Tight oil reservoir fluid saturation quantitative prediction method based on acoustoelectric joint model

Abstract

The invention discloses a tight oil reservoir fluid saturation quantitative prediction method based on an acoustoelectric joint model, particularly relates to the technical field of tight oil reservoir prediction, and utilizes an H-S boundary equation, an elastic and electrical differential effective medium theory and a Gurevich jet flow model to predict the fluid saturation of a tight oil reservoir. And tight oil elasticity and electrical rock physical models with the same microstructure are respectively constructed. And finally, constructing a three-dimensional rock physical drawing board suitable for the tight oil reservoir in combination with rock elasticity and electrical response, extracting a logging data correction joint drawing board of an actual stratum, and applying the logging data correction joint drawing board to the tight oil reservoir.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tight oil reservoir prediction, and particularly relates to a quantitative prediction method for fluid saturation of a tight oil reservoir based on an acoustic-electric combined model. BACKGROUND

[0002] Tight oil refers to oil enriched in non-shale reservoirs such as clastic rock or carbonate rock reservoirs, and the overpressure permeability thereof is less than 0.1*10 -3 μm 2 The development of tight oil can significantly increase the supply of oil resources, thereby effectively relieving energy supply pressure. Compared with traditional oil and gas resources, tight oil reservoirs have the characteristics of low porosity, poor permeability, complex mineral composition, high shale content, poor oil-water differentiation, and the like, which greatly affect the reservoir fluid flow and rock physical characteristics. Due to the complex mineral composition and fluid characteristics of tight oil reservoirs, there are certain limitations in reservoir fine characterization and fluid identification by using conventional geophysical exploration methods, and it is difficult to effectively evaluate the reservoirs.

[0003] Fluid saturation is considered to be one of the key parameters for evaluating tight oil reservoirs. Fluid saturation is usually related to geophysical parameters by a theoretical model or an experimental empirical formula, so that the fluid saturation can be estimated based on the measured geophysical data. A commonly used model is the Archie formula, which establishes a mathematical relationship between rock resistivity and fluid saturation.

[0004] By obtaining an elastic parameter sensitive to fluid saturation, a quantitative relationship between the elastic parameter and the fluid saturation is established, and the fluid saturation prediction accuracy is improved by using a fluid factor index. A pore volume modulus method for quantitatively predicting fluid saturation based on logging data and prestack seismic inversion parameters establishes a relationship between water saturation and pore volume modulus. Reservoir original formation fluid models, drilling fluid invasion models, and mathematical models of logging responses propose corresponding fluid saturation calculation methods and reservoir fluid-containing property discrimination methods.

[0005] With the introduction of nuclear magnetic resonance (NMR) logging technology, the difference spectrum method and the shift spectrum method have been widely used to identify the characteristics of reservoir fluids. By using nuclear magnetic resonance and micron-nanometer CT scanning technology, the micro-existence state of tight oil in a Chang 7 section of a certain formation is quantitatively analyzed, and the relationship between the tight oil content and the initial water saturation of the reservoir, the clay mineral content, and the pore structure is revealed.

[0006] Clay mineral content can be a key controlling factor affecting the physical properties of tight reservoirs and has a significant impact on porosity, permeability, pore throat type and size distribution. The increase of clay mineral content will lead to the decrease of pore throat connectivity, and thus the decrease of mobile fluid saturation. Due to the high electrical conductivity of clay minerals, the overall electrical conductivity of the reservoir will also increase with the increase of clay mineral content. Considering the clay content and type can help improve the accuracy of inversion, so as to more accurately estimate the fluid saturation and porosity.

[0007] In recent years, with the deepening of the research on the elastic and electrical properties of reservoirs, the elastic-electric coupling rock physics model can not only provide complementary information, but also significantly improve the accuracy of reservoir characterization results. The rock physical properties such as elasticity and electrical property of reservoir rock are closely related to the pore structure, fluid distribution, pressure and saturation of the reservoir. Among them, the reservoir elastic parameters have limited sensitivity in oil-water differentiation, and the fluid identification ability has great uncertainty. The lithology of tight oil reservoirs is complex and it is difficult to distinguish oil and water, so it is difficult to distinguish oil and water only by relying on elastic properties. SUMMARY

[0008] In contrast, electrical parameters are more sensitive to fluid type and can more effectively reflect the characteristics of oil-water distribution. When combining elastic and electrical models, the consistency of the rock microstructure must be maintained. By using a three-dimensional rock physics modeling method, the Poisson's ratio, longitudinal wave impedance and resistivity of different rock-fluid combinations are calculated by discretizing the reservoir parameter space and constructing the corresponding elastic-electric coupling model, realizing the parameter prediction of the characteristics of tight oil reservoirs. By combining the reconstructed electrical differential effective medium (DEM) expression with the existing elastic DEM expression, and using the chain rule for cross-attribute modeling, a new relationship between the elastic and electrical properties of the composite medium is obtained, which simulates the combined elastic and electrical properties of tight oil reservoirs. By combining the White patch saturation model and the Gurevich jet model to construct a partially saturated-jet model, the equivalent medium model is combined to simulate the response characteristics of frequency dispersion, attenuation and electrical conductivity with respect to fracture porosity and saturation.

[0009] Therefore, the present application provides a method for quantitatively predicting the fluid saturation of tight oil reservoirs based on an acoustic-electric coupling model to solve the problems in the background art.

[0010] The present application firstly analyzes the microstructure characteristics of the layer by using a scanning electron microscope, combines ultrasonic experimental measurement and core sample analysis, analyzes the change rule of the elastic wave velocity of the sample with porosity and shale content in the saturated water and saturated oil states, and simultaneously carries out fluid sensitivity analysis of the elastic parameters based on the experimental data. In addition, the relationship between the reservoir elasticity and electrical characteristics and the porosity and shale content is studied by using logging data to reveal the rock physical characteristics. By using the H-S boundary equation, the elastic and electrical differential effective medium theory and the Gurevich jet flow model, the elastic and electrical rock physical models of the tight oil with the same microstructure are respectively constructed. Finally, the three-dimensional rock physical chart suitable for the tight oil reservoir is constructed by combining the rock elastic and electrical responses, the logging data of the actual formation is extracted to correct the combined chart, and the combined chart is applied to the tight oil reservoir.

