Method and device for improving the accuracy of seismic characterization of thin layer structures

Abstract

This invention relates to a method for improving the accuracy of seismic characterization of thin-layer structures, comprising: acquiring raw seismic data; establishing a multi-channel deconvolution objective function based on the raw seismic data; acquiring the prior probability distribution function of the reflection coefficient and applying a probability distribution constraint to the reflection coefficient in the longitudinal direction; acquiring dip angle information and constructing a spatial constraint term using the dip angle information, the constraint term including the dip angle information of the in-phase axes in the entire seismic trace; constructing a multi-channel deconvolution objective functional based on the dip angle; solving the multi-channel deconvolution objective functional to obtain seismic record data with improved resolution. This invention is applicable to seismic prediction and description of complex reservoirs, can more effectively reflect thin reservoirs, and the inversion results are closer to the actual geological characteristics. It can effectively compensate for the defects of constrained sparse deconvolution and achieve a fine description of reservoir characteristics.

Description

Technical Field

[0001] This invention belongs to the field of seismic data processing, interpretation and inversion technology for oil and gas geophysical exploration and development, and particularly relates to methods and devices for improving the accuracy of seismic characterization of thin-layer structures. Background Technology

[0002] As oilfield exploration and development continue to deepen, conventional seismic data, limited by its seismic resolution, can no longer meet the needs of oil and gas exploration and development in complex geological conditions, especially for detailed interpretation of thin interbedded layers, reservoir prediction, and reservoir characterization in complex target areas. Therefore, improving the resolution of seismic data has become a key technical challenge for solving the problems of detailed structural interpretation and reservoir prediction in the stage of oil and gas exploration and development.

[0003] Deconvolution is a primary method for improving the resolution of seismic data. This method recovers the true subsurface reflection coefficient sequence by compressing the seismic wavelet. However, traditional linear deconvolution is limited by the effective frequency band of the original seismic data and cannot recover reflection information outside this band. To obtain high-resolution seismic data, many researchers artificially assume that the reflection coefficients satisfy a certain functional distribution, such as the p-norm distribution, Huber distribution, Sech distribution, Cauchy distribution, or modified Cauchy distribution. These constraints can maintain a good signal-to-noise ratio while improving resolution. However, the actual reflection coefficient distribution characteristics of the actual seismic field often do not conform to these constraints, significantly reducing the processing effectiveness. Furthermore, sparse constraints suppress information about weak reflectors, which contradicts the goal of improving seismic data resolution to identify thin-layer reflections. Therefore, how to improve the resolution of seismic data while reducing the suppression of weak reflection information becomes a crucial issue. Additionally, this technique, based on a single-channel deconvolution method, does not consider the spatial relationships of reservoir structures, resulting in lateral discontinuities in the processing results.

[0004] To alleviate the above problems, we provide a method to improve the accuracy of seismic characterization of thin-layer structures. This invention first extracts the probability distribution of reflection coefficients from reliable well logging data. The selected wells should reflect the distribution characteristics of reflection coefficients in the target area, especially the target reservoir. This statistical probability distribution function represents the true distribution of reflection coefficients in the work area, thereby improving the accuracy of reflection coefficient prediction and reducing the influence of human factors on the inversion results. Then, the dip angle information of the seismic data is introduced into the objective function of the inversion, establishing a dip-constrained multi-channel nonlinear deconvolution, which improves seismic resolution while ensuring the lateral continuity of the processing results.

[0005] Therefore, based on these issues, this paper proposes a method and apparatus for seismic prediction and description of complex reservoirs, which can more effectively reflect thin reservoirs, and whose inversion results are closer to the actual geological characteristics. This method and apparatus can effectively compensate for the defects of constrained sparse deconvolution and achieve a fine description of reservoir characteristics, thereby improving the accuracy of seismic characterization of thin-layer structures. This has important practical significance. Summary of the Invention

[0006] This invention proposes a method and apparatus for seismic prediction and description of complex reservoirs, which can more effectively reflect thin reservoirs, and the inversion results are closer to the actual geological characteristics. It can effectively make up for the defects of constrained sparse deconvolution and improve the accuracy of seismic characterization of thin-layer structures by achieving a fine description of reservoir characteristics.

