CN111077568B

CN111077568B - Method and equipment for detecting oil and gas reservoir by fluid factor of tight oil and gas reservoir

Info

Publication number: CN111077568B
Application number: CN201911327436.0A
Authority: CN
Inventors: 李生杰; 康永尚; 赵群; 王红岩; 张方南
Original assignee: China National Petroleum Corp Science And Technology Research Institute Co ltd; China University of Petroleum Beijing
Current assignee: China National Petroleum Corp Science And Technology Research Institute Co ltd; China University of Petroleum Beijing
Priority date: 2019-12-20
Filing date: 2019-12-20
Publication date: 2021-04-23
Anticipated expiration: 2039-12-20
Also published as: CN111077568A

Abstract

The embodiment of the invention provides a method and equipment for detecting a hydrocarbon reservoir by using a fluid factor of a tight hydrocarbon reservoir, wherein the method comprises the following steps: constructing a matrix modulus prediction model of the tight sandstone according to the actual core sample of the tight reservoir and the test data thereof; according to the actually measured core porosity and sound wave speed, a rock physical model under the dry condition of a compact reservoir is constructed by adopting a cemented sandstone theory; adopting a Gassmann equation, and combining the prediction result of the dry compact sandstone rock physical model to perform fluid replacement analysis and conversion of parameters related to the Lame modulus, and performing fluid sensitivity analysis of the parameters related to the Lame modulus; determining an equivalent fluid factor according to the attribute parameters of fluid replacement calculation and by combining actual drilling logging information; acquiring elastic parameters of a target layer by adopting a seismic inversion method, and calculating a seismic fluid factor data volume by combining logging data; and performing seismic fluid detection analysis according to the newly constructed seismic fluid factor to predict the distribution of the seismic oil and gas reservoir.

Description

Method and equipment for detecting oil and gas reservoir by fluid factor of tight oil and gas reservoir

Technical Field

The embodiment of the invention relates to the technical field of unconventional oil and gas exploration, in particular to a method and equipment for detecting a tight oil and gas reservoir by using fluid factors.

Background

The research of the oil and gas reservoir exploration technology is always important for research and exploration of oil and gas explorationists at home and abroad because the formation mechanism and the distribution rule of the compact oil and gas reservoir are complex, the exploration difficulty is high, the technical requirement is high, and the exploration has the characteristics of high difficulty and high risk. A method for predicting the characteristics and distribution of dense oil and gas is disclosed. Particularly, reservoir fluid detection technology is widely applied at home and abroad. The seismic inversion and analysis in the seismic method play an important role in reservoir oil-gas-bearing detection and fluid identification. For fluid identification, from the seventies of the last century, exploration researchers have conducted various analysis and research on reservoir fluid identification methods based on seismic data, and have also obtained various methods which are sensitive to fluid identification. Ostrander (1984) proposed identifying high porosity areas in gas sands using the Poisson ratio; smith and Gidlow (1987) innovatively applied Castagna mudstone baselines to weighted prestack seismic data, initially proposed fluid identification factors to estimate rock properties for gas reservoir detection, and discovered through studies that when hydrocarbons are present or lithology changes, the data curves deviate from the mudstone baselines

The conventional fluid factor detection method at present adopts the velocity weighted difference of longitudinal waves and transverse waves to predict reservoir fluid. However, due to the restriction of factors such as low porosity, weak reservoir stratum difference, large density inversion result difference and the like of the dense oil-gas rock, the fluid factor profile obtained by the conventional fluid factor detection method is poor in identification effect, and the distribution of reservoir fluids cannot be accurately predicted.

Disclosure of Invention

The embodiment of the invention provides a method and equipment for detecting a hydrocarbon reservoir by using a dense hydrocarbon reservoir fluid factor, which are used for solving the problems that the fluid factor profile obtained by a conventional fluid factor detection method is poor in identification effect and the distribution of reservoir fluid cannot be accurately predicted due to the restriction of low porosity, weak reservoir stratum difference, large density inversion result difference and other factors of dense hydrocarbon rock.

In a first aspect, an embodiment of the present invention provides a method for detecting a hydrocarbon reservoir by using a tight hydrocarbon reservoir fluid factor, including:

step A: constructing a matrix modulus prediction model of the tight sandstone according to the actual core sample of the tight reservoir and the test data thereof;

and B: according to the actually measured core porosity and sound wave speed, a rock physical model under the dry condition of a compact reservoir is constructed by adopting a cemented sandstone theory;

and C: adopting a Gassmann equation, and combining the prediction result of the dry compact sandstone rock physical model to perform fluid replacement analysis and conversion of parameters related to the Lame modulus, and performing fluid sensitivity analysis of the parameters related to the Lame modulus;

step D: determining an equivalent fluid factor according to the attribute parameters of fluid replacement calculation and by combining actual drilling logging information;

step E: acquiring elastic parameters of a target layer by adopting a seismic inversion method, and calculating a seismic fluid factor data volume by combining logging data to obtain a newly constructed seismic fluid factor;

step F: and performing seismic fluid detection analysis according to the newly constructed seismic fluid factor to predict the distribution of the seismic oil and gas reservoir.

In one possible design, step a specifically includes:

step A1: performing rock physical parameter tests including X-ray diffraction (XRD) analysis of the core sample, porosity test of a dry sample and longitudinal and transverse wave velocity test according to the core sample of the actual compact sandstone reservoir, and determining mineral components, the porosity and the longitudinal and transverse wave velocity information of the dry sample;

step A2: according to the mineral composition and proportion relation determined by XRD analysis, a Hill average method is used for calculating the matrix elastic modulus of the compact reservoir, including the volume modulus and the shear modulus, determining the matrix elastic modulus variation range of the compact sandstone with different clay contents, and constructing a prediction model of the rock matrix elastic modulus, as shown in equations (1) and (2):

in the formula, K_mAnd mu_mRespectively the matrix modulus of the rock; k_iAnd mu_iVolume and shear modulus of different minerals, respectively; f. of_iExpressing the volume ratio of the mineral in the i; n represents the number of minerals constituting the rock.

