Quantitative prediction method for fluid saturation of tight oil reservoir based on acoustic-electric combined model

Abstract

The application discloses a method for quantitatively predicting fluid saturation of a tight oil reservoir based on a sound-electricity combined model, and particularly relates to the technical field of tight oil reservoir prediction, and utilizes H-S boundary equation, elastic and electrical differential effective medium theory and Gurevich jet flow model to respectively construct tight oil elastic and electrical rock physical models with the same microstructure. Finally, in combination with rock elastic and electrical response, a three-dimensional rock physical model suitable for the tight oil reservoir is constructed, logging data of an actual formation is extracted to correct the combined model, and the combined model is applied to the tight oil reservoir.

Description

Technical Field

[0001] This invention relates to the field of tight oil reservoir prediction technology, specifically to a method for quantitative prediction of fluid saturation in tight oil reservoirs based on a combined acoustic and electrical model. Background Technology

[0002] Tight oil refers to petroleum enriched in non-shale formations such as clastic or carbonate rock reservoirs, with an overpressure permeability of less than 0.1 × 10⁻⁶. -3 μm 2 The development of tight oil can significantly increase the supply of petroleum resources, thereby effectively alleviating energy supply pressure. Compared with traditional oil and gas resources, tight oil reservoirs are characterized by low porosity, poor permeability, complex mineral composition, high clay content, and significant oil-water heterogeneity, which greatly affect reservoir fluid flow and rock physical characteristics. Due to the complex mineral composition and fluid characteristics of tight oil reservoirs, conventional geophysical exploration methods have certain limitations in terms of detailed reservoir characterization and fluid identification, making it difficult to achieve effective reservoir assessment.

[0003] Fluid saturation is considered one of the key parameters for evaluating tight oil reservoirs. Fluid saturation is typically correlated with geophysical parameters through theoretical models or empirical formulas, allowing estimation based on measured geophysical data. A commonly used model is the Archie formula, which establishes a mathematical relationship between rock resistivity and fluid saturation.

[0004] By acquiring elastic parameters sensitive to fluid saturation, a quantitative relationship between these parameters and fluid saturation was established, and the accuracy of fluid saturation prediction was improved using the fluid factor index. A quantitative fluid saturation prediction method based on well logging data and pre-stack seismic inversion parameters—the pore volume modulus method—was proposed, establishing a relationship between water saturation and pore volume modulus. Corresponding methods for calculating fluid saturation and identifying reservoir fluid-bearing properties were proposed based on the original formation fluid model, drilling fluid invasion model, and mathematical model of well logging response.

[0005] With the introduction of nuclear magnetic resonance (NMR) logging technology, difference spectroscopy and displacement spectroscopy have been widely used to identify reservoir fluid characteristics. Using NMR and micro / nano CT scanning techniques, the microscopic state of tight oil in a certain formation's Chang 7 section was quantitatively analyzed, revealing the relationship between tight oil content and the initial water saturation, clay mineral content, and pore structure of the reservoir.

[0006] Clay mineral content is likely a key controlling factor influencing the physical properties of tight reservoirs, significantly affecting porosity, permeability, pore throat type, and their size distribution. Increased clay mineral content leads to decreased pore throat connectivity, consequently reducing mobile fluid saturation. Due to the high electrical conductivity of clay minerals, the overall reservoir conductivity also increases with increasing clay mineral content. Considering clay content and type helps improve the accuracy of inversion, thereby providing a more accurate estimate of fluid saturation and porosity.

[0007] In recent years, with the deepening research on reservoir elasticity and electrical properties, acoustic-electric coupled rock physics models can not only provide complementary information but also significantly improve the accuracy of reservoir characterization results. The elastic and electrical properties of reservoir rocks are closely related to the reservoir's pore structure, fluid distribution, pressure, and saturation. Among these, reservoir elastic parameters have limited sensitivity in distinguishing between oil and water, and their fluid identification ability has significant uncertainties. Tight oil reservoirs have complex lithological characteristics and are difficult to distinguish between oil and water; relying solely on elastic properties is insufficient for this purpose. Summary of the Invention

[0008] In contrast, electrical parameters are more sensitive to fluid type and can more effectively reflect oil-water distribution characteristics. Maintaining consistency in rock microstructure is crucial when jointly characterizing elastic and electrical models. Using a three-dimensional rock physics modeling method, by discretizing the reservoir parameter space and constructing a corresponding acoustic-electrical joint model, parameters such as Poisson's ratio, P-wave impedance, and resistivity for different rock-fluid combinations were calculated, enabling parameter prediction of tight oil reservoir characteristics. By combining the reconstructed differential electrical 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 was derived, simulating the joint elastic and electrical properties of tight oil reservoirs. A partially saturated-jet flow model was constructed by combining the White patch saturation model and the Gurevich jet flow model, and combined with the acoustic-electrical equivalent medium model, the response characteristics of dispersion, attenuation, and conductivity with fracture porosity and saturation were simulated.