[0007] The technical problem solved by this invention is achieved through the following technical solution:

[0008] Methods to improve the seismic characterization accuracy of thin-layer structures include the following steps:

[0009] Acquire raw seismic data; establish a multi-channel deconvolution model based on the raw seismic data:

[0010] d = Gm

[0011] Where d = [s1, s2, ..., s N ] represents the observed seismic data composed of multiple seismic traces connected end to end, d i This represents the observation data of the i-th seismic trace; G is a block diagonal matrix, where the elements on its diagonal are the convolution matrices of the seismic wavelets of multiple seismic traces; m = [r1, r2, ..., r N ] represents the multi-trace model parameters formed by connecting the first and last parameters of the multi-trace model sequentially, r i Let N represent the reflection coefficient sequence of the i-th seismic trace; N is the total number of seismic traces.

[0012] Establish the objective function for multi-channel deconvolution:

[0013]

[0014] Obtain the prior probability distribution function P of the reflection coefficient w (m), applying a probability distribution constraint to the reflection coefficient in the longitudinal direction, such that the parameter distribution function P est (m) and the probability distribution function P of the reflectance coefficient in this region w (m) consistent;

[0015] The probability distribution constraint is as follows: P est (m) represents the probability distribution of the multichannel model parameter m; it is obtained by performing probability statistics on m.

[0016] Obtain dip angle information and use the dip angle information to construct a spatial constraint term H, which contains the dip angle information of the in-phase axes in the entire seismic trace;

[0017] Construct a multi-channel deconvolution objective functional based on tilt angle:

[0018]

[0019] Where, μ z μ represents the weighting factor controlling the time direction constraint term within the seismic trace. x Adjust the proportion of the tilt angle constraint term in the objective function;

[0020] The multichannel deconvolution objective functional is solved to obtain seismic record data with improved resolution.

[0021] Furthermore, the method for obtaining the prior probability distribution function of the reflection coefficient is as follows:

[0022] A reference well is selected, and the probability distribution histogram of the reflection coefficient is extracted based on the well logging data of the reference well to obtain the prior probability distribution function p. w (x) is a function that describes the distribution characteristics of the reflection coefficient of the target area.

[0023] Furthermore, the logging data from the reference well can reflect the distribution characteristics of the reflection coefficient in the target area.

[0024] Furthermore, the method for obtaining tilt angle information is as follows:

[0025] Dip scanning of the seismic profile yields dip information θ(x,t) from the seismic data.

[0026] The cross-correlation coefficient in the tilt scan is recorded as the confidence level c(x,t) of the tilt angle. A threshold c0 is set. When the confidence level c(x,t) < c0, the tilt angle information is obtained by fitting the surrounding tilt angles.

[0027] in,

[0028] w is the weighting coefficient matrix, the size of which is related to the distance between the sampling point of the dip angle to be determined and the surrounding sampling points, and w(0,0)=0; t is the time series, it is the index number of the time series, x is the seismic trace series, ix is ​​the seismic trace index number, and a and b are index variables.

[0029] A device for classifying weathering crust structure using well logging curves and petrological characteristics includes the following modules:

[0030] The module for establishing the multichannel deconvolution objective function is used to acquire raw seismic data; a multichannel deconvolution model is then established based on the raw seismic data.

[0031] d = Gm

[0032] Where d = [s1, s2, ..., s N ] represents the observed seismic data composed of multiple seismic traces connected end to end, d i This represents the observation data of the i-th seismic trace; G is a block diagonal matrix, where the elements on its diagonal are the convolution matrices of the seismic wavelets of multiple seismic traces; m = [r1, r2, ..., r N ] represents the multi-trace model parameters formed by connecting the first and last parameters of the multi-trace model sequentially, r i Let N represent the reflection coefficient sequence of the i-th seismic trace; N is the total number of seismic traces.

