Combined attenuation-elastic-electric modeling and parameter prediction method for shale oil reservoir

Abstract

The application discloses a kind of shale oil reservoir's attenuation-elastic-electricity combined modeling and parameter prediction method, belong to geophysical exploration and oil and gas reservoir evaluation technical field.First, obtain mineral components, pore structure and elastic-attenuation-electricity full-dimensional experimental data through core experiment, determine attenuation and resistivity as high sensitivity parameter after sensitivity analysis;Again, based on Hashin-Shtrikman boundary, differential equivalent medium theory, triple-pore medium wave propagation equation, construct the acoustic and electrical rock physics model of completely consistent micro-pore structure;Finally, establish longitudinal wave impedance-attenuation coefficient-resistivity three-dimensional joint rock physics chart, after multi-band correction, simultaneously quantitative prediction reservoir porosity, shale content and fracture porosity.The application breaks through the limitation of conventional acoustic-electricity combined model, significantly reduces the multi-solution of reservoir parameter prediction, and is suitable for fine evaluation of various shale oil tight reservoirs.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration and oil and gas reservoir evaluation technology, specifically involving a combined modeling method for attenuation-elasticity-electricity of tight reservoirs in shale oil formations, and a quantitative prediction method for reservoir porosity, clay content, and fracture porosity based on this model. Background Technology

[0002] Shale oil is an important unconventional alternative resource for ensuring my country's oil and gas energy security. Its reservoirs are generally characterized by low porosity, extremely poor permeability, complex mineral composition, high clay content, well-developed microfractures, and strong heterogeneity, making it difficult for conventional reservoir evaluation methods to accurately characterize reservoir properties and oil-bearing potential. Accurately and quantitatively predicting the three core parameters of reservoir porosity, clay content, and fracture porosity is a crucial prerequisite for evaluating the effectiveness of shale oil reservoirs and determining the optimal exploitation area.

[0003] In existing technologies, conventional geophysical reservoir prediction methods are mainly based on single elastic wave data, using elastic parameters such as P-wave velocity, S-wave velocity, wave impedance, and Poisson's ratio to characterize the reservoir. However, for tight shale oil reservoirs, the complex pore structure and high clay content significantly reduce the sensitivity of elastic parameters to reservoir properties and fluid types, making accurate quantitative reservoir evaluation impossible.

[0004] In recent years, some scholars have conducted research on elastic-electrical joint rock physics modeling, utilizing the high sensitivity of resistivity to clay content and fluid type to compensate for the shortcomings of a single elastic parameter. However, existing acoustic-electrical joint models have three major defects: First, most only focus on the coupling of elastic and electrical two-dimensional parameters, completely ignoring the high sensitivity of seismic wave attenuation parameters to pore structure, microfractures, and reservoir heterogeneity, and thus cannot effectively characterize the fracture development characteristics of tight reservoirs; Second, the modeling process fails to ensure that the micropore structure (pore / fracture aspect ratio, inclusion content, addition order, etc.) of the acoustic model and the electrical model are completely consistent, resulting in serious ambiguity in the acoustic-electrical joint interpretation; Third, existing models cannot achieve simultaneous quantitative prediction of the three key parameters of porosity, clay content, and fracture porosity, making it difficult to meet the needs of fine evaluation of shale oil tight reservoirs.