[0009] To address this issue, the present invention provides a method for quantitative prediction of fluid saturation in tight oil reservoirs based on a combined acoustic and electrical model.

[0010] This invention first utilizes scanning electron microscopy (SEM) to analyze the microstructural characteristics of the reservoir. Combined with ultrasonic testing and core sample analysis, it examines the variation of elastic wave velocity with porosity and clay content under water-saturated and oil-saturated conditions. Simultaneously, fluid sensitivity analysis of elastic parameters is conducted based on experimental data. Furthermore, well logging data is used to study the relationship between reservoir elastic and electrical properties and porosity and clay content, revealing its petrophysical characteristics. Elastic and electrical petrophysical models of tight oil reservoirs with identical microstructures are constructed using the HS boundary equation, the theory of effective media based on elastic and electrical differentials, and the Gurevich jet flow model. Finally, combining the elastic and electrical responses of the rock, a three-dimensional petrophysical model suitable for tight oil reservoirs is constructed. Well logging data from actual formations is extracted to correct the combined model, which is then applied to tight oil reservoirs.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a quantitative prediction method for fluid saturation in tight oil reservoirs based on a combined acoustic and electrical model, which analyzes the mineral distribution of rocks based on the core scanning electron microscopy analysis results of tight oil reservoirs, and calculates the matrix elastic modulus and matrix electrical conductivity of the mineral mixture after removing clay minerals using the elastic HS boundary equation;

[0012] Using a DEM model, pores and fractures were added to the rock matrix as hard pores and soft pores, respectively, to obtain a rock skeleton model containing pore and fracture structures. The elastic modulus of the rock skeleton model containing inclusions was then calculated.

[0013] Then, the clay minerals were added as argillaceous ellipsoids to the rock skeleton model containing inclusions using the DEM model. At this time, a rock skeleton model containing pores, fractures and argillaceous content was obtained, and the elastic modulus of the rock skeleton model with different argillaceous contents was calculated.

[0014] The Gurvich model was used to simulate the jet flow effect under arbitrary saturation. Based on the obtained rock skeleton model, the improved bulk modulus and shear modulus with jet flow effect were calculated. Based on the obtained wave response characteristics of partially saturated rocks, the elastic model of tight oil rocks was obtained.

[0015] After constructing the elastic model of tight oil rock, an electro-rock physics model with the same pore structure and pore fluid is also constructed. The mineral mixture is used as the matrix, and the electrical conductivity of the mineral mixture is given by the electrical HS boundary equation. The rock skeleton model containing pores and fractures is obtained by using the electrical differential effective medium model. The electrical conductivity of the rock skeleton model containing inclusions is calculated. Then, clay minerals are added as argillaceous ellipsoids to the rock skeleton model containing inclusions to obtain the electro-rock physics model of tight oil. The electrical conductivity of the electro-rock physics model of tight oil with different argillaceous contents is calculated.

[0016] By combining rock elasticity and electrical response, an acoustic-electric joint model is constructed. The model is calibrated and corrected using well logging data and applied to actual tight oil reservoirs to predict the fluid saturation of actual tight oil reservoirs.

[0017] Preferably, the DEM model uses equivalent volume. and shear modulus 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 , ; and These are the bulk modulus and shear modulus of the initial phase, i.e., phase 1; and These are the bulk modulus and shear modulus of phase 2, which are the inclusions gradually added into the rock matrix. The content of phase 2; and The geometric factor representing the package.

[0021] Preferably, the improved bulk modulus includes the jet flow effect. and shear modulus The calculation is as follows:

[0022] ;

[0023] ;

[0024] In the formula, Angular frequency, For fluid viscosity, , The content and aspect ratio of micropores, respectively. It is the bulk modulus of the skeleton of a rock containing only hard pores; and These are the bulk modulus and shear modulus of the rock skeleton model, respectively.

[0025] P-wave velocity of partially saturated rock and transverse wave velocity Calculations based on bulk modulus and shear modulus:

[0026] ;

[0027] = ;

[0028] ;

[0029] ;

[0030] ;

[0031] in, , and These represent the bulk modulus, shear modulus, and density of partially saturated rock, respectively. and These are the bulk modulus of the skeleton containing water and the bulk modulus of the skeleton containing oil, respectively. To incorporate the skeletal shear modulus of porous, fractured, and clay minerals, Porosity The content of clay, The density of the matrix, The density of the mixed fluid, The density of clay minerals is given; based on the wave response characteristics of partially saturated rocks, an elastic model of tight oil rocks is obtained.

[0032] The preferred electrical rock physics model design for tight oil is as follows:

[0033] ;

[0034] in, To introduce the conductivity of phase 2; initial conditions are: (e=0)= ; Let be the conductivity of phase 1; denoted as ε, where ε is the conductivity of phase 2; and e is the content of phase 2. It is the depolarization factor of phase 2 A function consisting of (P=1,2,3);

[0035] ;

[0036] in, It is a depolarization factor related to the shape of phase 2, taking into account aspect ratio. ellipsoidal inclusions with a value less than 1;

[0037] ;

[0038]

[0039] According to Archie's formula, the electrical conductivity of pores and fissures is a function of water saturation:

[0040] ;

[0041] in, The electrical conductivity of the salt water. The water saturation of the rock; The electrical conductivity of pores or fissures; It is the saturation index; This represents the lithology coefficient.