[0033] Establish the objective function for multi-channel deconvolution:

[0034]

[0035] Obtain the prior probability distribution function P of the reflection coefficient w (m), applying a probability distribution constraint to the reflection coefficient in the longitudinal direction, such that the parameter distribution function P est (m) and the probability distribution function P of the reflectance coefficient in this region w (m) consistent;

[0036] The probability distribution constraint is as follows: P est (m) represents the probability distribution of the multichannel model parameter m; it is obtained by performing probability statistics on m.

[0037] The spatial constraint term establishment module is used to obtain dip angle information and construct spatial constraint term H using dip angle information. This constraint term contains dip angle information of the in-phase axes in the entire seismic trace.

[0038] A module for constructing multichannel deconvolution objective functionals is used to build tilt-angle-based multichannel deconvolution objective functionals.

[0039]

[0040] Where, μ z μ represents the weighting factor controlling the time direction constraint term within the seismic trace. x Adjust the proportion of the tilt angle constraint term in the objective function;

[0041] The multichannel deconvolution objective functional solver module is used to solve the multichannel deconvolution objective functional to obtain seismic record data with improved resolution.

[0042] A computing device includes: one or more processing units; and a storage unit for storing one or more programs, wherein when the one or more programs are executed by the one or more processing units, the one or more processing units perform the method described above for improving the accuracy of seismic characterization of thin-layer structures.

[0043] A computer-readable storage medium having processor-executable non-volatile program code, wherein the computer program, when executed by a processor, implements the steps of the method described above for improving the accuracy of seismic characterization of thin-layer structures.

[0044] The advantages and positive effects of this invention are:

[0045] This invention extracts the probability distribution function of the reflection coefficient of the target area from well logging data and imposes vertical constraints on it; it utilizes dip information from seismic data to construct lateral constraints, improving the reliability and stability of the multi-channel deconvolution target functional; it alleviates the instability and discontinuity in the lateral direction of traditional single-channel inversion, and solves the influence of mathematical distributions such as sparsity constraints on the inversion results by using the probability distribution of statistical reflection coefficients for vertical constraints, thereby improving the accuracy of high-resolution inversion results, enhancing the interpretability of seismic data, and demonstrating strong feasibility. Attached Figure Description

[0046] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that these drawings are designed for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, unless specifically indicated, these drawings are intended only to conceptually illustrate the structural construction described herein and are not necessarily drawn to scale.

[0047] Figure 1 This is a flowchart illustrating the method for improving the seismic characterization accuracy of thin-layer structures in this embodiment of the invention.

[0048] Figure 2 This refers to a seismic record containing noise, as described in Embodiment 1 of the present invention.

[0049] Figure 3 This is a histogram of the probability distribution of reflection coefficients based on well data in Embodiment 1 of the present invention.

[0050] Figure 4 In Embodiment 1 of the present invention Figure 2 The dip field of the earthquake record;

[0051] Figure 5(a) shows the result of traditional sparse deconvolution processing in Embodiment 1 of the present invention;

[0052] Figure 5(b) shows the result of processing using this method in Embodiment 1 of the present invention;

[0053] Figure 6(a) shows actual seismic data in Embodiment 2 of the present invention;

[0054] Figure 6(b) shows the seismic record processed by traditional sparse deconvolution in Embodiment 2 of the present invention;

[0055] Figure 6(c) shows the seismic record processed by the method in Embodiment 2 of the present invention; Detailed Implementation

[0056] First, it should be noted that the specific structure, features, and advantages of the present invention will be described in detail below by way of examples. However, all descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way. Furthermore, any single technical feature described or implied in the embodiments mentioned herein, or any single technical feature shown or implied in the accompanying drawings, can still be arbitrarily combined or deleted among these technical features (or their equivalents) to obtain more other embodiments of the present invention that may not be directly mentioned herein. Additionally, for the sake of simplifying the drawings, the same or similar technical features may be indicated only in one place in the same drawing.