[0005] Therefore, the industry urgently needs to develop a joint rock physics modeling and parameter prediction method that integrates three types of sensitive parameters—attenuation, elasticity, and electrical properties—while maintaining complete homogeneity of the microstructure of the acoustic-electric model, in order to solve the technical problems of strong multiple solutions, low prediction accuracy, and incomplete parameter characterization in existing technologies. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method for combined attenuation-elasticity-electricity modeling and parameter prediction of shale oil reservoirs. This method overcomes the limitations of conventional acoustic-electrical combined models, enabling simultaneous high-precision quantitative prediction of reservoir porosity, clay content, and fracture porosity, reducing the ambiguity of interpretations, and providing reliable technical support for the fine evaluation of tight shale oil reservoirs.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a method for joint modeling and parameter prediction of attenuation-elasticity-electricity properties of shale oil reservoirs, comprising the following steps: S1. Core Data Acquisition and Experimental Testing: Collect core samples from the tight reservoir of the target shale oil formation, conduct X-ray diffraction experiments and cast thin section analysis, and obtain the mineral composition, content and pore structure characteristics of the rock through experiments; as well as the full-dimensional experimental data of the rock's elasticity, attenuation and resistivity; and simultaneously acquire sonic logging, resistivity logging and seismic inversion data of the target work area.

[0008] S2. Sensitivity analysis of reservoir parameters: Based on the experimental data obtained in step S1, the sensitivity index of each parameter, such as attenuation coefficient, resistivity and elasticity, is calculated. The sensitivity of attenuation coefficient and resistivity to the above-mentioned key reservoir parameters is quantitatively verified to be higher than that of conventional elastic parameters. The core sensitive parameters for joint modeling are then determined.

[0009] S3. Construction of the acoustic rock physics model: Based on the mineral composition and pore structure obtained in step S1, a complete acoustic rock physics model is constructed using the elastic Hashin-Shtrikman boundary equation.

[0010] S4. Construction of a homologous electrical rock physics model: Using the same pore structure parameters as in step S3, construct an electrical rock physics model that is completely homologous to the microstructure of the acoustic wave model.

[0011] S5. Construction and Multi-Band Correction of Three-Dimensional Joint Rock Physics Chart: Based on the acoustic rock physics model in step S3 and the electrical rock physics model in step S4, the reasonable range of values ​​for porosity, fracture porosity, and clay content is explored. The corresponding longitudinal wave impedance, attenuation coefficient, and resistivity are calculated by forward modeling. A three-dimensional attenuation-elasticity-electricity joint rock physics chart with the above three parameters as orthogonal coordinate axes is established. The high-frequency part of the chart is corrected using core experimental data in the ultrasonic band, and the low-frequency part is corrected using measured well logging data in the well logging band. The scale conversion of different frequency bands is achieved by adjusting the inclusion radius.

[0012] S6. Quantitative prediction of reservoir parameters: Project the measured longitudinal wave impedance, attenuation coefficient and resistivity data of the target work area onto the three-dimensional joint rock physics chart after correction in step S5. Through data matching and intersection, the porosity, clay content and fracture porosity of the reservoir are predicted quantitatively at the same time.

[0013] Further, in step S1, ultrasonic and resistivity experiments are conducted on the core sample under varying confining pressure conditions. The longitudinal wave attenuation coefficient is estimated using the spectral ratio method to obtain comprehensive experimental data on the rock's elasticity, attenuation, and resistivity. The center frequency of the ultrasonic experiment is 0.5~0.6MHz, the confining pressure is set in the range of 20MPa~60MPa, and the experiment is conducted under conditions of water saturation, in-situ reservoir temperature, and pore pressure. The resistivity experiment is conducted at a test frequency of 100~150Hz and a test voltage of 1V. The core sample is in a fully saturated brine state, and the brine salinity is consistent with the in-situ formation water salinity of the target reservoir.

[0014] Furthermore, the formula for estimating the P-wave attenuation coefficient using the spectral ratio method is as follows: In the formula, , These are the geometric factors for the rock sample and the standard aluminum block, respectively. For the longitudinal wave velocity, The distance the wave travels in the rock sample. , The amplitude spectra of the rock sample and the standard aluminum block are shown below. For frequency, The quality factor is denoted by , and the attenuation coefficient is the reciprocal of the quality factor.