[0042] The present invention has the following advantages:

[0043] 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 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 integrated rock-physical model suitable for tight oil reservoirs is constructed. The three-dimensional acoustic-electrical integrated rock-physical model 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

[0044] Figure 1 Scanning electron microscope image of a dense oil sample of the target layer provided for this invention;

[0045] 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) is shown.

[0046] Figure 3 The graph shows the results of the sensitivity analysis of the hydroelastic parameters of the core sample provided by this invention.

[0047] Figure 4 The well logging A correlation curve provided by this invention;

[0048] Figure 5 The well logging B correlation curve provided by this invention;

[0049] Figure 6 The electrical conductivity of the well logging data provided by this invention is related to the clay content ( Figure 6 (a) and porosity ( Figure 6 The relationship diagram in (b));

[0050] 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 clay content;

[0051] 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;

[0052] Figure 9 A flowchart for the elastic-electric joint modeling of tight oil rocks provided by this invention;

[0053] 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 middle and attenuation Figure 10 The graph showing the variation of (d) with frequency;

[0054] 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.

[0055] 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.

[0056] 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;

[0057] Figure 14 The relationship between electrical conductivity and porosity and fracture porosity is provided by the present invention.

[0058] Figure 15 The graph showing the relationship between electrical conductivity, water saturation, and clay content provided by this invention;

[0059] Figure 16 The physical model and experimental data diagram of elastic rock in the ultrasonic band (1MHz) provided by this invention;

[0060] Figure 17 This invention provides a graph showing the longitudinal wave velocity dispersion relationship between logging scale and ultrasonic experimental scale under different porosity conditions.

[0061] Figure 18The acoustic-electric combined rock physics model and well A data diagram provided by this invention;

[0062] Figure 19 The model prediction results and well A data provided for this invention ( Figure 19 (a) porosity, ( Figure 19 (b) mud content, ( Figure 19 (c) Oil saturation diagram;

[0063] Figure 20 The model prediction results and well B data provided for this invention Figure 20 (a) porosity, ( Figure 20 (b) mud content, ( Figure 20 (c) Oil saturation diagram. Detailed Implementation

[0064] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Tight oil reservoirs are a typical type of unconventional oil and gas resource, characterized by low porosity, complex mineral composition, high clay content, and difficulty in distinguishing between oil and water. Currently, evaluating tight oil reservoirs based solely on acoustic data and elastic characteristics is insufficient to accurately identify the oil-water distribution. Combining electrical properties can provide a more intuitive reflection of this distribution.

[0066] In this embodiment, the source and reservoir layers are in close interbedded contact, forming an integrated source-reservoir combination with favorable oil generation conditions and a high oil saturation of over 40%. The reservoir features well-developed sandstone bodies, favorable formation pressure conditions, and strong overpressure, resulting in favorable conditions for reservoir formation. The target layer thickness is 70-110m, with lithology dominated by siltstone and argillaceous siltstone, and a high clay content. Reservoir porosity is mainly between 4-12%, with an average of 8.5%, and permeability is mainly between 0.01-0.5mD. This embodiment uses a tight oil reservoir in a certain formation as the research object, collecting 11 core samples from the reservoir, conducting ultrasonic experiments and fluid sensitivity analysis of elastic parameters, and using well logging data from the work area to analyze the relationship between the reservoir's elastic and electrical properties and porosity and clay content. Elastic and electrical differential effective medium theory and jet flow model were used to construct elastic and electrical rock-physical models of tight oil reservoirs with the same microstructure, respectively. The effects of rock porosity, clay content, and water saturation on elastic wave velocity and electrical conductivity were further analyzed. Combining the elastic and electrical responses of the rock, a three-dimensional acoustic-electrical combined rock-physical model suitable for tight oil reservoirs was constructed. The three-dimensional acoustic-electrical combined rock-physical model was calibrated and corrected using well logging data and applied to actual tight oil reservoirs. The porosity, clay content, and oil saturation of the reservoirs were predicted, and a comparative analysis was conducted using well logging interpretation results and actual oil production reports. The results show that the acoustic-electrical combined rock-physical model of tight oil reservoirs constructed in this invention, with both fluid and structural heterogeneity, can effectively interpret the acoustic and electrical data of tight oil rocks and predict reservoir oil-water saturation.