[0057] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed during use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0058] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0059] Example 1

[0060] like Figure 1 As shown in this embodiment, the method for improving the seismic characterization accuracy of thin-layer structures includes the following steps:

[0061] Step 1, Obtain raw seismic data: Load the seismic data to be upgraded into the system. In this embodiment, the data contains 350 seismic traces, each containing 286 sampling points, such as... Figure 2 As shown;

[0062] Step 2, establish a multi-channel deconvolution model:

[0063] The single-channel convolution model can be represented as:

[0064] s = Wr

[0065] Where s represents single-channel seismic data, W represents the convolution matrix form of the seismic wavelet, and r represents the reflection coefficient sequence corresponding to the seismic channel;

[0066] Based on the single-channel convolution model described above, the following multi-channel forward model can be established:

[0067] d = Gm

[0068] Where d = [s1, s2, ..., s N ] represents the observed seismic data composed of multiple seismic traces connected end to end, d i Represents the observation data of the i-th seismic trace; G is a block diagonal matrix, and the elements on its diagonal are the convolution matrices W of the seismic wavelets of multiple seismic traces; m = [r1, r2, ..., r N ] represents the multi-trace model parameters formed by connecting the first and last parameters of the multi-trace model sequentially, r i Let represent the reflection coefficient sequence of the i-th seismic trace. N represents the total number of seismic traces, which is 350. Based on the above formula, the following multi-trace deconvolution objective function can be established:

[0069]

[0070] Step 3, Calculate the prior probability distribution function of the reflection coefficient:

[0071] A high-quality well is selected as a reference well. The logging data of the reference well can reflect the distribution characteristics of the reflection coefficient in the target area. A probability distribution histogram of the reflection coefficient is extracted based on the reliable well data. Figure 3 This histogram describes the distribution characteristics of the reflectance coefficient in this region, and the reflectance coefficient r can be obtained. i The probability distribution p at time w (r i ).

[0072] Obtain the prior probability distribution function P of the reflection coefficient w (m), applying a probability distribution constraint to the reflection coefficient in the longitudinal direction, such that the parameter distribution function P est (m) and the probability distribution function P of the reflectance coefficient in this region w (m) consistent;

[0073] The probability distribution constraint is as follows: P est (m) represents the probability distribution of the multichannel model parameter m; it is obtained by performing probability statistics on m.

[0074] Step 4, obtain the tilt angle information:

[0075] Dip scanning of the seismic profile yields the dip distribution characteristics θ(x,t) of the seismic data. Simultaneously, to assess the accuracy of the dip scanning, the cross-correlation coefficients from the dip scans are recorded as the confidence level c(x,t). A threshold of c0 = 0.6 is set. When the confidence level c(x,t) < c0, the dip estimation accuracy at that point is considered low, and the value should be fitted from surrounding dip values.

[0076]

[0077] Where w is the weighting coefficient matrix, the size of which is related to the distance between the sampling point of the tilt angle to be determined and the surrounding sampling points, and w(0,0)=0; the final tilt field is as follows Figure 4 As shown.

[0078] Step 5: Construct a spatial constraint term H using dip angle information. This constraint term contains dip angle information of the in-phase axes in the entire seismic trace.

[0079] Step 6: Based on the above steps, construct a multi-channel deconvolution objective functional with tilt angle constraints:

[0080]

[0081] μ z μ represents the weighting factor controlling the time direction constraint term within the seismic trace. x Adjust the weight of the tilt angle constraint term in the objective function; in this example, μ z =0.3, μ x =0.2;

[0082] Step 7: Solve the objective function of step 6 and output the seismic record with improved resolution, as shown in Figure 5(b); the traditional single-channel deconvolution result is shown in Figure 5(a). By comparison, it can be seen that the seismic profile processed by the method of this invention recovers more weak reflections and the phase axis is more continuous.

