A method and device for selecting seismic attributes based on rock physics model forward modeling

Abstract

The present invention discloses a method and device for selecting seismic attributes based on rock physics model forward modeling, comprising the following steps: step S1, constructing a rock physics model; step S2, constructing a forward geological model according to the rock physics model; step S3, extracting seismic attributes according to the forward geological model; step S4, optimizing the extracted seismic attributes. The technical solution of the present invention is adopted, and the relationship between reservoir physical parameters and elastic parameters can be quantitatively characterized by using the rock physics model, which is used as the criterion for selecting seismic attributes, which not only avoids the influence of human subjective factors, but also effectively enhances the connection between seismic attributes and underground reservoirs while reducing the requirement for the number of wells in the study area.

Description

Technical Field

[0001] The invention belongs to the technical field of seismic exploration, and in particular relates to a method and a device for optimizing seismic attributes based on forward modeling of a rock physics model. Background Art

[0002] With the development of seismic attribute extraction and optimization technology, the types of seismic attributes are constantly increasing, and the optimization methods are also constantly improving. At present, seismic attribute optimization technology is mainly divided into three categories: expert experience method, mathematical analysis method and seismic forward simulation method. The optimization of seismic attributes based on expert experience and knowledge is highly subjective and usually needs to be used in conjunction with other methods. Mathematical analysis method refers to the optimization of sensitive attributes through mathematical analysis. Commonly used methods include simulated annealing method, cluster analysis method, rough set theory method and genetic algorithm. These methods reduce the influence of human subjective factors, but the selected seismic attributes sometimes do not have clear geological significance, and require more wells in the study area, otherwise some mathematical analysis methods are no longer applicable, and the selected attributes will have large errors. The main advantage of the seismic forward analysis method is that it has a certain degree of credibility, and the selected seismic attributes usually have clear geological significance. However, the disadvantage is that it requires the collection of a large amount of logging, core and other data for reservoir simulation, which requires large calculation and workload, and there is a large error between the reservoir simulation and the actual reservoir situation.

[0003] Seismic attribute optimization needs to be combined with the reservoir characteristics of the study area to select seismic attributes that are sensitive to reservoir physical parameters. Affected by various factors, the existing seismic attribute optimization methods cannot well describe the relationship between reservoir physical parameters and seismic wave geometry, kinematics and dynamics. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for optimizing seismic attributes based on forward modeling of a rock physics model. The rock physics model can be used to quantitatively characterize the relationship between reservoir physical parameters and elastic parameters, and this is used as a criterion for selecting seismic attributes. This not only avoids the influence of human subjective factors, but also the optimized seismic attributes have certain geological significance. While reducing the requirement for the number of well locations in the study area, it effectively enhances the connection between seismic attributes and underground reservoirs.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A method for optimizing seismic attributes based on rock physics model forward modeling comprises the following steps:

[0007] Step S1, constructing a rock physics model;

[0008] Step S2, constructing a forward geological model based on the rock physics model;

[0009] Step S3, extracting earthquake attributes according to the forward geological model;

[0010] Step S4: Optimize the extracted seismic attributes.

[0011] Preferably, the longitudinal wave velocity, the shear wave velocity and the density elastic parameters of the rock are calculated according to the rock physics model to obtain the impedance curve of the well section;

[0012] The impedance curves are arranged and combined according to the corresponding porosity and water saturation to form a forward geological model.

[0013] Preferably, step S4 comprises:

[0014] Normalize the seismic attributes;

[0015] Calculate the Pearson correlation coefficient between the extracted seismic attributes and perform self-organizing classification according to the absolute value of the Pearson correlation coefficient;

[0016] The Spearman correlation coefficients between seismic attributes and storage physical parameters in each category are calculated;

[0017] In each group, a seismic attribute with the largest absolute value of the Spearman correlation coefficient is selected as the optimal sensitive attribute reflecting the change of reservoir physical properties.