[0067] In this embodiment, 11 tight oil reservoir cores were extracted from the tight oil reservoir in this region. Figure 1 Scanning electron microscope images of tight oil rock samples at different scales ( Figure 1 (a) and Figure 1 (b) shows the development of pores and fissures; Figure 1 (c) in the text represents the development of quartz minerals; Figure 1 (d) indicates the development of clay minerals. This tight oil rock sample mainly contains reservoir spaces such as intergranular pores, intergranular dissolution pores, and microfractures. The mineral composition is mainly quartz, feldspar, and clay, with lower contents of dolomite and calcite. The feldspar types are mainly plagioclase and potassium feldspar, while the clay mineral content is relatively high, mainly illite. The samples were collected at a depth of about 2200m. These core samples were processed into cylinders with a diameter of 25mm and a length between 50-56mm. Table 1 shows the basic physical parameters of the samples.

[0068] Table 1 Physical properties of rock samples

[0069]

[0070] Ultrasonic experiments simulated the temperature, confining pressure, pore fluid pressure, and saturation conditions of different fluids in underground rocks. In water-saturated and oil-saturated ultrasonic experiments, the wave propagation velocity in rock samples was measured using the ultrasonic pulse method (frequency approximately 1 MHz). The samples were fully saturated using a vacuum-pressure saturation method. Subsequently, the samples were placed in a high-pressure container and a constant confining pressure of 50 MPa was applied. The pore pressure was increased to 25 MPa, while the temperature was raised to 80°C. After 30 minutes of equilibration, the P-wave velocity of the samples under saturation was recorded.

[0071] Figure 2 Experimental results for core samples are presented, showing the variation trends of P-wave and S-wave velocities with porosity (a) and clay content (b) under water-saturated and oil-saturated conditions. Figure 2 The blue and red scatter dots represent the P-wave velocities in water- and oil-bearing states, respectively, while the black and green scatter dots represent the S-wave velocities in water- and oil-bearing states, respectively. In the water-saturated state, the P-wave velocity is generally slightly higher than that in the oil-saturated state. The difference between the two gradually increases with increasing porosity. In contrast, the S-wave velocities in the water- and oil-saturated states are relatively similar, showing less influence from fluid saturation. Based on the P- and S-wave velocities of rock samples measured by ultrasonic experiments in water- and oil-saturated states, fluid sensitivity analysis was conducted. For the case containing two fluids, the water-saturated sample was used as the benchmark, and the following fluid sensitivity parameters were defined:

[0072] ;

[0073] In the formula, A represents the physical parameters of dense siltstone; the subscript w represents water; and the subscript o indicates that the fluid is oil. The values ​​are generally between 0 and 1, and the larger the value, the higher the sensitivity of parameter A to fluid. Based on the experimental results, the basic elastic parameters of rock samples under corresponding conditions, such as longitudinal and transverse wave impedance, shear modulus, Lamé constant, Poisson's ratio, and Young's modulus, are calculated and determined. Further screening of fluid-sensitive rock elastic parameters and their combinations is then conducted.

[0074] Figure 3 The results of fluid (oil-water) sensitivity analysis of core samples ( Figure 3 middle denoted as the first Lamé coefficient; v is Poisson's ratio; vp is the P-wave velocity; vs is the S-wave velocity; zp is the P-wave impedance; zs is the S-wave impedance; E is the shear modulus; E is Young's modulus; (Density). Among various elastic parameters, the first Lamé coefficient (λ), Poisson's ratio (ν), P-wave velocity (vp), P-wave impedance (zp), and P-wave / S-wave velocity ratio (vp / vs) exhibit good sensitivity. However, the variation range of these parameters under different fluid states is relatively small, not exceeding 0.02, indicating that the overall sensitivity of these reservoir elastic parameters to fluids is not ideal. Related studies have shown that the electrical conductivity of tight reservoirs is highly sensitive to fluid type and can effectively reflect oil-water distribution. Using a combined acoustic-electric method is beneficial for improving the accuracy of reservoir fluid identification and optimizing fluid saturation prediction.

[0075] Resistivity, porosity, density, and natural gamma ray values ​​of the target formation from wells A and B in the study area were extracted for well logging data analysis. The amplitude of the natural gamma ray logging curve depends on the formation's clay content. Using the natural gamma ray logging data, the formation clay content was estimated using the relative value method. The gamma ray logging value of pure mudstone was taken as the maximum value, and the gamma ray logging value of pure sandstone as the minimum value. The gamma ray logging value of the target formation rock was compared with these values ​​to calculate the relative value of the formation's clay volume content. The magnitude of the relative value reflects the formation's clay content; a larger relative value indicates a higher clay content. The calculation method for the relative value of clay content is as follows:

[0076]

[0077] ;

[0078] In the formula: The natural gamma relative value of the target layer, also known as the clay content index, is dimensionless. , , These represent the natural gamma logging values ​​for the target layer, pure sandstone layer, and pure mudstone layer, respectively. The content of clay in the formation; The Hill index is set to 3.7.

[0079] Analyze reservoir rock properties using borehole data from wells A and B. Figure 4 and Figure 5 The data shows the porosity, clay content, P-wave velocity, S-wave velocity, density, and electrical conductivity of two wells. The reservoir has high porosity and clay content, but low P-wave and S-wave velocities. Well A exhibits a higher elastic wave velocity compared to well B. Figure 4 and Figure 5 In the table, (a) represents porosity; (b) represents clay content; (c) represents longitudinal wave velocity; (d) represents transverse wave velocity; (e) represents density; and (f) represents electrical conductivity.

