Fluid fluidity prediction method and device based on coKriging estimation

Abstract

The invention provides a fluid mobility prediction method and device based on coKriging. The method comprises the following steps: carrying out generalized S transformation on an original time domain seismic signal to obtain seismic frequency data; obtaining logging fluid fluidity data of the reservoir from the logging information; adopting a coKriging algorithm, taking the logging fluid fluidity data as a main variable, taking the seismic frequency data as a secondary variable, constructing a coKriging estimation equation for estimating the fluid fluidity, and determining coefficient items in the coKriging estimation equation; and predicting the three-dimensional fluid fluidity distribution of the research area based on the constructed coKriging estimation equation. According to the technical scheme, on one hand, the logging fluid fluidity and the seismic frequency data can be effectively integrated, on the other hand, calculation can be simplified, the calculation efficiency can be improved, the reservoir fluid fluidity can be directly obtained, and the accuracy and intuition of a prediction result are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration in the petrochemical industry, and more specifically, to a method and device for predicting fluid mobility based on co-kriging estimation. Background Art

[0002] Reservoir fluid mobility has good oil and gas reservoir imaging ability, and this property provides important guidance for the prediction of high-quality reservoirs. Depending on the existing reservoir prediction technologies, currently two methods can be used to obtain reservoir fluid mobility from seismic data. One is to extract this property from seismic low-frequency information using time-frequency analysis methods, and the other is to use inversion algorithms to obtain this property.

[0003] The first approach: Silin et al. (2004, 2006) derived the propagation equation of low-frequency harmonics in the asymptotic mode in a water-saturated elastic porous medium from the basic seepage theory, and obtained an asymptotic representation of the reflection coefficient of a fluid-saturated reservoir that depends on frequency, i.e., the low-frequency reflection coefficient is proportional to the square root of the product of fluid mobility, fluid density, and seismic signal frequency. Korneev et al. (2004) used this reflection coefficient to image the low-frequency components in actual seismic data to predict the oilfield productivity. Goloshubin et al. (2006) formally introduced the concept of low-frequency imaging attributes based on Silin's low-frequency asymptotic analysis theory and Korneev's research work, verified the relationship between the imaging attributes and the actual oil production rate, and applied this attribute to the discrimination of the oil-water interface and the prediction of the oil production rate of the reservoir. According to the low-frequency asymptotic analysis theory, Goloshubin et al. (2008) further derived the low-frequency imaging attributes as seismic analysis attributes related to seismic frequency by applying fluid flow performance and scattering mechanisms, and used this attribute to predict the fluid flow capacity and permeability in the reservoir. Dai Shuanghe et al. (2010) extracted the reservoir fluid mobility attribute based on actual seismic data and used this attribute to predict the oil and gas content of the reservoir, achieving good results. Cai Hanpeng (2012) systematically discussed the relationships among imaging attributes, reservoir fluid mobility attributes, permeability, and reservoir oil production rate, and verified the direct use of low-frequency information to predict reservoir productivity by combining well logging data and production test data. Chen Xuehua et al. (2012) derived a calculation method for reservoir fluid mobility attributes using the generalized S transform and provided a theoretical method for determining the dominant frequency using the low-frequency asymptotic analysis theory. Chen Xuehua et al. (2013) combined the "low-frequency shadow technique" with reservoir fluid mobility to identify reservoir fluids and reduced its non-uniqueness and uncertainty. Ren et al. (2013) estimated reservoir fluid mobility based on frequency-dependent azimuthal AVO and explained the important influence of frequency on fluid mobility. Zhang Shengqiang et al. (2015) derived a calculation method for reservoir fluid mobility based on high-resolution sparse inversion spectral decomposition, improving the resolution of reservoir fluid mobility imaging. Rusakov et al. (2016) applied reservoir fluid mobility attributes to estimate formation permeability from 3D seismic data.

