Method and apparatus for predicting reservoirs based on petrophysical volume versions

Abstract

The application discloses a reservoir prediction method and device based on a rock physical quantity version, and the method comprises the following steps: fitting parameter points with a porosity less than a preset porosity in the rock physical quantity version to obtain a first coordinate rotation axis; counterclockwise rotating the rock physical quantity version, and taking λ*ρ in the rock physical quantity version with the first coordinate rotation axis in a horizontal state as a lithology identification factor; fitting a porosity size variation trend of parameter points with a porosity greater than or equal to the preset porosity in the rock physical quantity version, taking an axis perpendicular to the axis obtained by fitting as a second coordinate rotation axis; counterclockwise rotating the rock physical quantity version, and taking λ*ρ in the rock physical quantity version with the second coordinate rotation axis in a horizontal state as a reservoir identification factor; and generating a reservoir prediction result according to the lithology identification factor and the reservoir identification factor. The application can improve the rationality and accuracy of reservoir prediction.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of seismic data processing in the field of earth science, and more particularly, to a reservoir prediction method and device based on a rock physical quantity version. BACKGROUND

[0002] With the deepening of oil and gas exploration, the exploration depth is increasing, the reservoir physical property is deteriorating, and the high-precision reservoir prediction is more and more difficult. The accurate identification of lithology and high-quality reservoirs becomes one of the core targets of seismic exploration. However, due to the deficiency of conventional lithology and reservoir identification factors in accurate identification of complex reservoirs, a method for improving the accuracy of lithology and reservoir identification factors is urgently needed. SUMMARY

[0003] Therefore, the present application discloses a scheme for improving the accuracy of reservoir prediction results of rocks.

[0004] According to one aspect of the present application, a reservoir prediction method based on a rock physical quantity version is provided, which comprises: fitting parameter points with a porosity less than a preset porosity in the rock physical quantity version to obtain a first coordinate rotation axis; wherein the parameter points in the rock physical quantity version are used to represent corresponding values among λ*ρ, impedance, and porosity, the λ is the Lamé constant of the rock, the ρ is the density of the rock, the horizontal coordinate of the rock physical quantity version is impedance, and the vertical coordinate is λ*ρ; the rock physical quantity version is counterclockwise rotated until the first coordinate rotation axis is in a horizontal state, and the λ*ρ in the rock physical quantity version with the first coordinate rotation axis in the horizontal state is taken as a lithology identification factor; the porosity size variation trend of the parameter points with a porosity greater than or equal to the preset porosity in the rock physical quantity version is fitted, and an axis perpendicular to the fitted axis is taken as a second coordinate rotation axis; the rock physical quantity version is counterclockwise rotated until the second coordinate rotation axis is in a horizontal state, and the λ*ρ in the rock physical quantity version with the second coordinate rotation axis in the horizontal state is taken as a reservoir identification factor; and a reservoir prediction result is generated according to the lithology identification factor and the reservoir identification factor.

[0005] In some embodiments, the lithology identification factor Rotate_Y_lith is expressed as Rotate_Y_lith=(λ*ρ-b_lith)*cos(θ_lith)-AI*sin(θ_lith)+b_lith; wherein θ_lith and b_lith are used to represent corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the first coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(θ_lith)*X+b_lith, and AI is impedance.

[0006] In some embodiments, the reservoir identification factor Rotate_Y_res is expressed as Rotate_Y_res=(λ*ρ-b_res)*cos(θ_res)-AI*sin(θ_res)+b_res; wherein θ_res and b_res are used to represent corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the second coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(θ_res)*X+b_res.

[0007] In some embodiments, the rock physical quantity version is obtained by determining λ*ρ according to the P-wave velocity V p of the rock in the saturated fluid state, the S-wave velocity V s of the rock in the saturated fluid state, and the density ρ of the rock; wherein, K sat is the effective bulk modulus of the saturated rock, and μ sat is the effective shear modulus of the saturated rock. The rock physical quantity version is constructed by taking λ*ρ as the vertical coordinate, impedance as the horizontal coordinate, and porosity as the attribute of the parameter point.