[0080] Analyze reservoir rock properties using borehole data from wells A and B. Figure 4 and Figure 5The data shows the porosity, clay content, P-wave velocity, S-wave velocity, density, and electrical conductivity of the two wells. The reservoir has high porosity and clay content, but low P-wave and S-wave velocities. Well A exhibits a higher elastic wave velocity compared to well B.

[0081] Figure 6 The relationship between electrical conductivity of well logging data and clay content (a) and porosity (b) is given. It can be seen from the figure that electrical conductivity increases with the increase of clay content and porosity, and the change is quite obvious. Figure 7 The results show that both longitudinal and transverse wave velocities decrease with increasing clay content, and the variation pattern is relatively clear. Figure 8 Further analysis shows that as porosity increases, both P-wave and S-wave velocities gradually decrease, with the P-wave velocity showing a more pronounced trend. The clay content of the reservoirs in the study area is mainly concentrated between 5% and 30%, while the porosity is primarily distributed between 3% and 15%.

[0082] Considering the complex pore structure and high clay content of tight oil, this embodiment constructs elastic and electrical models with the same microstructure, and combines the two models to establish a combined elastic-electrical model of tight oil rock, such as... Figure 9 Provide a modeling flowchart.

[0083] This embodiment provides a quantitative prediction method for fluid saturation in tight oil reservoirs based on a combined acoustic and electrical model. The mineral distribution of the rock is analyzed based on the core scanning electron microscopy analysis results of the tight oil reservoir. The elastic modulus of the mineral mixture after removing clay minerals is calculated using the elastic HS boundary equation, including the matrix elastic modulus and matrix electrical conductivity.

[0084] Based on the pore structure characteristics of tight oil rocks, a DEM model was used to add pores and fractures as hard pores and soft pores into the rock matrix, respectively. The aspect ratios of the hard pores and soft pores were 0.25 and 0.003, respectively, to obtain a rock skeleton model containing pores and fractures. The elastic modulus of the rock skeleton model containing inclusions was then calculated.

[0085] Subsequently, the clay minerals were added as argillaceous ellipsoids (aspect ratio of 0.18) to the rock skeleton model containing inclusions using the DEM model. This yielded a rock skeleton model containing pores, fissures, and argillaceous content. The elastic modulus of the rock skeleton models with different argillaceous contents was then calculated. );

[0086] DEM model through equivalent volume and shear modulus The equivalent elastic parameters are calculated using a coupled differential equation, which is as follows: ;

[0087] ;

[0088] The initial condition is , ; and These are the bulk modulus and shear modulus of the initial phase, i.e., phase 1; and These are the bulk modulus and shear modulus of phase 2, which are the inclusions gradually added into the rock matrix. The content of phase 2; and The geometric factor representing the package.

[0089] Considering the rock in the reservoir environment, calculate the bulk modulus and density of the reservoir fluid under different temperature and pressure conditions.

[0090] bulk modulus of water and density The calculation equation is as follows:

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] in: For temperature, For pressure, Salt content, The speed of sound in pure water.

[0096] Oil bulk modulus and density The calculation equation is as follows:

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] in The density of crude oil was measured at 15.6℃ and normal pressure. To account for the density after considering the pressure effect, This represents the crude oil density after considering the combined effects of temperature and pressure.

[0102] Based on the rock skeleton model modulus calculated above, the Gurvich model is used to simulate jet flow effects at arbitrary saturation. An improved bulk modulus incorporating jet flow effects is then calculated based on the rock skeleton model. and shear modulus ;

[0103] Specifically, the improved bulk modulus with jet flow effect and shear modulus The calculation is as follows:

[0104] ;

[0105] ;

[0106] In the formula, Angular frequency, For fluid viscosity, , The content and aspect ratio of micropores, respectively. It is the bulk modulus of the skeleton of a rock containing only hard pores; and These are the bulk modulus and shear modulus of the rock skeleton model containing all pore structures obtained by DEM, respectively.

[0107] P-wave velocity of partially saturated rock and transverse wave velocity Calculations based on bulk modulus and shear modulus:

[0108] ;

[0109] = ;

[0110] ;

[0111] ;

[0112] ;

[0113] in, , and These represent the bulk modulus, shear modulus, and density of partially saturated rock, respectively. and These are the bulk modulus of the skeleton containing water and the bulk modulus of the skeleton containing oil, respectively. To incorporate the skeletal shear modulus of porous, fractured, and clay minerals, Porosity The content of clay, The density of the matrix, The density of the mixed fluid, The density of clay minerals is given; based on the obtained wave response characteristics of partially saturated rocks, an elastic model of tight oil rocks is obtained.