In a possible design, the step B specifically includes:

step B1: determining a prediction range of the dry rock sample model according to the maximum value of the actually measured porosity; setting critical porosity phi of tight reservoir_c40 percent;

step B2: calculating the bulk modulus and shear modulus of the dry rock at the high porosity end by adopting a contact cemented sandstone model,

in the formula, K_HMAnd mu_HMBulk and shear moduli of the dry rock, respectively; phi and phi_cRock porosity and critical porosity, respectively; p is the effective formation pressure, i.e., the difference between the confining pressure and the pore pressure; μ and v are the shear modulus and poisson's ratio of the rock, respectively; n is the coordination number of the rock particles, i.e. the average number of contact points of all particles;

step B3: according to the porosity and the longitudinal and transverse wave speeds tested by different samples, calibrating equations (3) and (4), and determining the variation range of the coordination number n of the compact sandstone;

step B4: combining the dry rock volume K determined in step B3 with a Hashin-Shtrikman model, namely the HS model_HMAnd shear modulus mu_HMExtrapolating the dry volume and the shear modulus of the compact sandstone with different porosity by using a lower limit formula of the HS model, thereby constructing and obtaining a dry compact reservoir rock physical model; as shown in equations (5) - (7);

bulk modulus K of dry tight sandstone_drvAnd shear modulus mu_dryThe prediction model of (2) is as follows:

in one possible design, the step C specifically includes:

step C1: according to the physical theory model based on the tight sandstone rocks, namely equations (1), (2), (5), (6) and (7); and (3) filling pore fluid by adopting Gassaman equations, namely equations (8) and (9), and respectively determining the bulk modulus and the density of the rock in a water-saturated, gas-saturated or oil-saturated state, wherein the calculation equation is as follows:

μ_wet＝μ_dry (9)

ρ_b＝ρ_m(1-φ)+φρ_f(10)

in the formula, K_wetIs the bulk modulus of the rock after saturation with fluid; k_fIs the bulk modulus of the pore fluid; mu.s_wetAnd mu_dryVolume and shear modulus of saturated and dry rock, respectively; rho_b、ρ_mAnd ρ_fSaturated fluid rock density, dry rock density and pore fluid density, respectively;

step C2: respectively calculating attribute parameters of the Lame modulus, the Poisson ratio and the longitudinal and transverse wave impedance of the fluid-containing rock according to the volume and the shear modulus of rocks saturated with different fluids; and (3) carrying out fluid sensitivity attribute analysis to obtain an absolute change rate FA and a relative change rate FR of the elastic parameter, wherein:

FA＝|A_w-A_i| (11)

in the formula, A is an attribute parameter, a subscript w represents water, and a subscript i represents gas or oil;

step C3: and determining the fluid sensitivity of the parameter related to the Lame modulus according to the absolute change rate FA and the relative change rate FR of the elastic parameter.

In one possible design, step D specifically includes:

step D1: according to the drilling and logging data, the longitudinal and transverse wave speeds and the density of the oil and gas reservoir and the cover layer are statistically analyzed; respectively determining the attributes of the Lame modulus, the longitudinal wave impedance and the transverse wave impedance of the cover layer and the reservoir; respectively calculating difference values and average values of the Lame modulus, the shear modulus and the density of the reservoir and the cover layer by combining the attribute parameters of the reservoir with different lithologies and fluid properties calculated in the step C2;

step D2: the equivalent longitudinal wave modulus AP and the equivalent shear wave modulus AS are calculated according to the following equations (13) and (14):

in the formula, AP and AS are respectively equivalent longitudinal wave modulus and equivalent transverse wave modulus; λ, μ and ρ represent the (first) lamel modulus, shear modulus and density, respectively; the delta symbol represents the difference between the cap layer and the reservoir layer as the subsequent parameter;

step D3: determining an equivalent fluid factor F according to the following formula (15);

F＝APsinθ+ASconθ (15)

and theta is a fluid factor rotation angle and can be determined according to the elasticity parameters of the actual oil and gas reservoir.

In one possible design, step E specifically includes:

step E1: fitting coefficients k, m, a, and b in the equation according to the relationship between the velocity of longitudinal waves, the velocity of transverse waves, and the density shown in equations (16) and (17) based on actual logging data:

V_p＝kV_s+m (16)

in the formula, V_pAnd V_sThe longitudinal wave speed and the transverse wave speed of the stratum are respectively; ρ is the density of the formation; k, m, a and b are fitting coefficients;

step E2: performing prestack inversion on the seismic data, and determining the seismic longitudinal wave impedance and the seismic transverse wave impedance of a target layer;

step E3: substituting equations (16) and (17) into equations (13) and (14) eliminates the density ρ, resulting in equations (18) and (19), as follows:

in the formula I_pLongitudinal wave impedance for seismic inversion; v_SIs the transverse wave velocity; gamma is the ratio of the longitudinal wave velocity to the transverse wave velocity;

step E4: respectively calculating seismic equivalent longitudinal and transverse wave modulus data volumes according to equations (18) and (19) according to fitting coefficients fitted by the equations (16) and (17) and longitudinal and transverse wave impedance of seismic inversion; and constructing a seismic equivalent fluid factor data volume according to equation (15).

In one possible design, step F specifically includes:

step F1: determining a threshold value for identifying the equivalent fluid factor of the oil and gas reservoir according to the known equivalent fluid factor of the oil and gas reservoir and the equivalent fluid factor of the water layer;

step F2: performing threshold analysis on the equivalent fluid factor seismic data volume, taking the seismic data with the equivalent fluid factor value smaller than the threshold as a background, and depicting a distribution area with a higher value of the equivalent fluid factor;

step F3: calibrating the equivalent fluid factor prediction result according to the information of the known oil-gas well; and adjusting the equivalent fluid factor threshold until the optimal oil and gas prediction result of the research area is obtained.