[0008] According to an aspect of the present application, a reservoir prediction device based on a rock physical quantity version is also provided. The prediction device comprises: a first coordinate rotation axis generation module, which is used to fit parameter points in the rock physical quantity version, whose porosity is less than a preset porosity, to obtain a first coordinate rotation axis; wherein the parameter points in the rock physical quantity version are used to represent corresponding values among λ*ρ, impedance, and porosity, the λ is the Lamé constant of the rock, the ρ is the density of the rock, the horizontal coordinate in the rock physical quantity version is impedance, and the vertical coordinate is λ*ρ; a lithology identification factor generation module, which is used to rotate the rock physical quantity version counterclockwise until the first coordinate rotation axis is in a horizontal state, and take λ*ρ in the rock physical quantity version when the first coordinate rotation axis is in the horizontal state as the lithology identification factor; a second coordinate rotation axis generation module, which is used to fit the porosity size variation trend of parameter points in the rock physical quantity version, whose porosity is greater than or equal to the preset porosity, and take an axis perpendicular to the fitted axis as the second coordinate rotation axis; a reservoir identification factor generation module, which is used to rotate the rock physical quantity version counterclockwise until the second coordinate rotation axis is in a horizontal state, and take λ*ρ in the rock physical quantity version when the second coordinate rotation axis is in the horizontal state as the reservoir identification factor; and a reservoir prediction result generation module, which is used to generate a reservoir prediction result according to the lithology identification factor and the reservoir identification factor.

[0009] In some embodiments, the lithology identification factor Rotate_Y_lith is expressed as Rotate_Y_lith=(λ*ρ-b_lith)*cos(θ_lith)-AI*sin(θ_lith)+b_lith; wherein θ_lith and b_lith are used to represent corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the first coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(θ_lith)*X+b_lith, and AI is the impedance.

[0010] In some embodiments, the reservoir identification factor Rotate_Y_res is expressed as Rotate_Y_res=(λ*ρ-b_res)*cos(θ_res)-AI*sin(θ_res)+b_res; wherein θ_res and b_res are used to represent corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the second coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(θ_res)*X+b_res.

[0011] In some embodiments, the rock physical quantity version is obtained by determining λ*ρ according to the P-wave velocity V p of the rock in the saturated fluid state, the S-wave velocity V s of the rock in the saturated fluid state, and the density ρ of the rock; wherein, K sat is the effective bulk modulus of the saturated rock, and μ sat is the effective shear modulus of the saturated rock. The rock physical quantity version is constructed by taking λ*ρ as the vertical coordinate, taking the impedance as the horizontal coordinate, and taking the porosity as the attribute of the parameter point.

[0012] According to another aspect of the present application, an electronic device is also provided, which comprises a memory storing executable instructions, and a processor running the executable instructions in the memory to implement the above-mentioned reservoir prediction method based on the rock physical quantity version.

[0013] According to another aspect of the present application, a computer readable storage medium is also provided, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned reservoir prediction method based on the rock physical quantity version.

[0014] The technical scheme has at least the following advantages: the embodiment of the application provides a reservoir prediction method based on a rock physical quantity version, parameters of the rock physical quantity version with a porosity less than a preset porosity are fitted to obtain a first coordinate rotation axis, the rock physical quantity version is then counterclockwise rotated until the first coordinate rotation axis is in a horizontal state, and lambda*rho in the rock physical quantity version when the first coordinate rotation axis is in the horizontal state is taken as a lithology identification factor, the porosity size variation trend of parameters of the rock physical quantity version with a porosity greater than or equal to the preset porosity is fitted, an axis perpendicular to the fitted axis is taken as a second coordinate rotation axis, the rock physical quantity version is then counterclockwise rotated until the second coordinate rotation axis is in a horizontal state, and lambda*rho in the rock physical quantity version when the second coordinate rotation axis is in the horizontal state is taken as a reservoir identification factor, and finally, a reservoir prediction result is generated according to the lithology identification factor and the reservoir identification factor. In the embodiment of the application, the rock physical quantity version is established based on lambda*rho, impedance and porosity, the physical characteristics of rocks are fully utilized, the influence of mineral component and porosity variation on the lithology identification factor and the reservoir identification factor can be analyzed, and the rationality and accuracy of reservoir prediction are improved.

[0015] The method and apparatus of the application have other characteristics and advantages that will be apparent from or will be described in detail in the drawings and the following detailed description, which are incorporated herein for the purpose of explaining certain principles of the application. BRIEF DESCRIPTION OF DRAWINGS

[0016] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which like reference characters refer to like parts throughout and in which:

[0017] Figure 1 A flow chart of a reservoir prediction method based on a rock physical quantity version according to one embodiment of the application is shown.

[0018] Figure 2 A reference schematic diagram of a reservoir prediction method based on a rock physical quantity version according to one embodiment of the application is shown.

[0019] Figure 3 A reference schematic diagram of a reservoir prediction method based on a rock physical quantity version according to one embodiment of the application is shown.