[0018] The present invention also provides a seismic attribute optimization device based on rock physics model forward modeling, comprising:

[0019] A first building module is used to build a rock physics model;

[0020] The second construction module is used to construct a forward geological model based on the rock physics model;

[0021] An extraction module, used to extract seismic attributes based on the forward geological model;

[0022] The optimization module is used to optimize the extracted seismic attributes.

[0023] Preferably, the second building block comprises:

[0024] The first processing unit is used to calculate the longitudinal wave velocity, the transverse wave velocity and the density elastic parameters of the rock according to the rock physics model to obtain the impedance curve of the well section;

[0025] The second processing unit is used to arrange and combine the impedance curves according to the corresponding porosity and water saturation to form a forward geological model.

[0026] Preferably, the preferred modules include:

[0027] A normalization unit is used to normalize seismic attributes;

[0028] The first calculation unit is used to calculate the Pearson correlation coefficient between the extracted seismic attributes and perform self-organization classification according to the absolute value of the Pearson correlation coefficient;

[0029] The second calculation unit is used to calculate the Spearman correlation coefficient between the seismic attributes and the storage physical property parameters in each category;

[0030] The selection unit is used to select a seismic attribute with the largest absolute value of the Spearman correlation coefficient in each group as the optimal sensitive attribute reflecting the change of reservoir physical properties.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows: using rock physics as a reference standard to optimize seismic attributes, on the one hand, greatly reduces the influence of human subjective factors; on the other hand, the seismic attributes calculated in the optimization stage are determined by the reservoir properties, so the seismic attributes optimized in this way have certain geological significance. In addition, the present invention is less dependent on the number of wells. When the number of wells in the study area is small, the present invention can still be used to optimize the seismic attributes. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 This is a flow chart of a method for optimizing seismic attributes based on rock physics model forward modeling according to an embodiment of the present invention;

[0034] Figure 2 It is a schematic diagram of the optimization process;

[0035] Figure 3 It is the rock physics template map;

[0036] Figure 4 This is a schematic diagram of the design model;

[0037] Figure 5(a) is a schematic diagram of the RMS amplitude;

[0038] Figure 5(b) is a schematic diagram of the total negative amplitude;

[0039] Figure 5(c) is a schematic diagram of the amplitude slope;

[0040] Figure 6(a) is a heat map of the Pielson correlation coefficients among the 18 extracted attributes.

[0041] Figure 6(b) is a schematic diagram of the classification results. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Embodiment 1:

[0045] like Figure 1 , 2 As shown, an embodiment of the present invention provides a method for optimizing seismic attributes based on rock physics model forward modeling, comprising the following steps:

[0046] Step S1: Building a rock physics model

[0047] The establishment of rock physics model includes the calculation of rock matrix elastic parameters, fluid modulus, dry rock sample modulus and saturated rock sample elastic modulus. The specific steps are as follows.

[0048] I. Calculation of rock matrix parameters: The Voigt-Reuss-Hill model is used to calculate the rock matrix elastic parameters. The calculation formula is as follows:

[0049]

[0050] Among them, M V is the Voigt model, M R It is the Reuss model. f i is the volume fraction of the i-th mineral; M i is the elastic modulus of the ith mineral.

[0051] II. Calculation of fluid modulus: The bulk modulus of the saturated fluid is calculated using the fluid uniform mixing formula. The calculation formula is as follows:

[0052]

[0053] Among them, S W Represents water saturation; K h and K W represent the bulk modulus of hydrocarbons and brine, respectively; K fl is the bulk modulus of the fluid.

[0054] III. Calculation of dry rock sample modulus: The Pride consolidation coefficient method is used to calculate the elastic modulus of dry rock samples. The calculation formula is as follows:

[0055]

[0056] Among them, K dry and μ dry represent the bulk modulus and shear modulus of dry rock samples respectively; K m and μ m represent the bulk modulus and shear modulus of the matrix, respectively; represents porosity; α represents the consolidation coefficient, which is generally between 2 and 20; γ is a function of α.