[0004] The second approach is that the seismic reflection coefficient is the key to inverting reservoir fluid information and reservoir fluid mobility. Since Biot's theory (Biot, 1941, 1956, 1957, and 1962) describing the propagation of elastic waves in fluid-saturated porous media was established, more and more research has been conducted on the derivation of the interface reflection coefficient. For example, Korneev et al. (2004) explained the frequency dependence of reflection in a fluid-saturated porous layer. They observed that in gas- and fluid-saturated porous reservoirs, seismic reflections vary greatly and show a decreasing trend with increasing frequency at low frequencies (15 - 50 Hz). Silin et al. (2006) derived the reflection of seismic waves from a planar interface between two elastic media and found that if one of the media is poroelastic and fluid-saturated, the reflection becomes frequency-dependent. Zhao et al. (2014) studied the reflectivity in a diffusive viscous medium and demonstrated that the magnitude of the reflection coefficient in such a medium is not only related to the incident angle and the parameters of the media (such as velocity, density, diffusion, and viscous attenuation) but also closely related to frequency. Qin et al. (2018) studied the frequency dependence of the reflection coefficient of saturated gas-bearing P-waves. They found that the dispersion of longitudinal wave reflection increases with increasing gas saturation, and the absolute value of the reflection coefficient decreases with increasing frequency. Yin Xingyao et al. and Zhou Dongyong et al. (2019) derived the equations for the reflection coefficient and transmission coefficient at the interface between a porous medium and a viscous fluid and proposed that the magnitudes of the reflection coefficient and transmission coefficient are affected by frequency, fluid viscosity, and different pore fluid types.

[0005] Regarding the first approach, using time-frequency analysis methods to extract from the low-frequency components of seismic information is also the main technical means to obtain reservoir fluid mobility at present. However, the existing time-frequency analysis methods have limited time-frequency resolution and it is difficult to accurately extract the fluid mobility of thin reservoirs. Regarding the second approach, since the fluid mobility in the formation has received increasing attention in recent years, seismic inversion is an effective way to convert the interface information of seismic data into formation information. By combining the results of formation seismic inversion and fluid mobility attributes, the distribution and thickness of gas reservoirs can be comprehensively predicted. However, there is currently a lack of a method for directly inverting reservoir fluid mobility to improve the intuitiveness of the prediction results, resulting in low accuracy and lack of intuitiveness of the obtained reservoir fluid mobility results. Summary of the Invention

[0006] In view of this, the present invention proposes a technical solution that can accurately and intuitively predict reservoir fluid mobility.

[0007] According to one aspect of the present invention, a method for predicting fluid mobility based on co-kriging estimation is proposed. The method includes:

[0008] Step 1, perform a generalized S-transform on the original time-domain seismic signal to obtain seismic frequency data;

[0009] Step 2: Obtain the logging fluid mobility data of the reservoir from logging data;

[0010] Step 3: Use the co-Kriging algorithm, take the logging fluid mobility data as the primary variable and the seismic frequency data as the secondary variable, construct a co-Kriging estimation equation for estimating fluid mobility, and determine the coefficient terms in the co-Kriging estimation equation;

[0011] Step 4: Predict the three-dimensional fluid mobility distribution of the study area based on the constructed co-Kriging estimation equation.

[0012] In some embodiments, in the said Step 1, perform a generalized transform on the original time-domain seismic signal x(t) based on the following formula to obtain the seismic frequency data GST(τ, f):

[0013]

[0014] where λ > 0, p > 0, and λ and p are the parameter factors of the generalized S transform.

[0015] In some embodiments, in the said Step 2, calculate the logging fluid mobility data F of the reservoir based on the following formula:

[0016]

[0017] where K is the permeability and η is the viscosity coefficient.

[0018] In some embodiments, in the said Step 3, construct a co-Kriging estimation equation for evaluating fluid mobility based on the following formula:

[0019]

[0020] where F * (u 0 ) is the fluid mobility to be estimated at the position of u 0 , is the sampling value of the primary variable at the position of u Fi , that is, the logging fluid mobility data, is the weighting coefficient assigned to this sampling point, is the sampling value of the secondary variable at the position of , that is, the seismic frequency data, 0 is the constant coefficient of the co-Kriging estimation equation.

[0021] According to another aspect of the present invention, a fluid mobility prediction device based on co-Kriging estimation is also proposed. The device includes:

[0022] An earthquake frequency data acquisition unit for performing a generalized S - transform on the original time - domain earthquake signal to obtain earthquake frequency data;

[0023] A logging mobility data acquisition unit for obtaining the logging fluid mobility data of the reservoir from logging data;

[0024] A prediction model construction unit for using the co - Kriging algorithm, taking the logging fluid mobility data as the main variable and the earthquake frequency data as the secondary variable, constructing a co - Kriging estimation equation for estimating fluid mobility, and determining the coefficient terms in the co - Kriging estimation equation;

[0025] A mobility distribution prediction unit for predicting the three - dimensional fluid mobility distribution of the research area based on the constructed co - Kriging estimation equation.