[0020] Figure 4 A reference schematic diagram of a reservoir prediction method based on a rock physical quantity version according to one embodiment of the application is shown. DETAILED DESCRIPTION

[0021] In the related art, the lithology identification and the reservoir identification factor are constructed by using elastic parameters or elastic parameter combinations to effectively identify the lithology and the reservoir and improve the identification ability of the lithology and the reservoir. In general, the lithology and the reservoir are identified by setting a threshold value of the elastic parameters on the basis of the intersection analysis of the logging data and the analysis of the typical elastic characteristics of the target layer. The method has high subjectivity and low precision.

[0022] Therefore, the embodiment of the present application provides a reservoir prediction method based on a rock physical quantity version. The parameter points with a porosity less than a preset porosity in the rock physical quantity version are fitted to obtain a first coordinate rotation axis. Then, the rock physical quantity version is counterclockwise rotated until the first coordinate rotation axis is in a horizontal state. The λ*ρ in the rock physical quantity version when the first coordinate rotation axis is in the horizontal state is taken as a lithology identification factor. The porosity size variation trend of the parameter points with a porosity greater than or equal to the preset porosity in the rock physical quantity version is fitted. An axis perpendicular to the fitted axis is taken as a second coordinate rotation axis. Then, the rock physical quantity version is counterclockwise rotated until the second coordinate rotation axis is in a horizontal state. The λ*ρ in the rock physical quantity version when the second coordinate rotation axis is in the horizontal state is taken as a reservoir identification factor. Finally, a reservoir prediction result is generated according to the lithology identification factor and the reservoir identification factor. In the embodiment of the present application, the rock physical quantity version is established based on λ*ρ, impedance, and porosity. The physical characteristics of the rock are fully utilized. The influence of the mineral component and the porosity variation on the lithology identification factor and the reservoir identification factor can be analyzed. Therefore, the rationality and the accuracy of the reservoir prediction are improved.

[0023] Preferred embodiments of the present application will be described in more detail with reference to the drawings. Although the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0024] Example 1

[0025] Figure 1 A flowchart of a reservoir prediction method based on a rock physical quantity version according to an embodiment of the present application is shown. As shown in the figure, the method includes steps 1-5.

[0026] Step 1, fitting the parameter points in the rock physical quantity version with porosity less than a preset porosity to obtain a first coordinate rotation axis. The parameter points in the rock physical quantity version are used to represent the corresponding values among λ*ρ, impedance and porosity, the horizontal coordinate in the rock physical quantity version is impedance, and the vertical coordinate is λ*ρ. Exemplarily, the specific value of the preset porosity is not limited in the embodiment of the present application, and the developer can set it according to the actual situation. Here, taking the rock including shale and quartz as an example, the smaller the porosity (which can also be higher than a preset value, which is less than the preset porosity), the more shale, and the more representative the final lithology identification factor. The above λ can be defined as the relationship between the stress and strain of the rock, and the related definitions of impedance, porosity and the like can be referred to the related technologies, which are not described herein again. ρ here refers to the comprehensive density of the rock, which can include fluid. The specific algorithm of the above fitting is not limited in the embodiment of the present application, and the approximate distribution of the parameter points can be embodied, that is, the parameter points with porosity less than the preset porosity are distributed according to the axis obtained by fitting.