[0083] Example 2

[0084] This embodiment uses a work area in an eastern oilfield as an example to test the feasibility and effectiveness of the present invention. The original post-stack seismic profile is shown in Figure 6(a), which contains 220 seismic traces, each with 402 sampling points and a sampling interval of 2ms. The processing effects of traditional single-trace sparse deconvolution and the present invention are shown in Figures 6(b) and 6(c), respectively. It can be seen that single-trace sparse deconvolution suppresses weak reflections in the original seismic record, and due to noise, the lateral continuity of the inverted phase axis is poor, making it difficult to further process and interpret. The method of the present invention recovers more weak reflection information and maintains the spatial structure relationship of the phase axis in the original data, more effectively reflecting the thin-layer structure.

[0085] Example 3

[0086] A device for classifying weathering crust structure using well logging curves and petrological characteristics includes the following modules:

[0087] The module for establishing the multichannel deconvolution objective function is used to acquire raw seismic data; a multichannel deconvolution model is then established based on the raw seismic data.

[0088] d = Gm

[0089] Where d = [s1, s2, ..., s N ] represents the observed seismic data composed of multiple seismic traces connected end to end, d i This represents the observation data of the i-th seismic trace; G is a block diagonal matrix, where the elements on its diagonal are the convolution matrices of the seismic wavelets of multiple seismic traces; m = [r1, r2, ..., r N ] represents the multi-trace model parameters formed by connecting the first and last parameters of the multi-trace model sequentially, r i Let N represent the reflection coefficient sequence of the i-th seismic trace; N is the total number of seismic traces.

[0090] Establish the objective function for multi-channel deconvolution:

[0091]

[0092] Obtain the prior probability distribution function P of the reflection coefficient w (m), applying a probability distribution constraint to the reflection coefficient in the longitudinal direction, such that the parameter distribution function P est (m) and the probability distribution function P of the reflectance coefficient in this region w (m) consistent;

[0093] The probability distribution constraint is as follows: P est (m) represents the probability distribution of the multichannel model parameter m; it is obtained by performing probability statistics on m.

[0094] The spatial constraint term establishment module is used to obtain dip angle information and construct spatial constraint term H using dip angle information. This constraint term contains dip angle information of the in-phase axes in the entire seismic trace.

[0095] A module for constructing multichannel deconvolution objective functionals is used to build tilt-angle-based multichannel deconvolution objective functionals.

[0096]

[0097] Where, μ z μ represents the weighting factor controlling the time direction constraint term within the seismic trace. x Adjust the proportion of the tilt angle constraint term in the objective function;

[0098] The multichannel deconvolution objective functional solver module is used to solve the multichannel deconvolution objective functional to obtain seismic record data with improved resolution.

[0099] A computing device, comprising:

[0100] One or more processing units;

[0101] A storage unit is used to store one or more programs.

[0102] When the one or more programs are executed by the one or more processing units, the one or more processing units perform the above-described method of classifying weathering crust structures using well logging curves and petrological characteristics. It should be noted that the computing device may include, but is not limited to, processing units and storage units. Those skilled in the art will understand that the inclusion of processing units and storage units in the computing device does not constitute a limitation on the computing device. It may include more components, or combine certain components, or different components. For example, the computing device may also include input / output devices, network access devices, buses, etc.

[0103] A computer-readable storage medium having processor-executable non-volatile program code, wherein the computer program, when executed by a processor, implements the steps of the method described above for classifying weathering crust structures using well logging curves and petrological characteristics; it should be noted that the readable storage medium may be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof; the program contained on the readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. For example, the program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on a user's computing device, partially on a user's device, as a standalone software package, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to the user's computing device via any type of network—including a local area network (LAN) or a wide area network (WAN), or they can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0104] The above embodiments have provided a detailed description of the present invention, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent variations and improvements made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. Method for improving the accuracy of seismic characterization of thin layer structures, characterized in that: Includes the following steps: Acquire raw seismic data; establish a multi-channel deconvolution model based on the raw seismic data: ； in, This represents observational seismic data composed of multiple seismic traces joined end-to-end. Indicates the first Observational data of seismic traces; It is a block diagonal matrix, and the elements on its diagonal are the convolution matrices of the seismic wavelets of multiple seismic traces. This represents the multi-trace model parameters formed by connecting the parameters of multiple seismic traces sequentially. Indicates the first The reflection coefficient sequence of the seismic trace; This represents the total number of seismic traces. Establish the objective function for multi-channel deconvolution: ； Obtain the prior probability distribution function of the reflection coefficient Applying a probability distribution constraint to the reflection coefficient in the longitudinal direction makes the parameter distribution function The probability distribution function of the reflectance coefficient in this region Consistent; The probability distribution constraint is as follows: ; Let m be the probability distribution of the multichannel model parameter m; Obtain tilt angle information and use it to construct spatial constraint terms. This constraint includes dip information of the in-phase axes throughout the entire seismic trace; Construct a multi-channel deconvolution objective functional based on tilt angle: ； in, This represents the weighting factor controlling the time direction constraint term within the seismic trace. Adjust the proportion of the tilt angle constraint term in the objective function; The multichannel deconvolution objective functional is solved to obtain seismic record data with improved resolution.