In a second aspect, an embodiment of the present invention provides an apparatus for tight hydrocarbon reservoir fluid factor detection of a hydrocarbon reservoir, including:

the first model building module is used for building a matrix modulus prediction model of the tight sandstone according to the actual core sample of the tight reservoir and the test data thereof;

the second model building module is used for building a rock physical model under the drying condition of the compact reservoir by adopting a cemented sandstone theory according to the actually measured core porosity and the actually measured sound wave speed;

the fluid sensitivity analysis module is used for performing fluid replacement analysis and conversion of parameters related to the Lame modulus by adopting a Gassmann equation and combining the prediction result of the dry compact sandstone rock physical model, and performing fluid sensitivity analysis of the parameters related to the Lame modulus;

the equivalent fluid factor determining module is used for determining an equivalent fluid factor according to the attribute parameters of the fluid replacement calculation and by combining with actual drilling logging information;

the fluid factor data volume calculation module is used for acquiring elastic parameters of a target layer by adopting a seismic inversion method, and calculating a seismic fluid factor data volume by combining logging information to obtain a newly constructed seismic fluid factor;

and the oil and gas reservoir prediction module is used for carrying out seismic fluid detection analysis according to the newly constructed seismic fluid factor and predicting the distribution of the seismic oil and gas reservoir.

In a third aspect, an embodiment of the present invention provides a computer device, including: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method for tight hydrocarbon reservoir fluid factor detection of a hydrocarbon reservoir as set forth in the first aspect above and in various possible designs of the first aspect.

In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement a method for tight hydrocarbon reservoir fluid factor testing of a hydrocarbon reservoir as set forth in the first aspect above and in various possible designs of the first aspect.

According to the method and the device for detecting the oil and gas reservoir by the fluid factor of the tight oil and gas reservoir, a matrix modulus prediction model of the tight sandstone is constructed according to the actual core sample of the tight reservoir and the test data of the core sample; according to the actually measured core porosity and sound wave speed, a rock physical model under the dry condition of a compact reservoir is constructed by adopting a cemented sandstone theory; adopting a Gassmann equation, and combining the prediction result of the dry compact sandstone rock physical model to perform fluid replacement analysis and conversion of parameters related to the Lame modulus, and performing fluid sensitivity analysis of the parameters related to the Lame modulus; determining an equivalent fluid factor according to the attribute parameters of fluid replacement calculation and by combining actual drilling logging information; acquiring elastic parameters of a target layer by adopting a seismic inversion method, and calculating a seismic fluid factor data volume by combining logging data; and (3) performing seismic fluid detection and analysis according to the newly constructed seismic fluid factor, predicting the distribution of the seismic oil and gas reservoir, obtaining a fluid factor profile with a better recognition effect, and accurately predicting the distribution of reservoir fluid.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a schematic flow chart of a method for detecting a hydrocarbon reservoir by using a tight hydrocarbon reservoir fluid factor according to an embodiment of the invention;

FIG. 2 is a schematic diagram of a rock physics model construction provided by an embodiment of the invention;

FIG. 3 is a graph illustrating changes in a fluid-sensitive parameter before and after a different fluid replacement provided by an embodiment of the present invention;

FIG. 4 is a first fitting graph of the elastic equivalent fluid factor of the pores of the gas layer and the water layer according to the present invention;

FIG. 5 is a second fitting graph of the elastic equivalent fluid factor of the pores of the gas and water layers according to the present invention;

FIG. 6 is a cross plot of the apparent longitudinal and transverse wave moduli of the measured data in accordance with the teachings of the present invention;

FIG. 7 is a cross-sectional view of the equivalent fluid factor A and the equivalent fluid factor B in the present invention;

FIG. 8 is a diagram of a seismic horizon interpretation and velocity model for a study area, which is the subject of the present invention;

FIG. 9 is a schematic diagram of the seismic attribute parameter extraction for the study area according to the present invention;

FIG. 10 is a schematic diagram of the longitudinal and transverse wave impedance profiles obtained by seismic inversion according to the teachings of the present invention;

FIG. 11 is a sectional view of an equivalent flow factor according to the present invention;

FIG. 12 is a cross-sectional view of a conventional flow factor for the purpose of the description of the present invention;

FIG. 13 is a schematic structural diagram of an apparatus for tight hydrocarbon reservoir fluid factor testing of a hydrocarbon reservoir according to an embodiment of the present invention;

fig. 14 is a schematic diagram of a hardware structure of the equipment for detecting a hydrocarbon reservoir by using a tight hydrocarbon reservoir fluid factor according to the embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, fig. 1 is a schematic flow chart of a method for detecting a hydrocarbon reservoir by using a tight hydrocarbon reservoir fluid factor according to an embodiment of the present invention, where an execution subject of the embodiment may be a server or a computer terminal. Referring to fig. 1, the method includes:

s11: and constructing a matrix modulus prediction model of the tight sandstone according to the actual core sample of the tight reservoir and the test data thereof.

Specifically, step S11 may specifically include:

s111: and performing rock physical parameter tests including X-ray diffraction (XRD) analysis of the core sample, porosity test of the dry sample and longitudinal and transverse wave velocity test according to the core sample of the actual compact sandstone reservoir, and determining mineral components, the porosity and the longitudinal and transverse wave velocity information of the dry sample.

S112: according to the mineral composition and proportion relation determined by XRD analysis, a Hill average method is used for calculating the matrix elastic modulus of the compact reservoir, including the volume modulus and the shear modulus, determining the matrix elastic modulus variation range of the compact sandstone with different clay contents, and constructing a prediction model of the rock matrix elastic modulus, as shown in equations (1) and (2):

S12: and constructing a rock physical model under the dry condition of the compact reservoir by adopting a cemented sandstone theory according to the actually measured core porosity and the actually measured sound wave speed.