[0027] In one possible implementation, the rock physical quantity version is obtained by determining λ*ρ according to the P-wave velocity V p of the rock in the saturated fluid state, s the S-wave velocity V sat of the rock in the saturated fluid state, and the density ρ of the rock; wherein, K sat is the effective bulk modulus of the saturated rock, and μ m is the effective shear modulus of the saturated rock. The rock physical quantity version is constructed by taking λ*ρ as the vertical coordinate, impedance as the horizontal coordinate, and porosity as the attribute of the parameter points. Figure 2 Figure 2 Fig. 1 shows a reference diagram of a prediction method of a reservoir based on a rock physical quantity version according to one embodiment of the present application, Figure 2 The coordinate system in Fig. 1 can be regarded as a rock physical quantity version, wherein the horizontal coordinate is impedance (or AI), the vertical coordinate is λ*ρ (or Lamda*Rho), and each reference point (i.e. the point with different gray scales in the figure) can correspond to porosity (or PHIT). Exemplarily, the elastic modulus of the mixed mineral can be calculated first. In this example, the rock includes quartz and shale, and it should be understood that in the case that the rock is composed of multiple minerals, the corresponding parameters can be appropriately added in the formula. The elastic modulus of the mixed mineral can be expressed as: wherein K m and μ V are the bulk modulus and shear modulus of the mixed mineral, respectively. qu qu ​​+V sh *K sh , U V =V qu *U qu +V sh *U sh , where K qu and U qu These are the bulk modulus and shear modulus of quartz, K. sh and U sh These are the bulk modulus and shear modulus of clay, V, respectively. qu and V sh These represent the normalized quartz and clay content as matrix volume percentages, respectively. Then, a differential equivalent medium model (or DEM) is used to incorporate all pores into the system to calculate the bulk modulus and shear modulus of the dry rock skeleton, which are K0 and K1, respectively. dry μ dry The specific calculation process can be found in relevant technologies, and will not be elaborated upon here in this embodiment of the invention. The Wood equation can be used to mix the porous fluids and calculate the bulk modulus K of the mixed fluid. f , K o K g and K w These are the bulk moduli of oil, gas, and water, respectively, S o S g and S w These are the saturation levels of oil, gas, and water, respectively, and S... o +S g +S w =1. The bulk density ρ of the mixed fluid. f The expression is as follows: ρ f =S w ρ w +S o ρ o +S g ρ g In the formula, ρ o ρ g and ρ w These are the volume densities of oil, gas, and water, respectively. Depending on the specific application, developers can also use the following formula: K f =S w K w +(1-S w )K g , Where e is an empirical coefficient, which can be 2-5. Finally, the Biot-Gassmann equation can be used to calculate K by adding the fluid mixture into the pore space. sat μ sat, and μ are the effective bulk modulus and effective shear modulus of the saturated rock, respectively, μ sat = μ dry , where K dry , μ dry is the effective bulk modulus of the dry rock skeleton, K m is the bulk modulus of the mixed mineral, K f is the bulk modulus of the mixed fluid, and φ is the porosity. Accordingly, the longitudinal wave velocity V P and the transverse wave velocity V S of the rock in the saturated fluid state can be obtained, and λ*ρ can be further obtained.

[0028] Continuing to refer to Figure 1 , in step 2, the rock physics quantity chart is rotated counterclockwise until the first coordinate rotation axis is in a horizontal state, and λ*ρ in the rock physics quantity chart in which the first coordinate rotation axis is in the horizontal state is taken as a lithology identification factor. In an example, the lithology identification factor Rotate_Y_lith is represented as Rotate_Y_lith=(λ*ρ-b_lith)*cos(θ_lith)-AI*sin(θ_lith)+b_lith; where θ_lith and b_lith are used to represent corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the first coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(θ_lith)*X+b_lith, and AI is the impedance. Another parameter Rotate_X_lith=AI*cos(θ_lith)+(λ*ρ-b_lith)*sin(θ_lith) is listed here, and in another example, the value can also be taken as a lithology identification factor. Refer to Figure 3 , Figure 3 A reference diagram illustrating a prediction method of a reservoir based on a rock physics quantity chart according to an embodiment of the present application is shown. In the diagram, the parameter points with a porosity close to 0 are distributed according to the oblique line in the diagram, and the parameter points can be taken as a first coordinate rotation axis. The rock physics quantity chart is rotated counterclockwise, and the horizontal coordinate after rotation can be taken as a lithology identification factor. As can be seen in the right diagram, the lithology identification factor can distribute the parameter points with a porosity less than a preset porosity (in the above example, the parameter points are mud) in a rectangular interval close to a parallel distribution, and the classification effect is relatively obvious.

[0029] Continuing to refer to Figure 1 , in step 3, the porosity size variation trend of the parameter points with a porosity greater than or equal to a preset porosity in the rock physics quantity chart is fitted, and an axis perpendicular to the axis obtained by fitting is taken as a second coordinate rotation axis. Exemplarily, in the above example, the parameter points with a porosity greater than or equal to a preset porosity are parameter points in which quartz can exist.

[0030] Step 4, rotate the rock physical quantity version counterclockwise until the second coordinate rotation axis is in a horizontal state, and take λ*ρ in the rock physical quantity version with the second coordinate rotation axis in a horizontal state as the reservoir identification factor. In one example, the reservoir identification factor Rotate_Y_res is expressed as Rotate_Y_res=(λ*ρ-b_res)*cos(θ_res)-AI*sin(θ_res)+b_res; wherein θ_res and b_res are used to represent the corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the second coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(θ_res)*X+b_res. Another parameter is listed here: Rotate_X_res=AI*cos(θ_res)+(λ*ρ-b_res)*sin(θ_res), which can also be used as a reservoir identification factor in another example. In combination with Figure 4 , Figure 4 A reference diagram of a reservoir prediction method based on a rock physical quantity version according to one embodiment of the present application is shown, in which the diagonal line of the left figure is the second coordinate rotation axis, and the size change trend of the porosity is approximately from the upper right corner of the coordinate axis to the lower left corner. The diagonal line in the left figure is perpendicular to each other, and the right figure can be obtained by counterclockwise rotation of the rock physical quantity version, and the vertical coordinate after rotation is the reservoir identification factor. In the right figure, it can be seen that the reservoir identification factor can make the parameter points with porosity greater than or equal to the preset porosity according to the porosity distribution, and the relationship between the porosity and the reservoir is relatively close, which can improve the prediction effect of the reservoir.