[0057] IV. Calculation of elastic modulus of saturated rock samples: The elastic modulus of saturated rock samples is calculated using the Gassmann equation. The calculation formula is as follows:

[0058]

[0059] μ sat =μ dry (6)

[0060] Where φ is the porosity, K sat , K m , K dry With K f are the bulk modulus of saturated rock, the bulk modulus of rock matrix, the bulk modulus of dry rock and the bulk modulus of pore fluid, μ sat and μ dry are the shear modulus of saturated rock and the shear modulus of dry rock, respectively.

[0061] Step S2: construct a forward geological model based on the rock physics model

[0062] According to the rock physics model, the elastic parameters of the rock such as P-wave velocity and density are calculated, and the impedance curve of the well section is obtained by multiplying the P-wave velocity curve and the density curve. By changing the porosity and water saturation of the rock physics model of the target layer, the impedance curves under different saturations and porosities can be obtained. The impedance curve is used as the horizontal and vertical coordinates according to the corresponding porosity and water saturation, and an impedance curve is stored under one coordinate to form a three-dimensional forward geological model.

[0063] Step S3: Extracting earthquake attributes based on the forward geological model

[0064] The reflection coefficient model is calculated based on the forward geological model, and the reflection coefficient model is convolved with the seismic wavelet to obtain a series of synthetic seismic records. The calculation formula is as follows:

[0065]

[0066] S=R*W (8)

[0067] Where R is the reflection coefficient, W is the seismic wavelet, S is the synthetic seismic record, * is the convolution operation, Zp1 is the lower layer impedance value, and Zp2 is the upper layer impedance value.

[0068] Forward seismic waveforms can characterize changes in reservoir physical parameters. Finally, by extracting multiple seismic attributes from synthetic seismic records, different seismic attributes under different porosities and water saturations can be obtained. When extracting seismic attributes, the target layer is identified, and then a time window is opened on it based on existing data. Mathematical operations such as summation, averaging, and maximum and minimum values ​​are performed on the seismic data within the time window to achieve the extraction of attributes.

[0069] Step S4: Optimizing earthquake attributes

[0070] The obtained seismic attributes are normalized, and then the Pearson correlation coefficients between the extracted seismic attributes are calculated and extracted, and self-organized classification is performed according to the absolute value of the Pearson correlation coefficient; finally, the Spearman correlation coefficient between the seismic attributes and storage physical parameters in each category is obtained. The specific steps are as follows:

[0071] I. Normalization of seismic attributes: If all seismic attribute values ​​are less than 0, they are normalized to between -1 and 0; if some seismic attribute values ​​are both greater than 0 and less than 0, they are normalized to between -1 and 1; if all seismic attribute values ​​are greater than 0, they are normalized to between 0 and 1.

[0072] II. Calculation of Pearson correlation coefficient of seismic attributes and self-organizing classification: The Pearson correlation coefficient can measure the linear relationship between two random variables and is usually represented by the letter r. The overall Pearson correlation coefficient calculation formula between two seismic attributes is:

[0073]

[0074] Among them, x i and i Respectively represent two groups of seismic attributes; n is the number of seismic attribute samples; and Represent the mean of two groups of seismic attributes. According to the absolute value of the Pearson correlation coefficient, the seismic attributes are self-organized and classified, that is, the absolute values ​​greater than 0.95 are grouped into one group, those between 0.9 and 0.95 are grouped into one group, and those less than 0.9 are grouped into one group.