Specifically, step S12 may specifically include:

s121: determining a prediction range of the dry rock sample model according to the maximum value of the actually measured porosity; setting critical porosity phi of tight reservoir_c40 percent;

s122: calculating the bulk modulus and shear modulus of the dry rock at the high porosity end by adopting a contact cemented sandstone model,

s123: according to the porosity and the longitudinal and transverse wave speeds tested by different samples, calibrating equations (3) and (4), and determining the variation range of the coordination number n of the compact sandstone;

s124: the dry rock volume K determined in step B3 is combined with a Hashin-Shtrikmann HS model, namely an HS model_HMAnd shear modulus mu_HMExtrapolating the dry volume and the shear modulus of the compact sandstone with different porosity by using a lower limit formula of the HS model, thereby constructing and obtaining a dry compact reservoir rock physical model; as shown in equations (5) - (7);

bulk modulus K of dry tight sandstone_dryAnd shear modulus mu_dryThe prediction model of (2) is as follows:

s13: and performing fluid replacement analysis and conversion of parameters related to the Lame modulus by adopting a Gassmann equation and combining the prediction result of the dry and compact sandstone rock physical model, and performing fluid sensitivity analysis of the parameters related to the Lame modulus.

Specifically, step S13 may specifically include:

s131: according to the physical theory model based on the tight sandstone rocks, namely equations (1), (2), (5), (6) and (7); and (3) filling pore fluid by adopting Gassaman equations, namely equations (8) and (9), and respectively determining the bulk modulus and the density of the rock in a water-saturated, gas-saturated or oil-saturated state, wherein the calculation equation is as follows:

μ_wet-μ_dry (9)

ρ_b＝ρ_m(1-φ)+φρ_f (10)

s132: respectively calculating attribute parameters of the Lame modulus, the Poisson ratio and the longitudinal and transverse wave impedance of the fluid-containing rock according to the volume and the shear modulus of rocks saturated with different fluids; and (3) carrying out fluid sensitivity attribute analysis to obtain an absolute change rate FA and a relative change rate FR of the elastic parameter, wherein:

FA＝|A_w-A_i| (11)

s133: and determining the fluid sensitivity of the seismic attribute parameter related to the Lame modulus according to the absolute change rate FA and the relative change rate FR of the elastic parameter.

S14: and determining an equivalent fluid factor according to the attribute parameters of the fluid replacement calculation and by combining actual drilling logging information.

Specifically, step S14 may specifically include:

s141: according to the drilling and logging data, the longitudinal and transverse wave speeds and the density of the oil and gas reservoir and the cover layer are statistically analyzed; respectively determining the attributes of the Lame modulus, the longitudinal wave impedance and the transverse wave impedance of the cover layer and the reservoir; respectively calculating difference values and average values of the Lame modulus, the shear modulus and the density of the reservoir and the cover layer by combining the attribute parameters of the reservoir with different lithologies and fluid properties calculated in the step C2;

s142: the equivalent longitudinal wave modulus AP and the equivalent shear wave modulus AS are calculated according to the following equations (13) and (14):

in the formula, AP and AS are respectively equivalent longitudinal wave modulus and equivalent transverse wave modulus; λ, μ and ρ represent the (first) lamel modulus, shear modulus and density, respectively; the delta symbol indicates that its subsequent parameter is the difference between the cap and reservoir parameters.

S143: determining an equivalent fluid factor F according to the following formula (15);

F＝APsinθ+ASconθ (15)

where θ is the fluid factor rotation angle. Can be determined according to the elasticity parameters of the actual oil and gas reservoir.

S15: and acquiring the elastic parameters of the target layer by adopting a seismic inversion method, and calculating a seismic fluid factor data volume by combining logging data to obtain a newly constructed seismic fluid factor.

Specifically, step S15 may specifically include:

s151: fitting coefficients k, m, a, and b in the equation according to the relationship between the velocity of longitudinal waves, the velocity of transverse waves, and the density shown in equations (16) and (17) based on actual logging data:

V_p＝kV_s+m (16)

in the formula, V_pAnd V_sThe longitudinal wave speed and the transverse wave speed of the stratum are respectively; ρ is the density of the formation; k, m, a and b are the study region fitting coefficients.

S152: performing prestack inversion on the seismic data, and determining the seismic longitudinal wave impedance and the seismic transverse wave impedance of a target layer;

s153: substituting equations (16) and (17) into equations (13) and (14) eliminates the density ρ, resulting in equations (18) and (19), as follows:

in the formula I_pLongitudinal wave impedance for seismic inversion; v_SIs the transverse wave velocity; γ is the ratio of the longitudinal wave velocity to the transverse wave velocity.

S154: respectively calculating seismic equivalent longitudinal and transverse wave modulus data volumes according to equations (18) and (19) according to the fitting coefficients of the study areas fitted by the equations (16) and (17) and the longitudinal and transverse wave impedance of seismic inversion; and constructing a seismic equivalent fluid factor data volume according to equation (15).

S16: and (3) applying the newly constructed seismic fluid factor to carry out seismic fluid detection and analysis and predicting the distribution of the seismic oil and gas reservoir.

Specifically, step S16 specifically includes:

s161: determining a threshold value for identifying the equivalent fluid factor of the oil and gas reservoir according to the known equivalent fluid factor of the oil and gas reservoir and the equivalent fluid factor of the water layer;

s162: performing threshold analysis on the equivalent fluid factor seismic data volume, taking the seismic data with the equivalent fluid factor value smaller than the threshold as a background, and depicting a distribution area with a higher value of the equivalent fluid factor;

s163: calibrating the equivalent fluid factor prediction result according to the information of the known oil-gas well; and adjusting the equivalent fluid factor threshold until the optimal oil and gas prediction result of the research area is obtained.

According to the description of the embodiment, a matrix modulus prediction model of the tight sandstone is constructed according to the actual core sample of the tight reservoir and the test data of the core sample; according to the actually measured core porosity and sound wave speed, a rock physical model under the dry condition of a compact reservoir is constructed by adopting a cemented sandstone theory; adopting a Gassmann equation, and combining the prediction result of the dry compact sandstone rock physical model to perform fluid replacement analysis and conversion of parameters related to the Lame modulus, and performing fluid sensitivity analysis of the parameters related to the Lame modulus; determining an equivalent fluid factor according to the attribute parameters of fluid replacement calculation and by combining actual drilling logging information; acquiring elastic parameters of a target layer by adopting a seismic inversion method, and calculating a seismic fluid factor data volume by combining logging data; and (3) performing seismic fluid detection and analysis according to the newly constructed seismic fluid factor, predicting the distribution of the seismic oil and gas reservoir, obtaining a fluid factor profile with a better recognition effect, and accurately predicting the distribution of reservoir fluid.