[0031] Continuing to refer to Figure 1, step 5, generating a reservoir prediction result according to the lithology identification factor and the reservoir identification factor. On the basis of obtaining the lithology identification factor and the reservoir identification factor, a developer can carry out post-stack and pre-stack seismic inversion to obtain impedance, P-wave velocity, S-wave velocity and density data bodies. The post-stack seismic inversion is to obtain high-fidelity post-stack seismic data on the basis of amplitude-preserved seismic processing, to realize reasonable well-seismic calibration through well logging synthetic seismogram, to estimate a wavelet suitable for waveform characteristics of a research area, and to establish a low-frequency impedance model based on well data, and to realize three-dimensional impedance inversion by using a sparse pulse inversion method. The pre-stack seismic inversion is to first determine an angle-dependent stacking range through well logging AVO characteristic analysis on the basis of pre-stack migration gather amplitude-preserved processing, to perform angle-dependent volume stacking on the pre-stack gather to obtain near-biased, middle-biased and far-biased partial stacking data. On the basis of well logging synthetic seismogram, a wavelet of each partial stacking data is estimated, and a low-frequency elastic impedance model is established based on well data to perform elastic impedance inversion of each partial stacking data. The specific inversion process is not limited in the embodiments of the present application, and can be referred to related technologies. The P-wave velocity body, the S-wave velocity body and the density body can be substituted into a formula corresponding to the lithology identification factor (which can be induced according to a rock physical quantity version corresponding to the lithology identification factor) to obtain effective division of the lithology (for example, effective division of the argillaceous matter in the above example), and then substituted into a formula corresponding to the reservoir identification factor (which can be induced according to a rock physical quantity version corresponding to the reservoir identification factor) to obtain accurate prediction of the high-quality reservoir, so that high-precision three-dimensional reservoir prediction result can be realized.

[0032] Example 2

[0033] According to one embodiment of the present application, a reservoir prediction device based on a rock physical quantity chart is provided. The prediction device comprises: a first coordinate rotation axis generation module configured to fit parameter points in the rock physical quantity chart with a porosity less than a preset porosity to obtain a first coordinate rotation axis; wherein the parameter points in the rock physical quantity chart are used to represent corresponding values among lambda*porosity, impedance, and porosity, the lambda is a Lame constant of rock, the porosity is a density of rock, and a horizontal coordinate of the rock physical quantity chart is impedance and a vertical coordinate is lambda*porosity; a lithology identification factor generation module configured to rotate the rock physical quantity chart counterclockwise until the first coordinate rotation axis is in a horizontal state, and take lambda*porosity in the rock physical quantity chart with the first coordinate rotation axis in the horizontal state as a lithology identification factor; a second coordinate rotation axis generation module configured to fit a porosity size variation trend of parameter points in the rock physical quantity chart with a porosity greater than or equal to the preset porosity, take an axis perpendicular to the fitted axis as a second coordinate rotation axis; a reservoir identification factor generation module configured to rotate the rock physical quantity chart counterclockwise until the second coordinate rotation axis is in a horizontal state, and take lambda*porosity in the rock physical quantity chart with the second coordinate rotation axis in the horizontal state as a reservoir identification factor; and a reservoir prediction result generation module configured to generate a reservoir prediction result according to the lithology identification factor and the reservoir identification factor.

[0034] In some embodiments, the lithology identification factor Rotate_Y_lith is represented as Rotate_Y_lith=(lambda*porosity-b_lith)*cos(theta_lith)-AI*sin(theta_lith)+b_lith; wherein theta_lith and b_lith are used to represent corresponding parameters of a horizontal coordinate X and a vertical coordinate Y of the first coordinate rotation axis, the corresponding relationship is as follows: Y=tan(theta_lith)*X+b_lith, and AI is impedance.

[0035] In some embodiments, the reservoir identification factor Rotate_Y_res is represented as Rotate_Y_res=(lambda*porosity-b_res)*cos(theta_res)-AI*sin(theta_res)+b_res; wherein theta_res and b_res are used to represent corresponding parameters of a horizontal coordinate X and a vertical coordinate Y of the second coordinate rotation axis, the corresponding relationship is as follows: Y=tan(theta_res)*X+b_res.