[0011] In order to achieve the above object, the present application provides the following technical scheme: a tight oil reservoir fluid saturation quantitative prediction method based on an acoustic-electric combined model, the mineral distribution of the rock is analyzed according to the core scanning electron microscope analysis result of the rock tight oil reservoir, the matrix elastic modulus and matrix conductivity of the mineral mixture after removing the clay mineral are calculated by using the elastic HS boundary equation;

[0012] The DEM model is used to add the pores and cracks as hard holes and soft holes into the rock matrix respectively to obtain the rock skeleton model containing the pore and crack structure, and the elastic modulus is calculated;

[0013] Then the DEM model is also used to add the clay mineral as the shale ellipsoid into the rock skeleton, and at this time the dry rock skeleton model containing the pore, crack and shale content is obtained, and the elastic modulus containing different shale contents is calculated;

[0014] The Gurvich model is used to simulate the jet flow effect under any saturation, the improved bulk modulus and shear modulus containing the jet flow effect are calculated based on the obtained dry rock skeleton model, and the tight oil rock elastic model is obtained based on the wave response characteristics of the partially saturated rock;

[0015] After the tight oil rock elastic model is constructed, the electrical rock physical model with the same pore structure and pore fluid is simultaneously constructed, the rock minerals are mixed into the matrix, the conductivity of the mineral mixture is given by using the electrical HS boundary equation, the rock skeleton containing the same pore structure is obtained by using the electrical differential effective medium model, the conductivity thereof is calculated, the clay mineral is added into the rock skeleton as the shale ellipsoid by using the model, the conductivity containing different shale contents is calculated, and the tight oil electrical rock physical model is obtained;

[0016] The acoustic-electric combined model is constructed by combining the rock elastic and electrical responses, the template is calibrated and corrected by using the logging data, and is applied to the actual tight oil reservoir to predict the fluid saturation of the reservoir.

[0017] Preferably, the DEM model uses the equivalent volume K * and shear modulus μ * The equivalent elastic parameters are calculated using a coupled differential equation, which is as follows:

[0018] Coupled differential equations:

[0019]

[0020] The initial condition is K. * (0) = K1, μ * (0) = μ1; K1 and μ1 are the bulk modulus and shear modulus of the initial phase, i.e., phase 1; K2 and μ2 are the bulk modulus and shear modulus of phase 2, i.e., the inclusions gradually added to the rock matrix; y is the content of phase 2; P and Q represent the geometric factors of the inclusions.

[0021] Preferred, improved volumetric model K incorporating jet flow effect bf and shear modulus μ bf The calculation is as follows:

[0022]

[0023] In the formula, ω is the angular frequency, η is the fluid viscosity, and φ is the angular frequency. c α c The content and aspect ratio of micropores, respectively, K d K is the bulk modulus of a rock containing only hard pores; dry and μ dry These are the volume and shear modulus of the dry rock skeleton containing all pore structures obtained by DEM, respectively.

[0024] P-wave and S-wave velocities V of partially saturated rocks S and V P Calculations based on bulk modulus and shear modulus:

[0025]

[0026] μ sat =μdry;

[0027] ρ sat =(1-φ-V) sh )ρ0+φρ b +V sh ρ sh ;

[0028]

[0029] Among them, Ksat , μ sat and ρ sat are the bulk modulus, shear modulus and density of partially saturated rock respectively, K bf1 and K bf2 are the water-saturated and oil-saturated bulk modulus of the matrix respectively, μ b is the shear modulus of the matrix added with pores, fractures and clay, φ is the porosity, V sh is the shale content, ρ0 is the density of the matrix, ρ b is the density of the mixed fluid, ρ sh is the clay density; based on the obtained wave response characteristics of the partially saturated rock, a tight oil rock elastic model is obtained.

[0030] Preferably, the tight oil electrical rock physics model is designed as follows:

[0031]

[0032] wherein σ * is the conductivity added with phase 2; the initial condition is σ * (e=0)=σ1; σ1 is the conductivity of phase 1; σ2 is the conductivity of phase 2; e is the content of phase 2; λ * is a function composed of depolarization factors L P (P=1,2,3) of phase 2;

[0033]

[0034] wherein L P is a depolarization factor related to the shape of phase 2, considering an ellipsoidal inclusion with an aspect ratio α<1;

[0035]

[0036] L1=L2=(1-L3) / 2;

[0037] According to the Archie formula, the conductivity of pores and fractures is a function of water saturation:

[0038]

[0039] wherein σ w is the conductivity of salt water, S w is the water saturation of rock; σ2 is the conductivity of pores or fractures; n is the saturation index; β is the lithology coefficient.

[0040] The present application has the following advantages:

[0041] This invention utilizes the theory of elastic and electrical differential effective media and a jet flow model to construct elastic and electrical rock-physical models of tight oil reservoirs with identical microstructures. It further analyzes the influence of factors such as rock porosity, clay content, and water saturation on elastic wave velocity and electrical conductivity. Combining the elastic and electrical responses of the rock, a three-dimensional acoustic-electrical combined rock-physical template suitable for tight oil reservoirs is constructed. The template is calibrated and corrected using well logging data and applied to actual tight oil reservoirs to predict reservoir porosity, clay content, and oil saturation. Attached Figure Description

[0042] Figure 1 Scanning electron microscope image of a dense oil sample of the target layer provided for this invention;

[0043] Figure 2 The wave velocity and porosity of the core sample provided by this invention Figure 2 (a) and clay content ( Figure 2 The relationship diagram in (b)).

[0044] Figure 3 The graph shows the results of the sensitivity analysis of the hydroelastic parameters of the core sample provided by this invention.