[0026] In some embodiments, in the earthquake frequency data acquisition unit, the original time - domain earthquake signal x(t) is specifically subjected to a generalized transform based on the following formula to obtain the earthquake frequency data GST(τ, f):

[0027]

[0028] where λ > 0, p > 0, and λ and p are the parameter factors of the generalized S - transform.

[0029] In some embodiments, the logging mobility data acquisition unit specifically calculates the logging fluid mobility data F of the reservoir based on the following formula:

[0030]

[0031] where K is the permeability and η is the viscosity coefficient.

[0032] In some embodiments, the prediction model construction unit specifically constructs a co - Kriging estimation equation for evaluating fluid mobility based on the following formula:

[0033]

[0034] where F * (u 0 ) is the fluid mobility to be estimated at the position of u 0 , is the main variable sampling value at the position of u Fi , that is, the logging fluid mobility data, is the weighting coefficient assigned to this sampling point, is the secondary variable sampling value at the position of , that is, the earthquake frequency data, 0is the constant coefficient of the co-Kriging estimation equation.

[0035] According to another aspect of the present invention, an electronic device is also provided, which includes:

[0036] a memory storing executable instructions;

[0037] a processor that runs the executable instructions in the memory to implement the fluid mobility prediction method based on co-Kriging estimation as described above.

[0038] According to another aspect of the present invention, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the fluid mobility prediction method based on co-Kriging estimation as described above.

[0039] The technical solution proposed by the present invention has at least the following beneficial effects:

[0040] 1. Effectively comprehensively utilize two information sources of well logging mobility data and seismic frequency data, significantly improving the accuracy of the prediction results;

[0041] 2. By considering the correlation between data through the co-Kriging algorithm, the prediction deviation can be reduced;

[0042] 3. Compared with directly inverting the fluid mobility, the solution of the present invention has higher calculation efficiency and is more intuitive;

[0043] 4. Avoid the trouble of time-frequency analysis being limited by time-frequency resolution;

[0044] 5. Can directly obtain the three-dimensional fluid mobility distribution, improving the intuitiveness of the results;

[0045] 6. The flow calculation process is simplified, and the calculation efficiency is significantly improved;

[0046] 7. Can be widely applied to the prediction and description of oil and gas reservoirs;

[0047] 8. Helps to improve the efficiency of oil and gas exploration and reduce development risks.

[0048] The method and device of the present invention have other characteristics and advantages, which will be obvious in the accompanying drawings and the subsequent specific embodiments incorporated herein, or will be described in detail in the accompanying drawings and the subsequent specific embodiments incorporated herein. These accompanying drawings and specific embodiments are used together to explain the specific principles of the present invention. Description of the Drawings

[0049] The above and other objects, features, and advantages of the present invention will become more apparent by describing the exemplary embodiments of the present invention in more detail with reference to the accompanying drawings, in which, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0050] Figure 1 The flowchart of a fluid mobility prediction method based on co-Kriging estimation according to an embodiment of the present invention is shown.

[0051] Figure 2 The structural block diagram of a fluid mobility prediction device based on co-Kriging estimation according to an embodiment of the present invention is shown.

[0052] Figure 3 The schematic diagram of the reservoir fluid mobility prediction result according to an exemplary embodiment of the present invention is shown. Detailed implementation manners

[0053] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the drawings, 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.

[0054] Example 1

[0055] Figure 1 The flowchart of a fluid mobility prediction method based on co-Kriging estimation according to an embodiment of the present invention is shown. As shown, the method includes Step 1 to Step 4.

[0056] Step 1, perform a generalized S transform on the original time-domain seismic signal to obtain seismic frequency data.

[0057] In some embodiments, the original time-domain seismic signal x(t) is subjected to a generalized transform based on the following formula to obtain the seismic frequency data GST(τ, f):

[0058]

[0059] where λ > 0, p > 0, and λ and p are the parameter factors of the generalized S transform.