[0075] III. Spearman correlation analysis and attribute optimization of seismic attributes and reservoir properties: Calculate the Spearman correlation coefficient between the seismic attributes and reservoir physical parameters within each attribute group obtained in step II. In each group, select a seismic attribute with the largest absolute value of the Spearman correlation coefficient as the optimal sensitive attribute reflecting the change of reservoir physical properties. The Spearman correlation coefficient is a non-parametric measure of rank correlation. The calculation formula of the Spearman correlation coefficient between seismic attributes and reservoir parameters is:

[0076]

[0077] R(a) and R(a) are the ranks of seismic attribute a and reservoir parameter c, respectively. and Respectively represent the average ranking. The simplified calculation formula is as follows:

[0078]

[0079] Among them, d i Represents the difference in the rank value of the i-th data pair; n is the total number of samples.

[0080] The embodiment of the present invention is based on a rock physics model. By inputting different porosity and water saturation parameters, velocity and density curves are calculated, and these curves are arranged and combined according to the corresponding porosity and water saturation to form a forward geological model. A convolution forward simulation algorithm is used to obtain synthetic seismic records, and then multiple seismic attributes are extracted. The seismic attributes are classified according to the size of the Pearson correlation coefficient between each two, and then one or two attributes with the largest absolute value of the correlation coefficient with the reservoir physical property parameters in each category are selected to characterize and predict the physical property changes of the target layer in the study area.

[0081] Embodiment 2:

[0082] The embodiment of the present invention provides a method for optimizing seismic attributes based on rock physics model forward modeling, comprising the following steps:

[0083] (1) When establishing a rock physics template, the density of the matrix, the density of the fluid, the elastic modulus of the matrix, and the elastic modulus of the fluid are required. The determination of the fluid modulus and density can be obtained through laboratory measurements; the determination of the matrix modulus is mainly based on the test results of the core experiment to determine the matrix elastic modulus.

[0084] According to the determined matrix elastic modulus and the rock physics modeling calculation formula, by inputting different porosities, water saturations and mud contents, the P- and S-wave velocities of the corresponding lithology can be calculated. The porosity, water saturation and mud content can be set according to the actual situation and drawn into a template diagram.

[0085] Take a certain work area as an example. Figure 3 is the rock physics template map, Figure 3 The middle grid lines indicate the changes in mud content and porosity. The range of mud content is 0%-100% and the range of porosity is 0%-10%. Figure 3 The scattered points in the figure are the actual P- and S-wave velocities of the well.

[0086] (2) According to the reservoir characteristics of the study area, a Figure 4 In the N*M model shown, N is the number of sample points in the Inline direction, and a parameter is selected as a variable, such as: the Inline direction is the porosity interval from 0% to 0.1% to 9.9%, and M is the number of sample points in the Xline direction. A parameter is selected as a variable, such as: the Xline direction is the water saturation interval from 0% to 100%. At each determined Inline and determined Xline, the synthetic seismic record under the corresponding parameters is calculated by the rock physics model established in step (1), for example: the Inline direction is porosity, and the Xline direction is water saturation, then each track is a synthetic seismic record under the corresponding porosity and water saturation.

[0087] exist Figure 4 Based on the data, seismic attributes are extracted. Taking amplitude attributes as an example, a total of 18 amplitude attributes are extracted. These 18 attributes are normalized. Attribute values ​​less than 0 are normalized to between -1 and 0; attribute values ​​greater than 0 and less than 0 are normalized to between -1 and 1; attribute values ​​greater than 0 are normalized to between 0 and 1. Figures 5(a), 5(b), and 5(c) show the effects of normalization of three attributes. The Pearson correlation coefficient is calculated between each pair, and then the absolute value of the Pearson correlation coefficient is divided into one group, 0.9 to 0.95 is divided into one group, and less than 0.9 is divided into one group. Finally, the 18 seismic attributes are divided into 5 categories. Figure 6(a) is the Pearson correlation coefficient heat map between the 18 extracted attributes, and Figure 6(b) is the classification result.

[0088] (3) The Spearman correlation coefficient is calculated between the seismic attributes and the reservoir physical property parameters (the reservoir physical property parameters can be porosity, shale content, etc., and POR*(100-SW) is used as an example here). In each type of seismic attribute, the attribute with the largest absolute value of the Spearman coefficient is selected. The results are shown in Table 1. The attribute with the largest absolute value of the correlation coefficient is the total positive amplitude and attribute, and its correlation coefficient is 0.6863, which means that among the 18 amplitude attributes, the total positive amplitude and attribute can best reflect the changes in reservoir physical properties.