The method for detecting the hydrocarbon reservoir by the tight hydrocarbon reservoir fluid factor of the embodiment is described in detail by a specific application example. The reservoir of the target layer in the research area mainly comprises rock debris, quartz sandstone and dense rock, and the cementing materials mainly comprise feldspar, calcite, argillaceous substances and the like, as shown in figure 2.

The method comprises the following steps: constructing a matrix modulus prediction model of the tight sandstone according to the actual core sample of the tight reservoir and the test data thereof; and constructing a rock physical model under the dry condition of the compact reservoir by adopting a cemented sandstone theory according to the actually measured core porosity and the actually measured sound wave speed.

According to the main mineral components and the content of the actual core sample of the compact reservoir, physical analysis is carried out to obtain the relevant parameters such as the porosity, the permeability, the water saturation, the pore fluid property, the cementation index and the like of the core, and the longitudinal and transverse wave speed and density information of the rock.

And combining the actual core slice with the data obtained by the physical property analysis to construct a reasonable rock physical model.

Because the target stratum reservoir in the research area mainly comprises rock debris, namely quartz sandstone, and the rock is compact, and the cementing materials mainly comprise feldspar, calcite, argillaceous substances and the like, a normal cementing sandstone rock physical model is adopted for modeling.

The overall modeling process is as follows:

1. determining the matrix modulus from the rock mineral composition;

2. and calculating the volume and the shear modulus of the high-porosity end framework by using a contact cementation model.

3. Calculating the rock volume and the shear modulus under different porosities according to the type of the pore filler;

4. carrying out argillaceous replacement on skeleton mineral components, and determining the drying volumes and the shear moduli of different lithologies;

5. and (4) replacing pore fluid, determining the volume or speed change of the water-saturated rock, and establishing a compact sandstone calculation model.

The specific construction process refers to the calculation processes of equations (1) - (7), and is not described herein.

Step two: and performing fluid replacement analysis and conversion of parameters related to the Lame modulus by adopting a Gassmann equation and combining the prediction result of the dry and compact sandstone rock physical model, and performing fluid sensitivity analysis of the parameters related to the Lame modulus.

The screening of the fluid sensitive attribute mainly comprises the step of comparing and analyzing the sensitive change degree of the fluid sensitive attribute before and after fluid replacement according to a fluid replacement method of a rock physical model.

Study of Density longitudinal wave V_PAnd transverse wave velocity V_SVelocity ratio V of longitudinal and transverse waves_P/V_SLongitudinal wave impedance I_PTransverse wave impedance I_SImpedance difference of longitudinal wave and transverse wave I_P-I_SThe sensitivity change degree of 14 seismic elasticity parameters such as volume modulus K, shear modulus mu, modulus difference K-G, Lame constant lambda rho and mu rho. Research shows that when the lithology, the pore fluid properties or the fluid occurrence space of reservoirs in a research area are changed, different seismic elastic parameters have different characteristics, and it is generally not determined which seismic elastic parameters are sensitive to the change of the fluid and the lithology, so that certain difficulty exists in parameter selection intersection. Therefore, it is necessary to provide a quantitative evaluation criterion for the fluid change sensitivity of the extracted elastic parameters, which describes the relative magnitude of the change of the property parameters of the specific reservoir rock before and after the pore fluid changes.

The fluid sensitivity evaluation method takes absolute and relative change rates as a standard, wherein the absolute change rate FA refers to the absolute difference of rock properties of two saturated different fluids, and the relative change rate FR refers to the change of relative values aiming at the rock properties of the saturated different fluids.

The equations for the absolute rate of change FA and the relative rate of change FR refer to equations (11) and (12).

As can be seen from fig. 3, it can be analyzed that the more sensitive elastic parameter of the reservoir in the research area is the petrophysical elastic parameter attribute with the lame modulus as the dominant.

Step three: determining an equivalent fluid factor according to the attribute parameters of fluid replacement calculation and by combining actual drilling logging information; and acquiring the elastic parameters of the target layer by adopting a seismic inversion method, and calculating a seismic fluid factor data volume by combining logging data.

And (3) counting the storage-cover relation of the reservoir by using logging information, and carrying out fluid replacement on the data by combining the constructed physical model so as to expand the data volume. And obtaining an empirical coefficient of the projected fluid factor through the fitting parameters.

The fluid of the seismic data is constructed, the reflection coefficient is firstly researched, a rock physical model is utilized, the reflection system is contrastively analyzed, a Lamei modulus combination form sensitive to angle change is obtained, and a new equivalent fluid factor is constructed by using the characteristic of taking the intercept gradient as a main factor.

By counting the reservoir data of the well log data, the data as shown in table 1 can be obtained.

TABLE 1 statistical results of the well logging data of the study area

Forward modeling analysis of the data:

the theoretical basis for forward modeling is the zopritz equation. This equation is used to describe the case of wave propagation at the interface of two different media, and for simple calculations, the latter proposes many approximations of the approximate reflection coefficient.

For these approximate reflection coefficient approximations we mainly fall into the following categories:

1. speed class

2. Impedance difference class

3. Lame modulus class

4. Modulus of pore elasticity class

The reflectance versus angle of incidence was calculated for these five calculations in combination with statistical information for the study area. The four approximate reflection coefficients obtained according to the drawn relation graph of the five reflection coefficients and the incident angle can be used for describing the reflection coefficient relation between two layers of the storage cover. In order to see that of the four reflection coefficients, the parameter affecting the most reflection coefficient is received as the incident angle varies. The four equations (21) to (24) can be subjected to split comparison, and a "reflection coefficient component variation graph with the variation of the incident angle" of the four equations can be obtained. The reflection coefficient characteristics of the oil-gas well area in the research area are compared with the effects of different elastic properties in various approximate solutions, and the changes of the Laume modulus and the elastic modulus inversion fluid are the most sensitive.