[0015] Furthermore, in step S2, the sensitivity indices of P-wave velocity, S-wave velocity, P-wave impedance, attenuation coefficient, and resistivity to reservoir porosity, clay content, and fracture porosity are calculated. The specific calculation formula is as follows: In the formula, This represents the average value of rock physical parameters within the range of low physical properties. This represents the average value of rock physical parameters within the high physical property parameter range, where the physical property parameters are porosity, clay content, and fracture porosity. The larger the value, the more sensitive the rock's physical parameters are to the corresponding reservoir properties.

[0016] Further, step S3 specifically involves: mixing the framework minerals (excluding clay minerals) using the elastic Hashin-Shtrikman boundary equation and calculating the elastic modulus of the rock matrix; based on the acoustic differential equivalent medium theory, adding the hard pores, soft pores, and clay mineral ellipsoidal inclusions sequentially into the matrix and calculating the elastic modulus of the dry rock framework; combining the triple-porous medium wave propagation equation to simulate the longitudinal wave velocity dispersion and attenuation characteristics of fluid-containing rocks, constructing a complete acoustic rock physics model, wherein the coupled differential equations used in the acoustic differential equivalent medium theory are: In the formula, This refers to the volume content of the inclusion phase. , For the bulk modulus and shear modulus of the inclusion phase, , , , The bulk modulus and shear modulus of the background phase are given. , This is the geometric factor of the corresponding package.

[0017] Furthermore, the propagation equation for the triple porous medium considers the four-phase coupled motion of solid skeleton displacement, fracture fluid displacement, intergranular pore fluid displacement, and clay pore fluid displacement, and solves for the complex wave number through plane wave analysis. Then, the longitudinal wave velocity and quality factor are calculated using the following formula: In the formula, For complex longitudinal wave velocity, Let be the real part of the complex wavenumber. is the imaginary part of the complex wave number.

[0018] Further, step S4 specifically involves: using the same pore structure parameters as in step S3, including pore / fracture aspect ratio, inclusion volume content, addition order, and inclusion geometric characteristics, firstly, mixing the framework minerals (excluding clay minerals) using the electrical Hashin-Shtrikman boundary equation, and calculating the electrical conductivity of the rock matrix; then, based on the electrical differential equivalent medium theory, sequentially adding saturated pores, fractures, and clay mineral inclusions into the matrix in the same order as the acoustic model, simulating the equivalent electrical conductivity and resistivity of the rock, and constructing an electrical rock physics model with a microstructure completely homologous to the acoustic model. The calculation formula used in the electrical differential equivalent medium theory is as follows: In the formula, The equivalent conductivity after adding the inclusion phase is given. , The conductivity of the background phase. The conductivity of the inclusion phase, This represents the volume content of the inclusion phase. This is the depolarization factor for the inclusion phase.

[0019] The fluid conductivity of pores and fissures is calculated based on Archie's formula: In the formula, The lithology coefficient is set to 1. The electrical conductivity of the formation water. This represents the water saturation level. This is the saturation index.

[0020] Furthermore, in steps S3 and S4, the aspect ratio of hard pores is 0.1 to 1, the aspect ratio of soft pores and fissures is fixed at 0.001, and the aspect ratio of clay mineral ellipsoidal inclusions is 0.15 to 0.20.

[0021] Furthermore, the color codes of the three-dimensional combined rock physics chart correspond to the values ​​of porosity, clay content, and fracture porosity, respectively; during frequency band conversion, the radius of fracture inclusions and clay inclusions corresponding to the ultrasonic frequency band is 25~35μm, and the radius of inclusions corresponding to the well logging frequency band is 0.8~1.2mm.

[0022] Furthermore, the lithology of the tight reservoir in the shale oil system is mainly terrestrial tight sandstone, with a porosity ranging from 2% to 15% and a permeability ranging from 0.01 mD to 0.2 mD.

[0023] Compared with the prior art, the present invention has the following advantages: 1. This invention quantitatively verifies the high sensitivity of attenuation to microfractures and resistivity to clay content through core experiments, making up for the shortcomings of conventional elastic parameters in characterizing the heterogeneity of tight reservoirs. It is the first to achieve joint modeling of three parameters: attenuation, elasticity and electrical properties, breaking through the limitations of conventional two-dimensional joint modeling of elasticity and electrical properties.

