Porosity geophysical prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a porosity geophysical prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: fitting a relational expression between porosity and elastic parameters according to logging data fractal facies; performing pre-stack inversion under different lithofacies hypotheses to obtain elastic parameter inversion results under different lithofacies hypotheses; calculating porosity prediction results under different lithofacies according to the fitting relation on the basis of the elastic parameter inversion results under different lithofacies assumptions; calculating the probability of each lithofacies based on the inversion result of the fractal lithofacies elastic parameters; and calculating a final porosity prediction result by using the porosity prediction results under different lithofacies and the probability of each lithofacies. By using the method of the invention, a high-precision porosity prediction result can be obtained according to the pre-stack seismic data and the logging data, a high-quality reservoir can be depicted more finely, and support is provided for oil-gas exploration and development.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas exploration and development, specifically in the direction of seismic interpretation and reservoir prediction. The main content is to provide a new geophysical prediction method for porosity, a prediction device, an electronic device, and a storage medium. Background Art

[0002] Rock porosity is one of the important parameters characterizing a reservoir. High-precision prediction of porosity is beneficial for more precisely depicting the reservoir and providing support for oil and gas exploration and development. Rock porosity refers to the ratio of the sum of the volumes of all pore spaces in a rock sample to the volume of the rock sample, expressed as a percentage. Among them, only interconnected pores are of practical significance because they can not only store oil and gas but also allow oil and gas to percolate through them. In the field of oil and gas field development and production, rock porosity is an important index for evaluating reservoir characteristics, which determines whether oil and gas can be stored and transported. Larger porosity is beneficial for the storage and transport of oil and gas, while smaller porosity will limit the storage and transport of oil and gas.

[0003] Conventional porosity prediction methods are mainly divided into two categories. One category uses machine learning or deep learning algorithms to establish the mapping relationship between porosity and elastic parameters or well-side seismic traces, and then porosity is predicted from seismic inversion results or seismic data. This type of method requires sufficient well data as labels and is more suitable for cases with a large amount of well data. The other category is to perform elastic parameter inversion and then predict porosity using the fitting relationship or rock physics relationship between elastic parameters and porosity. However, the inversion methods widely used in the industry currently usually do not consider the influence of lithofacies, resulting in limited inversion accuracy.

[0004] In view of the above problems, the present invention proposes a new porosity prediction method. The present invention considers the influence of lithofacies, fits the relationship between porosity and elastic parameters by lithofacies, performs pre-stack inversion by lithofacies, and the final porosity prediction result can be obtained using lithofacies probability. Due to considering the influence of lithofacies, the prediction result has higher accuracy. Summary of the Invention

[0005] Provide a new geophysical prediction method for porosity to be used in seismic interpretation links such as high-quality reservoir prediction and description, and provide support for the optimization of favorable oil and gas zones and well location deployment.

[0006] To achieve the above object, the present invention provides a geophysical prediction method for porosity, including:

[0007] Fitting the relationship between porosity and elastic parameters by lithofacies according to well logging data;

[0008] Performing pre-stack inversion under different lithofacies assumptions to obtain the elastic parameter inversion results under different lithofacies assumptions;

[0009] Based on the elastic parameter inversion results under different lithofacies assumptions, calculate the porosity prediction results for different lithofacies according to the fitting relationship formula.

[0010] Calculate the probabilities of each lithofacies based on the elastic parameter inversion results of different lithofacies.

[0011] Using the porosity prediction results under different lithofacies and the probabilities of each lithofacies, calculate the final porosity prediction result.

[0012] Furthermore, the relationship formula for fitting porosity and elastic parameters by lithofacies based on well logging data includes respectively fitting the multiple linear or non - linear relationship formulas between the porosity and elastic parameters of different lithofacies according to the statistical well logging data.

[0013] Furthermore, the multiple linear relationship formula between the porosity and elastic parameters of different lithofacies is:

[0014]

[0015] Where i represents different lithofacies types, is the porosity of lithofacies i, E1 i , E2 i , E3 i are the 3 elastic parameters that are most sensitive to porosity among the elastic parameters of lithofacies i, a i , b i , c i , d i are the fitting coefficients of lithofacies i.