The core data is combined with the logging data to perform parameter fitting, so that a 'gas-water layer pore elastic equivalent fluid factor fitting graph' (refer to fig. 4 and 5) can be obtained, and further, the longitudinal and transverse wave speeds, the density and other related parameters of the oil-gas reservoir and the cover layer are analyzed.

The specific equivalent fluid factor constructing process of the present application embodiment refers to S141-S143 and S151-S154, which are not described herein.

Step four: and performing seismic fluid detection analysis according to the newly constructed seismic fluid factor to predict the distribution of the seismic oil and gas reservoir.

And combining the fitting of rock data and well logging data and seismic data prestack inversion rugged parameters to identify the fluid in the research area. And the recognition effect of the conventional fluid and thus the newly constructed seismic fluid factor is compared.

From the measured data, an intersection of the measured data on the apparent longitudinal and transverse wave moduli (refer to fig. 6) and an intersection of the equivalent flow factor a and the equivalent flow factor B (refer to fig. 7) are obtained. The equivalent fluid factors A and B are attribute parameters calculated according to fluid replacement, and the equivalent fluid factors are determined by combining actual drilling logging information.

The equivalent fluid factor can effectively identify gas, oil and water parameters in a high-porosity reservoir, but the result is inaccurate when a low-porosity reservoir is predicted; through equivalent fluid factor component rotation processing, the method can improve the effective identification of the fluid properties of the compact low-porosity reservoir.

Referring to fig. 4 and 5, the coefficients a and b in (16) and (17) and the ratio γ of the shear wave velocity to the longitudinal wave velocity can be solved from the "gas-water pore elastic equivalent fluid factor fitting chart".

The calculation of the angle θ in equation (15) requires selection for different reservoirs in different regions. By preference, the recognition effect of the best fluid is obtained.

The prestack synchronous inversion method is a prestack and poststack combined inversion method which utilizes different detection range gather data and logging data, parameters such as longitudinal wave velocity (wave impedance) and density can be obtained simultaneously, the relationship among the longitudinal wave velocity, the transverse wave velocity and the density is considered in the inversion process by the novel method, and the reservoir lithology and the fluid identification capability are improved.

The well-seismic calibration or the horizon calibration can effectively connect different data of logging, geology and earthquake, the depth domain logging data and the time domain seismic data can be corresponded and matched, the earthquake homophase axis has geological meaning, and the correctness of structure and lithology explanation is effectively improved. The logging curve is depth domain data, the seismic data is time domain data, the well-seismic calibration process is to adjust the time-depth relation, and the depth domain logging data and the time domain seismic data are accurately corresponded. Reference is made to the constructed "study area seismic horizon interpretation and velocity model map (refer to fig. 8)" and "seismic attribute parameter extraction map" (refer to fig. 9).

In order to ensure the accuracy and precision of inversion impedance, pre-stack inversion analysis is carried out on logging wave impedance data, seismic wave impedance data and low-frequency model wave impedance data, so that the wave impedance data are well fitted, and the quality of an inversion result can be ensured.

By combining the information of the longitudinal wave impedance and the transverse wave impedance obtained by inversion (refer to fig. 10, fig. 10 is a longitudinal and transverse wave impedance profile obtained by seismic inversion), and by combining the equivalent fluid factors constructed according to the formulas (16), (17) and (15), an equivalent fluid factor profile can be obtained (refer to fig. 11, fig. 11 is an equivalent fluid factor profile). By comparing with the conventional fluid factor profile (refer to fig. 12, fig. 12 is a conventional fluid factor profile), it can be found that the conventional fluid factor has no recognition effect of the equivalent fluid factor in the partial region.

Referring to fig. 13, fig. 13 is a schematic structural diagram of an apparatus for tight hydrocarbon reservoir fluid factor testing of a hydrocarbon reservoir according to an embodiment of the present invention. As shown in fig. 13, the apparatus 40 includes: a first model building module 401, a second model building module 402, a fluid sensitivity analysis module 403, an equivalent fluid factor determination module 404, a fluid factor data volume calculation module 405, and a hydrocarbon reservoir prediction module 406.

The first model building module 401 is used for building a matrix modulus prediction model of the tight sandstone according to the actual core sample of the tight reservoir and the test data thereof;

the second model building module 402 is used for building a rock physical model under a compact storage and drying condition by adopting a cemented sandstone theory according to the actually measured core porosity and the actually measured sound wave speed;

the fluid sensitivity analysis module 403 is configured to perform fluid replacement analysis and conversion of parameters related to the lame modulus by using a Gassmann equation in combination with a prediction result of the dry tight sandstone rock physical model, and perform fluid sensitivity analysis of parameters related to the lame modulus;

an equivalent fluid factor determination module 404, configured to determine an equivalent fluid factor according to the attribute parameters of the fluid replacement calculation in combination with actual drilling logging data;

a fluid factor data volume calculation module 405, configured to obtain elastic parameters of a target zone by using a seismic inversion method, and calculate a seismic fluid factor data volume by combining logging data to obtain a newly constructed seismic fluid factor;

and the hydrocarbon reservoir prediction module 406 is used for performing seismic fluid detection analysis according to the newly constructed seismic fluid factor and predicting the distribution of the seismic hydrocarbon reservoir.

The device provided in this embodiment may be used to implement the technical solution of the above method embodiment, and the implementation principle and technical effect are similar, which are not described herein again.