[0036] In some embodiments, the rock physical quantity chart is obtained by: determining lambda*porosity according to a P-wave velocity V p of rock in a saturated fluid state, a S-wave velocity V s of rock in the saturated fluid state, and a density p of rock; wherein, Ksat μ is the effective bulk modulus of the saturated rock, sat G is the effective shear modulus of the saturated rock, A rock physics volume is constructed with λ*ρ as the ordinate, impedance as the abscissa, and porosity as the attribute of the parameter point.

[0037] Example 3

[0038] According to another aspect of the present application, an electronic device is also provided. The electronic device comprises:

[0039] a memory storing executable instructions:

[0040] a processor running the executable instructions in the memory to implement the prediction method of a reservoir based on a rock physics volume according to the present application.

[0041] The prediction method comprises: fitting parameter points in the rock physics volume with a porosity less than a preset porosity to obtain a first coordinate rotation axis; wherein the parameter points in the rock physics volume are used to represent corresponding values among λ*ρ, impedance, and porosity, the λ is the Lame constant of the rock, the ρ is the density of the rock, the abscissa in the rock physics volume is impedance, and the ordinate is λ*ρ; rotating the rock physics volume counterclockwise until the first coordinate rotation axis is in a horizontal state, and taking λ*ρ in the rock physics volume with the first coordinate rotation axis in the horizontal state as a lithology identification factor; fitting a porosity size variation trend corresponding to parameter points in the rock physics volume with a porosity greater than or equal to the preset porosity, taking an axis perpendicular to the axis obtained by fitting as a second coordinate rotation axis; rotating the rock physics volume counterclockwise until the second coordinate rotation axis is in a horizontal state, and taking λ*ρ in the rock physics volume with the second coordinate rotation axis in the horizontal state as a reservoir identification factor; and generating a reservoir prediction result according to the lithology identification factor and the reservoir identification factor.

[0042] In some embodiments, the lithology identification factor Rotate_Y_lith is expressed as Rotate_Y_lith=(λ*ρ-b_lith)*cos(θ_lith)-AI*sin(θ_lith)+b_lith; wherein θ_lith and b_lith are used to represent corresponding parameters of the abscissa X and the ordinate Y of the first coordinate rotation axis, the corresponding relationship is as follows: Y=tan(θ_lith)*X+b_lith, and AI is impedance.

[0043] In some embodiments, the reservoir identification factor Rotate_Y_res is expressed as Rotate_Y_res=(λ*ρ-b_res)*cos(θ_res)-AI*sin(θ_res)+b_res; wherein θ_res and b_res are used to represent corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the second coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(θ_res)*X+b_res.

[0044] In some embodiments, the petrophysical quantity version is obtained by determining λ*ρ according to the P-wave velocity V p of the rock in the saturated fluid state, the S-wave velocity V s of the rock in the saturated fluid state, and the density ρ of the rock; wherein, K sat is the effective bulk modulus of the saturated rock, and μ sat is the effective shear modulus of the saturated rock. The petrophysical quantity version is constructed by taking λ*ρ as the vertical coordinate, impedance as the horizontal coordinate, and porosity as the attribute of the parameter point.

[0045] Example 4

[0046] According to another aspect of the present application, there is also provided a computer readable storage medium storing a computer program which, when executed by a processor, implements the reservoir prediction method based on the petrophysical quantity version according to the present application.

[0047] The prediction method comprises: fitting the parameter points in the petrophysical quantity version with porosity less than a preset porosity to obtain a first coordinate rotation axis; wherein the parameter points in the petrophysical quantity version are used to represent corresponding values among λ*ρ, impedance, and porosity, the λ is the Lamé constant of the rock, the ρ is the density of the rock, the horizontal coordinate in the petrophysical quantity version is impedance, and the vertical coordinate is λ*ρ; rotating the petrophysical quantity version counterclockwise until the first coordinate rotation axis is in a horizontal state, and taking λ*ρ in the petrophysical quantity version with the first coordinate rotation axis in the horizontal state as a lithology identification factor; fitting the porosity size variation trend of the parameter points in the petrophysical quantity version with porosity greater than or equal to the preset porosity, taking an axis perpendicular to the fitted axis as a second coordinate rotation axis; rotating the petrophysical quantity version counterclockwise until the second coordinate rotation axis is in a horizontal state, and taking λ*ρ in the petrophysical quantity version with the second coordinate rotation axis in the horizontal state as a reservoir identification factor; and generating a reservoir prediction result according to the lithology identification factor and the reservoir identification factor.

