Tight sandstone gas reservoir microfracture prediction method based on elastic modulus frequency dispersion inversion

Abstract

The invention is suitable for the technical field of oil and gas resource exploration, and provides a tight sandstone gas reservoir microfracture prediction method based on elastic modulus frequency dispersion inversion. According to the method, a rock physical model is constructed based on an equivalent embedded body stress averaging theory, frequency dispersion attributes are defined through elasticity modulus differences in a non-relaxed state and a relaxed state, and a microfracture parameter influence mechanism is disclosed; inversion is driven by a non-relaxed state model, the calculated elastic modulus frequency dispersion attribute and the microfracture density are in significant positive correlation, the quantitative correlation of the two is verified through measured data, and the inversion precision of the microfracture characteristics in the well is improved; an L-BFGS algorithm is introduced to carry out seismic inversion, frequency dispersion attribute quantitative prediction of pre-stack seismic data is realized, physical consistency between logging and seismic data is ensured, traditional limitation is broken through, a micro-fracture development area of a gas production well target layer is successfully identified in practical application, and the method has a good application prospect. And an effective solution is provided for high-precision prediction of the regional scale of the compact sandstone gas reservoir microcracks.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas resource exploration, and in particular relates to a method for predicting microcracks in tight sandstone gas reservoirs based on elastic modulus dispersion inversion. Background Art

[0002] The development of microfractures in tight sandstone gas reservoirs is a key factor controlling their permeability, directly affecting reservoir permeability and oil and gas recovery. Therefore, using seismic methods to achieve high-precision prediction of microfractures at a regional scale is crucial for the efficient development of tight sandstone gas reservoirs.

[0003] Rock physics experiments and theoretical studies have shown that the development of microfractures causes elastic parameters to respond with frequency, providing a reliable physical basis for identifying microfractures using seismic dispersion. Currently, many studies have used the frequency-dependent AVO (Amplitude Variation with Offset) inversion method to extract seismic dispersion attributes. However, these studies primarily apply this method to fluid identification, while dispersion attribute inversion methods for microfracture prediction are still under exploratory.

[0004] The dispersion properties calculated by traditional frequency-varying AVO inversion rely on a simplified frequency gradient approximation, which greatly limits the ability to accurately characterize the dispersion response of rocks. In addition, although existing studies have improved the frequency-varying AVO inversion by introducing advanced time-frequency analysis techniques, and to a certain extent improved the resolution of the inversion results, they have not fundamentally broken through the inherent limitations of dispersion property prediction. Therefore, it is urgent to develop a new seismic inversion method that can quantitatively predict the dispersion response induced by microfractures in order to improve the accuracy of microfracture identification in tight sandstone gas reservoirs. To this end, the present invention proposes a microfracture prediction method for tight sandstone gas reservoirs based on elastic modulus dispersion inversion. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion, aiming to solve the problems raised in the above background technology.

[0006] The purpose of the present invention is achieved through the following technical solutions: The micro-fracture prediction method for tight sandstone gas reservoirs based on elastic modulus dispersion inversion includes the following steps: Step 1: Rock physics model construction; A rock physics model is constructed based on the equivalent embedded volume stress averaging theory to quantitatively describe the elastic modulus response characteristics of tight sandstone gas reservoirs in the unrelaxed state under pore pressure imbalance and the relaxed state under pore pressure equilibrium. Step 2: Rock physics inversion of microfracture parameters in the well; Driven by the rock physics model in the unrelaxed state, the logging data is input to invert the microfracture parameters and calculate the microfracture density parameters; Step 3: Prediction of elastic modulus in the relaxed state in the well; The microfracture parameters obtained by inversion and the logging data are input into the rock physics model of the relaxed state to predict the elastic modulus of the relaxed state; Step 4: Calculation and verification of the elastic modulus dispersion properties in the well; Based on the difference between the elastic moduli in the relaxed and unrelaxed states, the elastic modulus dispersion properties in the well are calculated to verify the effectiveness of microfracture density identification. Step 5: Calculation of elastic impedance curve and establishment of seismic inversion constraints; Based on the elastic impedance equation, the elastic impedance curves of the unrelaxed and relaxed states are calculated as the seismic inversion constraints; Step 6: Seismic inversion of elastic modulus dispersion properties based on L-BFGS; The pre-stack seismic data spectrum is decomposed. Using the elastic impedance curve as a constraint, the elastic impedance of the unrelaxed and relaxed states is inverted using the L-BFGS algorithm. The elastic modulus is converted and the dispersion properties are calculated. Step 7: Seismic inversion verification and microcrack prediction; The inversion results are verified by combining the microfracture density in the well to achieve microfracture prediction.