[0089] Table 1

[0090]

[0091] Embodiment 3:

[0092] The embodiment of the present invention further provides a seismic attribute optimization device based on rock physics model forward modeling, comprising:

[0093] A first building module is used to build a rock physics model;

[0094] The second construction module is used to construct a forward geological model based on the rock physics model;

[0095] An extraction module, used to extract seismic attributes based on the forward geological model;

[0096] The optimization module is used to optimize the extracted seismic attributes.

[0097] As an implementation manner of the embodiment of the present invention, the second building block includes:

[0098] The first processing unit is used to calculate the longitudinal wave velocity, the transverse wave velocity and the density elastic parameters of the rock according to the rock physics model to obtain the impedance curve of the well section;

[0099] The second processing unit is used to arrange and combine the impedance curves according to the corresponding porosity and water saturation to form a forward geological model.

[0100] As an implementation of an embodiment of the present invention, the preferred module includes:

[0101] A normalization unit is used to normalize seismic attributes;

[0102] The first calculation unit is used to calculate the Pearson correlation coefficient between the extracted seismic attributes and perform self-organization classification according to the absolute value of the Pearson correlation coefficient;

[0103] The second calculation unit is used to calculate the Spearman correlation coefficient between the seismic attributes and the storage physical property parameters in each category;

[0104] The selection unit is used to select a seismic attribute with the largest absolute value of the Spearman correlation coefficient in each group as the optimal sensitive attribute reflecting the change of reservoir physical properties.

[0105] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for optimizing seismic attributes based on rock physics model forward modeling, characterized in that: The following steps are involved: Step S1, constructing a rock physics model; Step S2, constructing a forward geological model based on the rock physics model; Step S3, extracting earthquake attributes according to the forward geological model; Step S4, optimizing the extracted seismic attributes; Step S2 includes: The longitudinal wave velocity, shear wave velocity and density elastic parameters of the rock are calculated based on the rock physics model to obtain the impedance curve of the well section; The impedance curves are arranged and combined according to the corresponding porosity and water saturation to form a forward geological model; Step S4 includes: Normalize the seismic attributes; Calculate the Pearson correlation coefficient between the extracted seismic attributes and perform self-organizing classification according to the absolute value of the Pearson correlation coefficient; The Spearman correlation coefficients between seismic attributes and storage physical parameters in each category are calculated; In each group, a seismic attribute with the largest absolute value of the Spearman correlation coefficient is selected as the optimal sensitive attribute reflecting the change of reservoir physical properties.

2. A seismic attribute optimization device based on rock physics model forward modeling, characterized in that: include: A first building module is used to build a rock physics model; The second construction module is used to construct a forward geological model based on the rock physics model; An extraction module, used to extract seismic attributes based on the forward geological model; A selection module is used to select the extracted seismic attributes; The second building block includes: The first processing unit is used to calculate the longitudinal wave velocity, the transverse wave velocity and the density elastic parameters of the rock according to the rock physics model to obtain the impedance curve of the well section; The second processing unit is used to arrange and combine the impedance curves according to the corresponding porosity and water saturation to form a forward geological model; Preferred modules include: A normalization unit is used to normalize seismic attributes; The first calculation unit is used to calculate the Pearson correlation coefficient between the extracted seismic attributes and perform self-organization classification according to the absolute value of the Pearson correlation coefficient; The second calculation unit is used to calculate the Spearman correlation coefficient between the seismic attributes and the storage physical property parameters in each category; The selection unit is used to select a seismic attribute with the largest absolute value of the Spearman correlation coefficient in each group as the optimal sensitive attribute reflecting the change of reservoir physical properties.

Images