[0016] The above - mentioned 3 elastic parameters that are most sensitive to porosity refer to calculating the correlation coefficients between each elastic parameter and porosity, and the 3 elastic parameters with the largest correlation coefficients.

[0017] The relationship between the porosity and elastic parameters of different lithofacies can also be a non - linear relationship, such as an exponential relationship, a polynomial relationship, etc. For example, it is possible that there is a linear relationship between lithofacies 1 and elastic parameters 1 and 2, and an exponential relationship between lithofacies 2 and elastic parameters 1, 3, and 4, etc.

[0018] Furthermore, performing pre - stack inversion under different lithofacies assumptions includes:

[0019] For different lithofacies, perform Gaussian probability distribution fitting on the P - wave velocity, S - wave velocity, and density to obtain the mean and variance of the P - wave velocity, S - wave velocity, and density.

[0020] For each lithofacies, respectively use the mean and variance of the P - wave velocity, S - wave velocity, and density fitted under this lithofacies to perform pre - stack inversion to obtain the P - wave velocity, S - wave velocity, and density inversion results under each lithofacies assumption.

[0021] Further, using the longitudinal wave velocity, transverse wave velocity, and density inversion results under each lithofacies assumption, the corresponding elastic parameters are calculated, and the porosity prediction values under each lithofacies assumption are calculated according to formula (1) or the non-linear relationship

[0022] Further, calculating the probabilities of each lithofacies based on the inversion results of elastic parameters of different lithofacies includes:

[0023] Using well logging data for lithofacies interpretation, and statistically analyzing porosity, longitudinal wave velocity, transverse wave velocity, and density data for different lithofacies;

[0024] Using well logging data and lithofacies interpretation results to train a Bayesian classifier for longitudinal wave velocity, transverse wave velocity, density, and lithofacies type;

[0025] Using the Bayesian discriminator to respectively classify and determine the inversion results of longitudinal wave velocity, transverse wave velocity, and density under each lithofacies assumption, and obtain all lithofacies probabilities.

[0026] Further, normalizing the lithofacies probabilities so that the sum of the lithofacies probabilities is 1, and obtaining the final lithofacies probability p of each lithofacies i ;

[0027] Based on the lithofacies probability p i and the porosity prediction values under each lithofacies assumption Calculate the final prediction result of porosity according to the following formula:

[0028]

[0029] where n represents the number of lithofacies types.

[0030] According to another aspect of the present invention, there is provided a porosity geophysical prediction device, including:

[0031] A fitting module that fits the relationship between porosity and elastic parameters by lithofacies according to well logging data;

[0032] An inversion module that performs pre-stack inversion under different lithofacies assumptions to obtain the inversion results of elastic parameters under different lithofacies assumptions;

[0033] A prediction module that calculates the porosity prediction results under different lithofacies based on the inversion results of elastic parameters under different lithofacies assumptions according to the fitting relationship;

[0034] A calculation module that calculates the probabilities of each lithofacies based on the inversion results of elastic parameters of different lithofacies;

[0035] A result module that calculates the final prediction result of porosity using the porosity prediction results under different lithofacies and the probabilities of each lithofacies.

[0036] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0037] A memory storing executable instructions;

[0038] A processor that runs the executable instructions in the memory to implement the porosity geophysical prediction method described above.

[0039] According to another aspect of the present invention, there is provided a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the porosity geophysical prediction method described above is implemented.

[0040] Using the method of the present invention, high-precision porosity prediction results can be obtained based on pre-stack seismic data and logging data, and high-quality reservoirs can be characterized more precisely, providing support for oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] By describing the exemplary embodiments of the present invention in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present invention will become more apparent. Among them, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0042] Figure 1 It is a flowchart of the porosity geophysical prediction method according to the present invention.

[0043] Figure 2 It is seismic data at a certain incident angle according to an embodiment of the present invention.

