WO2022198363A1

WO2022198363A1 - Method and device for predicting elastic parameters of shale reservoir, and storage medium

Info

Publication number: WO2022198363A1
Application number: PCT/CN2021/082006
Authority: WO
Inventors: 印兴耀; 印林杰; 宗兆云; 吴国忱; 张广智; 曹丹平; 张繁昌; 梁锴; 张佳佳; 李坤
Original assignee: 中国石油大学(华东)
Priority date: 2021-03-22
Filing date: 2021-03-22
Publication date: 2022-09-29

Abstract

The present application provides a method and device for predicting elastic parameters of a shale reservoir, and a storage medium. The method comprises: acquiring well logging data of shale; using a SCA model to determine the effective elastic modulus of a background matrix; performing free gas addition on hard pores by using an inclusion model, so as to determine the effective elastic modulus of the background matrix containing hard pores; using a micro-nano pore model that uses an adsorption gas ratio and the elastic modulus of an adsorption gas as surface elastic parameters to determine the effective elastic modulus of a nano-porous organic substance considering the adsorption gas, the surface effect of micro-nano pores in the micro-nano pore model being positively correlated with the adsorption gas ratio; using a Gassmann equation to determine the effective elastic modulus of a saturated nanoporous organic substance; using an anisotropic inclusion model to obtain an equivalent elastic matrix of the shale; and determining, according to the equivalent elastic matrix and density of the shale, the equivalent longitudinal wave velocity and the equivalent transverse wave velocity of the shale. Thus, the reasonable prediction of elastic parameters of a shale reservoir is achieved.

Description

A method, device and storage medium for predicting elastic parameters of shale reservoir

technical field

The present application relates to the field of oil and gas exploration, and in particular, to a method, device and storage medium for predicting elastic parameters of shale reservoirs.

Background technique

The acquisition of rock elastic parameters (elastic modulus, velocity) is an important step in seismic exploration. Especially in seismic forward and inversion methods, the relevant seismic convolution model or wave equation is a function of rock compressional wave velocity, shear wave velocity and density. The current seismic exploration technology cannot directly obtain the elastic parameters of the underground medium. In order to better simulate the actual situation or obtain the physical properties of the underground reservoir, it is necessary to obtain accurate rock elastic parameters by means of petrophysical methods.

At present, there are mainly two types of methods for obtaining rock elastic parameters by petrophysical methods, namely 1) core acquisition based on laboratory measurements, and 2) theoretical estimation based on petrophysical modeling methods. The latter is based on reasonable assumptions that the actual rock is equivalent to an ideal medium, and the quantitative relationship between petrophysical parameters and reservoir elastic parameters is established by using the principles of physics. Because of its convenience, this method is often used in the prediction of rock elastic parameters in seismic exploration.

Petrophysical modeling typically divides rocks into three parts: background minerals, pores, and pore fluids. Accordingly, the petrophysical modeling process can also be divided into three parts: 1) Calculate the elastic modulus of the background matrix; 2) Calculate the elastic modulus of the dry rock (matrix rock with dry pores); 3) Calculate the elastic modulus of the fluid-bearing rock quantity. In particular, Xu and White, 1995, 'A new velocity model for clay-sand mixtures', Geophysical prospecting, 43(1): 91-118, first proposed a new method using the wyllie equation, the DEM model, the KT model and the Gassmann equation A method for establishing petrophysical models of clastic rock reservoirs, which has been widely used in the quantitative estimation of the equivalent elastic modulus of clastic rock reservoirs. With the deepening of seismic exploration, exploration objects have shifted from conventional clastic reservoirs to unconventional reservoirs, such as shale reservoirs. The minerals and pores with special physical properties in these reservoirs have attracted the attention of scholars, and the corresponding petrophysical modeling methods have also been improved. Zhu, et al., 2012, ‘Improved rock-physics model for shale gas reservoir’, SEG Annual Meeting, established an anisotropic rock physics model considering the influence of TOC in shale on rock elastic properties. Zhao et al.,2016,'Rock-physics modeling for the elastic properties of organic shale at different maturity stages',Geophysics,81(5):D527-D541, considering the relationship between TOC maturity and internal pore development in shale , the corresponding shale petrophysical model was constructed, and the influence of TOC on the overall elastic properties of shale under different maturity models was analyzed.