[0024] 2. The acoustic-electric model constructed in this invention adopts completely consistent mineral composition, pore / fracture aspect ratio, inclusion addition order and geometric parameters, which fundamentally solves the problem of multiple solutions in joint interpretation caused by the mismatch of existing model structures, realizes that the microscopic pore structure of the acoustic wave and electrical model is completely homologous, and provides a unified theoretical basis for the collaborative interpretation of acoustic-electric data.

[0025] 3. Compared with the limitations of traditional two-dimensional intersection charts which can only constrain two parameters, this invention constructs a three-dimensional joint rock physics chart. Through three-dimensional parameter constraints, the ambiguity of reservoir parameter prediction is greatly reduced, and the synchronous quantitative prediction of the three core reservoir parameters is realized, thereby improving the prediction accuracy.

[0026] 4. This invention achieves scale conversion of different frequency bands by adjusting the radius of the inclusion body, and completes frequency band correction using experimental data and well logging data respectively. It realizes multi-frequency cross-scale adaptation of core-logging-seismic data, effectively eliminates prediction errors caused by wave dispersion differences, and has a wide range of applications.

[0027] 5. The method of this invention is customized for the strong heterogeneity of tight shale oil reservoirs. The predicted reservoir parameters are in high agreement with the well logging interpretation results. It can effectively identify high-porosity, high-fracture-development, and low-mud content high-quality reservoirs, providing reliable technical support for predicting the optimal exploitation area of ​​shale oil. Attached Figure Description

[0028] Figure 1 The image shows a thin section of rock sample A in Embodiment 1 of the present invention, where (a) is a planar polarized light view and (b) is a cross-polarized light view.

[0029] Figure 2 The graph shows the relationship between the longitudinal wave velocity and the transverse wave velocity of the rock sample in Embodiment 1 of the present invention and the confining pressure; where (a) is the longitudinal wave velocity and (b) is the transverse wave velocity.

[0030] Figure 3 This is a graph showing the relationship between the longitudinal wave attenuation coefficient of the rock sample and the confining pressure in Example 1 of the present invention.

[0031] Figure 4 This is a graph showing the relationship between the resistivity of a rock sample and the confining pressure in Example 1 of the present invention.

[0032] Figure 5 The graph shows the sensitivity analysis results of rock physical parameters on porosity, clay content, and fracture porosity / confining pressure in Example 1 of the present invention, where (a) represents porosity, (b) represents clay content, and (c) represents fracture porosity / confining pressure.

[0033] Figure 6 The diagrams show the actual structure and model structure of the present invention; where (a) is a schematic diagram of the rock microstructure and (b) is a model diagram of the triple pore structure.

[0034] Figure 7 This is a flowchart illustrating the construction process of the acoustic rock physics model of the present invention.

[0035] Figure 8 This is a flowchart illustrating the construction process of the electrical rock physics model of the present invention.

[0036] Figure 9 This is a comparison diagram of the three-dimensional combined rock physics chart and core experimental data in the ultrasonic band in Embodiment 1 of the present invention, where (a) represents porosity and (b) represents clay content.

[0037] Figure 10This is a comparison chart of the three-dimensional combined rock physics chart and logging data in the logging frequency band in Embodiment 1 of the present invention, where (a) represents porosity and (b) represents clay content.

[0038] Figure 11 This is a comparison chart of the reservoir parameter prediction results and well logging interpretation results for wells A(a) and B(b) in Embodiment 1 of the present invention. Detailed Implementation

[0039] The present invention will be further described below.

[0040] In all embodiments of the present invention, the experimental tests were carried out under the conditions of in-situ reservoir temperature of 25°C and pore pressure of 15MPa. The core samples were all processed into standard cylinders with a diameter of 25mm±0.2mm and a length of 50mm±0.2mm to ensure the consistency and comparability of the experimental data.