[0045] Figure 4 The well logging A correlation curve provided by this invention;

[0046] Figure 5 The well logging B-related curve provided by this invention;

[0047] Figure 6 The electrical conductivity of the logging data provided by this invention is related to the clay content ( Figure 6 (a) and porosity ( Figure 6 The relationship diagram in (b));

[0048] Figure 7 The well logging data P-wave (P-wave) provided by this invention Figure 7 (a)), transverse wave ( Figure 7 (b) The relationship between velocity and mud content in the figure;

[0049] Figure 8 The well logging data P-wave (P-wave) provided by this invention Figure 8 (a)), transverse wave ( Figure 8 (b) The relationship between velocity and porosity in the figure;

[0050] Figure 9 A flowchart for the elastic-electric joint modeling of tight oil rocks provided by this invention;

[0051] Figure 10 The present invention provides different porosities and longitudinal wave velocities under saturation conditions. Figure 10(a) and attenuation ( Figure 10 The graph showing the variation of (b) with frequency and the longitudinal wave velocity under different clay contents and saturation conditions. Figure 10 (c) in the text is related to attenuation. Figure 10 The graph shows the variation of (d) with frequency.

[0052] Figure 11 The longitudinal wave velocity under saturated conditions provided by this invention ( Figure 11 (a) in the middle), transverse wave velocity ( Figure 11 (b) shows the relationship between porosity and clay content, respectively.

[0053] Figure 12 The longitudinal wave velocity under saturated oil conditions provided by this invention ( Figure 12 (a) in the middle), transverse wave velocity ( Figure 12 (b) shows the relationship between porosity and clay content, respectively.

[0054] Figure 13 The longitudinal wave provided by the present invention ( Figure 13 (a)), transverse wave ( Figure 13 (b) The relationship between velocity and clay content and porosity, and a comparison with experimental data;

[0055] Figure 14 The relationship between electrical conductivity and porosity and fracture porosity is shown in the diagram provided by this invention.

[0056] Figure 15 A graph showing the relationship between electrical conductivity, water saturation, and clay content provided for this invention;

[0057] Figure 16 The physical model and experimental data diagram of elastic rock in the ultrasonic band (1MHz) provided by this invention;

[0058] Figure 17 The diagram showing the longitudinal wave velocity dispersion relationship between logging scale and ultrasonic experimental scale under different porosity conditions provided by this invention;

[0059] Figure 18 The elastic-electric combined rock physics model and well A data diagram provided by this invention;

[0060] Figure 19 The model prediction results and well A data provided by this invention Figure 19 (a) porosity, ( Figure 19 (b) mud content, ( Figure 19 (c) Oil saturation diagram;

[0061] Figure 20 The model prediction results and well B data provided by this invention Figure 20(a) porosity, Figure 20 (b) shale content, Figure 20 (c) oil saturation map. DETAILED DESCRIPTION

[0062] The following describes embodiments of the present application by specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0063] The tight oil reservoir is a typical unconventional oil and gas resource, which has low porosity, complex mineral composition, high shale content, and difficult to distinguish oil and water. At present, if the evaluation of tight oil reservoir is only based on single acoustic data and elastic characteristics, it is difficult to accurately identify the oil and water distribution of the reservoir, and the combination of electrical characteristics can more directly reflect the oil and water distribution.

[0064] The tight source-reservoir interbedded contact in the present embodiment forms a combined mode of source-reservoir integration, has good oil generation conditions, and has a high oil saturation of more than 40%. The reservoir sand body in the region develops well, the formation pressure condition is good, the overpressure is strong, and the reservoir conditions are good. The thickness of the target layer is 70-110m, the lithology is mainly siltstone and argillaceous siltstone, and the shale content is high. The reservoir porosity is mainly between 4-12%, and the average is 8.5%. The permeability is mainly between 0.01-0.5mD. The present embodiment takes a certain tight oil reservoir as the research object, collects 11 core samples of the reservoir, carries out ultrasonic experiment measurement and fluid sensitivity analysis of elastic parameters, and analyzes the relationship between the elastic and electrical characteristics of the reservoir and the porosity and shale content by using the logging data of the working area. The elastic and electrical differential effective medium theory and the jet flow model are used to construct a tight oil elastic rock physics model and an electrical rock physics model with the same microstructure, and further analyze the influence of factors such as porosity, shale content and water saturation on elastic wave velocity and electrical conductivity. Combined with the elastic and electrical responses of the rock, a three-dimensional acoustic-electric combined rock physics template suitable for tight oil reservoirs is constructed. The template is calibrated and corrected by logging data, applied to actual tight oil reservoirs, and the porosity, shale content and oil saturation of the reservoir are predicted, and comparative analysis is carried out through logging interpretation results and actual oil production reports. The results show that the tight oil acoustic-electric combined rock physics model constructed by the present application can effectively interpret the acoustic and electrical data of tight oil rocks and predict the oil and water saturation of the reservoir.

[0065] In the present embodiment, 11 tight oil reservoir cores are extracted from the tight oil reservoir in the region.Figure 1 SEM images of tight oil rock samples at different scales Figure 1 (a) and Figure 1 (b) are pore and fracture development; Figure 1 (c) is quartz mineral development; Figure 1 (d) is clay mineral development). The tight oil rock samples mainly contain intergranular pores, intergranular dissolved pores, and microfractures, etc. The mineral composition is mainly quartz, feldspar, and clay, with lower content of dolomite and calcite. The feldspar types are mainly plagioclase and potassium feldspar, and the clay mineral content is high, mainly illite. The sample collection depth is about 2200m. These core samples are processed into cylinders with a diameter of 25mm and a length of 50-56mm. Table 1 shows the basic physical parameters of the samples.

[0066] Table 1 Physical parameters of rock samples

[0067]

[0068] The ultrasonic experiment simulates the temperature, confining pressure, pore fluid pressure, and different fluid saturation of the underground rock. In the water-saturated and oil-saturated ultrasonic experiments, the ultrasonic pulse method (frequency about 1MHz) is used to measure the wave propagation velocity in the rock samples. The vacuum-pressure saturation method is used to completely saturate the samples. Then, the samples are placed in a high-pressure container and a constant confining pressure of 50MPa is applied. The pore pressure is increased to 25MPa, and the temperature is increased to 80℃. After 30 minutes of equilibrium, the P-wave velocity of the sample in the saturated state is recorded.