[0048] In some embodiments, the lithology identification factor Rotate_Y_lith is expressed as Rotate_Y_lith = (λ*ρ - b_lith)*cos(θ_lith) - AI*sin(θ_lith) + b_lith; where θ_lith and b_lith are used to represent corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the first coordinate rotation axis, and the corresponding relationship is as follows: Y = tan(θ_lith)*X + b_lith, and AI is the impedance.

[0049] In some embodiments, the reservoir identification factor Rotate_Y_res is expressed as Rotate_Y_res = (λ*ρ - b_res)*cos(θ_res) - AI*sin(θ_res) + b_res; where θ_res and b_res are used to represent corresponding parameters of the horizontal coordinate X and the vertical coordinate Y of the second coordinate rotation axis, and the corresponding relationship is as follows: Y = tan(θ_res)*X + b_res.

[0050] In some embodiments, the rock physical quantity version is obtained by determining λ*ρ according to the P-wave velocity V p of the rock in the saturated fluid state, the S-wave velocity V s of the rock in the saturated fluid state, and the density ρ of the rock; and determining the rock physical quantity version according to λ*ρ as the vertical coordinate, the impedance as the horizontal coordinate, and the porosity as the attribute of the parameter point. K sat is the effective bulk modulus of the saturated rock, μ sat is the effective shear modulus of the saturated rock, The rock physical quantity version is constructed by taking λ*ρ as the vertical coordinate, the impedance as the horizontal coordinate, and the porosity as the attribute of the parameter point.

[0051] Example 5

[0052] In combination with the actual application scenarios, in the case of obtaining the lithology identification factor and the reservoir identification factor, the developer can carry out post-stack and pre-stack seismic inversion to obtain the impedance, P-wave velocity, S-wave velocity, and density data volume. The post-stack seismic inversion is to obtain high-fidelity post-stack seismic data on the basis of amplitude-preserved seismic processing, to realize reasonable well-seismic calibration through well logging synthetic seismograms, to estimate the wavelet suitable for the waveform characteristics of the study area, and to establish a low-frequency impedance model based on well data, and to realize three-dimensional impedance inversion by using sparse pulse inversion and other methods. Then, the P-wave velocity volume, the S-wave velocity volume, and the density volume are substituted into the formula corresponding to the rock identification factor (which can be induced according to the rock physical quantity version corresponding to the rock identification factor) to obtain the effective differentiation of the lithology (for example, the effective differentiation of the mud in the above example), and then substituted into the formula corresponding to the reservoir identification factor (which can be induced according to the rock physical quantity version corresponding to the reservoir identification factor) to obtain the accurate prediction of the high-quality reservoir, that is, to realize the high-precision three-dimensional reservoir prediction result.

[0053] Further details regarding the present exemplary embodiments can be found in the corresponding description of the foregoing embodiments, which are not repeated here.

[0054] Embodiments of the application have been described above with the intent to be illustrative rather than limiting. Many modifications and enhancements are possible and within the scope of the embodiments. Although exemplary embodiments have been described in some detail, by way of example, for purposes of illustration and description, it is to be understood that such detailed description, is not intended to limit the scope of the embodiments or of any patent claims. Variations and modifications of the embodiments described herein can be made based on the description set forth herein, without departing from the scope and spirit of the claimed technology. The above description is that of current embodiments of the application. Various modifications and changes can be made thereto without departing from the spirit of the application, and it is to be understood that the application is not to be limited to any one particular material or construction. The intention is that modifications and variations of the embodiments described are considered to lie within the scope of the application, the scope of which is to be determined by the following claims.

Claims

1. A method of predicting a reservoir based on a rock physics volume, characterized by, The prediction method comprises: Fitting parameter points with a porosity less than a preset porosity in a rock physical quantity version to obtain a first coordinate rotation axis; wherein the parameter points in the rock physical quantity version are used to represent corresponding values among lambda*porosity, impedance, and porosity, the lambda is a Lame constant of rock, the porosity is a density of rock, a horizontal coordinate in the rock physical quantity version is impedance, and a vertical coordinate is lambda*porosity; Counterclockwise rotating the rock physical quantity version until the first coordinate rotation axis is in a horizontal state, and taking lambda*porosity in the rock physical quantity version when the first coordinate rotation axis is in the horizontal state as a lithology identification factor; Fitting a porosity size change trend corresponding to parameter points with a porosity greater than or equal to the preset porosity in the rock physical quantity version, and taking an axis perpendicular to an axis obtained by fitting as a second coordinate rotation axis; Counterclockwise rotating the rock physical quantity version until the second coordinate rotation axis is in a horizontal state, and taking lambda*porosity in the rock physical quantity version when the second coordinate rotation axis is in the horizontal state as a reservoir identification factor; Generating a reservoir prediction result according to the lithology identification factor and the reservoir identification factor.