The complex characteristics of shale make it difficult to model petrophysically. The pore types of shale reservoirs are various, and the shale elastic parameters calculated by the models in the related technologies will have certain errors.

SUMMARY OF THE INVENTION

In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a method, a device and a storage medium for predicting elastic parameters of a shale reservoir.

In a first aspect, the present application provides a method for predicting elastic parameters of a shale reservoir, comprising: acquiring logging data of shale, wherein the logging data includes elastic modulus of minerals, quartz content, clay content, Adsorbed gas fraction, density, and porosity; use the SCA model to determine the effective elastic modulus of the background matrix based on the quartz content and clay content of the minerals; use the inclusion model to add free gas to hard pores to determine the effective elastic modulus of the background matrix The effective elastic modulus of the background matrix containing hard pores is determined according to the porosity of shale and the bulk modulus of free gas; the micro-nano surface elastic modulus is used with the proportion of adsorbed gas and the elastic modulus of adsorbed gas as the surface elastic parameters. The pore model determines the effective elastic modulus of the nanoporous organic matter considering the adsorbed gas according to the porosity of the shale, the proportion of adsorbed gas and the elastic modulus of the nanoporous organic matter. The effect is positively related to the proportion of adsorbed gas; using the Gassmann equation, the porosity of saturated nanoporous organic matter considering adsorbed gas and free gas is determined based on porosity, bulk modulus of free gas, and effective elastic modulus of nanoporous organic matter considering adsorbed gas and free gas. Effective elastic modulus; using the anisotropic inclusion model, adding saturated nanoporous organic matter to the hard pore-bearing background matrix to obtain the equivalent elastic matrix of the shale; and determining the shale based on the equivalent elastic matrix and density of the shale The equivalent longitudinal wave velocity and the equivalent shear wave velocity.

In certain embodiments, the effective elastic modulus of the nanoporous organic matter considering adsorbed gas is determined as follows:

in

ΔK=K ₁ -K ₂ ,

in,

represents the porosity, K1 and _μ1 represent the bulk modulus and shear modulus _of the _nanoporous organic matter, respectively, K2 and _μ2 represent the bulk modulus and shear modulus of the adsorbed gas, respectively, ΔK represents the volume of the nanoporous organic matter The difference between the modulus and the bulk modulus of the adsorbed gas, K ^eff represents the effective bulk modulus of the nanoporous organic matter considering the adsorbed gas, and α represents the proportion of the adsorbed gas.

In certain embodiments, the effective elastic modulus of the background matrix is determined as follows:

in,

and

represent the effective bulk modulus and effective shear modulus of the background matrix, respectively, P ^*Qua and Q ^*Qua are the geometric factors of quartz, P ^*Clay and Q ^*Clay are the geometric factors of clay, Qua and Clay represent the quartz content and Clay content, K _Qua is the bulk modulus of quartz, μ _Qua is the shear modulus of quartz, K _Clay is the bulk modulus of clay, and μ _Clay is the shear modulus of clay.

In certain embodiments, the inclusion model for determining the effective elastic modulus of the hard pore-containing background matrix includes: the SCA model, the KT model, or the DEM model.