[0060] Let the short-time Fourier transform of the time-domain seismic signal x(t) be:

[0061]

[0062] It can be seen from the experiments that the window function used directly determines the time-frequency resolution of the STFT. According to the characteristics of the time-frequency uncertainty principle, the time-frequency area of the Gaussian-like window function is the smallest. Then, the window function in the above formula is defined as follows:

[0063]

[0064] where δ is the scale factor that determines the time width of the window function. Let

[0065]

[0066] where λ > 0, p > 0 are parameter factors.

[0067] Then, the scale factor δ is a function of the frequency f. It not only has the ability to adaptively adjust the time window width with the frequency in the time-frequency plane but also has the characteristic of multi-resolution. Combining the above derivations, the expression of the generalized S transform is obtained as:

[0068]

[0069] where λ > 0, p > 0, and λ is also called the adjustment factor.

[0070] The generalized S transform discusses a new generalized S transform by introducing two parameter factors λ and p, and modifies the window function of the standard S transform. It can flexibly adjust the window function according to the frequency distribution characteristics of non-stationary signals and can meet different time-frequency analysis purposes. It can not only flexibly change the width of the time window according to the characteristics of the actual seismic signal but also make the connection line of the peak points of the window function show diversity by adjusting the parameters.

[0071] When λ = 1, p = 1, the generalized S transform degenerates into the standard S transform:

[0072]

[0073] Step 2: Obtain the logging fluid mobility data of the reservoir from the logging data.

[0074] In some embodiments, the logging fluid mobility data F of the reservoir can be calculated based on the following formula:

[0075]

[0076] where K is the permeability and η is the viscosity coefficient. The permeability K and the viscosity coefficient η can be directly obtained from the logging data.

[0077] Step 3: Use the co-Kriging algorithm, take the logging fluid mobility data as the main variable and the seismic frequency data as the secondary variable, construct a co-Kriging estimation equation for estimating the fluid mobility, and determine the coefficient term in the co-Kriging estimation equation.

[0078] In some embodiments, a co-kriging estimation equation for evaluating fluid mobility is constructed based on the following formula:

[0079]

[0080] where F * (u 0 ) is the fluid mobility to be estimated at the position of u 0 , is the sampling value of the primary variable at the position of , that is, the logging fluid mobility data, is the weighting coefficient assigned to this sampling point, is the sampling value of the secondary variable at the position of , that is, the seismic frequency data, 0 is the constant coefficient of the co-kriging estimation equation.

[0081] Both n and m represent the number of sampling points. If the number of sampling points is too small, the amount of information is insufficient and it is difficult to ensure the accuracy of prediction; if the number of sampling points is too large, the features cannot be highlighted and the differences are smoothed. Therefore, those skilled in the art can select appropriate n and m according to experience and experiments.

[0082] The following introduces the process of solving λ 0 , and .

[0083] To make the solution process more concise, the co-kriging equation can be written in the following form:

[0084]

[0085] where X * (u 0 ) is the estimated value at the position of u 0 ; is the sampling value of the primary variable at the position of , is the value of the weighting coefficient assigned to this sampling point; is the sampling value of the secondary variable at the position of , is the value of the weighting coefficient assigned to this sampling point.

[0086] Applying the unbiased condition:

[0087] E[X(u 0 ) - X * (u 0 )] = 0

[0088] Substituting equation (6) into the above equation, we get:

[0089]

[0090] Using first-order stationarity, we obtain:

[0091]

[0092] where m X and m Y are the means of variables X and Y respectively. Therefore,

[0093]

[0094] For simple co-kriging, the above equation satisfies the unbiased condition. If we do not know the mean, we can make λ 0 equal to 0 using the following equation:

[0095]

[0096] Equation (10) is the assumption condition of traditional ordinary co-kriging.