In an embodiment of the present invention, the first model building module 401 is specifically configured to:

performing rock physical parameter tests including X-ray diffraction (XRD) analysis of the core sample, porosity test of a dry sample and longitudinal and transverse wave velocity test according to the core sample of the actual compact sandstone reservoir, and determining mineral components, the porosity and the longitudinal and transverse wave velocity information of the dry sample;

according to the mineral composition and proportion relation determined by XRD analysis, a Hill average method is used for calculating the matrix elastic modulus of the compact reservoir, including the volume modulus and the shear modulus, determining the matrix elastic modulus variation range of the compact sandstone with different clay contents, and constructing a prediction model of the rock matrix elastic modulus, as shown in equations (1) and (2):

In an embodiment of the present invention, the second model building module 402 is specifically configured to:

determining a prediction range of the dry rock sample model according to the maximum value of the actually measured porosity; setting critical porosity phi of tight reservoir_c40 percent;

calculating the bulk modulus and shear modulus of the dry rock at the high porosity end by adopting a contact cemented sandstone model,

according to the porosity and the longitudinal and transverse wave speeds tested by different samples, calibrating equations (3) and (4), and determining the variation range of the coordination number n of the compact sandstone;

using a Hashin-Shtrikmann HS model, i.e., HS model, in combination with the dry rock volume K determined above_HMAnd shear modulus mu_HMExtrapolating the dry volume and the shear modulus of the compact sandstone with different porosities by using a lower limit formula of an HS (high-speed materials) model so as to construct and obtain a dry compact reservoir rock objectA physical model; as shown in equations (5) - (7);

in an embodiment of the present invention, the fluid sensitivity analysis module 403 is specifically configured to:

according to the physical theory model based on the tight sandstone rocks, namely equations (1), (2), (5), (6) and (7); and (3) filling pore fluid by adopting Gassaman equations, namely equations (8) and (9), and respectively determining the bulk modulus and the density of the rock in a water-saturated, gas-saturated or oil-saturated state, wherein the calculation equation is as follows:

μ_wet＝μ_dry (9)

ρ_b＝ρ_m(1-φ)+φρ_f (10)

respectively calculating attribute parameters of the Lame modulus, the Poisson ratio and the longitudinal and transverse wave impedance of the fluid-containing rock according to the volume and the shear modulus of rocks saturated with different fluids; and (3) carrying out fluid sensitivity attribute analysis to obtain an absolute change rate FA and a relative change rate FR of the elastic parameter, wherein:

FA＝|A_w-A_i| (11)

and determining the fluid sensitivity of the parameter related to the Lame modulus according to the absolute change rate FA and the relative change rate FR of the elastic parameter.

In an embodiment of the present invention, the equivalent fluid factor determining module 404 is specifically configured to:

according to the drilling and logging data, the longitudinal and transverse wave speeds and the density of the oil and gas reservoir and the cover layer are statistically analyzed; respectively determining the attributes of the Lame modulus, the longitudinal wave impedance and the transverse wave impedance of the cover layer and the reservoir; respectively calculating difference values and average values of the Lame modulus, the shear modulus and the density of the reservoir stratum and the cover stratum by combining the calculated attribute parameters of the reservoir stratum with different lithologies and fluid properties;

the equivalent longitudinal wave modulus AP and the equivalent shear wave modulus AS are calculated according to the following equations (13) and (14):

determining an equivalent fluid factor F according to the following formula (15);

F＝APsinθ+ASconθ (15)

In an embodiment of the present invention, the fluid factor data volume calculation module 405 is specifically configured to:

fitting coefficients k, m, a, and b in the equation according to the relationship between the velocity of longitudinal waves, the velocity of transverse waves, and the density shown in equations (16) and (17) based on actual logging data:

V_p＝kV_s+m (16)

in the formula, V_pAnd V_sThe longitudinal wave speed and the transverse wave speed of the stratum are respectively; ρ is the density of the formation; k, m, a and b are study region fitting coefficients;

performing prestack inversion on the seismic data, and determining the seismic longitudinal wave impedance and the seismic transverse wave impedance of a target layer;

substituting equations (16) and (17) into equations (13) and (14) eliminates the density ρ, resulting in equations (18) and (19), as follows:

respectively calculating seismic equivalent longitudinal and transverse wave modulus data volumes according to equations (18) and (19) according to the fitting coefficients of the study areas fitted by the equations (16) and (17) and the longitudinal and transverse wave impedance of seismic inversion; and constructing a seismic equivalent fluid factor data volume according to equation (15).

In one embodiment of the present invention, the hydrocarbon reservoir prediction module 406 is configured to:

determining a threshold value for identifying the equivalent fluid factor of the oil and gas reservoir according to the known equivalent fluid factor of the oil and gas reservoir and the equivalent fluid factor of the water layer;

performing threshold analysis on the equivalent fluid factor seismic data volume, taking the seismic data with the equivalent fluid factor value smaller than the threshold as a background, and depicting a distribution area with a higher value of the equivalent fluid factor;

calibrating the equivalent fluid factor prediction result according to the information of the known oil-gas well; and adjusting the equivalent fluid factor threshold until the optimal oil and gas prediction result of the research area is obtained.

Fig. 14 is a schematic hardware structure diagram of a computer device according to an embodiment of the present invention. As shown in fig. 14, the equipment for detecting a hydrocarbon reservoir by using a tight hydrocarbon reservoir fluid factor according to the embodiment includes: a processor 501 and a memory 502; wherein

A memory 502 for storing computer-executable instructions;

the processor 501 is configured to execute the computer execution instructions stored in the memory to implement the steps performed by the server or the computer terminal in the above embodiments. Reference may be made in particular to the description relating to the method embodiments described above.

Alternatively, the memory 502 may be separate or integrated with the processor 501.

When the memory 502 is independently arranged, the equipment for detecting the tight hydrocarbon reservoir fluid factor also comprises a bus 503 which is used for connecting the memory 502 and the processor 501.

The embodiment of the invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method for detecting the hydrocarbon reservoir by the tight hydrocarbon reservoir fluid factor is realized.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of modules may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to implement the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit. The unit formed by the modules can be realized in a hardware form, and can also be realized in a form of hardware and a software functional unit.

The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a processor to execute some steps of the methods described in the embodiments of the present application.

It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc.