In a second aspect, the present application provides an apparatus for predicting elastic parameters of a shale reservoir, comprising: an acquisition module configured to acquire logging data of shale, wherein the logging data includes elastic modulus of minerals, Quartz content, clay content, proportion of adsorbed gas, density and porosity; a first determination module, configured to use the SCA model, to determine the effective elastic modulus of the background matrix based on the quartz content and clay content of the minerals; a second determination module, Configured to perform free gas addition to hard pores using an inclusion model, with the effective elastic modulus of the background matrix as an initial value, the effective elastic modulus of the background matrix containing hard pores is determined from the porosity of the shale and the bulk modulus of free gas The third determination module is configured to use the micro-nano pore model with the proportion of adsorbed gas and the elastic modulus of the adsorbed gas as the surface elastic parameters, according to the porosity of the shale, the proportion of adsorbed gas and the elasticity of the nanoporous organic matter modulus, determining the effective elastic modulus of the nanoporous organic matter considering adsorbed gas, wherein in the micro-nanopore model, the surface effect of micro-nanopores is positively correlated with the proportion of adsorbed gas; a fourth determination module, configured to use Gassmann equation, according to the porosity, the bulk modulus of free gas and the effective elastic modulus of nanoporous organic matter considering adsorbed gas, to determine the effective elastic modulus of saturated nanoporous organic matter considering adsorbed gas and free gas; the fifth determination module, is configured to use an anisotropic inclusion model to add saturated nanoporous organic matter to the hard pore containing background matrix to obtain an equivalent elastic matrix of the shale; The effective elastic matrix and density determine the equivalent P-wave velocity and equivalent shear-wave velocity of the shale.

In certain embodiments, the third determination module is configured to determine the effective elastic modulus of the nanoporous organic matter considering the adsorbed gas in the following manner:

in

ΔK=K ₁ -K ₂ ,

in,

In certain embodiments, the first determination module is configured to determine the effective elastic modulus of the background matrix in the following manner:

in,

and

In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, predicting shale storage is realized. The steps of the method for layer elasticity parameters.

In a fourth aspect, the present application provides a computer-readable storage medium, where a program for predicting elastic parameters of a shale reservoir is stored, and the program for predicting elastic parameters of a shale reservoir is executed by a processor to achieve prediction Steps of a method for elastic parameters of a shale reservoir.

Compared with the prior art, the above technical solutions provided in the embodiments of the present application have the following advantages: the method provided in the embodiments of the present application combines the adsorption of micro-nano pores with organic matter to realize the overall rock elastic parameters of the shale reservoir. reasonable forecast.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. In other words, on the premise of no creative labor, other drawings can also be obtained from these drawings.

Fig. 1 is the schematic diagram of adsorption phenomenon caused by surface effect;

Figure 2 shows the relationship between the effective bulk modulus and the proportion of adsorbed gas under different porosity;

3 is a flowchart of an embodiment of a method for predicting elastic parameters of a shale reservoir provided in an embodiment of the present application;

4 is a flowchart of a preferred embodiment of the method for predicting elastic parameters of a shale reservoir provided in the embodiment of the present application;

Fig. 5 shows the variation of p-wave velocity with the proportion of adsorbed gas and the corresponding logging data under different porosity;

Fig. 6 shows the variation of p-wave velocity with the proportion of adsorbed gas and the corresponding logging data under different TOCs;

FIG. 7 is a structural block diagram of an embodiment of the apparatus for predicting elastic parameters of shale reservoirs provided by the embodiment of the present application; and

FIG. 8 is a schematic hardware diagram of an implementation manner of a computer device provided in an embodiment of the present application.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

In the following description, suffixes such as "module", "component" or "unit" used to represent elements are used only to facilitate the description of the present application, and have no specific meaning per se. Thus, "module", "component" or "unit" may be used interchangeably.

A large number of micro- and nano-pores are developed in shale organic matter. When the pore size reaches nanoscale, its elastic properties change greatly due to adsorption and size effects. Conventional shale physical models do not consider the above factors. Therefore, the present application provides a method to avoid the above problems and defects, and provides a method to consider the adsorption effect of micro-nano pores, so as to realize the prediction of elastic parameters of shale reservoirs. In the embodiment of the present application, the adsorption of micro-nano pores is combined with organic matter, so as to realize a reasonable prediction of the overall rock elastic parameters of the shale reservoir.