[0007] Furthermore, in step 1, the elastic modulus in the unrelaxed state includes the bulk modulus and shear modulus , and its calculation formula is: ; ; The elastic modulus in the relaxed state includes the bulk modulus and shear modulus , and its calculation formula is: ; ; in, and are the bulk modulus and shear modulus of the rock solid matrix, respectively; is the bulk modulus of the fluid; is the total porosity of the rock, which is equal to the spherical pore porosity and microfracture porosity sum; and Microcrack content and microcrack aspect ratio The relevant parameters, and Corresponding to The situation at that time.

[0008] Furthermore, in step 2, the inversion objective function for ; in, and are microcrack content and microcrack aspect ratio, respectively; and are the well logging bulk modulus and shear modulus, respectively; Microcrack parameters obtained by inversion and Further used to calculate the microcrack density : .

[0009] Furthermore, in step 4, the formula for quantitatively calculating the elastic modulus dispersion property in the well is as follows: ; ; in, is the bulk modulus dispersion property; is the shear modulus dispersion property.

[0010] Furthermore, in step 5, the elastic impedance equation is: ; in, is the elastic impedance; is the angle of incidence; is the elastic impedance normalization factor; is the bulk modulus; is the shear modulus; is the density; 、 and represent the average values ​​of bulk modulus, shear modulus, and density in the well, respectively; ,in is the ratio of the longitudinal and transverse wave velocities; ; .

[0011] Furthermore, the specific process of step 6 is as follows: Pre-stack seismic data are spectrally decomposed. The elastic impedance curves in the unrelaxed and relaxed states are used as logging constraints. The AVO formula for elastic impedance is extended to the frequency domain, and an objective function for seismic inversion is constructed. The objective function is iteratively solved using the L-BFGS algorithm. By minimizing the difference between the synthetic seismic records and the spectral decomposition results, the elastic impedances in the unrelaxed and relaxed states are inverted. The elastic impedances are then converted into the corresponding elastic moduli in the unrelaxed and relaxed states, and the difference between the two is used to quantitatively predict the elastic modulus dispersion properties.

[0012] Furthermore, in step 6, the reflection coefficient of the elastic impedance extended to the frequency domain is: ; in, for wave reflection coefficient; represents frequency; Δ and — represent the difference and average value of the elastic impedance of the upper and lower layers, respectively; Objective function of seismic inversion for: ; in, Elastic impedance model for unrelaxed / relaxed states ; is the spectral decomposition result of pre-stack seismic data; For time; Indicates that the current model Calculated reflection coefficient Synthetic seismic record obtained by convolution with seismic wavelet.

[0013] Furthermore, in step 7, the elastic modulus dispersion attribute is positively correlated with the microcrack density. The stronger the abnormal response of the elastic modulus dispersion attribute, the higher the degree of microcrack development in the corresponding tight sandstone gas reservoir.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention constructs a rock physics model based on the equivalent embedded body stress averaging theory, quantitatively describes the elastic response characteristics of tight sandstone in the unrelaxed state when pore pressure is unbalanced, and in the relaxed state when pore pressure is balanced, defines the dispersion properties by the difference in elastic modulus between the unrelaxed state and the relaxed state, and accurately reveals the influence mechanism of microcrack parameters on elastic modulus dispersion; the unrelaxed state model is used to drive the inversion of microcrack parameters in the well, and the calculated elastic modulus dispersion properties are significantly positively correlated with the microcrack density. The quantitative correlation between the two is verified by measured data, which improves the microcracks in the well. The inversion accuracy of the characteristics is improved; the innovative L-BFGS algorithm is introduced to carry out seismic inversion of elastic modulus dispersion attributes. The elastic moduli of the unrelaxed and relaxed states calculated in the well are constrained. The elastic impedance conversion and spectral decomposition technology is used to achieve quantitative prediction of the dispersion attributes of pre-stack seismic data, ensuring the physical consistency between logging and seismic data. It breaks through the limitation of traditional frequency-varying AVO inversion that relies on simplified frequency gradient approximation, and successfully identifies the micro-fracture development area of ​​the target layer of the gas well in practical applications, providing an effective solution for regional-scale high-precision prediction of micro-fractures in tight sandstone gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flow chart of the method of the present invention.