[0097] Since the sum of the weighting coefficients of variable Y needs to be equal to 0, some of the weighting coefficients will be less than zero, which may lead to a negative estimation result for the point to be estimated, and this may have no physical meaning. To avoid this situation, one solution is to satisfy the following two equations:

[0098]

[0099]

[0100] If equation (10) is substituted into equation (6), we get:

[0101]

[0102] If equations (11) and (12) are substituted into equation (6), we can obtain:

[0103]

[0104] The estimation variance is:

[0105]

[0106] Because:

[0107] Var[X - Y - Z] = Var[X] + Var[Y] + Var[Z] - 2C[X,Y] - 2C[Y,Z] (16)

[0108] Therefore, Equation (15) can be expanded to obtain:

[0109]

[0110] Under the conditions of Equation (10), to minimize the variance of Equation (17), the Lagrangian method can be used to define the function Flag as follows:

[0111]

[0112] Taking the derivatives of λ and μ in the above equation respectively, we can obtain:

[0113]

[0114] In Equation (19), C X represents the covariance of the main variables; C C represents the cross - covariance between two variables; C Y represents the covariance between the secondary variable seismic frequency data. Equation (19) written in matrix form is:

[0115]

[0116] Solving the above equations gives μ X and μ Y The values, substituting into Equation (13) can obtain the estimated value. Substituting Equation (19) into Equation (17) can obtain:

[0117]

[0118] If we choose the assumption conditions of Equation (11) and Equation (12), after similar derivations, the final system of equations, written in matrix form, is as follows:

[0119]

[0120] Solving the above equations gives The value of μ, substituting into Equation (14) can obtain the estimated value. The variance of the corresponding error is:

[0121]

[0122] Through Equation (22) and (23), the λ 0 , and in Equation (6) can be finally obtained, corresponding to the covariance - Kriging estimation equation λ 0, and Thus, all the coefficient terms in the cokriging estimation equation can be determined.

[0123] Step 4: Predict the three-dimensional fluid mobility distribution in the study area based on the constructed cokriging estimation equation.

[0124] According to the fluid mobility prediction method based on cokriging estimation of this embodiment, on the one hand, it can effectively integrate well logging fluid mobility and seismic frequency data, and on the other hand, it can simplify the calculation and improve the calculation efficiency, and can directly obtain the reservoir fluid mobility, significantly improving the accuracy and intuitiveness of the prediction results.

[0125] Example 2

[0126] Figure 2 The structural block diagram of a fluid mobility prediction device based on cokriging estimation according to an embodiment of the present invention is shown. As shown in the figure, the device includes a seismic frequency data acquisition unit 201, a well logging mobility data acquisition unit 202, a prediction model construction unit 203, and a mobility distribution prediction unit 204.

[0127] The seismic frequency data acquisition unit 201 is used to perform a generalized S transform on the original time-domain seismic signal to obtain seismic frequency data.

[0128] The well logging mobility data acquisition unit 202 is used to obtain the well logging fluid mobility data of the reservoir from well logging data.

[0129] The prediction model construction unit 203 is used to adopt the cokriging algorithm, use the well logging fluid mobility data as the main variable, use the seismic frequency data as the secondary variable, construct a cokriging estimation equation for estimating fluid mobility, and determine the coefficient terms in the cokriging estimation equation.

[0130] The mobility distribution prediction unit 204 is used to predict the three-dimensional fluid mobility distribution in the study area based on the constructed cokriging estimation equation.

[0131] For other detailed descriptions of this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0132] In some embodiments, in the seismic frequency data acquisition unit, the original time-domain seismic signal x(t) is specifically subjected to a generalized transform based on the following formula to obtain the seismic frequency data GST(τ, f):

[0133]

[0134] where λ > 0, p > 0, and λ and p are the parameter factors of the generalized S transform.

[0135] Let the short-time Fourier transform of the time-domain seismic signal \(x(t)\) be:

[0136]

[0137] It can be known from experiments that the window function used directly determines the time-frequency resolution of the STFT. According to the characteristics of the time-frequency uncertainty principle, the time-frequency area of the Gaussian-like window function is the smallest. Then, the window function in the above formula is defined as follows:

[0138]

[0139] where \(\delta\) is the scale factor that determines the time width of the window function. Let

[0140]

[0141] where \(\lambda\gt0, p\gt0\) are parameter factors.

[0142] Then, the scale factor \(\delta\) is a function of the frequency \(f\). It not only has the ability to adaptively adjust the time window width with frequency in the time-frequency plane but also has the characteristic of multi-resolution. Combining the above derivations, the expression of the generalized S-transform is obtained as:

[0143]

[0144] where \(\lambda\gt0, p\gt0\), and \(\lambda\) is also called the adjustment factor.