[0041] Example 1: The target work area in this example is the Chang 7 section of the Yanchang Formation in the Ordos Basin. This formation is the core producing layer of continental shale oil in my country. The lithology is mainly tight sandstone, the reservoir porosity is generally 3%~9%, and the permeability is 0.019mD~0.177mD. It belongs to a typical low-porosity and low-permeability tight reservoir with well-developed microfractures, large variation in clay content, and extremely strong heterogeneity.

[0042] The specific implementation steps of this embodiment are as follows: S1. Core Sample Collection and Experimental Testing: Fifteen core samples of tight sandstone with a length of 7 sections were collected from the target work area. After standard sample processing, the following tests were conducted: S11. Basic physical property test: The density, porosity and permeability of the rock samples were measured using a fully automatic porosity and permeability meter. The results are shown in Table 1. The porosity of the 15 rock samples ranged from 3.22% to 8.85%, and the permeability ranged from 0.019 mD to 0.177 mD, which is consistent with the characteristics of tight reservoirs.

[0043] Table 1. Basic physical property parameters of core samples from Example 1 S12. Mineral and Pore Structure Characterization: X-ray diffraction whole-rock mineral analysis was conducted to obtain the mineral composition and content of the rock samples. The results are shown in Table 2. The main minerals in the rock samples were quartz and feldspar, with clay mineral content ranging from 4.62% to 15.23%. Simultaneously, thin section analysis was performed to obtain the pore structure characteristics of the rock samples. The thin section of sample A is shown in Table 2. Figure 1 As shown, the rock is dense and has developed various types of pore structures, such as intergranular pores, dissolution pores, and microfractures.

[0044] Table 2 Mineral composition of core samples from Example 1 S13. Variable Confining Pressure Acoustoelectric Experiments: Ultrasonic and resistivity experiments were conducted under confining pressures ranging from 20 MPa to 60 MPa. The ultrasonic experiment used the pulse transmission method with a center frequency of 0.55 MHz to measure the longitudinal and transverse wave velocities of the rock samples. A standard aluminum block of the same size was selected as a reference sample, and the longitudinal wave attenuation coefficient was calculated using the spectral ratio method. The resistivity experiment used a test frequency of 120 Hz and a test voltage of 1 V. The rock samples were fully saturated with brine (mineralization 56.5 g / L, consistent with the formation water mineralization in the work area), and the resistivity of the rock samples under different confining pressures was measured.

[0045] Experimental results are as follows Figure 2-4 As shown, with increasing confining pressure, the longitudinal and transverse wave velocities of the rock sample gradually increase, while the attenuation coefficient and resistivity gradually decrease. This reflects the significant impact of increased confining pressure on the elastic, attenuation, and electrical characteristics of the rock due to the closure of microcracks.

[0046] S2. Reservoir Parameter Sensitivity Analysis: Based on experimental data under a confining pressure of 20 MPa, the sensitivity index formula was used to calculate the sensitivity indices of P-wave velocity, S-wave velocity, P-wave impedance, attenuation coefficient, and resistivity to porosity, clay content, and fracture porosity (confining pressure). The results are as follows: Figure 5 As shown.

[0047] The analysis results show that longitudinal wave impedance and resistivity are most sensitive to porosity; resistivity and attenuation coefficient are significantly more sensitive to clay content than conventional elastic parameters; and attenuation coefficient and resistivity are far more sensitive to fracture porosity / confining pressure than other parameters. Therefore, attenuation coefficient, resistivity, and longitudinal wave impedance are determined to be the core sensitive parameters for joint modeling.

[0048] S3. Construction of the acoustic rock physics model: Based on the mineral composition and pore structure obtained in step S1, according to... Figure 7 The process of constructing an acoustic rock physics model involves the following steps: S31. Calculation of matrix elastic modulus: Using the elastic Hashin-Shtrikman boundary equation, the matrix matrix is ​​mixed with skeletal minerals such as quartz, feldspar, and carbonate minerals other than clay minerals. The bulk modulus and shear modulus of the rock matrix are calculated, and the matrix bulk modulus is 39 GPa and the shear modulus is 35 GPa.