[0016] Figure 2 Schematic diagram of the relationship between the elastic modulus dispersion property and the microcrack density simulated based on the EIAS model; (a) is the bulk modulus dispersion property D K With the density of microcracks ε c (b) is the shear modulus dispersion property D μ With the density of microcracks ε c Changing relationships.

[0017] Figure 3 The results of the inversion of microfracture parameters and elastic modulus dispersion attribute prediction driven by the EIAS model are shown in Figure 1. (a) is the bulk modulus K , including the well logging bulk modulus K Log , the unrelaxed bulk modulus predicted by the rock physics model K Unrelaxed and the relaxed bulk modulus K Relaxed ; (b) is the shear modulus μ , including the logging shear modulus μ Log , unrelaxed shear modulus predicted by rock physics model μ Unrelaxed and the relaxed shear modulus μ Relaxed ; (c) is the bulk modulus dispersion property D K ; (d) is the shear modulus dispersion property D μ ; (e) is the microfracture content obtained by rock physics inversion in the well f c ; (f) is the aspect ratio of microfractures obtained by rock physics inversion in the well α ; (g) is the content of microcracks f c and microcrack aspect ratio α Calculated microcrack density ε c .

[0018] Figure 4 The cross-correlation analysis diagram between the elastic modulus dispersion property and the microfracture density in the well; (a) is the bulk modulus dispersion property D K Microcrack density ε c (b) is the shear modulus dispersion property. D μ Microcrack density ε c The intersection diagram.

[0019] Figure 5 is the cross-sectional diagram of the elastic impedance in the unrelaxed state and the relaxed state; where (a) is the elastic impedance EI in the unrelaxed state Unrelaxed , (b) is the elastic impedance EI in the relaxed state Relaxed .

[0020] Figure 6 is a cross-sectional diagram of bulk modulus in the unrelaxed and relaxed states; (a) is the bulk modulus in the unrelaxed state K Unrelaxed , (b) is the bulk modulus in the relaxed state K Relaxed .

[0021] Figure 7 is a cross-sectional diagram of the shear modulus in the unrelaxed and relaxed states; (a) is the shear modulus in the unrelaxed state μ Unrelaxed , (b) is the shear modulus in the relaxed state μ Relaxed .

[0022] Figure 8 Quantitative prediction profile of bulk modulus and shear modulus dispersion properties; (a) is the bulk modulus dispersion property D K, (b) is the shear modulus dispersion property D μ . DETAILED DESCRIPTION

[0023] In order to have a clearer understanding of the technical features, objectives and beneficial effects of the present invention, the technical solution of the present invention is now described in detail below, but it should not be understood as limiting the scope of implementation of the present invention.

[0024] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0025] One embodiment of the present invention provides a method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion, as shown in the flowchart. Figure 1 As shown, the following steps are included: Step 1: Rock physics model construction; Based on the equivalent inclusion-average stress (EIAS) theory, a rock physics model suitable for tight sandstone gas reservoirs in wells (i.e., the EIAS model) is constructed to quantitatively describe the elastic modulus response characteristics of tight sandstone gas reservoirs with complex pore-fracture structures in the unrelaxed state when pore pressure is unbalanced and the relaxed state when pore pressure is balanced (Equations 1-4). The elastic modulus dispersion is defined by the difference in elastic modulus under these two states, and the influence mechanism of microfracture parameters on the elastic modulus dispersion is studied.

[0026] The elastic modulus corresponding to the unrelaxed state when the pore pressure is unbalanced is expressed as: Formula 1: ; Formula 2: ; The elastic modulus corresponding to the relaxed state when pore pressure is balanced is expressed as: Formula 3: ; Formula 4: ; Among them, the subscript and represent the unrelaxed state and the relaxed state, respectively; and are the bulk modulus and shear modulus of saturated rock, respectively; and are the bulk modulus and shear modulus of the rock solid matrix, respectively; is the bulk modulus of the fluid; is the total porosity of the rock, which is equal to the spherical pore porosity Microfracture porosity sum; and Microcrack content and microcrack aspect ratio The relevant parameters, and Corresponding to The situation at that time.

