are interpreted using these three parameters. At this time, the hydrocarbon-bearing section represented by the interpretation of the three parameters is consistent with the hydrocarbon-bearing section interpreted from the well logging data.

[0115] Device Examples

[0116] Referring to Figure 6, this embodiment provides a reservoir property detection device, which includes a data acquisition module and an AVO inversion module connected in sequence.

[0117] The data acquisition module is used to acquire real-time P-wave azimuth AVO gather data of the well area.

[0118] The AVO inversion module is used to input the P-wave azimuth AVO gather data into the constructed AVO inversion model to invert the reservoir physical parameters of the well area. The reservoir physical parameters include shear wave velocity reflectivity, P-wave velocity reflectivity, and density reflectivity.

[0119] The AVO inversion model uses historical P-wave azimuth AVO gather data as the data source.

[0120] The process of determining the inversion objective function of the AVO inversion model is as follows:

[0121] Based on a system of linear equations constructed using multichannel reflection coefficients, the objective function S = (AX - b) for least squares inversion is determined. T (AX-b);

[0122] By adding constraints to the objective function, the objective function is modified to S = (AX - b). T (AX-b)+λX T X, where the size of λ cannot be estimated in advance;

[0123] For the first term (AX-b) of the objective function T (AX-b) includes the weighting coefficient P and the second term X. T With the addition of weight coefficient Q to X, the objective function is modified to S = (AX - b). T P(AX-b)+X T QX;

[0124] Correspondingly, the solution X = (A) of the AVO inversion model T PA+Q) -1 (A T Pb);

[0125] Wherein, the weight coefficient P represents the confidence level of the first term of the objective function, the weight coefficient Q represents the confidence level of the second term of the objective function, A represents the coefficient matrix of the linear equation system, X represents the reservoir physical property parameters to be solved, and b represents the historical P-wave azimuth AVO gather data.

[0126] Optionally, in the AVO inversion module, the matrix form of the linear equation system is:

[0127]

[0128] in, R PP (θ n ) indicates that the reflection angle is θ n Longitudinal wave reflection coefficient, R PP (θ m ) indicates that the reflection angle is θ m Longitudinal wave reflection coefficient, R PP (θ f ) indicates that the reflection angle is θ f The longitudinal wave reflection coefficient at time γ is the pre-estimated ratio of the transverse wave velocity to the longitudinal wave velocity, R Vp R represents the longitudinal wave velocity reflectivity. Vs R represents the transverse wave velocity reflectivity. ρ This indicates density reflectance.

[0129] Optionally, when constructing the AVO inversion model, the weighting coefficient P is the inverse of the covariance matrix of the P-wave azimuth AVO gather data.

[0130] Optionally, when constructing the AVO inversion model, the weighting coefficient Q is calculated based on the pre-stack composite gather of the well area corresponding to the historical P-wave azimuth AVO gather data, and is the inverse matrix of the covariance matrix of the solution X.

[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0132] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the reservoir property detection method as described in any one of the method embodiments one to three.

[0133] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0134] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] In another aspect, the present invention also provides a machine-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reservoir property detection method described in any one of the method embodiments one to three.

[0136] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting reservoir properties, characterized in that, include: Acquire real-time P-wave azimuth AVO gather data for the well area; The P-wave azimuth AVO gather data is input into the constructed AVO inversion model to obtain the reservoir physical parameters of the well area. These parameters include shear wave velocity reflectivity, P-wave velocity reflectivity, and density reflectivity. Historical P-wave azimuth AVO gather data is used as the data source during the AVO inversion model construction. The determination process of the objective function of the AVO inversion model is as follows: Based on a system of linear equations constructed from multi-channel reflection coefficients, the least-squares inversion objective function S = (AX - b) is determined. T (AX-b); By adding constraints to the objective function, the objective function is modified to S = (AX-b). T (AX-b)+λX T X, where the magnitude of the first parameter λ cannot be estimated in advance; for the first term (AX-b) of the objective function T (AX-b) includes the weighting coefficient P and the second term X. T With the addition of weight coefficient Q to X, the objective function is modified to S = (AX - b). T P(AX-b)+X T QX; correspondingly, the solution X = (A T PA+Q) -1 (A T Pb); where the weight coefficient P represents the confidence level of the first term of the objective function, the weight coefficient Q represents the confidence level of the second term of the objective function, A represents the coefficient matrix of the linear equation system, X represents the reservoir physical property parameters to be solved, and b represents the historical P-wave azimuth AVO gather data.