[0044] Figure 3 It is the logging lithofacies interpretation result according to an embodiment of the present invention.

[0045] Figure 4 It is the porosity prediction result according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0047] The present invention proposes a new seismic prediction method for porosity. Fit the linear or non-linear relationship between porosity and elastic parameters (longitudinal wave velocity, shear wave velocity, density, wave impedance, elastic impedance, Poisson's ratio, etc.) according to logging data for different lithofacies; perform prestack inversion under different lithofacies assumptions to obtain the inversion results of elastic parameters under different lithofacies assumptions; based on the inversion results of elastic parameters under different lithofacies assumptions, calculate the porosity prediction results for different lithofacies according to the fitting relationship; use logging data to train a Bayesian classifier for elastic parameters and lithofacies types, and calculate the probabilities of each lithofacies based on the inversion results of elastic parameters for different lithofacies; use the porosity prediction results for different lithofacies and the probabilities of each lithofacies to calculate the final porosity prediction result, realizing high-precision porosity prediction.

[0048] Example 1

[0049] As Figure 1 shown, this embodiment provides a geophysical prediction method for porosity, including:

[0050] Fit the relationship between porosity and elastic parameters according to logging data for different lithofacies;

[0051] Perform prestack inversion under different lithofacies assumptions to obtain the inversion results of elastic parameters under different lithofacies assumptions;

[0052] Based on the inversion results of elastic parameters under different lithofacies assumptions, calculate the porosity prediction results for different lithofacies according to the fitting relationship;

[0053] Calculate the probabilities of each lithofacies based on the inversion results of elastic parameters for different lithofacies;

[0054] Use the porosity prediction results for different lithofacies and the probabilities of each lithofacies to calculate the final porosity prediction result.

[0055] Furthermore, the step of fitting the relationship between porosity and elastic parameters according to logging data for different lithofacies includes respectively fitting the multiple linear or non-linear relationships between porosity and elastic parameters for different lithofacies according to the statistical logging data.

[0056] Furthermore, the multiple linear relationship between porosity and elastic parameters for different lithofacies is:

[0057]

[0058] where i represents different lithofacies types, is the porosity of lithofacies i, and E1 i , E2 i , E3 i are the 3 elastic parameters that are most sensitive to porosity among the elastic parameters of lithofacies i, and a i , b i , c i , di is the fitting coefficient for lithofacies i.

[0059] The three elastic parameters most sensitive to porosity mentioned above refer to calculating the correlation coefficients between each elastic parameter and porosity, and the three elastic parameters with the largest correlation coefficients.

[0060] The relationship between porosity and elastic parameters of different lithofacies can also be a non-linear relationship, such as an exponential relationship, a polynomial relationship, etc. For example, the relationship between lithofacies 1 and elastic parameters 1 and 2 may be a linear relationship, and the relationship between lithofacies 2 and elastic parameters 1, 3, and 4 may be an exponential relationship, etc.

[0061] Furthermore, prestack inversion under different lithofacies assumptions includes:

[0062] For different lithofacies, perform Gaussian probability distribution fitting on the P-wave velocity, S-wave velocity, and density to obtain the mean and variance of the P-wave velocity, S-wave velocity, and density;

[0063] For each lithofacies, respectively use the mean and variance of the P-wave velocity, S-wave velocity, and density fitted under this lithofacies to perform prestack inversion to obtain the inversion results of the P-wave velocity, S-wave velocity, and density under each lithofacies assumption.

[0064] Furthermore, using the inversion results of the P-wave velocity, S-wave velocity, and density under each lithofacies assumption, calculate the corresponding elastic parameters, and calculate the porosity prediction values under each lithofacies assumption according to formula (1) or the non-linear relationship formula

[0065] Furthermore, calculating the probabilities of each lithofacies based on the inversion results of elastic parameters of different lithofacies includes:

[0066] Use well logging data for lithofacies interpretation, and statistically analyze porosity, P-wave velocity, S-wave velocity, and density data for different lithofacies;

[0067] Use well logging data and lithofacies interpretation results to train a Bayesian classifier for P-wave velocity, S-wave velocity, density, and lithofacies type;

[0068] Use the Bayesian discriminator to respectively perform classification and determination on the inversion results of the P-wave velocity, S-wave velocity, and density under each lithofacies assumption to obtain all lithofacies probabilities.