[0027] Step 2: Rock physics inversion of microfracture parameters in the well; A rock physics inversion method for microfracture parameters in wells driven by the unrelaxed EIAS model is established. The well logging data (such as mineral composition, porosity, gas saturation, etc.) are input, and the microfracture parameters (microfracture content and microfracture aspect ratio) are used as the inversion target parameters. The objective function (Equation 5) is used to find the relationship between the well logging elastic modulus ( and ) is best fitted to obtain the microcrack content and microcrack aspect ratio parameters. Based on the inversion results, the microcrack density parameter (Equation 6) that comprehensively characterizes the microcrack characteristics is further calculated.

[0028] The objective function is: Formula 5: ; in, is the objective function of inversion; and represent the logging bulk modulus and shear modulus, respectively.

[0029] Microcrack parameters obtained by inversion and Further used to calculate the microcrack density : Formula 6: .

[0030] The iterative process for best fit is as follows: Given and The initial value of is based on the EIAS model (Equation 1, Equation 2) in the unrelaxed state, and the current assumption is substituted. and , calculate the elastic modulus in the unrelaxed state and ;Use the objective function to quantify the elastic modulus of the unrelaxed state and the logging elastic modulus 、 The difference, adjustment and , until the objective function converges to the minimum value (or below the preset threshold), at this time and The optimal parameters are substituted into Equation 6 to calculate the microcrack density and complete the quantitative characterization of microcrack characteristics.

[0031] Step 3: Prediction of elastic modulus in the relaxed state in the well; The microfracture parameters obtained by inversion and logging data (such as mineral composition, porosity, gas saturation, etc.) are input into the relaxed state EIAS model (Equations 3 and 4) to predict the relaxed state elastic modulus of the tight sandstone gas reservoir in the well ( and ).

[0032] Step 4: Calculation and verification of the elastic modulus dispersion properties in the well; Based on the difference between the predicted elastic modulus in the relaxed and unrelaxed states, the elastic modulus dispersion attribute in the wellbore was quantitatively calculated (Equations 7 and 8). By comparing this with the inversion results of the microfracture density in the wellbore, the effectiveness of the elastic modulus dispersion attribute in identifying microfracture density was verified, and a quantitative correlation between the two was established. The stronger the abnormal response of the elastic modulus dispersion attribute, the more developed the microfractures in the tight sandstone gas reservoir.

[0033] The formula for quantitatively calculating the elastic modulus dispersion property in the well is as follows: Formula 7: ; Formula 8: ; in, is the bulk modulus dispersion property; is the shear modulus dispersion property.

[0034] Step 5: Calculation of elastic impedance curve and establishment of seismic inversion constraints; Based on the elastic impedance equation (Eq. 14), according to the elastic modulus of the unrelaxed state of the well logging ( and ) Calculate the elastic impedance in the unrelaxed state , similarly, according to the predicted relaxation state elastic modulus ( and ) Calculate the elastic impedance in the relaxed state Finally, the elastic impedance curves in the unrelaxed and relaxed states are obtained, which serve as logging constraint information for seismic inversion of elastic modulus dispersion properties.

[0035] Step 6: Seismic inversion of elastic modulus dispersion properties based on L-BFGS; The pre-stack seismic data is spectrally decomposed, and the elastic impedance curves in the unrelaxed and relaxed states are used as logging constraints. The AVO formula for elastic impedance is extended to the frequency domain. The static relationship between reflection coefficient and elastic impedance is first established by Equation 9, and then the frequency variable is introduced using Equation 10. f, forming a frequency-domain reflection coefficient expression, and constructing the seismic inversion objective function (Equation 11). The objective function is iteratively solved using the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. The elastic impedances in the unrelaxed and relaxed states are inverted by minimizing the difference between the synthetic seismic record (resulting from the reflection coefficient calculated using Equations 9-10 and convolved with the seismic wavelet) and the spectral decomposition results. During this process, the objective function gradient is calculated using Equations 12-13 to drive the iterative optimization and ensure that the inversion results meet the wellbore constraints. Subsequently, based on the elastic impedance equation (Equation 14) and combined with Equations 15-16, the elastic impedance is converted into the corresponding elastic moduli in the unrelaxed and relaxed states. The difference between the two is used to quantitatively predict the elastic modulus dispersion properties (Equations 7 and 8).