[0069] Figure 2 The experimental results of the core samples are given, i.e. the change trend of the P-S wave velocity with porosity (a) and shale content (b) in the water-saturated and oil-saturated states Figure 2 The blue and red scatter points in the figure represent the P-wave velocity in the water-saturated and oil-saturated states, and the black and green scatter points represent the S-wave velocity in the water-saturated and oil-saturated states). In the water-saturated state, the P-wave velocity is slightly higher than that in the oil-saturated state. With the increase of porosity, the difference between the two gradually increases. In contrast, the S-wave velocity in the water-saturated and oil-saturated states is relatively close, and is relatively less affected by the fluid saturation state. Based on the P-S wave velocity of the rock samples measured by the ultrasonic experiment in the water-saturated and oil-saturated states, fluid sensitivity analysis is carried out. For the case of containing two fluids, the water-saturated sample is taken as the reference, and the fluid sensitivity parameter is defined as:

[0070]

[0071] where A is the petrophysical parameter of the dense siltstone; subscript w indicates water; subscript o indicates that the fluid is oil. The value of x is generally between 0 and 1, and the greater the value, the higher the sensitivity of the parameter A to the fluid. According to the experimental results, the basic elastic parameters of the rock sample under the corresponding conditions, such as the longitudinal and transverse wave impedances, the shear modulus, the Lame constant, the Poisson's ratio and the Young's modulus, are calculated and determined. Further, the rock elastic parameters and their combinations sensitive to the fluid are screened.

[0072] Figure 3 is the fluid (oil-water) sensitivity analysis result of the core sample Figure 3 where λ is the first Lame constant; v is the Poisson's ratio; vp is the longitudinal wave velocity; vs is the transverse wave velocity; zp is the longitudinal wave impedance; zs is the transverse wave impedance; μ is the shear modulus; E is the Young's modulus; and ρ is the density. Among various elastic parameters, the first Lame constant (λ), the Poisson's ratio (v), the longitudinal wave velocity (vp), the longitudinal wave impedance (zp) and the ratio of the longitudinal and transverse wave velocities (vp / vs) show good sensitivity. However, the change amplitudes of these parameters under different fluid states are all less than 0.02, indicating that the sensitivity of the elastic parameters of the reservoir to the fluid is not ideal as a whole. Related researches show that the conductivity of the dense reservoir has strong sensitivity to the type of fluid and can effectively reflect the oil-water distribution. The acoustic-electric combined method is beneficial to improving the accuracy of reservoir fluid identification and optimizing the prediction of fluid saturation.

[0073] The resistivity, porosity, density and natural gamma value of the target layer of well A and well B in the study area are extracted for well logging data analysis. The amplitude change of the natural gamma logging curve depends on the shale content of the formation. The relative value method is used to estimate the shale content of the formation by using the natural gamma logging data. The gamma logging value of pure mudstone is taken as the maximum value, the gamma logging value of pure sandstone is taken as the minimum value, the gamma logging value of the rock of the target layer is compared with the two values, and the relative value of the shale volume content of the rock is calculated. The size of the relative value reflects the shale content of the formation, and the larger the relative value, the higher the shale content of the formation. The calculation method of the relative value of the shale content is as follows:

[0074] I GR = (GR min - GR max ) / (GR min - GR GR );

[0075]

[0076] where I GR is the relative value of the natural gamma of the target layer, also known as the shale content index, which is dimensionless; GR, GR min and GR max represent the natural gamma logging values of the target layer, pure sandstone layer and pure mudstone layer, respectively; V shShale content of the formation; GCUR is the Hillier index, taking 3.7.

[0077] The drilling data of well A and well B are used to analyze the reservoir rock physical properties, Figure 4 and Figure 5 The porosity, clay content, P-wave velocity, S-wave velocity, density and conductivity of the two wells are shown. The reservoir porosity and clay content are high, and the P-wave and S-wave velocities are low. Among them, well A shows higher elastic wave velocity than well B. Figure 4 and Figure 5 (a) is the porosity; (b) is the clay content; (c) is the P-wave velocity; (d) is the S-wave velocity; (e) is the density; (f) is the conductivity.

[0078] The drilling data of well A and well B are used to analyze the reservoir rock physical properties, Figure 4 and Figure 5 The porosity, clay content, P-wave velocity, S-wave velocity, density and conductivity of the two wells are shown. The reservoir porosity and clay content are high, and the P-wave and S-wave velocities are low. Among them, well A shows higher elastic wave velocity than well B.

[0079] Figure 6 The relationship between the conductivity of the logging data and the shale content (a) and the porosity (b) is given. From the figure, it can be seen that the conductivity increases with the increase of the shale content and the porosity, and the change range is relatively obvious. Figure 7 The P-wave and S-wave velocities decrease with the increase of the shale content, and the change rule is relatively clear, Figure 8 Further, with the increase of the porosity, the P-wave and S-wave velocities gradually decrease, and the change trend of the P-wave velocity is more obvious. The shale content of the reservoir in the study area mainly concentrates in 5%-30%, and the porosity mainly distributes between 3%-15%.

[0080] In view of the characteristics of complex pore structure and high shale content of tight oil, the elastic and electrical models with the same microstructure are constructed in this embodiment, and the elastic-electric combined model of tight oil rock is established by combining the two models, such as Figure 9 , the modeling flowchart is given.

[0081] The embodiment provides a quantitative prediction method for fluid saturation of tight oil reservoir based on the acoustic-electric combined model. According to the core scanning electron microscope analysis result of the rock of the tight oil reservoir, the mineral distribution of the rock is analyzed, and the elastic modulus of the mineral mixture after removing the clay mineral is calculated by using the elastic HS boundary equation, including the matrix elastic modulus and the matrix conductivity.