[0069] Furthermore, normalize the lithofacies probabilities so that the sum of the lithofacies probabilities is 1 to obtain the final lithofacies probability p of each lithofacies i ;

[0070] Based on the lithofacies probability p i and the porosity prediction values under each lithofacies assumption Calculate the final prediction result of porosity according to the following formula:

[0071]

[0072] Among them, n represents the number of lithofacies types.

[0073] Example Two

[0074] This example provides a geophysical prediction method for porosity, which specifically includes the following steps:

[0075] Step 1: Collect well logging data in the work area, use the well logging data for lithofacies interpretation, and statistically analyze the porosity and elastic parameter data such as longitudinal wave velocity, transverse wave velocity, density, wave impedance, elastic impedance, Poisson's ratio, and Young's modulus for different lithofacies; calculate the correlation coefficients between each elastic parameter and porosity for each lithofacies. Assume that for lithofacies i (i≠2), the three elastic parameters with the largest correlation coefficients in descending order are wave impedance, longitudinal wave velocity, and Poisson's ratio, and the porosity has a polynomial relationship with wave impedance, longitudinal wave velocity, and Poisson's ratio; for lithofacies 2, the two elastic parameters with the largest correlation coefficients in descending order are wave impedance and transverse wave velocity, and the porosity has a linear relationship with wave impedance and transverse wave velocity.

[0076] Step 2: According to the statistically analyzed well logging data, respectively fit the polynomial relationships between the porosity and wave impedance, longitudinal wave velocity, and Poisson's ratio for different lithofacies:

[0077]

[0078] Among them, i represents different lithofacies types (i≠2), is the porosity of different lithofacies, Ip i , Vp i , σ i are the wave impedance, longitudinal wave velocity, and Poisson's ratio of different lithofacies, a i , b i , c i , d i are the fitting coefficients of different lithofacies.

[0079] When i = 2, respectively fit the linear relationships between the porosity and wave impedance, transverse wave velocity:

[0080]

[0081] Among them is the porosity of lithofacies 2, Ip 2 , Vs 2 are the wave impedance and transverse wave velocity of lithofacies 2, a 2 , b 2 , c 2 are the fitting coefficients of lithofacies 2.

[0082] Step 3: Using the logging data and the results of lithofacies interpretation, train a Bayesian classifier for P-wave velocity, S-wave velocity, density, and lithofacies type.

[0083] Step 4: For different lithofacies, perform Gaussian probability distribution fitting on P-wave velocity, S-wave velocity, and density to obtain the mean and variance of P-wave velocity, S-wave velocity, and density.

[0084] Step 5: For each lithofacies, respectively use the mean and variance of the fitted P-wave velocity, S-wave velocity, and density under this lithofacies to perform prestack inversion to obtain the inversion results of P-wave velocity, S-wave velocity, and density under each lithofacies hypothesis.

[0085] Step 6: Using the inversion results of P-wave velocity, S-wave velocity, and density under each lithofacies hypothesis in Step 5, calculate the inversion results of wave impedance and Poisson's ratio under each lithofacies hypothesis, and according to Step 2, calculate the predicted porosity values under each lithofacies hypothesis

[0086] Step 7: Using the Bayesian discriminator trained in Step 3, classify and determine the inversion results of P-wave velocity, S-wave velocity, and density under a certain lithofacies hypothesis in Step 5 to obtain the probability of this lithofacies; repeat the above steps to classify and determine the inversion results under other lithofacies hypotheses, and finally obtain the probabilities of all lithofacies.

[0087] Step 8: Normalize the lithofacies probabilities calculated in Step 7 so that the sum of the probabilities of each lithofacies is 1 to obtain the final probability p of each lithofacies i 。

[0088] Step 9: According to the lithofacies probability p in Step 8 i and the predicted porosity values under each lithofacies hypothesis in Step 6 Calculate the final predicted result of porosity according to the following formula.