[0049] S32. Calculation of dry rock skeleton modulus: Based on the acoustic differential equivalent medium theory, hard pores (aspect ratio 0.5) and soft pores and fissures (aspect ratio 0.001) are first added to the matrix in sequence to obtain a rock skeleton containing pores and fissures; then clay minerals are added to the skeleton as ellipsoidal inclusions (aspect ratio 0.18) to calculate the equivalent elastic modulus of the dry rock skeleton.

[0050] S33. Simulation of Fluid Wave Response: Combining the wave propagation equation of a triple porous medium, considering the four-phase coupled motion of the solid skeleton, fractured fluid, intergranular pore fluid, and clay pore fluid, the fluid parameters are set as follows: formation water bulk modulus 2.24 GPa, density 1.002 g / cm³. 3 Viscosity 9.8×10 -4 Pa•s; By solving the complex wave number through plane wave analysis, the longitudinal wave velocity and attenuation coefficient at different frequencies are calculated, and the acoustic rock physics model is completed.

[0051] The forward modeling results show that with the increase of porosity, fracture porosity and clay content, the dispersion of longitudinal wave velocity and the attenuation peak value both increase significantly, which is completely consistent with the experimental test results.

[0052] S4. Construction of a Homogeneous Electrical Rock Physics Model: Using pore structure parameters completely identical to those in step S3 (pore aspect ratio 0.5, fracture aspect ratio 0.001, clay inclusion aspect ratio 0.18, inclusion addition order, and volume content are exactly the same), following... Figure 8 The process of constructing an electrical rock physics model is as follows: S41. Calculation of matrix electrical conductivity: Using the electrical Hashin-Shtrikman boundary equation, the skeletal minerals other than clay minerals are mixed, and the equivalent electrical conductivity of the rock matrix is ​​calculated, yielding a matrix electrical conductivity of 0.003 S / m.

[0053] S42. Equivalent conductivity simulation: Based on the electrical differential equivalent medium theory, following the same order as the acoustic wave model, the pores and fissures of the saturated salt water are first added to the matrix in sequence, followed by the addition of clay mineral inclusions (conductivity 0.5 S / m). The equivalent conductivity and resistivity of the rock are calculated, thus completing the construction of an electrical rock physics model that is completely homologous to the microstructure of the acoustic wave model.

[0054] The forward modeling results show that as porosity, fracture porosity, and clay content increase, the rock resistivity gradually decreases, which is completely consistent with the experimental test results.

[0055] S5. Construction and multi-band correction of three-dimensional joint rock physics chart: S51. Construction of a 3D rock physical map: Traversing the reasonable range of porosity (1%~11%), fracture porosity (0.03%~0.075%), and clay content (1%~15%), the corresponding longitudinal wave impedance, attenuation coefficient, and resistivity are calculated based on the forward model of the acoustic-electric joint model. A 3D joint rock physical map is established with the above three parameters as orthogonal coordinate axes. The color scales of the map correspond to porosity, clay content, and fracture porosity, respectively.

[0056] S52. Multi-band calibration: The ultrasonic band (0.55MHz) was set with an inclusion radius of 30μm. The high-frequency chart was calibrated using the core experimental data from step 1. The results are as follows: Figure 9 As shown, the experimental data perfectly match the trend on the chart; the logging frequency band (10kHz) was set with a 1mm radius, and the low-frequency chart was corrected using measured logging data from wells A and B in the work area. The results are as follows. Figure 10 As shown, the well logging data and the chart have excellent matching degree.