[0036] The formulas involved in steps 5 and 6 are as follows: The AVO equation for elastic impedance (EI) is: Formula 9: ; in, for wave reflection coefficient; represents the angle of incidence; n +1 with n are the lower and upper media respectively; Δ and — represent the difference and average value of the elastic impedance of the upper and lower media respectively.

[0037] Expand Equation 9 to the frequency domain to construct a frequency-dependent AVO expression: Formula 10: ; in, Represents frequency.

[0038] Objective function of seismic inversion Defined as: Formula 11: ; in, Elastic impedance model for unrelaxed / relaxed states ; is the spectral decomposition result of pre-stack seismic data; For time; Indicates that the current model Calculated reflection coefficient Synthetic seismic record obtained by convolution with seismic wavelet.

[0039] The objective function gradient required in the L-BFGS algorithm is: Formula 12: ; Formula 13: ; in, Represents seismic wavelet.

[0040] The elastic impedance equation is: Equation 14: ; in, is the elastic impedance normalization factor; is the bulk modulus; is the shear modulus; is the density; 、 and represent the average values ​​of bulk modulus, shear modulus, and density in the well, respectively; ,in is the ratio of the longitudinal and transverse wave velocities; ; .

[0041] Taking the logarithm of Equation 14 and expanding it to the frequency domain, we obtain: Equation 15: .

[0042] Combined reference frequency f 0, eliminating the influence of density, and constructing the inverse equation of the elastic modulus in the unrelaxed and relaxed states: Equation 16: .

[0043] Finally, the elastic modulus dispersion properties of the unrelaxed state and the relaxed state obtained by inversion are quantitatively calculated according to Equations 7 and 8 by the difference between the elastic moduli of the two states: D K and D μ , used for micro-fracture prediction in tight sandstone gas reservoirs.

[0044] Step 7: Seismic inversion verification and microcrack prediction; Combined with the results of wellbore microfracture density prediction, the effectiveness of seismic inversion results using elastic modulus dispersion attributes for microfracture identification was verified and applied to microfracture prediction in tight sandstone gas reservoirs. Specifically, the stronger the abnormal response of the elastic modulus dispersion attribute, the higher the degree of microfracture development in the tight sandstone gas reservoir, thus achieving quantitative prediction of reservoir microfractures.

[0045] In an embodiment of the present invention, the EIAS model of Formulas 1-4 is first used to quantitatively describe the elastic response characteristics of the tight sandstone gas reservoir corresponding to the unrelaxed state when the pore pressure is unbalanced and the relaxed state when the pore pressure is balanced. The unrelaxed state EIAS model defined by Formulas 1-2 is used as a driver, and the microcrack content and microcrack aspect ratio in the well are inverted using Formula 5 using logging data. The microcrack parameters obtained by inversion are then used to calculate the microcrack density that comprehensively characterizes the microcrack characteristics using Formula 6. At the same time, the relaxed state EIAS model defined by Formulas 3-4 is used to calculate the relaxed state elastic modulus. Combined with Formulas 7-8, the difference between the elastic moduli in the unrelaxed state and the relaxed state is used to quantitatively predict the elastic modulus dispersion properties in the well, thereby clarifying the quantitative relationship between the microcrack density in the well and the elastic modulus dispersion properties. Subsequently, seismic inversion of elastic modulus dispersion properties was carried out. The elastic moduli in the unrelaxed and relaxed states based on the EIAS model in the well were used as constraints for the seismic inversion. The L-BFGS algorithm was used to solve the inversion objective function shown in Equation 11 to minimize the elastic impedances in the unrelaxed and relaxed states. The elastic impedances were then converted into elastic moduli in the unrelaxed and relaxed states using Equations 14-16. Finally, Equations 7-8 were used to quantitatively predict the elastic modulus dispersion properties based on the difference in the elastic moduli in the unrelaxed and relaxed states, thereby enabling the identification of microcracks in tight sandstone gas reservoirs.