The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (Extended Industry standard architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

The storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in an electronic device or host device.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting a hydrocarbon reservoir by a tight hydrocarbon reservoir fluid factor is characterized by comprising the following steps:

step F: performing seismic fluid detection analysis according to the newly constructed seismic fluid factor to predict the distribution of the seismic oil and gas reservoir;

the step D specifically comprises the following steps:

step D1: according to the drilling and logging data, the longitudinal and transverse wave speed and density relations of the oil and gas reservoir and the cover layer are statistically analyzed; respectively determining the attributes of the Lame modulus, the longitudinal wave impedance and the transverse wave impedance of the cover layer and the reservoir; respectively calculating attribute parameters of the Lame modulus, the Poisson ratio and the longitudinal and transverse wave impedance of the fluid-containing rock according to the volume and the shear modulus of rocks saturated with different fluids; respectively calculating difference values and average values of the Lame modulus, the shear modulus and the density of the reservoir stratum and the cover stratum by combining the calculated attribute parameters of the reservoir stratum with different lithologies and fluid properties;

step D2: the equivalent longitudinal wave modulus AP and the equivalent shear wave modulus AS are calculated according to the following equations (1) and (2):

in the formula, AP and AS are respectively equivalent longitudinal wave modulus and equivalent transverse wave modulus; λ, μ and ρ represent the lamel modulus, shear modulus and density, respectively; the delta symbol represents the difference between the cap layer and the reservoir layer as the subsequent parameter;

step D3: determining an equivalent fluid factor F according to the following formula (3);

F＝AP sinθ+AS cosθ (3)

2. The method according to claim 1, wherein the step a specifically comprises:

step A2: according to the mineral composition and proportion relation determined by XRD analysis, a Hill average method is used for calculating the matrix elastic modulus of the compact reservoir, including the volume modulus and the shear modulus, determining the matrix elastic modulus variation range of the compact sandstone with different clay contents, and constructing a prediction model of the rock matrix elastic modulus, as shown in equations (4) and (5):

in the formula, K_mAnd mu_mRespectively the matrix modulus of the rock; k_iAnd mu_iVolume and shear modulus of different minerals, respectively; f. of_iExpressing the volume ratio of the mineral in the i; n represents the number of minerals constituting the rock; n is the coordination number of the rock particles, i.e. the average number of contacts of all particles.

3. The method according to claim 2, wherein step B specifically comprises:

in the formula, K_HMAnd mu_HMBulk and shear moduli of the dry rock, respectively; phi and phi_cRock porosity and critical porosity, respectively; p is the effective formation pressure, i.e., the difference between the confining pressure and the pore pressure; μ and v are the shear modulus and poisson's ratio of the rock, respectively;

step B3: according to the porosity and the longitudinal and transverse wave speeds tested by different samples, calibrating equations (6) and (7), and determining the variation range of the coordination number n of the compact sandstone;

step B4: combining the dry rock volume K determined in step B3 with a Hashin-Shtrikman model, namely the HS model_HMAnd shear modulus mu_HMExtrapolating the dry volume and the shear modulus of the compact sandstone with different porosity by using a lower limit formula of the HS model, thereby constructing and obtaining a dry compact reservoir rock physical model; as shown in equation (8) - (10);

4. the method according to claim 3, wherein step C specifically comprises:

step C1: according to the physical theory model based on the tight sandstone rocks, namely equations (4), (5), (8), (9) and (10); and (3) filling pore fluid by adopting Gassaman equation, namely equation (11) -12, and respectively determining the bulk modulus and the density of the rock in a water-saturated, gas-saturated or oil-saturated state, wherein the calculation equation is as follows:

μ_wet＝μ_dry (12)

ρ_b＝ρ_m(1-φ)+φρ_f (13)

FA＝|A_w-A_i| (14)

step C3: and determining the fluid sensitivity of related parameters such as the Lame modulus and the like according to the absolute change rate FA and the relative change rate FR of the elastic parameters.

5. The method according to claim 4, wherein the step E specifically comprises:

V_p＝kV_s+m (16)

step E3: substituting equations (16) and (17) into equations (1) and (2) eliminates the density ρ, resulting in equations (18) and (19), as follows:

step E4: respectively calculating seismic equivalent longitudinal and transverse wave modulus data volumes according to equations (18) and (19) according to fitting coefficients fitted by the equations (16) and (17) and longitudinal and transverse wave impedance of seismic inversion; and constructing a seismic equivalent fluid factor data volume according to equation (3).

6. The method according to claim 5, wherein step F specifically comprises:

step F3: calibrating the equivalent fluid factor prediction result according to the information of the known oil-gas well; and adjusting the equivalent fluid factor threshold until the optimal oil and gas prediction result is obtained.

7. A device for detecting a hydrocarbon reservoir by using a fluid factor of a compact hydrocarbon reservoir is characterized in that,

the oil and gas reservoir prediction module is used for carrying out seismic fluid detection analysis according to the newly constructed seismic fluid factor and predicting the distribution of the seismic oil and gas reservoir;

the equivalent fluid factor determination module is specifically used for carrying out statistical analysis on the longitudinal wave speed, the transverse wave speed and the density relation of the oil and gas reservoir and the cover layer according to the drilling logging information; respectively determining the attributes of the Lame modulus, the longitudinal wave impedance and the transverse wave impedance of the cover layer and the reservoir; respectively calculating attribute parameters of the Lame modulus, the Poisson ratio and the longitudinal and transverse wave impedance of the fluid-containing rock according to the volume and the shear modulus of rocks saturated with different fluids; respectively calculating difference values and average values of the Lame modulus, the shear modulus and the density of the reservoir stratum and the cover stratum by combining the calculated attribute parameters of the reservoir stratum with different lithologies and fluid properties;

in the formula, AP and AS are respectively equivalent longitudinal wave modulus and equivalent transverse wave modulus; and respectively expressing the lamel modulus, shear modulus and density; the delta symbol represents the difference between the cap layer and the reservoir layer as the subsequent parameter;

F＝AP sinθ+AS cosθ (3)

the rotation angle of the fluid factor can be determined according to the elasticity parameters of the actual oil and gas reservoir.

8. A computer device, comprising: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method for tight hydrocarbon reservoir fluid factor detection of a hydrocarbon reservoir as claimed in any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon computer executable instructions which, when executed by a processor, carry out the method for tight hydrocarbon reservoir fluid factor testing of a hydrocarbon reservoir as claimed in any one of claims 1 to 6.