2. The prediction method of claim 1, wherein, The lithology identification factor Rotate_Y_lith is expressed as Rotate_Y_lith=(lambda*porosity-b_lith)*cos(theta_lith)-AI*sin(theta_lith)+b_lith; wherein theta_lith and b_lith are used to represent corresponding parameters of a horizontal coordinate X and a vertical coordinate Y of the first coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(theta_lith)*X+b_lith, and AI is impedance.

3. The prediction method of claim 1, wherein, The reservoir identification factor Rotate_Y_res is expressed as Rotate_Y_res=(lambda*porosity-b_res)*cos(theta_res)-AI*sin(theta_res)+b_res; wherein theta_res and b_res are used to represent corresponding parameters of a horizontal coordinate X and a vertical coordinate Y of the second coordinate rotation axis, and the corresponding relationship is as follows: Y=tan(theta_res)*X+b_res.

4. The prediction method of claim 1, wherein, The rock physical quantity version is obtained by the following method: According to the longitudinal wave velocity V p of the rock in the saturated fluid state s , the density p of the rock, to determine λ*ρ; wherein, K sat is the effective bulk modulus of the saturated rock, and μ sat is the effective shear modulus of the saturated rock, Constructing a rock physical quantity version by taking lambda*porosity as a vertical coordinate, impedance as a horizontal coordinate, and porosity as an attribute of a parameter point.

5. A device for predicting a reservoir based on a rock physics volume, characterized by, The prediction device comprises: A first coordinate rotation axis generation module is used to fit parameter points with a porosity less than a preset porosity in a rock physical quantity version to obtain a first coordinate rotation axis; wherein the parameter points in the rock physical quantity version are used to represent corresponding values among lambda*porosity, impedance, and porosity, the lambda is a Lame constant of rock, the porosity is a density of rock, a horizontal coordinate in the rock physical quantity version is impedance, and a vertical coordinate is lambda*porosity; A lithology identification factor generation module is used to counterclockwise rotate the rock physical quantity version until the first coordinate rotation axis is in a horizontal state, and take lambda*porosity in the rock physical quantity version when the first coordinate rotation axis is in the horizontal state as a lithology identification factor; The second coordinate rotation axis generation module is configured to fit a variation trend of a porosity size corresponding to a parameter point with a porosity greater than or equal to a preset porosity in the rock physical quantity version, and take an axis perpendicular to the fitted axis as a second coordinate rotation axis. The reservoir identification factor generation module is configured to rotate the rock physical quantity version counterclockwise until the second coordinate rotation axis is in a horizontal state, and take a rock physical quantity version with the second coordinate rotation axis in the horizontal state as a reservoir identification factor. The reservoir prediction result generation module is configured to generate a reservoir prediction result according to the lithology identification factor and the reservoir identification factor.

6. The prediction device of claim 5, wherein, The lithology identification factor Rotate_Y_lith is represented as Rotate_Y_lith=(λ*ρ-b_lith)*cos(θ_lith)-AI*sin(θ_lith)+b_lith; wherein θ_lith and b_lith are used to represent corresponding parameters of a horizontal coordinate X and a vertical coordinate Y of the first coordinate rotation axis, the corresponding relationship is as follows: Y=tan(θ_lith)*X+b_lith, and AI is impedance.

7. The prediction device of claim 5, wherein, The reservoir identification factor Rotate_Y_res is represented as Rotate_Y_res=(λ*ρ-b_res)*cos(θ_res)-AI*sin(θ_res)+b_res; wherein θ_res and b_res are used to represent corresponding parameters of a horizontal coordinate X and a vertical coordinate Y of the second coordinate rotation axis, the corresponding relationship is as follows: Y=tan(θ_res)*X+b_res.

8. The prediction device of claim 5, wherein, The petrophysical quantity chart is obtained by determining λ*ρ according to the longitudinal wave velocity V p of the rock in the saturated fluid state, the transverse wave velocity V s of the rock in the saturated fluid state, and the density ρ of the rock; wherein, K sat is the effective bulk modulus of the saturated rock, and μ sat is the effective shear modulus of the saturated rock, The petrophysical quantity chart is constructed by taking λ*ρ as the longitudinal coordinate, impedance as the transverse coordinate, and porosity as the attribute of the parameter point.

9. An electronic device, comprising: The electronic device comprises: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the prediction method in any one of claims 1-4. 10.A computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the prediction method in any one of claims 1-4.

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