[0114] Based on the elastic model of tight oil rocks, an electro-rock physics model with the same pore structure and pore fluid is constructed. The mineral mixture is used as the matrix, and the electrical conductivity of the mineral mixture is given by the electrical HS boundary equation. The pores and fractures containing fluids are added to the rock matrix using the electrical differential effective medium model. The same pore structure is taken (the aspect ratios of the pores and fractures are 0.25 and 0.003, respectively), and a rock skeleton model containing pores and fractures is obtained. The electrical conductivity of the rock skeleton model containing inclusions is calculated. Then, clay minerals are added as argillaceous ellipsoids (the aspect ratio is 0.18) to the rock skeleton model containing inclusions to obtain the electro-rock physics model of tight oil. The electrical conductivity with different argillaceous contents is calculated.

[0115] Specifically, the electrical rock physics model design for tight oil is as follows:

[0116] ;

[0117] in, To introduce the conductivity of phase 2; initial conditions are: (e=0)= ; Let be the conductivity of phase 1; denoted as ε, where ε is the conductivity of phase 2; and e is the content of phase 2. It is the depolarization factor of phase 2 A function consisting of (P=1,2,3);

[0118] ;

[0119] in, It is a depolarization factor related to the shape of phase 2, taking into account aspect ratio. ellipsoidal inclusions with a value less than 1;

[0120] ;

[0121]

[0122] According to Archie's formula, the electrical conductivity of pores and fissures is a function of water saturation:

[0123] ;

[0124] in, The electrical conductivity of the salt water. The water saturation of the rock; The electrical conductivity of pores or fissures; It is the saturation index; This represents the lithology coefficient.

[0125] By combining rock elasticity and electrical response, an acoustic-electric joint model is constructed. The model is calibrated and corrected using well logging data and applied to actual tight oil reservoirs to predict the fluid saturation of actual tight oil reservoirs.

[0126] 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.

[0127] 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 porosities 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.

[0128] Figure 11 and Figure 12 The relationships between P-wave velocity and S-wave velocity as a function of porosity and clay content are presented under water-saturated and oil-saturated conditions, respectively. Assuming the elastic modulus of the pores and fractures is 0, the model is set to water-saturated (…). Figure 11 ) or saturated oil ( Figure 12 By setting the fracture porosity to 1% of the total porosity, the porosity and clay content of the model can be varied, allowing for the analysis of the relationship between P-wave and S-wave velocities and porosity and clay content. Under saturated conditions, [the following text appears to be a separate, unrelated sentence: "by setting the fracture porosity to 1% of the total porosity, the porosity and clay content of the model can be changed, allowing for the analysis of the relationship between P-wave and S Figure 11 (a) and Figure 11 As shown in (b), the longitudinal and transverse wave velocities gradually decrease with increasing porosity and clay content. Compared to longitudinal wave velocity, transverse wave velocity varies more significantly with clay content but less significantly with porosity. Similarly, under oil-saturated conditions, its variation trend is similar to that under water-saturated conditions.

[0129] Based on ultrasonic experimental data (oil-saturated condition), the effects of different porosities and clay content on the elastic response of rocks were analyzed, and the experimental results were compared with the predictions of the elastic model to evaluate the applicability of the model. In this embodiment, porosity and clay content in the model parameters were set as variables, with fracture porosity taken as 2.5% of the total porosity, saturation state set as fully oil-saturated, and other parameters kept constant. Further calculations were performed under different porosity conditions to analyze the relationship between P-wave and S-wave velocities and clay content, and the experimental data were compared with the model predictions. Figure 13 As shown ( Figure 13 The blue, black, red, and purple lines represent the P-wave and S-wave velocities at porosities of 1%, 5%, 9%, and 13%, respectively. The results show that both P-wave and S-wave velocities decrease with increasing porosity and clay content, and the model calculations generally agree with the experimental data, verifying the applicability of this model in tight oil reservoirs.

[0130] This embodiment utilizes the electrical equivalent medium theory to construct a tight oil electrophysiological model. Based on this model, the effects of parameters such as rock porosity, fracture porosity, clay content, and water saturation on the electrical properties of the rock are analyzed. In the model parameters, the conductivity of brine is 5 S / m, the conductivity of the mineral matrix is ​​0.038 S / m, and the conductivity of clay is 0.5 S / m. n and β are set to 2 and 1, respectively. By setting the model to water saturation and the clay content to 5%, and adjusting the porosity and fracture porosity of the model, the variation characteristics of the electrical conductivity of tight oil rocks with porosity and fracture porosity can be observed. In tight oil reservoirs, pores and fractures constitute the main fluid transport channels, and their good connectivity enhances the overall conductivity of the reservoir, thereby strengthening the electrical response. Figure 14 As shown, electrical conductivity increases with increasing porosity and fracture porosity, and the response of electrical conductivity to changes in fracture porosity is more significant. Therefore, the electrical properties of rocks are more sensitive to fracture porosity than to overall porosity.

[0131] 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.