Micro-Nano Pore Model

A micro-nano pore model established by Sharma, et al., 2004, 'Size-Dependent Eshelby's Tensor for Embedded Nano-Inclusions Incorporating Surface/Interface Energies', Journal of Applied Mechanics, 72(4):663-671, is It is used to characterize the surface effect of media containing micro-nano spherical pores. The effective elastic modulus of the medium is obtained by introducing the surface elastic parameter, as shown in formula (1).

in

In formula (1), K ^s represents the bulk modulus of the surface, which is defined as K ^s =2(λ ^s + μ ^s ), where λ ^s and μ ^s are defined as surface Lame parameters.

is the porosity, and R represents the pore size. K ₁ and μ ₁ are the bulk and shear moduli of the matrix, and K ₂ and μ ₂ are the bulk and shear moduli of the inclusions. Q is defined as shown in formula (2), ΔK is the difference between the bulk modulus of the matrix and the bulk modulus of the inclusions, and ^Keff is the effective bulk modulus.

In shale, pores developed in organic matter are usually nano-scale. When filled with shale gas, adsorption occurs on the pore surface, adsorbing gas molecules of a certain thickness until the energy balance on both sides of the pore surface. At this time, the adsorption thickness is determined by the surface elastic modulus, as shown in the following formula (3).

In formula (3), λ ₂ , μ ₂ are the Lame constants of the adsorbed gas, h ₂ is the thickness of the adsorbed gas, K ^s is the surface bulk modulus, and λ ^s and μ ^s are the surface Lame parameters. It is worth noting that the shear modulus of the gas is zero under normal conditions, while the adsorbed gas molecules are directional and exhibit solid-like properties. It can be considered that this part of the gas has a certain shear modulus.

Therefore, in the embodiments of the present application, the gases in the pores are divided into two categories: one is adsorbed on the surface of the pores, and the other is freely distributed in the remaining pore space, which are called adsorbed gas and free gas respectively. gas), as shown in Figure 1.

Referring to Fig. 1, h ₁ is the thickness of the surface atoms different from the bulk elasticity, h ₂ is the thickness of the adsorbed gas, and R represents the pore size. Then the volume ratio of free gas to total gas is expressed as formula (4).

In formula (4), V _free and V _total are the free gas volume and total gas volume in a single pore, respectively, α is the proportion of adsorbed gas, R represents the pore size, and h ₂ is the thickness of adsorbed gas.

Substitute formula (4) into formula (3) to obtain formula (5):

Among them, α is the proportion of adsorbed gas, R represents the pore size, K ₂ and μ ₂ are the bulk modulus and shear modulus of the inclusions, and k is defined as shown in formula (5).

Substituting formula (5) into formula (1), the effective bulk modulus is obtained as:

in,

is the porosity, K ₁ and μ ₁ are the bulk modulus and shear modulus of the nanoporous organic matter, K ₂ and μ ₂ are the bulk modulus and shear modulus of the inclusions (adsorbed gas in the embodiment of the present application) , ΔK is the difference between the bulk modulus of the nanoporous organic matter and the bulk modulus of the inclusions, k and Q are defined as formula (6), K ^eff is the effective bulk modulus of the nanoporous organic matter containing adsorbed gas, α is The proportion of adsorbed gas.

Here, the surface effect of micro-nanopores is characterized by the proportion of adsorbed gas (α-dependent) rather than surface elastic modulus and pore size (size-dependent). That is, the adsorbed gas ratio affects the overall elastic properties of nanoporous shale.

The effect of the proportion of adsorbed gas α on the effective elastic modulus under different porosity is shown in Figure 2. As shown in Figure 2, an increase in α leads to a decrease in the effective bulk modulus, and the larger the α, the more pronounced the decrease in modulus.

Predicting elastic parameters of shale reservoirs

An embodiment of the present application provides a method for predicting elastic parameters of a shale reservoir. As shown in FIG. 3 , the method includes steps S302 to S314. In this embodiment, the surface adsorption of nano-scale pores is mainly considered. The shale matrix is mainly composed of quartz and clay, and the pores are mainly developed in the matrix, and the pore size is much larger than the nano-scale intergranular pores (hard pores) and nano-scale organic pores. Hard pores are only filled with free gas, while nanopores are filled with both free gas and adsorbed gas due to surface effects.

Step S302, acquiring logging data of shale.