[0082] According to the characteristics of the tight oil rock pore structure, DEM model is used to add the pores and fractures into the rock matrix as hard and soft pores respectively, wherein the pore aspect ratio of the hard and soft pores is 0.25 and 0.003 respectively, to obtain a rock skeleton model containing pores and fractures, and the elastic modulus is calculated;

[0083] Then, DEM model is also used to add the clay minerals into the rock skeleton as argillaceous ellipsoids (aspect ratio is 0.18), at this time, a dry rock skeleton model containing pores, fractures and argillaceous content is obtained, and the elastic modulus (K dry , μ dry ) of the rock containing different argillaceous content is calculated;

[0084] The DEM model realizes the calculation of equivalent elastic parameters through the coupling differential equation of equivalent volume K * and shear modulus μ * , and the coupling differential equation is as follows:

[0085]

[0086] Wherein, the initial condition is K * (0)=K1, μ * (0)=μ1; K1 and μ1 are the bulk modulus and shear modulus of the initial phase, i.e. phase 1; K2 and μ2 are the bulk modulus and shear modulus of phase 2, i.e. the inclusion gradually added into the rock matrix; y is the content of phase 2; P and Q represent the geometric factor of the inclusion.

[0087] The rock considering the reservoir environment is calculated, and the bulk modulus and density of the reservoir fluid under different temperature and pressure conditions are calculated.

[0088] The calculation equation of the bulk modulus K B and density ρ B of water is as follows:

[0089] ρ W =1+1×10 -6 (-80T-3.3T 2 +0.00175T 3 +489P-2TP+0.016T 2 P-1.3×10 -5 T 3 P-0.333P 2 -0;

[0090] ρ B =S{1×10 -6 [300P-2400PS+T(80+3T-3300S-13P+47PS)]+0.668+0.44S}+ρ W ;

[0091] V B = V W + S(1170 - 9.6T + 0.055T 2 - 8.5x10 -5 T 3 + 2.6P - 0.0029TP - 0.0476P 2 )+ S 1.5 (780 - 10P;

[0092] K B = p B V B 2 ;

[0093] where T is temperature, P is pressure, S is salt content, V W is the sound speed of pure water.

[0094] The calculation equations of the bulk modulus K O and density p O of oil are as follows:

[0095] p P = p c + (0.00277P - 1.71x10 -7 P 3 )(p c - 1.15) 2 + 3.49x10 -4 P;

[0096] p O = p P / [0.972 + 3.81x10 -4 (T + 17.78) 1.175 ;

[0097]

[0098] K O = p O V O 2 ;

[0099] where p c is the density of crude oil measured at 15.6°C and normal pressure, p P is the density after considering the pressure effect, and p O is the density of crude oil after considering the temperature and pressure effects.

[0100] Based on the rock skeleton modulus calculated in the foregoing, the jet flow effect under any saturation is simulated by using the Gurvich model. Based on the dry rock skeleton model, the improved bulk modulus Kbf and shear modulus μ bf ;

[0101] Specifically, the improved bulk model K bf and shear modulus μ bf are calculated as follows:

[0102]

[0103] where ω is the angular frequency, η is the fluid viscosity, φ c , α c are the content and aspect ratio of micro-micropores, respectively, K d is the bulk modulus of the skeleton containing only hard pores in the rock; K dry and μ dry are the bulk and shear modulus of the dry rock skeleton containing all the pore structures obtained by DEM;

[0104] The longitudinal and transverse wave velocities V S and V P of the partially saturated rock are calculated based on the bulk modulus and the shear modulus:

[0105]

[0106] μ sa t = μdry;

[0107] ρ sat = (1- φ - V sh ) ρ0+ φ ρ b + V sh ρ sh ;

[0108]

[0109] where K sat , μ sat and ρ sat are the bulk modulus, shear modulus and density of the partially saturated rock, K bf1 and K bf2 are the bulk modulus of the skeleton containing water and the bulk modulus of the skeleton containing oil, respectively, μ b is the bulk modulus of the skeleton with pores, fractures and clay, φ is the porosity, V sh is the shale content, ρ0is the density of the matrix, ρ b is the density of the mixed fluid, and ρ sh is the density of the clay; based on the obtained wave response characteristics of the partially saturated rock, a tight oil rock elastic model is obtained.

[0110] On the basis of the tight oil rock elastic model, an electrical rock physics model with the same pore structure and pore fluid is constructed. The rock minerals are mixed as a matrix, the electrical conductivity of the mineral mixture is given by using the electrical HS boundary equation, the pores and fractures containing fluid are added to the rock matrix by using the electrical differential effective medium model, the same pore structure (pore and fracture aspect ratio is 0.25 and 0.003 respectively) is taken, the rock skeleton containing pores and fractures is obtained, the electrical conductivity thereof is calculated, the clay minerals are added to the rock skeleton as argillaceous ellipsoids (aspect ratio is 0.18) by using the model again, the electrical conductivity of the rock containing different argillaceous content is calculated, and the tight oil electrical rock physics model is obtained.

[0111] Specifically, the tight oil electrical rock physics model is designed as follows:

[0112]

[0113] Wherein, σ * is the electrical conductivity of phase 2; the initial condition is σ * (e=0)=σ1; σ1 is the electrical conductivity of phase 1; σ2 is the electrical conductivity of phase 2; e is the content of phase 2; λ * is a function composed of depolarization factors L P (P=1,2,3) of phase 2;

[0114]

[0115] Wherein, L P is a depolarization factor related to the shape of phase 2, considering the ellipsoidal inclusions with aspect ratio α<1;

[0116]

[0117] L1=L2=(1-L3) / 2;

[0118] According to the Archie formula, the electrical conductivity of the pores and fractures is a function of the water saturation:

[0119]

[0120] Wherein, σ w is the electrical conductivity of the salt water, S w is the water saturation of the rock; σ2 is the electrical conductivity of the pores or fractures; n is the saturation index; β is the lithology coefficient.

[0121] Combined with the rock elastic and electrical responses, an acoustic-electric combined model is constructed, the template is calibrated and corrected through logging data, and is applied to the actual tight oil reservoir to predict the fluid saturation of the reservoir.