[0046] The rock physics model constructed based on the EIAS theory described in the present invention can quantitatively describe the elastic modulus response and dispersion characteristics of microfractures in tight sandstone gas reservoirs in the unrelaxed state when pore pressure is unbalanced and the relaxed state when pore pressure is balanced. On this basis, well logging data is used to predict microfracture parameters in the well and quantitatively characterize the elastic modulus dispersion properties. It also provides rock physics constraints for seismic inversion of the elastic modulus dispersion properties, ensuring the physical consistency of the elastic modulus dispersion properties calculated using well logging data and seismic data, and realizing the quantitative prediction of elastic modulus dispersion properties from pre-stack seismic data. The present invention realizes the quantitative prediction of elastic modulus dispersion properties using well logging data and seismic data, and the prediction of microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion properties.

[0047] Figure 2 The elastic modulus dispersion properties (bulk modulus dispersion properties) simulated by the EIAS model are shown. D K , shear modulus dispersion properties D μ With the density of microcracks ε c ) changes. Figure 2 As can be seen in (a) and (b), D K and D μ all ε cThe increase shows a monotonically increasing trend, which clearly shows a significant positive correlation between the elastic modulus dispersion attribute and the microcrack density, indicating that the elastic modulus dispersion attribute can effectively characterize the degree of microcrack development in tight sandstone gas reservoirs.

[0048] Figure 3 The results of the inversion of microfracture parameters and elastic modulus dispersion attribute prediction driven by the EIAS model are presented. The results show that the elastic modulus in the unrelaxed state predicted by the rock physics model is K Unrelaxed and μ Unrelaxed and logging elastic modulus K Log and μ Log Highly consistent (see Figure 3 (a) and (b) in the figure verify the effectiveness of rock physics modeling and inversion method; the microfracture parameters obtained by inversion f c and α Calculated microcrack density ε c (See Figure 3 (e), (f), (g)) comprehensively characterize the micro-fracture characteristics of tight sandstone gas reservoirs; elastic modulus dispersion properties D K and D μ Microcrack density ε c The curve fluctuation trend is associated and shows good correlation (see Figure 3 (c), (d), (g)), further demonstrating that the elastic modulus dispersion property can be used as an effective predictor of microcrack density.

[0049] Figure 4 The elastic modulus dispersion properties in the well are shown ( D K and D μ ) and microcrack density ( ε c ) intersection relationship analysis diagram. Figure 4 As can be seen in (a) and (b), the scattered points follow ε c The overall increase is in an upward distribution, clearly showing ε c and D K 、 D μ There is a significant positive correlation between Figure 2 The theoretical simulation results based on the rock physics model echoed and verified the law that the elastic modulus dispersion property increases with the microcrack density, indicating thatD K and D μ It can effectively predict the development degree of microcracks in tight sandstone.

[0050] Figure 5 The paper presents the elastic impedance profiles of the unrelaxed and relaxed states at medium incidence angles obtained by applying prestack seismic data to the elastic modulus dispersion attribute seismic inversion method based on the unrelaxed and relaxed state elastic modulus constraints in the well and the L-BFGS algorithm. Figure 5 As can be seen from (a) and (b), in the target layer of gas producing wells A and B, the elastic impedance in the unrelaxed state and the relaxed state exhibits obvious frequency-varying characteristics, which demonstrates the effectiveness of the inversion method.

[0051] Figure 6 The bulk modulus profiles of the unrelaxed and relaxed states obtained by converting the elastic impedance of the unrelaxed and relaxed states are shown. Figure 6 As can be seen from (a) and (b), in the target layers of gas producing wells A and B, the bulk moduli in the unrelaxed and relaxed states exhibit obvious frequency-varying characteristics, which demonstrates the effectiveness of the elastic modulus calculation method.

[0052] Figure 7 The unrelaxed and relaxed state shear modulus sections obtained by converting the unrelaxed and relaxed state elastic impedances are shown. Figure 7 As shown in (a) and (b), in the target layers of gas producing wells A and B, the shear moduli in the unrelaxed and relaxed states exhibit obvious frequency-varying characteristics, indicating the effectiveness of the elastic modulus calculation method.