2. The method for improving the seismic characterization accuracy of thin-layer structures according to claim 1, characterized in that: The method for obtaining the prior probability distribution function of the reflection coefficient is as follows: A reference well is selected, and the probability distribution histogram of the reflection coefficient is extracted based on the well logging data of the reference well to obtain the prior probability distribution function. This function describes the distribution characteristics of the reflectance coefficient of the target area.

3. The method for improving the seismic characterization accuracy of thin-layer structures according to claim 2, characterized in that: The logging data from the reference well can reflect the distribution characteristics of the reflection coefficient in the target area.

4. The method for improving the seismic characterization accuracy of thin-layer structures according to claim 3, characterized in that: The method for obtaining tilt angle information is as follows: Dip scanning of seismic profiles yields dip information from the seismic data. ; The cross-correlation coefficients in the tilt scans are recorded as the confidence level of the tilt angle. Set threshold When confidence level When this happens, the dip angle information is obtained by fitting the dip angles of the surrounding area; in, ; It is a weighting coefficient matrix, the size of which is related to the distance between the sampling point of the tilt angle to be determined and the surrounding sampling points, and ; t is the time series, it is the index number of the time series, x is the seismic trace sequence, ix is ​​the seismic trace index number, and a and b are index variables.

5. A device for classifying weathering crust structures using well logging curves and petrological characteristics, characterized in that: Includes the following modules: The module for establishing the multichannel deconvolution objective function is used to acquire raw seismic data; a multichannel deconvolution model is then established based on the raw seismic data. ； in, This represents observational seismic data composed of multiple seismic traces joined end-to-end. Indicates the first Observational data of seismic traces; It is a block diagonal matrix, and the elements on its diagonal are the convolution matrices of the seismic wavelets of multiple seismic traces. This represents the multi-trace model parameters formed by connecting the parameters of multiple seismic traces sequentially. Indicates the first The reflection coefficient sequence of the seismic trace; This represents the total number of seismic traces. Establish the objective function for multi-channel deconvolution: ； Obtain the prior probability distribution function of the reflection coefficient Applying a probability distribution constraint to the reflection coefficient in the longitudinal direction makes the parameter distribution function The probability distribution function of the reflectance coefficient in this region Consistent; The probability distribution constraint is as follows: ; Let m be the probability distribution of the multichannel model parameter m; The spatial constraint term creation module is used to obtain tilt angle information and construct spatial constraint terms using this information. This constraint includes dip information of the in-phase axes throughout the entire seismic trace; A module for constructing multichannel deconvolution objective functionals is used to build tilt-angle-based multichannel deconvolution objective functionals. ； in, This represents the weighting factor controlling the time direction constraint term within the seismic trace. Adjust the proportion of the tilt angle constraint term in the objective function; The multichannel deconvolution objective functional solver module is used to solve the multichannel deconvolution objective functional to obtain seismic record data with improved resolution.

6. A computing device, characterized in that: include: One or more processing units; A storage unit for storing one or more programs, wherein when the one or more programs are executed by the one or more processing units, the one or more processing units perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having processor-executable non-volatile program code, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

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