2. The reservoir property detection method according to claim 1, characterized in that, The matrix form of the linear equation system is: in, R PP (θ n ) indicates that the reflection angle is θ n Longitudinal wave reflection coefficient, R PP (θ m ) indicates that the reflection angle is θ m Longitudinal wave reflection coefficient, R PP (θ f ) indicates that the reflection angle is θ f The longitudinal wave reflection coefficient at time γ is the pre-estimated ratio of the transverse wave velocity to the longitudinal wave velocity, R Vp R represents the longitudinal wave velocity reflectivity. Vs R represents the transverse wave velocity reflectivity. ρ This indicates density reflectance.

3. The reservoir property detection method according to claim 1, characterized in that, When constructing the AVO inversion model, the weighting coefficient P is the inverse matrix of the covariance matrix of the P-wave azimuth AVO gather data.

4. The reservoir property detection method according to claim 1, characterized in that, When constructing the AVO inversion model, the weight coefficient Q is calculated based on the pre-stack synthesized well area gather corresponding to the historical P-wave azimuth AVO gather data, and is the inverse matrix of the covariance matrix of the solution X.

5. A reservoir property testing device, characterized in that, The device includes: a data acquisition module for acquiring real-time P-wave azimuth AVO gather data of the well area; and an AVO inversion module for inputting the P-wave azimuth AVO gather data into a constructed AVO inversion model to invert and obtain reservoir physical parameters of the well area, including shear wave velocity reflectivity, P-wave velocity reflectivity, and density reflectivity. The AVO inversion model is constructed using historical P-wave azimuth AVO gather data as the data source. The determination process of the inversion objective function of the AVO inversion model is as follows: based on a system of linear equations constructed from multi-channel reflection coefficients, the least squares inversion objective function S = (AX - b) is determined. T (AX-b); By adding constraints to the objective function, the objective function is modified to S = (AX-b). T (AX-b)+λX T X, where the magnitude of the first parameter λ cannot be estimated in advance; for the first term (AX-b) of the objective function T (AX-b) includes the weighting coefficient P and the second term X. T With the addition of weight coefficient Q to X, the objective function is modified to S = (AX - b). T P(AX-b)+X T QX; correspondingly, the solution X = (A T PA+Q) -1 (A T Pb); where the weight coefficient P represents the confidence level of the first term of the objective function, the weight coefficient Q represents the confidence level of the second term of the objective function, A represents the coefficient matrix of the linear equation system, X represents the reservoir physical property parameters to be solved, and b represents the historical P-wave azimuth AVO gather data.

6. The reservoir property testing device according to claim 5, characterized in that, In the AVO inversion module, the matrix form of the linear equation system is as follows: in, R PP (θ n ) indicates that the reflection angle is θ n Longitudinal wave reflection coefficient, R PP (θ m ) indicates that the reflection angle is θ m Longitudinal wave reflection coefficient, R PP (θ f ) indicates that the reflection angle is θ f The longitudinal wave reflection coefficient at time γ is the pre-estimated ratio of the transverse wave velocity to the longitudinal wave velocity, R Vp R represents the longitudinal wave velocity reflectivity. Vs R represents the transverse wave velocity reflectivity. ρ This indicates density reflectance.

7. The reservoir property testing device according to claim 5, characterized in that, When constructing the AVO inversion model, the weighting coefficient P is the inverse matrix of the covariance matrix of the P-wave azimuth AVO gather data.

8. A reservoir property testing device according to claim 5, characterized in that, When constructing the AVO inversion model, the weight coefficient Q is calculated based on the pre-stack synthesized well area gather corresponding to the historical P-wave azimuth AVO gather data, and is the inverse matrix of the covariance matrix of the solution X.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a reservoir property detection method as described in any one of claims 1 to 4.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a reservoir property detection method as described in any one of claims 1 to 4.

Images