Among them, the logging data includes elastic modulus of minerals, quartz content, clay content, proportion of adsorbed gas, density and porosity.

In step S304, the SCA model is used to determine the effective elastic modulus of the background matrix according to the quartz content and the clay content of the minerals.

Step S306, use the inclusion model to add free gas to the hard pores, take the effective elastic modulus of the background matrix as the initial value, and determine the effective elastic modulus of the background matrix containing hard pores according to the porosity of the shale and the bulk modulus of free gas. quantity.

Step S308 , using the micro-nano pore model with the proportion of adsorbed gas and the elastic modulus of the adsorbed gas as the surface elastic parameters, and according to the porosity of the shale, the proportion of adsorbed gas and the elastic modulus of the nano-porous organic matter, it is determined to consider the adsorbed gas. The effective elastic modulus of nanoporous organic matter.

Among them, in the micro-nano pore model, the surface effect of micro-nano pores is positively correlated with the proportion of adsorbed gas.

Step S310 , using the Gassmann equation, according to the porosity, the bulk modulus of the free gas, and the effective elastic modulus of the nanoporous organic matter considering the adsorbed gas, to determine the effective elastic modulus of the saturated nanoporous organic matter considering the adsorbed gas and the free gas.

Step S312, using the anisotropic inclusion model, adding saturated nanoporous organic matter into the background matrix containing hard pores to obtain an equivalent elastic matrix of shale.

In step S314, the equivalent longitudinal wave velocity and the equivalent shear wave velocity of the shale are determined according to the equivalent elastic matrix and the density of the shale.

In some embodiments, in the above step S308, the effective elastic modulus of the nanoporous organic matter considering the adsorbed gas is determined according to formula (6), wherein,

A preferred implementation of the embodiments of the present application will be described below. As shown in FIG. 4 , it mainly includes determining the effective elastic modulus of the background matrix (step S402 ), determining the effective elastic modulus of the background matrix containing hard pores (step S404 ), and determining the effective elastic modulus of the nanoporous organic matter (step S406 ). and saturated shale equivalent velocity estimation (step S408).

Step S402, determining the effective elastic modulus of the background matrix.

Its components are quartz and clay. The elastic modulus of the background matrix was solved using the SCA model, where the aspect ratios were 1.0 and 0.5, respectively. The SCA model is shown in formula (7).

in,

and

Step S404, determining the effective elastic modulus of the hard pore-containing background matrix.

Due to the low permeability and low porosity of shale, conventional fluid replacement theory such as Gassmann equation is not applicable, and the DEM model (or other inclusion models such as SCA model, KT model, etc.) is used to add free gas-containing hard pores. The pore aspect ratio was set to 0.5. The DEM model is shown in formula (8).

where φ _p is the hard porosity, K _f is the bulk modulus of free gas,

is the effective bulk modulus of the background matrix obtained in step S402; the calculation result

and

are the effective shear modulus and effective bulk modulus of the background matrix with hard pores, respectively,

Iterative initial value for the effective bulk modulus of the matrix with hard pore background, P ^* (φ _p ) geometric factor for pore shape.

Step S406, determining the effective elastic modulus of the nanoporous organic matter.

Due to adsorption, organic matter cannot be directly added to the background matrix, and at the same time, the traditional porous media theory is not applicable. Assuming that the nanopores are spherical, the effective elastic modulus of the nanoporous organic matter considering the adsorbed gas is first obtained by formula (6). It is worth noting that the modulus calculated by Equation (6) only considers the effect of adsorbed gas, and free gas should be added to the remaining pore space using the fluid replacement theory. The elastic modulus applies the Gassmann equation to output the effective elastic modulus of the saturated (including adsorbed and free) nanoporous organic matter.

Finally, the anisotropic SCA model (also DEM model) is used to add nanoporous organic matter into the background matrix containing hard pores, and the equivalent elastic matrix C ^SCA of shale is obtained. The anisotropic SCA model is shown in formula (9).

where N=1 and 2 represent the background matrix with hard pores and saturated nanoporous organic matter, respectively, v _n is the volume fraction of each component, C is the elastic matrix of each component,

is the geometric tensor of nanoporous organic matter.