[0145] The generalized S-transform discusses a new generalized S-transform by introducing two parameter factors \(\lambda\) and \(p\), and modifies the window function of the standard S-transform. It can flexibly adjust the window function according to the frequency distribution characteristics of non-stationary signals and can meet different time-frequency analysis purposes. It can not only flexibly change the width of the time window according to the characteristics of the actual seismic signal but also make the connection line of the peak points of the window function show diversity by adjusting the parameters.

[0146] When \(\lambda = 1, p = 1\), the generalized S-transform degenerates into the standard S-transform:

[0147]

[0148] In some embodiments, the logging mobility data acquisition unit specifically calculates the logging fluid mobility data \(F\) of the reservoir based on the following formula:

[0149]

[0150] where \(K\) is the permeability and \(\eta\) is the viscosity coefficient. The permeability \(K\) and the viscosity coefficient \(\eta\) can be directly obtained from the logging data.

[0151] In some embodiments, the prediction model construction unit specifically constructs a co-kriging estimation equation for evaluating fluid mobility based on the following formula:

[0152]

[0153] where F * (u 0 ) is the fluid mobility to be estimated at the position of u 0 , is the sampling value of the primary variable at the position of , that is, the logging fluid mobility data, is the weighting coefficient assigned to this sampling point, is the sampling value of the secondary variable at the position of, that is, the seismic frequency data, is the weighting coefficient assigned to this sampling point, n is the number of sampling points of the primary variable, m is the number of sampling points of the secondary variable, and λ 0 is the constant coefficient of the co-kriging estimation equation.

[0154] Both n and m represent the number of sampling points. If the number of sampling points is too small, the amount of information is insufficient and it is difficult to ensure the accuracy of prediction; if the number of sampling points is too large, the features cannot be highlighted and the differences are smoothed. Therefore, those skilled in the art can select appropriate n and m according to experience and experiments.

[0155] The following introduces the process of solving λ 0 , and .

[0156] To make the solution process more concise, the co-kriging equation can be written in the following form:

[0157]

[0158] where X * (u 0 ) is the estimated value at the position of u 0 ; is the sampling value of the primary variable at the position of , is the value of the weighting coefficient assigned to this sampling point; is the sampling value of the secondary variable at the position of , is the value of the weighting coefficient assigned to this sampling point.

[0159] Apply the unbiased condition:

[0160] E[X(u 0 ) - X * (u 0 )] = 0

[0161] Substituting Equation (6) into the above equation, we get:

[0162]

[0163] Using first-order stationarity, we obtain:

[0164]

[0165] where m X and m Y are the means of variables X and Y respectively. Therefore,

[0166]

[0167] For simple co-kriging, the above equation satisfies the unbiased condition. If we do not know the mean, we can make λ 0 equal to 0 using the following equation:

[0168]

[0169] Equation (10) is the assumption condition of traditional ordinary co-kriging.

[0170] Since the sum of the weighting coefficients of variable Y needs to be equal to 0, some of the weighting coefficients will be less than zero, which may lead to a negative estimation result for the point to be estimated, and this may have no physical meaning. To avoid this situation, one solution is to satisfy the following two equations:

[0171]

[0172]

[0173] If Equation (10) is substituted into Equation (6), we get:

[0174]

[0175] If Equations (11) and (12) are substituted into Equation (6), we can obtain:

[0176]

[0177] The estimation variance is:

[0178]

[0179] Because:

[0180] Var[X - Y - Z] = Var[X] + Var[Y] + Var[Z] - 2C[X,Y] - 2C[Y,Z] (16)

[0181] Therefore, Equation (15) can be expanded to obtain:

[0182]

[0183] Under the condition of Equation (10), to minimize the variance of Equation (17), the Lagrangian method can be used to define the function Flag as follows:

[0184]

[0185] Taking the derivatives of λ and μ in the above equation respectively, we can obtain:

[0186]

[0187] In Equation (19), C X represents the covariance of the main variables; C C represents the cross-covariance between two variables; C Y represents the covariance between the secondary variable seismic frequency data. Equation (19) written in matrix form is:

[0188]

[0189] Solving the above equations gives μ X and μ Y values. Substituting them into Equation (13) can obtain the estimated value. Substituting Equation (19) into Equation (17) can obtain:

[0190]

[0191] If we choose the hypothesis conditions of Equation (11) and Equation (12), after similar derivations, the final system of equations, written in matrix form, is as follows:

[0192]

[0193] Solving the above equations gives the value of μ. Substituting it into Equation (14) can obtain the estimated value. The variance of the corresponding error is:

[0194]

[0195] Through Equations (22) and (23), λ 0 , and in Equation (6) can be finally obtained, corresponding to the covariance kriging estimation equation λ 0, and All coefficient terms in the co-Kriging estimation equation can thus be determined.