[0132] To investigate the effects of porosity and clay content on the ultrasonic frequency band rock physics model, a two-dimensional elastic rock physics model 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 model was constructed, and all model parameters are given in Table 2. Figure 16 (a) in section 16 and (b) in section 16 present the comparison results between the two-dimensional elastic rock physics model constructed under oil-saturated conditions 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 Figure 16 and (c) in Figure 16. Figure 16(d) in the figure corresponds to the two-dimensional elastic rock physics model 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 two-dimensional elastic rock physics model.

[0133] 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.

[0134] Table 2 Relevant parameters of the rock physics model

[0135]

[0136] The target layer in the study area is an oil-bearing tight siltstone reservoir. Following the aforementioned rock physics modeling process, based on the ultrasonic-scale elastic rock physics model, the frequency was changed and an electrical conductivity parameter was introduced. Simultaneously considering the influence of porosity, clay content, and water saturation on the three-dimensional acoustic-electric joint model, total porosity, clay content, and water saturation were set as variables in the three-dimensional acoustic-electric joint model. Cross-interaction analysis was performed on the corresponding elastic and electrical parameters. The elastic parameters of the three-dimensional acoustic-electric joint model are given in Table 2. The rock electrical parameters are: matrix conductivity 0.038 S / m, water conductivity 5 S / m, clay conductivity 0.8 S / m, and saturation index (n) of 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 acoustic-electric model of tight oil-bearing rocks with respect to reservoir porosity, clay content, and water saturation was obtained. Figure 18 As shown, a comparison chart of the three-dimensional acoustic-electric model and well logging A data in tight oil rock is presented. Figure 18 The black, blue, and red lines represent fixed porosity, clay content, and water saturation, respectively. Comparison of the data with the three-dimensional acoustic-electrical joint model of tight oil rocks shows that the distribution patterns of porosity and clay content in the data are in good agreement with the three-dimensional acoustic-electrical joint model of tight oil rocks. Furthermore, as both increase, the changing trends of scatter elasticity and conductivity are the same as those in the three-dimensional acoustic-electrical joint model of tight oil rocks. Well logging data shows that porosity and conductivity are generally low, while clay content is high, consistent with the geological characteristics of the target formation. Based on Poisson's ratio, P-wave impedance, and conductivity from the well logging data, the data points are superimposed on the three-dimensional acoustic-electrical joint model of tight oil rocks to achieve reservoir parameter inversion.

[0137] Based on the obtained three-dimensional acoustic-electric combined model of tight oil and well logging data, quantitative predictions of porosity, clay content, and oil saturation were made for wells A and B within the reservoir parameter range of the three-dimensional acoustic-electric combined model of tight oil rock. Figures 19 and 20 show the comparison between the prediction results and well logging data for wells A and B, respectively. The analysis results show that the porosity of well A is mainly distributed in the range of 5%-15%, the clay content is concentrated between 5%-30%, and the oil saturation is distributed between 40%-80%. In contrast, the porosity of well B is distributed in the range of 3%-13%, the clay content is 3%-30%, and the oil saturation is 30%-70%. As can be seen from the figures, the prediction accuracy of well B is slightly lower than that of well A, but the porosity curves and clay content curves of both wells are basically consistent with the prediction curves, and the trends are similar. The predicted porosity and clay content of both wells show good correlation with the well logging curves. Among them, the clay content prediction curve of well A showed better fitting effect than that of well B, with a correlation coefficient of 0.8955. In the oil production report, the oil production depth ranges of wells A and B were mainly located at 2105-2200 meters and 2250-2300 meters, respectively. Further comparison of the data from wells A and B revealed that the average oil saturation of well A was 53.57%, while that of well B was 49.29%. It was also found that well A had higher porosity and oil saturation than well B, while its clay content was lower. This difference indicates that the target layer in the area where well A is located has better connectivity and more favorable reservoir space, demonstrating higher oil storage potential. The actual daily oil production of well A was 7.39 tons, and that of well B was 0.012 tons, consistent with the results predicted based on reservoir characteristics.

[0138] This embodiment analyzes the microstructure of a tight oil reservoir using scanning electron microscopy (SEM) based on 11 rock samples. Ultrasonic measurements were conducted to investigate the variation of elastic wave velocity with porosity and clay content under water-saturated and oil-saturated conditions, and fluid sensitivity analysis of elastic parameters was performed. Clay content was calculated using natural gamma rays from well logging data, and the relationship between reservoir elasticity and electrical properties and porosity and clay content was discussed based on conductivity. Results show that reservoir conductivity increases with increasing clay content and porosity, while P-wave and S-wave velocities gradually decrease with increasing clay content and porosity. Considering the complex lithological characteristics and difficulty in oil-water differentiation in tight oil reservoirs, this embodiment constructs an elastic rock physics model based on the HS boundary equation, equivalent medium theory, and the Gurevich jet flow model, investigating the influence of parameters such as porosity, clay content, and water saturation on rock elasticity. The results show that the P-wave and S-wave velocities gradually decrease with increasing porosity and clay content, while changes in water saturation have little effect on the P-wave and S-wave velocities. Simultaneously, based on the electrical HS boundary equation and the differential effective medium model, an electrical rock physics model was constructed to analyze the effects of rock porosity, clay content, and water saturation on electrical properties. The study found that electrical conductivity gradually increases with increasing values ​​of these physical parameters.