Step S408, estimating the equivalent velocity of saturated shale.

The equivalent elastic matrix of saturated shale is obtained through the above steps, and the equivalent longitudinal wave velocity and equivalent shear wave velocity are derived according to formula (10).

where c ₃₃ and c ₅₅ are obtained in step S406, and ρ is the density.

Fig. 5 shows the variation trend of p-wave velocity with the proportion of adsorbed gas under different porosity and the corresponding logging data. With the increase of the proportion of adsorbed gas α, the p-wave decreases, and the larger the α, the faster the decrease, indicating that the larger the α, the greater the impact on the effective p-wave velocity of shale. According to formula (2), the surface elastic modulus and pore radius are converted into α, while the surface elastic modulus of shale kerogen is constant, high α means smaller pore radius, so the nanopore parameters and longitudinal wave are established Relationship between velocities: A high proportion of adsorbed gas means a small pore size, which means a large surface effect. In addition, the logging data are in good agreement with the theoretical curve. Fig. 6 shows the variation trend of p-wave velocity with the proportion of adsorbed gas under different TOC and corresponding logging data. When TOC changes, the change trend of p-wave velocity with α is similar to that in Fig. 5.

An embodiment of the present application further provides an apparatus for predicting elastic parameters of a shale reservoir. As shown in FIG. 7 , the apparatus includes: an acquisition module 702 configured to acquire logging data of shale, wherein the logging data includes: The elastic modulus, quartz content, clay content, proportion of adsorbed gas, density and porosity of the mineral; the first determination module 704, connected to the acquisition module 702, is configured to use the SCA model to determine the mineral's quartz content and clay content The effective elastic modulus of the background matrix; the second determination module 706, connected to the first determination module 704, is configured to use the inclusion model to perform free gas addition to the hard pores, with the effective elastic modulus of the background matrix as the initial value, according to The porosity of the shale and the bulk modulus of the free gas determine the effective elastic modulus of the hard pore-containing background matrix; a third determination module 708, connected to the acquisition module 702, is configured to use the percentage of adsorbed gas and the elasticity of adsorbed gas The micro-nano pore model whose modulus is the surface elastic parameter, according to the porosity of shale, the proportion of adsorbed gas and the elastic modulus of nano-porous organic matter, the effective elastic modulus of nano-porous organic matter considering adsorbed gas is determined. The surface effect of the micro-nano pores in the micro-nano pore model is positively correlated with the proportion of adsorbed gas; the fourth determination module 710, connected to the third determination module 708, is configured to use the Gassmann equation, according to the porosity, the bulk modulus of the free gas amount and the effective elastic modulus of the nanoporous organic matter considering the adsorbed gas, determine the effective elastic modulus of the saturated nanoporous organic matter considering the adsorbed gas and the free gas; the fifth determination module 712, the second determination module 706 and the fourth determination module 710 connected and configured to add saturated nanoporous organic matter to the hard pore containing background matrix using an anisotropic inclusion model to obtain an equivalent elastic matrix of shale; and a sixth determination module 714 , which is connected with a fifth determination module 712 is connected and configured to determine the equivalent compressional wave velocity and the equivalent shear wave velocity of the shale according to the equivalent elastic matrix and density of the shale.

This embodiment also provides a computer device. The computer device 20 in this embodiment at least includes but is not limited to: a memory 21 and a processor 22 that can be communicatively connected to each other through a system bus, as shown in FIG. 8 . It should be noted that FIG. 8 only shows the computer device 20 having components 21-22, but it should be understood that it is not required to implement all of the illustrated components, and more or less components may be implemented instead.

In this embodiment, the memory 21 (ie, a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (eg, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), Magnetic Memory, Magnetic Disk, Optical Disk, etc. In some embodiments, the memory 21 may be an internal storage unit of the computer device 20 , such as a hard disk or a memory of the computer device 20 . In other embodiments, the memory 21 may also be an external storage device of the computer device 20, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc. Of course, the memory 21 may also include both the internal storage unit of the computer device 20 and its external storage device. In this embodiment, the memory 21 is generally used to store the operating system installed in the computer device 20 and various application software, such as program codes of a method for predicting elastic parameters of a shale reservoir, and the like. In addition, the memory 21 can also be used to temporarily store various types of data that have been output or will be output.