[0196] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0197] Example 3

[0198] According to another aspect of the present invention, an electronic device is further provided. The electronic device includes:

[0199] A memory storing executable instructions;

[0200] A processor that runs the executable instructions in the memory to implement the fluid mobility prediction method based on co-Kriging estimation as described above.

[0201] The method includes the following steps:

[0202] Step 1: Perform a generalized S-transform on the original time-domain seismic signal to obtain seismic frequency data;

[0203] Step 2: Obtain well logging fluid mobility data of the reservoir from well logging data;

[0204] Step 3: Use the co-Kriging algorithm, take the well logging fluid mobility data as the main variable and the seismic frequency data as the secondary variable, construct a co-Kriging estimation equation for estimating fluid mobility, and determine the coefficient terms in the co-Kriging estimation equation;

[0205] Step 4: Predict the three-dimensional fluid mobility distribution of the study area based on the constructed co-Kriging estimation equation.

[0206] Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0207] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0208] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0209] Example 4

[0210] According to another aspect of the present invention, there is also provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the fluid mobility prediction method based on co-Kriging estimation as described above.

[0211] The method includes the following steps:

[0212] Step 1, perform a generalized S-transform on the original time-domain seismic signal to obtain seismic frequency data;

[0213] Step 2, obtain well logging fluid mobility data of the reservoir from well logging data;

[0214] Step 3, adopt the co-Kriging algorithm, use the well logging fluid mobility data as the main variable and the seismic frequency data as the secondary variable to construct a co-Kriging estimation equation for estimating fluid mobility, and determine the coefficient terms in the co-Kriging estimation equation;

[0215] Step 4, predict the three-dimensional fluid mobility distribution of the study area based on the constructed co-Kriging estimation equation.

[0216] The computer-readable storage medium according to an embodiment of the present invention stores non-temporary computer-readable instructions. When the non-temporary computer-readable instructions are run by a processor, all or part of the steps of the methods of the various embodiments of the present invention described above are executed.

[0217] The above-mentioned computer-readable storage medium includes but is not limited to: optical storage media (such as CD-ROMs and DVDs), magneto-optical storage media (such as MOs), 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 ROMs (such as ROM cartridges).

[0218] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present invention.

[0219] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0220] Example 5

[0221] Figure 3The red part is the prediction result. It can be seen that by using the method of the present invention, the fluid sensitive area can be accurately predicted. By combining well and seismic data, it is different from the problem of low accuracy in directly inversing the reservoir fluid mobility by time-frequency analysis. By using the co-Kriging estimation algorithm, the accuracy of the prediction result is between well logging data and seismic data, that is, higher than seismic data. That is to say, compared with the seismic data participating in the calculation, it can improve the resolution and directly obtain the reservoir fluid mobility to improve the intuitiveness of the prediction result.

[0222] In summary, the present invention discloses a fluid mobility prediction method based on co-Kriging estimation, which mainly includes the following steps: performing a generalized S transform on the original time-domain seismic signal to obtain seismic frequency data; obtaining well logging fluid mobility data of the reservoir from well logging data; using the co-Kriging algorithm, taking the well logging fluid mobility data as the main variable and the seismic frequency data as the secondary variable, constructing a co-Kriging estimation equation for estimating fluid mobility, and determining the coefficient terms in the co-Kriging estimation equation; predicting the three-dimensional fluid mobility distribution of the study area based on the constructed co-Kriging estimation equation.