[0122] This invention constructs a physical model of tight oil elastic rocks based on the equivalent medium theory, and analyzes the influence of physical properties such as porosity, clay content, and water saturation on the elastic response of tight oil rocks. Based on the elastic H.S. boundary equation, the bulk modulus and shear modulus of the mineral mixture are calculated. In the rock parameters of the study area, the bulk modulus, shear modulus, and density of the matrix are 45 GPa, 30 GPa, and 2.55 g / cm³, respectively. 3 The volumetric and shear moduli and densities of the clay minerals were 10.5 GPa, 1.4 GPa, and 2.55 g / cm³, respectively. 3 The parameters of water are: bulk modulus of 2.24 GPa, density of 1.0016 g / cm³. 3 Viscosity coefficient 9.8*10 -4 The oil's parameters are: bulk modulus of 1.27 GPa, density of 0.79 g / cm³. 3 Viscosity coefficient 2.1*10 - 3 Pa s; the aspect ratios of the pores and fissures are 0.25 and 0.003, respectively.

[0123] Figure 10 The variation characteristics of longitudinal wave velocity and attenuation with frequency under different porosities, clay content, and saturation conditions. Figure 10 The black, blue, and red lines represent the longitudinal wave velocity and attenuation at porosity of 1%, 8%, and 15%, respectively. The solid and dashed lines represent the longitudinal wave velocity and attenuation at water and oil content, respectively. The variation of longitudinal wave velocity (c) and attenuation (d) with frequency under different clay contents and saturation states is shown in the figure. The black, blue, and red lines represent the longitudinal wave velocity and attenuation at clay contents of 1%, 15%, and 30%, respectively. The solid and dashed lines represent the water and oil content, respectively. Setting the clay content to 5% and the fracture porosity to 2% of the total porosity allows for adjustment of porosity and water saturation (e.g., ...). Figure 10 (a) and Figure 10 As shown in (b)), the porosity is set to 10%, and the fracture porosity is set to 0.1%, which allows for adjustment of the clay content and saturation. Figure 10 (c) and Figure 10 (d) describes the effects of rock porosity, clay content, and fluid saturation on the longitudinal wave velocity dispersion and attenuation of dense siltstone. Figure 10 It can be seen that the elastic characteristics of rocks are relatively similar under water-saturated and oil-saturated conditions. As the porosity and clay content of dense siltstone increase, the longitudinal wave velocity gradually decreases, while the wave dispersion effect and attenuation amplitude both show an increasing trend.

[0124] Figure 11 and Figure 12The relationships between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content are given respectively in the water-saturated state and the oil-saturated state. Assuming that the elastic modulus of the pore and fracture is 0, the model is set to be water-saturated ( Figure 11 ) or oil-saturated ( Figure 12 ), the fracture porosity is set to be 1% of the total porosity, the porosity and the shale content of the model can be changed, and the relationships between the longitudinal wave velocity and the transverse wave velocity and the porosity and the shale content are analyzed. In the water-saturated state, it can be known from (a) in Figure 11 and (b) in Figure 11 that the longitudinal wave velocity and the transverse wave velocity gradually decrease with the increase of the porosity and the shale content. The change amplitude of the transverse wave velocity is larger than that of the longitudinal wave velocity with the change of the shale content, and the change amplitude of the transverse wave velocity is smaller than that of the longitudinal wave velocity with the change of the porosity. Similarly, in the oil-saturated state, the change trend is similar to that in the water-saturated state.

[0125] Based on the ultrasonic experimental data (oil-saturated condition), the influence of different porosities and shale contents on the elastic response of the rock is analyzed, and the experimental results are compared with the predicted results of the elastic model to evaluate the applicability of the model. In this embodiment, the porosity and the shale content in the model parameters are set as variables, the fracture porosity is 2.5% of the total porosity, the saturated state is set to be completely oil-saturated, and other parameters remain unchanged. Further calculation under different porosity conditions is performed to analyze the relationship between the longitudinal wave velocity and the transverse wave velocity and the shale content, and the experimental data and the model prediction are compared as shown in ( Figure 13 ). The blue line, the black line, the red line and the purple line in ( Figure 13 ) represent the longitudinal wave velocity and the transverse wave velocity under the conditions of the porosity being 1%, 5%, 9% and 13%, respectively. The results show that the longitudinal wave velocity and the transverse wave velocity both show a downward trend with the increase of the porosity and the shale content, and the model calculation results are consistent with the experimental data as a whole, which verifies the applicability of the model in the tight oil reservoir.

[0126] In this embodiment, the electrical rock physics model of the tight oil is constructed by using the electrical equivalent medium theory, and the influence of the rock porosity, the fracture porosity, the shale content and the water saturation on the electrical characteristics of the rock is analyzed. In the model parameters, the conductivity of the salt water is 5 S / m, the conductivity of the mineral matrix is 0.038 S / m, the conductivity of the clay is 0.5 S / m, n and β are set to be 2 and 1 respectively. The model is set to be water-saturated, the shale content is set to be 5%, and the porosity and the fracture porosity of the model can be adjusted to observe the change characteristics of the electrical conductivity of the tight oil rock with the porosity and the fracture porosity. In the tight oil reservoir, the pores and the fractures constitute the main fluid migration channel, and the good connectivity thereof enhances the overall conductivity of the reservoir and further strengthens the electrical response. As shown in ( Figure 14 ), the electrical conductivity shows an upward trend with the increase of the porosity and the fracture porosity, and the electrical conductivity is more sensitive to the change of the fracture porosity. Therefore, the electrical characteristics of the rock are more sensitive to the fracture porosity than to the porosity. ​​​​​​​

[0127] The model's total porosity was set to 10%, and the fracture porosity to 0.05%, while other parameters remained unchanged. By adjusting the model's clay content and water saturation, the changes in rock electrical conductivity with varying clay content and water saturation could be observed. Figure 15 As shown, the electrical conductivity gradually increases with increasing clay content and water saturation. The electrical properties of the rock change significantly with variations in clay content and water saturation.