[0089]

[0090] Where n represents the number of lithofacies types.

[0091] Example 3

[0092] Refer to Figures 2 - 4 As shown, this example uses a calculation example to illustrate the implementation process of the present invention.

[0093] Appendix Figure 2 is the seismic data of a certain incident angle in a certain actual work area. According to the prediction method of the present invention, first perform logging lithofacies interpretation, as Figure 3The above is interpreted as two lithofacies, namely sandstone and mudstone. Then, the logging data are statistically analyzed for the two lithofacies, and it is found that there is a good fitting relationship between porosity and P-wave velocity, S-wave velocity, and density. Then, prestack inversion is carried out for each lithofacies to obtain the inversion results of P-wave velocity, S-wave velocity, and density under different lithofacies assumptions, and the lithofacies probability is calculated. Finally, the porosity is calculated according to the lithofacies probability and the fitting relationship. The final predicted porosity results are as Figure 4 shown.

[0094] The prediction results are in good agreement with the logging interpretation results, indicating that this method has high accuracy and verifies the effectiveness of the method.

[0095] Embodiment 4

[0096] This embodiment provides a geophysical prediction device for porosity, including:

[0097] A fitting module that fits the relationship between porosity and elastic parameters for each lithofacies according to logging data;

[0098] An inversion module that performs prestack inversion under different lithofacies assumptions to obtain the inversion results of elastic parameters under different lithofacies assumptions;

[0099] A prediction module that calculates the porosity prediction results for different lithofacies based on the inversion results of elastic parameters under different lithofacies assumptions according to the fitting relationship;

[0100] A calculation module that calculates the probability of each lithofacies based on the inversion results of elastic parameters for different lithofacies;

[0101] A result module that calculates the final predicted porosity result using the porosity prediction results for different lithofacies and the probability of each lithofacies.

[0102] Embodiment 5

[0103] This embodiment provides another aspect of the present invention, which provides an electronic device, and the electronic device includes:

[0104] A memory that stores executable instructions;

[0105] A processor that runs the executable instructions in the memory to implement the geophysical prediction method for porosity, and the method includes:

[0106] Fitting the relationship between porosity and elastic parameters for each lithofacies according to logging data;

[0107] Performing prestack inversion under different lithofacies assumptions to obtain the inversion results of elastic parameters under different lithofacies assumptions;

[0108] Based on the inversion results of elastic parameters under different lithofacies assumptions, calculating the porosity prediction results for different lithofacies according to the fitting relationship;

[0109] Calculate the probabilities of each lithofacies based on the inversion results of elastic parameters for different lithofacies;

[0110] Use the porosity prediction results under different lithofacies and the probabilities of each lithofacies to calculate the final porosity prediction result.

[0111] Example Six

[0112] This embodiment provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned porosity geophysical prediction method, and the method includes:

[0113] Fit the relationship between porosity and elastic parameters by lithofacies according to well logging data;

[0114] Perform pre-stack inversion under different lithofacies assumptions to obtain the inversion results of elastic parameters under different lithofacies assumptions;

[0115] Based on the inversion results of elastic parameters under different lithofacies assumptions, calculate the porosity prediction results under different lithofacies according to the fitting relationship;

[0116] Calculate the probabilities of each lithofacies based on the inversion results of elastic parameters for different lithofacies;

[0117] Use the porosity prediction results under different lithofacies and the probabilities of each lithofacies to calculate the final porosity prediction result.

[0118] The above-mentioned computer-readable storage medium includes but is not limited to: optical storage media (such as CD-ROM and DVD), magneto-optical storage media (such as MO), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROM (such as ROM cartridges).

[0119] In summary, the method of the present invention can obtain high-precision porosity prediction results based on pre-stack seismic data and well logging data, more finely characterize high-quality reservoirs, and provide support for oil and gas exploration and development.