[0139] Based on two models with the same microstructure, and combining the elastic and electrical responses of rocks, a three-dimensional acoustic-electrical combined rock-physics model suitable for tight oil reservoirs was constructed. The model was calibrated using measured data and applied to predict the porosity, clay content, and oil saturation of reservoirs A and B. The predicted porosity and clay content of both wells showed good correlation with the logging curves, with the clay content curve of well A showing the best correlation (correlation coefficient 0.8955). The results indicate that well A has higher porosity and oil saturation, and well B has higher clay content and higher oil production, consistent with the predicted results. The acoustic-electrical combined rock-physics model for tight oil rocks with the same microstructure can be effectively applied to tight oil reservoirs and can interpret the acoustic-electrical data of tight oil rocks well, demonstrating good prediction accuracy and reliability. The acoustic-electrical combined rock-physics model for tight oil rocks proposed in this embodiment can provide new ideas and methods for the interpretation of the physical properties and fluid prediction of tight oil reservoirs.

[0140] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for quantitative prediction of fluid saturation in tight oil reservoirs based on a combined acoustic-electric model, characterized by: Based on the core scanning electron microscopy analysis results of the tight oil reservoir, the mineral distribution of the rock was analyzed, and the matrix elastic modulus and matrix electrical conductivity of the mineral mixture after removing clay minerals were calculated using the elastic HS boundary equation. Using a DEM model, pores and fractures were added to the rock matrix as hard pores and soft pores respectively to obtain a rock skeleton model containing inclusions. The elastic modulus of the rock skeleton model containing inclusions was then calculated. Then, the clay minerals were added as argillaceous ellipsoids to the rock skeleton model containing inclusions using the DEM model. At this time, a rock skeleton model containing pores, fractures and argillaceous content was obtained, and the elastic modulus of the rock skeleton model with different argillaceous contents was calculated. The Gurvich model was used to simulate the jet flow effect under arbitrary saturation. Based on the obtained rock skeleton model, the improved bulk modulus and shear modulus with jet flow effect were calculated. Based on the obtained wave response characteristics of partially saturated rocks, the elastic model of tight oil rocks was obtained. After constructing the elastic model of tight oil rock, an electrical rock physics model with the same pore structure and pore fluid is also constructed. The mineral mixture is used as the matrix, and the electrical conductivity of the mineral mixture is given by the electrical HS boundary equation. The rock skeleton model containing inclusions is obtained by using the electrical differential effective medium model. The electrical conductivity of the rock skeleton model containing inclusions is calculated. Then, clay minerals are added as argillaceous ellipsoids to the rock skeleton model containing inclusions to obtain the electrical rock physics model of tight oil. The electrical conductivity of the electrical rock physics model of tight oil with different argillaceous contents is calculated. By combining rock elasticity and electrical response, an acoustic-electric joint model is constructed. The acoustic-electric joint model is calibrated and corrected using well logging data and applied to actual tight oil reservoirs to predict the fluid saturation of actual tight oil reservoirs. Among them, the improved bulk modulus with jet effect and shear modulus The calculation is as follows: ； ； In the formula, Angular frequency, For fluid viscosity, , The content and aspect ratio of micropores, respectively. It is the bulk modulus of the skeleton of a rock containing only hard pores; and These are the bulk modulus and shear modulus of the rock skeleton model, respectively. P-wave velocity of partially saturated rock and transverse wave velocity Calculations based on bulk modulus and shear modulus: ; = ； ； ; ; in, , and These represent the bulk modulus, shear modulus, and density of partially saturated rock, respectively. and These are the bulk modulus of the skeleton containing water and the bulk modulus of the skeleton containing oil, respectively. To incorporate the skeletal shear modulus of porous, fractured, and clay mineral frameworks, Porosity The content of clay, The density of the matrix, The density of the mixed fluid, The density of clay minerals is given; based on the wave response characteristics of partially saturated rocks, an elastic model of tight oil rocks is obtained.

2. The method for quantitative prediction of fluid saturation in tight oil reservoirs based on a combined acoustic and electrical model according to claim 1, characterized in that: The electrical rock physics model design for tight oil is as follows: ; in, To introduce the conductivity of phase 2; initial conditions are: (e=0)= ; Let be the conductivity of phase 1; denoted as ε, where ε is the conductivity of phase 2; and e is the content of phase 2. It is the depolarization factor of phase 2 A function consisting of (P=1,2,3); ； in, It is a depolarization factor related to the shape of phase 2, taking into account aspect ratio. ellipsoidal inclusions with a value less than 1; ； According to Archie's formula, the electrical conductivity of pores and fissures is a function of water saturation: ; in, The electrical conductivity of the salt water. The water saturation of the rock; The electrical conductivity of pores or fissures; It is the saturation index; This represents the lithology coefficient.

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