[0128] To investigate the effects of porosity and clay content on ultrasonic frequency band rock physics charts, a two-dimensional elastic rock physics template was constructed with longitudinal wave impedance and Poisson's ratio as the horizontal and vertical axes. Total porosity and clay content were set as variables, with both ranging from 0% to 17%. A two-dimensional physical property response chart was constructed, and all model parameters are given in Table 2. Figure 16 Figures (a) and (b) in 16 show the comparison results between the two-dimensional elastic rock physics template constructed in the oil-saturated state and the ultrasonic experimental data, where the color scale indicates the porosity of the data (…). Figure 16 (a) and clay content ( Figure 16 (b) in the middle. Figure 16 (c) and Figure 16 (d) in the figure corresponds to the two-dimensional elastic template and experimental data under water-saturated conditions. The black and blue lines represent fixed porosity and clay content, respectively. The results show a negative correlation between Poisson's ratio and longitudinal wave impedance; as porosity or clay content increases, longitudinal wave impedance gradually decreases, while Poisson's ratio increases accordingly. The overall difference in elastic parameters between water-saturated and oil-saturated states is small, making it difficult to accurately distinguish fluid types based solely on elastic parameters. The experimental data and model in the figure show good consistency in trend and distribution characteristics, indicating that the changes in scattered elastic parameters with increasing porosity and clay content follow the same trend as the template.

[0129] Figure 17 The response characteristics of P-wave velocity with frequency under different porosity conditions are presented, and the consistency between the model results and measured data (including well logging and ultrasonic experimental data) is compared. In the figure, the blue solid dots represent the average P-wave velocity at the well logging scale, and the red solid dots represent the average P-wave velocity at the ultrasonic experimental scale (black and purple lines represent the P-wave velocity at 8% and 15% porosity; blue and red hollow dots represent well logging data and experimental data; blue and red solid dots represent the average well logging value and the average experimental value). The P-wave velocity increases slightly with increasing frequency, showing certain dispersion characteristics; at the same time, the P-wave velocity is generally lower under higher porosity conditions. The model results reflect this trend well and show good consistency with the actual data at the well logging scale (low frequency) and the ultrasonic experimental scale (high frequency), indicating that the model has strong applicability under multi-scale conditions.

[0130] Table 2. Related parameters of rock physics model

[0131]

[0132] The target layer of the study area is an oil-bearing tight siltstone reservoir. According to the aforementioned rock physics modeling process, on the basis of the ultrasonic scale elastic rock physics model, the frequency is changed and the conductivity parameter is introduced. At the same time, the effects of porosity, shale content and water saturation on the model are considered, the total porosity, shale content and water saturation in the model are set as variables, the corresponding elastic and electrical parameters are cross-plotted, and the elastic parameters of the combined model are given in Table 2. The rock electrical parameters are as follows: the matrix conductivity is 0.038 S / m, the water conductivity is 5 S / m, the clay conductivity is 0.8 S / m, and the saturation index (n) is 2. The total porosity range is 0%-17%, the clay content range is 0%-37%, and the water saturation range is 0%-100%. A three-dimensional elastic-electric combined template about reservoir porosity, shale content and water saturation is obtained. As shown in FIG. 2, a comparison chart of the three-dimensional elastic-electric template of the tight oil rock and the logging A data is given (the black line, the blue line and the red line in the chart respectively represent the fixed porosity, the shale content and the water saturation). Figure 18 Figure 18 According to the comparison of the data and the template, the porosity and shale content distribution of the data are in good agreement with the template, and with the increase of the two, the change trend of the scattered point elasticity and conductivity is the same as that of the template. The logging data shows that the porosity and conductivity are generally low, and the shale content is high, which is consistent with the geological characteristics of the target layer. Based on the Poisson's ratio, the longitudinal wave impedance and the conductivity of the logging data, the data points are superimposed on the template to realize the inversion of the reservoir parameters.

[0133] Based on the obtained three-dimensional elastic-electric combined template of the tight oil and the logging data, the porosity, shale content and oil saturation of logging A and logging B are quantitatively predicted within the reservoir parameter range of the template. Figure 19 and Figure 20 ​The predicted results of well A and well B are compared with the logging data respectively. The analysis results show that the porosity of well A is mainly distributed in the range of 5%-15%, the shale content is concentrated in the range of 5%-30%, and the oil saturation is distributed in the range of 40%-80%. In comparison, the porosity of well B is distributed in the range of 3%-13%, the shale content is 3%-30%, and the oil saturation is 30%-70%. As can be seen from the figure, the prediction accuracy of well B is slightly lower than that of well A, but the porosity curve and the shale content curve of the two wells are basically consistent with the predicted result curve, and the trend is similar. The predicted results of the porosity and the shale content of the two wells show good correlation with the logging curve. Among them, the fitting effect of the shale content prediction curve of well A is better than that of well B, and the correlation coefficient is 0.8955. In the oil production report, the oil production depth interval of well A and well B is mainly located in the range of 2105-2200 meters and 2250-2300 meters. Further comparison of the data of well A and well B shows that the average oil saturation of well A is 53.57%, and the average oil saturation of well B is 49.29%. It can also be found that the porosity and oil saturation of well A are higher than those of well B, while the shale content is lower than that of well B. This difference shows that the target layer of well A has better connectivity and better reservoir space, and shows higher oil storage potential. The actual oil test of well A shows that the daily oil production is 7.39 tons, and the daily oil production of well B is 0.012 tons, which is consistent with the prediction result based on the reservoir characteristics.

[0134] Based on 11 rock samples of a certain tight oil reservoir, the microstructure characteristics of the reservoir are analyzed by scanning electron microscopy technology. Through ultrasonic experiment measurement, the variation of elastic wave velocity with porosity and shale content is studied under the conditions of water saturation and oil saturation. The fluid sensitivity of elastic parameters is analyzed. The shale content is calculated by using the natural gamma of logging data, and the relationship between the reservoir elasticity and electrical property and the porosity and shale content is discussed according to the electrical conductivity. The results show that the reservoir conductivity increases with the increase of shale content and porosity, and the P-wave and S-wave velocities gradually decrease with the increase of shale content and porosity. In view of the characteristics of complex lithology and difficult oil-water differentiation of the tight oil reservoir, an elastic rock physics model is constructed by combining the HS boundary equation, the equivalent medium theory and the Gurevich jet flow model, and the influence of porosity, shale content and water saturation on rock elasticity is studied. The results show that the P-wave and S-wave velocities gradually decrease with the increase of porosity and shale content, and the change of water saturation has little effect on the P-wave and S-wave velocities. At the same time, an electrical rock physics model is constructed based on the electrical HS boundary equation and the differential effective medium model, and the influence of rock porosity, shale content and water saturation on electrical property is analyzed. It is found that the electrical conductivity gradually increases with the increase of the above physical parameters.