[0057] S6. Quantitative prediction and verification of reservoir parameters: Select the 7-section sandstone reservoir of wells A and B in the work area, project the P-wave impedance, attenuation coefficient and resistivity in the measured logging data onto the corrected three-dimensional joint chart, and simultaneously quantitatively predict the reservoir porosity, clay content and fracture porosity.

[0058] Prediction results are as follows Figure 11 As shown, the predicted porosity distribution ranges from 2% to 15%, the clay content ranges from 4% to 15%, and the fracture porosity ranges from 0 to 0.4%, which shows a very high degree of agreement with the well logging interpretation results. Among them, the layers with high porosity and fracture porosity and low clay content correspond perfectly to the high-yield layers in the test area, verifying the reliability and practicality of the method of the present invention.

Claims

1. A method for combined modeling and parameter prediction of attenuation-elasticity-electricity properties of shale oil reservoirs, characterized in that, Includes the following steps: S1. Core Data Acquisition and Experimental Testing: Collect core samples and obtain the mineral composition, content, and pore structure characteristics of the rock through experiments; as well as the full-dimensional experimental data of the rock's elasticity, attenuation, and resistivity; simultaneously acquire sonic logging, resistivity logging, and seismic inversion data of the target work area; S2. Sensitivity analysis of reservoir parameters: Based on the experimental data obtained in step S1, the sensitivity index of each parameter, such as attenuation coefficient, resistivity and elasticity, is calculated. The sensitivity of attenuation coefficient and resistivity to the above-mentioned key reservoir parameters is quantitatively verified to be higher than that of conventional elastic parameters. The core sensitive parameters for joint modeling are then determined. S3. Construction of acoustic rock physics model: Based on the mineral composition and pore structure obtained in step S1, a complete acoustic rock physics model is constructed using the elastic Hashin-Shtrikman boundary equation. S4. Construction of a homologous electrical rock physics model: Using the same pore structure parameters as in step S3, construct an electrical rock physics model that is completely homologous to the microstructure of the acoustic wave model. S5. Construction and Multi-band Correction of Three-Dimensional Joint Rock Physics Chart: Based on the acoustic rock physics model in step S3 and the electrical rock physics model in step S4, a three-dimensional attenuation-elasticity-electricity joint rock physics chart is established with the above three parameters as orthogonal coordinate axes. The high-frequency part of the chart is corrected using core experimental data in the ultrasonic band and the low-frequency part is corrected using measured well logging data in the well logging band. The scale conversion of different frequency bands is achieved by adjusting the inclusion radius. S6. Quantitative prediction of reservoir parameters: Project the measured longitudinal wave impedance, attenuation coefficient and resistivity data of the target work area onto the three-dimensional joint rock physics chart after correction in step S5. Through data matching and intersection, the porosity, clay content and fracture porosity of the reservoir are predicted quantitatively at the same time.

2. The method according to claim 1, characterized in that, In step S1, ultrasonic and resistivity experiments are conducted on the core sample under varying confining pressure conditions. The longitudinal wave attenuation coefficient is estimated using the spectral ratio method to obtain comprehensive experimental data on the rock's elasticity, attenuation, and resistivity. The center frequency of the ultrasonic experiment is 0.5~0.6MHz, the confining pressure is set in the range of 20MPa~60MPa, and the experiment is conducted under conditions of water saturation, in-situ reservoir temperature, and pore pressure. The resistivity experiment is conducted at a test frequency of 100~150Hz and a test voltage of 1V. The core sample is in a fully saturated brine state, and the brine salinity is consistent with the in-situ formation water salinity of the target reservoir.

3. The method according to claim 2, characterized in that, The formula for estimating the P-wave attenuation coefficient using the spectral ratio method is as follows: In the formula, , These are the geometric factors for the rock sample and the standard aluminum block, respectively. For the longitudinal wave velocity, The distance the wave travels in the rock sample. , The amplitude spectra of the rock sample and the standard aluminum block are shown below. For frequency, The quality factor is denoted by , and the attenuation coefficient is the reciprocal of the quality factor.