[0223] The present invention has at least the following beneficial effects:

[0224] 1. Effectively comprehensively utilizes two information sources of well logging mobility data and seismic frequency data, significantly improving the accuracy of the prediction result;

[0225] 2. By considering the correlation between data through the co-Kriging algorithm, the prediction deviation can be reduced;

[0226] 3. Compared with directly inversing fluid mobility, the scheme of the present invention has higher calculation efficiency and is more intuitive;

[0227] 4. Avoids the trouble of being limited by time-frequency resolution in time-frequency analysis;

[0228] 5. Can directly obtain the three-dimensional fluid mobility distribution, improving the intuitiveness of the result;

[0229] 6. The flow calculation process is simplified, and the calculation efficiency is significantly improved;

[0230] 7. Can be widely applied to the prediction and description of oil and gas reservoirs;

[0231] 8. Helps to improve the efficiency of oil and gas exploration and reduce the development risk.

[0232] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is also 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. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the market, or to enable other ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A fluid mobility prediction method based on cokriging estimation, characterized in that: The method comprises: Step 1, performing generalized S transform on the original time-domain seismic signal to obtain seismic frequency data; Step 2, obtaining reservoir logging fluid mobility data from logging data; Step 3, using the cokriging algorithm, taking the well logging fluid mobility data as the main variable and the seismic frequency data as the secondary variable, constructing a cokriging estimation equation for estimating fluid mobility, and determining the coefficient term in the cokriging estimation equation; Step 4: Predict the three-dimensional fluid mobility distribution in the study area based on the constructed cokriging estimation equation.

2. The method according to claim 1, characterized in that In step 1, the original time-domain seismic signal x(t) is generalized transformed based on the following formula to obtain seismic frequency data GST(τ, f): Among them, λ>0,p>0, λ and p are parameter factors of generalized S transform.

3. The method according to claim 1, characterized in that In step 2, the logging fluid mobility data F of the reservoir is calculated based on the following formula: Where K is the permeability and η is the viscosity coefficient.

4. The method according to claim 1, characterized in that In step 3, a cokriging estimation equation for evaluating fluid mobility is constructed based on the following formula: Among them, F * (u0) is the fluid velocity to be estimated at position u0, is The sampling value of the main variable at the position, that is, the fluid mobility data of the logging fluid, is the weighting coefficient assigned to the sampling point, yes The subvariable sampling value at the location, that is, the earthquake frequency data, is the weighting coefficient assigned to the sampling point, n is the number of sampling points of the primary variable, m is the number of sampling points of the secondary variable, and λ0 is the constant coefficient of the cokriging estimation equation.

5. A fluid flow prediction device based on cokriging estimation, characterized in that: The device comprises: A seismic frequency data acquisition unit, used for performing a generalized S transform on the original time-domain seismic signal to acquire seismic frequency data; A well logging fluidity data acquisition unit, used for acquiring well logging fluid fluidity data of the reservoir from the well logging data; A prediction model building unit is used to adopt a cokriging algorithm, take the well logging fluid mobility data as the main variable, take the seismic frequency data as the secondary variable, build a cokriging estimation equation for estimating the fluid mobility, and determine the coefficient term in the cokriging estimation equation; The flow rate distribution prediction unit is used to predict the three-dimensional fluid flow rate distribution in the study area based on the constructed cokriging estimation equation.

6. The device according to claim 5, characterized in that The seismic frequency data acquisition unit performs a generalized transformation on the original time-domain seismic signal x(t) based on the following formula to obtain seismic frequency data GST(τ, f): Among them, λ>0,p>0, λ and p are parameter factors of generalized S transform.

7. The device according to claim 5, characterized in that The logging fluidity data acquisition unit specifically calculates the logging fluid fluidity data F of the reservoir based on the following formula: Where K is the permeability and η is the viscosity coefficient.

8. The device according to claim 5, characterized in that The prediction model building unit specifically builds a cokriging estimation equation for evaluating fluid mobility based on the following formula: Among them, F * (u0) is the fluid velocity to be estimated at position u0, is The sampling value of the main variable at the position, that is, the fluid mobility data of the logging fluid, is the weighting coefficient assigned to the sampling point, yes The subvariable sampling value at the location, that is, the earthquake frequency data, is the weighting coefficient assigned to the sampling point, n is the number of sampling points of the primary variable, m is the number of sampling points of the secondary variable, and λ0 is the constant coefficient of the cokriging estimation equation.

9. An electronic device, characterized in that: The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

Images