[0120] The above have described the embodiments of the present invention. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A porosity geophysical prediction method, characterized in that, it includes: fitting the relationship between porosity and elastic parameters for each lithofacies according to well logging data; performing prestack inversion under different lithofacies assumptions to obtain the inversion results of elastic parameters under different lithofacies assumptions; based on the inversion results of elastic parameters under different lithofacies assumptions, calculating the porosity prediction results for different lithofacies according to the fitting relationship; calculating the probability of each lithofacies based on the inversion results of elastic parameters of different lithofacies; using the porosity prediction results of different lithofacies and the probability of each lithofacies to calculate the final porosity prediction result.

2. The porosity geophysical prediction method according to claim 1, characterized in that, the step of fitting the relationship between porosity and elastic parameters for each lithofacies according to well logging data includes respectively fitting the multiple linear or non - linear relationship between porosity and elastic parameters of different lithofacies according to the statistical well logging data.

3. The porosity geophysical prediction method according to claim 2, characterized in that, the multiple linear relationship between porosity and elastic parameters of different lithofacies is: where i represents different lithofacies types, is the porosity of lithofacies i, E1 i , E2 i , E3 i are the three elastic parameters among the elastic parameters of lithofacies i that are most sensitive to porosity, a i , b i , c i , d i are the fitting coefficients of lithofacies i.

4. The porosity geophysical prediction method according to claim 3, characterized in that, performing prestack inversion under different lithofacies assumptions includes: for different lithofacies, fitting the Gaussian probability distribution of the longitudinal wave velocity, transverse wave velocity and density to obtain the mean and variance of the longitudinal wave velocity, transverse wave velocity and density; for each lithofacies, respectively using the mean and variance of the longitudinal wave velocity, transverse wave velocity and density fitted under this lithofacies to perform prestack inversion to obtain the inversion results of the longitudinal wave velocity, transverse wave velocity and density under each lithofacies assumption.

5. The porosity geophysical prediction method according to claim 4, characterized in that, Using the inversion results of P-wave velocity, S-wave velocity, and density under each lithofacies assumption, calculate the corresponding elastic parameters, and then calculate the predicted porosity values under each lithofacies assumption according to formula (1) or the nonlinear relationship 6. The porosity geophysical prediction method according to claim 5, characterized in that, calculating the probability of each lithofacies based on the inversion results of elastic parameters of different lithofacies includes: using well logging data for lithofacies interpretation and statistically analyzing porosity, longitudinal wave velocity, transverse wave velocity, and density data for different lithofacies; using well logging data and lithofacies interpretation results to train the Bayesian classifier of longitudinal wave velocity, transverse wave velocity, density and lithofacies type; using the Bayesian discriminator to respectively classify and determine the inversion results of the longitudinal wave velocity, transverse wave velocity and density under each lithofacies assumption to obtain the probabilities of all lithofacies.

7. The porosity geophysical prediction method according to claim 6, characterized in that, Normalize the lithofacies probability so that the sum of the lithofacies probabilities is 1, and obtain the final lithofacies probability p of each lithofacies i ; Based on the lithofacies probability p i and the predicted porosity values under each lithofacies hypothesis Calculate the final predicted result of porosity according to the following formula: where n represents the number of lithofacies types.

8. A porosity geophysical prediction device, characterized in that, it includes: a fitting module, which fits the relationship between porosity and elastic parameters for each lithofacies according to well logging data; an inversion module, which performs prestack inversion under different lithofacies assumptions to obtain the inversion results of elastic parameters under different lithofacies assumptions; a prediction module, which calculates the porosity prediction results for different lithofacies based on the inversion results of elastic parameters under different lithofacies assumptions according to the fitting relationship; a calculation module, which calculates the probability of each lithofacies based on the inversion results of elastic parameters of different lithofacies; a result module, which calculates the final porosity prediction result using the porosity prediction results of different lithofacies and the probability of each lithofacies.

9. An electronic device, characterized in that, the electronic device includes: A memory storing executable instructions; A processor that runs the executable instructions in the memory to implement the porosity geophysical prediction method according to any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the porosity geophysical prediction method according to any one of claims 1-7.

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