4. The method according to claim 1, characterized in that, In step S2, the sensitivity indices of P-wave velocity, S-wave velocity, P-wave impedance, attenuation coefficient, and resistivity to reservoir porosity, clay content, and fracture porosity are calculated. The specific calculation formula is as follows: In the formula, This represents the average value of rock physical parameters within the range of low physical properties. This represents the average value of rock physical parameters within the high physical property parameter range, where the physical property parameters are porosity, clay content, and fracture porosity. The larger the value, the more sensitive the rock's physical parameters are to the corresponding reservoir properties.

5. The method according to claim 1, characterized in that, Step S3 specifically involves: mixing the framework minerals (excluding clay minerals) using the elastic Hashin-Shtrikman boundary equation and calculating the elastic modulus of the rock matrix; based on the acoustic differential equivalent medium theory, adding the hard pores, soft pores, and clay mineral ellipsoidal inclusions sequentially into the matrix and calculating the elastic modulus of the dry rock framework; combining the triple-porous medium wave propagation equation to simulate the longitudinal wave velocity dispersion and attenuation characteristics of fluid-containing rocks, constructing a complete acoustic rock physics model, wherein the coupled differential equations used in the acoustic differential equivalent medium theory are: In the formula, This represents the volume content of the inclusion phase. , For the bulk modulus and shear modulus of the inclusion phase, , , , The bulk modulus and shear modulus of the background phase. , This is the geometric factor of the corresponding package.

6. The method according to claim 5, characterized in that, The proposed triple porous medium wave propagation equation considers the four-phase coupled motion of solid skeleton displacement, fracture fluid displacement, intergranular pore fluid displacement, and clay pore fluid displacement, and solves for the complex wave number through plane wave analysis. Then, the longitudinal wave velocity and quality factor are calculated using the following formula: In the formula, For complex longitudinal wave velocity, Let be the real part of the complex wavenumber. is the imaginary part of the complex wave number.

7. The method according to claim 1, characterized in that, Step S4 specifically involves: using the same pore structure parameters as in step S3, including pore / fracture aspect ratio, inclusion volume content, addition order, and inclusion geometry, firstly, mixing the framework minerals (excluding clay minerals) using the electrical Hashin-Shtrikman boundary equation, and calculating the electrical conductivity of the rock matrix; then, based on the electrical differential equivalent medium theory, sequentially adding saturated pores, fractures, and clay mineral inclusions into the matrix in the same order as the acoustic model, simulating the equivalent electrical conductivity and resistivity of the rock, and constructing an electrical rock physics model with a microstructure completely homologous to the acoustic model. The calculation formula used in the electrical differential equivalent medium theory is as follows: In the formula, The equivalent conductivity after adding the inclusion phase is given. , The conductivity of the background phase. The conductivity of the inclusion phase, This represents the volume content of the inclusion phase. The depolarization factor for the inclusion phase; The fluid conductivity of pores and fissures is calculated based on Archie's formula: In the formula, Lithology coefficient; The electrical conductivity of the formation water. Water saturation This is the saturation index.

8. The method according to claim 7, characterized in that, In steps S3 and S4, the aspect ratio of hard pores is 0.1 to 1, the aspect ratio of soft pores and fissures is fixed at 0.001, and the aspect ratio of clay mineral ellipsoidal inclusions is 0.15 to 0.

20.

9. The method according to claim 1, characterized in that, The color codes on the three-dimensional combined rock physics chart correspond to the values ​​of porosity, clay content, and fracture porosity, respectively. During frequency band conversion, the radius of fracture inclusions and clay inclusions corresponding to the ultrasonic frequency band is 25~35μm, and the radius of inclusions corresponding to the well logging frequency band is 0.8~1.2mm.

10. The method according to claim 1, characterized in that, The lithology of the tight reservoirs in the shale oil system is mainly terrestrial tight sandstone, with a porosity ranging from 2% to 15% and a permeability ranging from 0.01 mD to 0.2 mD.

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