[0135] Based on two models of the same microstructure, combined with the elastic and electrical response of the rock, a three-dimensional acoustic-electric rock physics model suitable for tight oil reservoirs was constructed. Through the measured data, the model was corrected, and the porosity, shale content and oil saturation of the reservoirs of well A and well B were predicted. The correlation between the predicted results of porosity and shale content of the two wells and the well logging curves was good, among which the correlation of well A's shale content curve was better, with a correlation coefficient of 0.8955. The results show that the porosity and oil saturation of well A are higher, and the shale content of well B is higher, so the oil production is higher, and the predicted results are consistent with the actual data. The elastic-electric rock physics model based on the same microstructure can be effectively applied to tight oil reservoirs, and can better explain the acoustic-electric data of tight oil rocks, showing good prediction accuracy and reliability. The model proposed in this example can provide a new idea and method for the research of physical property interpretation and fluid prediction method of tight oil reservoirs.

[0136] Although the present application has been described in detail above with general description and specific embodiments, some modifications or improvements can be made on the basis of the present application, which is obvious to those skilled in the art. Therefore, these modifications or improvements made on the basis of not deviating from the spirit of the present application, all belong to the scope of protection required by the present application.

Claims

1. A method for quantitatively predicting fluid saturation of a tight oil reservoir based on an acoustic-electric combined model, characterized in that: According to the results of core scanning electron microscopy analysis of rock, the mineral distribution of rock is analyzed, and the matrix elastic modulus and matrix conductivity of mineral mixture after removing clay minerals are calculated by using elastic HS boundary equation; The DEM model is used to add pores and fractures as hard holes and soft holes into the rock matrix respectively to obtain the rock skeleton model containing inclusions, and the elastic modulus is calculated; Then the DEM model is used to add clay minerals as argillaceous ellipsoids into the rock skeleton, and the dry rock skeleton model containing pores, fractures and argillaceous content is obtained, and the elastic modulus with different argillaceous content is calculated; The Gurvich model is used to simulate the effect of jet flow under any saturation, and the improved bulk modulus and shear modulus with jet flow effect are calculated based on the obtained dry rock skeleton model, and the elastic model of tight oil rock is obtained based on the obtained wave response characteristics of partially saturated rock; After building the elastic model of tight oil rock, the electrical rock physics model with the same pore structure and pore fluid is also built, the rock minerals are mixed into the matrix, the conductivity of mineral mixture is given by using the electrical HS boundary equation, the rock skeleton containing inclusions is obtained by using the electrical differential effective medium model with the same pore structure, and the conductivity is calculated, then the clay minerals are added to the rock skeleton as argillaceous ellipsoids, and the conductivity with different argillaceous content is calculated to obtain the electrical rock physics model of tight oil; Combined with the elastic and electrical response of rock, the acoustic-electric combined model is built, the template is calibrated and corrected through logging data, and is applied to actual tight oil reservoir to predict the fluid saturation of reservoir.

2. The method of claim 1, wherein the method is based on a combined acoustic and electrical model for quantitatively predicting fluid saturation in tight oil reservoirs. DEM model is realized by the coupling differential equation of equivalent volume K * and shear modulus μ * , which is as follows: Coupled differential equations: where the initial conditions are K * (0) = K1, μ * (0) = μ1; K1and μ1are the bulk modulus and shear modulus of phase 1, i.e. the initial phase of the rock matrix; K2and μ2are the bulk modulus and shear modulus of phase 2, i.e. the inclusions gradually added to the rock matrix; y is the content of phase 2; P and Q represent the geometric factors of the inclusions.

3. The method of claim 1, wherein the method is based on a combined acoustic and electrical model for tight oil reservoir fluid saturation quantification. Improved volume model with jet effect bf and shear modulus μ bf The calculation is as follows: where ω is the angular frequency, η is the fluid viscosity, φ c , α c are the micro-pore content and aspect ratio, respectively, K d is the bulk modulus of the rock skeleton containing only hard pores; K dry and μ dry are the bulk and shear modulus, respectively, of the dry rock skeleton containing the entire pore structure obtained by DEM. Longitudinal and transverse wave velocities V of partially saturated rock S and V P Based on bulk modulus and shear modulus calculation: μ sa t = μdry; p sat = (1 - φ - V sh ) p0+ φ p b + V sh p sh ; wherein K sat , μ sat and ρ sat are the bulk modulus, shear modulus and density of the partially saturated rock, respectively, K bf1 and K bf2 are the water-saturated and oil-saturated bulk modulus of the matrix, respectively, μ b is the shear modulus of the matrix with added pores, fractures and clays, φ is the porosity, V sh is the shale content, ρ0 is the density of the matrix, ρ b is the density of the mixture fluid, and ρ sh is the density of the clay; and obtaining a tight oil rock elastic model based on the obtained wave response characteristics of the partially saturated rock.

4. The method of claim 2, wherein the method is based on a combined acoustic and electrical model for tight oil reservoir fluid saturation quantification. The design of the electrical rock physics model of tight oil is as follows: where σ * is the conductivity of phase 2; the initial condition is σ * (e = 0) = σ1; σ1is the conductivity of phase 1; σ2is the conductivity of phase 2; e is the content of phase 2; λ * is a function of the depolarization factor L P (P = 1,2,3) of phase 2; where L P is a depolarization factor related to the shape of phase 2, considering an ellipsoidal inclusion with aspect ratio a < 1; L1=L2=(1-L3) / 2; According to Archie formula, the conductivity of pores and fractures is a function of water saturation: where σ w is the conductivity of the brine, S w is the water saturation of the rock; σ2 is the conductivity of the pore or fracture; n is the saturation exponent; and β is the lithology factor.

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