In some embodiments, the processor 22 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 22 is typically used to control the overall operation of the computer device 20 . In this embodiment, the processor 22 is configured to run program codes or process data stored in the memory 21, such as program codes for predicting elastic parameters of shale reservoirs, so as to implement a method for predicting elastic parameters of shale reservoirs.

This embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), only Read-only memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Programmable Read-Only Memory (PROM), magnetic memory, magnetic disk, optical disk, server, App application mall, etc., on which computer programs are stored, When the program is executed by the processor, the corresponding function is realized. The computer-readable storage medium of this embodiment is used for storing a program for predicting elastic parameters of shale reservoirs, and when executed by a processor, implements the steps of the method for predicting elastic parameters of shale reservoirs.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages or disadvantages of the embodiments.

From the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk, CD), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present invention.

The embodiments of the present application have been described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific embodiments, which are merely illustrative rather than restrictive. Under the inspiration of this application, many forms can be made without departing from the scope of protection of the purpose of this application and the claims, which all fall within the protection of this application.

Industrial Applicability

The method provided in the embodiment of the present application combines the adsorption of micro-nano pores with organic matter, and realizes the reasonable prediction of the overall rock elastic parameters of the shale reservoir, which has industrial practicability.

Claims

A method for predicting elastic parameters of a shale reservoir, comprising:

Obtaining logging data of shale, wherein the logging data includes elastic modulus, quartz content, clay content, proportion of adsorbed gas, density and porosity of minerals;

Use the SCA model to determine the effective elastic modulus of the background matrix based on the quartz content and clay content of the minerals;

Using the inclusion model to add free gas to the hard pores, the effective elastic modulus of the background matrix is used as the initial value, and the effective elastic modulus of the background matrix containing hard pores is determined according to the porosity of the shale and the bulk modulus of free gas;

Using the micro-nano pore model with the proportion of adsorbed gas and the elastic modulus of adsorbed gas as the surface elastic parameters, according to the porosity of shale, the proportion of adsorbed gas and the elastic modulus of nanoporous organic matter, the nanoporous pores considering adsorbed gas are determined. The effective elastic modulus of the organic matter, wherein in the micro-nano pore model, the surface effect of the micro-nano pores is positively correlated with the proportion of adsorbed gas;

Using the Gassmann equation, determine the effective elastic modulus of saturated nanoporous organic matter considering adsorbed gas and free gas based on porosity, bulk modulus of free gas, and effective elastic modulus of nanoporous organic matter considering adsorbed gas;

Using an anisotropic inclusion model, adding saturated nanoporous organic matter to a hard pore-bearing background matrix yields an equivalent elastic matrix for shale; and

The equivalent longitudinal wave velocity and the equivalent shear wave velocity of the shale are determined according to the equivalent elastic matrix and density of the shale.
The method of claim 1, wherein the effective elastic modulus of the nanoporous organic matter taking into account adsorbed gas is determined as follows:

in

in

ΔK=K 1 -K 2 ,

in,
represents the porosity, K1 and μ1 represent the bulk modulus and shear modulus of the nanoporous organic matter, respectively, K2 and μ2 represent the bulk modulus and shear modulus of the adsorbed gas, respectively, ΔK represents the volume of the nanoporous organic matter The difference between the modulus and the bulk modulus of the adsorbed gas, K eff represents the effective bulk modulus of the nanoporous organic matter considering the adsorbed gas, and α represents the proportion of the adsorbed gas.
The method of claim 1, wherein the effective elastic modulus of the background matrix is determined as follows:

in,
and
represent the effective bulk modulus and effective shear modulus of the background matrix, respectively, P *Qua and Q *Qua are the geometric factors of quartz, P *Clay and Q *Clay are the geometric factors of clay, Qua and Clay represent the quartz content and Clay content, K Qua is the bulk modulus of quartz, μ Qua is the shear modulus of quartz, K Clay is the bulk modulus of clay, and μ Clay is the shear modulus of clay.
The method of claim 1, wherein the inclusion model for determining the effective elastic modulus of the hard pore-containing background matrix comprises: an SCA model, a KT model, or a DEM model.
A device for predicting elastic parameters of a shale reservoir, comprising:

an acquisition module configured to acquire logging data of shale, wherein the logging data includes elastic modulus of minerals, quartz content, clay content, proportion of adsorbed gas, density and porosity;

a first determination module configured to use the SCA model to determine the effective elastic modulus of the background matrix based on the mineral's quartz content and clay content;

The second determination module is configured to use the inclusion model to add free gas to the hard pores, with the effective elastic modulus of the background matrix as the initial value, and determine the background of the hard pores according to the porosity of the shale and the bulk modulus of the free gas the effective elastic modulus of the matrix;

The third determination module is configured to use the micro-nano pore model with the proportion of adsorbed gas and the elastic modulus of the adsorbed gas as the surface elastic parameters, according to the porosity of the shale, the proportion of adsorbed gas and the elastic modulus of the nanoporous organic matter , determine the effective elastic modulus of the nanoporous organic matter considering the adsorbed gas, wherein, in the micro-nano pore model, the surface effect of the micro-nano pore is positively correlated with the proportion of the adsorbed gas;

A fourth determination module is configured to use the Gassmann equation to determine the effectiveness of saturated nanoporous organic matter considering adsorbed gas and free gas based on porosity, bulk modulus of free gas, and effective elastic modulus of nanoporous organic matter considering adsorbed gas. Elastic Modulus;

a fifth determination module configured to add saturated nanoporous organic matter to the hard pore-bearing background matrix to obtain an equivalent elastic matrix of the shale using an anisotropic inclusion model; and

The sixth determination module is configured to determine the equivalent longitudinal wave velocity and the equivalent shear wave velocity of the shale according to the equivalent elastic matrix and the density of the shale.
The apparatus of claim 5, wherein the third determination module is configured to determine the effective elastic modulus of the nanoporous organic matter considering adsorbed gas in the following manner:

in

in

ΔK=K 1 -K 2 ,

in,
represents the porosity, K1 and μ1 represent the bulk modulus and shear modulus of the nanoporous organic matter, respectively, K2 and μ2 represent the bulk modulus and shear modulus of the adsorbed gas, respectively, ΔK represents the volume of the nanoporous organic matter The difference between the modulus and the bulk modulus of the adsorbed gas, K eff represents the effective bulk modulus of the nanoporous organic matter considering the adsorbed gas, and α represents the proportion of the adsorbed gas.
The apparatus of claim 5, wherein the first determining module is configured to determine the effective elastic modulus of the background matrix in the following manner:

in,
and
represent the effective bulk modulus and effective shear modulus of the background matrix, respectively, P *Qua and Q *Qua are the geometric factors of quartz, P *Clay and Q *Clay are the geometric factors of clay, Qua and Clay represent the quartz content and Clay content, K Qua is the bulk modulus of quartz, μ Qua is the shear modulus of quartz, K Clay is the bulk modulus of clay, and μ Clay is the shear modulus of clay.
6. The apparatus of claim 5, wherein the inclusion model for determining the effective elastic modulus of the hard pore-containing background matrix comprises: an SCA model, a KT model, or a DEM model.
A computer device comprising:

a memory, a processor, and a computer program stored on the memory and executable on the processor;

The computer program when executed by the processor implements the steps of the method for predicting elastic parameters of a shale reservoir as claimed in any one of claims 1 to 4.
A computer-readable storage medium on which a program for predicting elastic parameters of shale reservoirs is stored, and when the program for predicting elastic parameters of shale reservoirs is executed by a processor, the implementation as claimed in claim 1 to The steps of any one of 4 methods for predicting elastic parameters of a shale reservoir.