[0053] Figure 8 The elastic modulus dispersion property profile is quantitatively predicted by the difference in elastic modulus between the unrelaxed and relaxed states. The results show that (see Figure 8 In (a) and (b), in the target layer of gas wells A and B, the elastic modulus dispersion attribute shows a high value abnormal response; at the same time, D K and D μ The strength of the abnormal response is consistent with the trend of the micro-fracture density prediction results in the well. D K and D μ The high-value areas correspond to the high-value microfracture density intervals in the well, further confirming that the elastic modulus dispersion attribute can effectively predict the distribution of tight sandstone gas reservoirs with developed microfractures.

[0054] The above are only preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several variations and improvements can be made without departing from the concept of the present invention. These should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion, characterized in that: The following steps are involved: Step 1: Rock physics model construction; A rock physics model is constructed based on the equivalent embedded volume stress averaging theory to quantitatively describe the elastic modulus response characteristics of tight sandstone gas reservoirs in the unrelaxed state under pore pressure imbalance and the relaxed state under pore pressure equilibrium. Step 2: Rock physics inversion of microfracture parameters in the well; Driven by the rock physics model in the unrelaxed state, the logging data is input to invert the microfracture parameters and calculate the microfracture density parameters; Step 3: Prediction of elastic modulus in the relaxed state in the well; The microfracture parameters obtained by inversion and the logging data are input into the rock physics model of the relaxed state to predict the elastic modulus of the relaxed state; Step 4: Calculation and verification of the elastic modulus dispersion properties in the well; Based on the difference between the elastic moduli in the relaxed and unrelaxed states, the elastic modulus dispersion properties in the well are calculated to verify the effectiveness of microfracture density identification. Step 5: Calculation of elastic impedance curve and establishment of seismic inversion constraints; Based on the elastic impedance equation, the elastic impedance curves of the unrelaxed and relaxed states are calculated as the seismic inversion constraints; Step 6: Seismic inversion of elastic modulus dispersion properties based on L-BFGS; The pre-stack seismic data spectrum is decomposed. Using the elastic impedance curve as a constraint, the elastic impedance of the unrelaxed and relaxed states is inverted using the L-BFGS algorithm. The elastic modulus is converted and the dispersion properties are calculated. Step 7: Seismic inversion verification and microcrack prediction; The inversion results are verified by combining the microfracture density in the well to achieve microfracture prediction.

2. The method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion according to claim 1, characterized in that: In step 1, the elastic modulus in the unrelaxed state includes the bulk modulus and shear modulus , and its calculation formula is: ； ； The elastic modulus in the relaxed state includes the bulk modulus and shear modulus , and its calculation formula is: ； ； in, and are the bulk modulus and shear modulus of the rock solid matrix, respectively; is the bulk modulus of the fluid; is the total porosity of the rock, which is equal to the spherical pore porosity and microfracture porosity sum; and Microcrack content and microcrack aspect ratio The relevant parameters, and Corresponding to The situation at that time.

3. The method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion according to claim 1, characterized in that: In step 2, the inversion objective function for ； in, and are microcrack content and microcrack aspect ratio, respectively; and are the well logging bulk modulus and shear modulus, respectively; Microcrack parameters obtained by inversion and Further used to calculate microcrack density : 。 4. The method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion according to claim 1, characterized in that: In step 4, the formula for quantitatively calculating the elastic modulus dispersion property in the well is as follows: ； ； in, is the bulk modulus dispersion property; is the shear modulus dispersion property.

5. The method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion according to claim 1, characterized in that: In step 5, the elastic impedance equation is: ； in, is the elastic impedance; is the angle of incidence; is the elastic impedance normalization factor; is the bulk modulus; is the shear modulus; is the density; 、 and represent the average values ​​of bulk modulus, shear modulus, and density in the well, respectively; ,in is the ratio of the longitudinal and transverse wave velocities; ； 。 6. The method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion according to claim 1, characterized in that: The specific process of step 6 is as follows: Pre-stack seismic data are spectrally decomposed. The elastic impedance curves in the unrelaxed and relaxed states are used as logging constraints. The AVO formula for elastic impedance is extended to the frequency domain, and an objective function for seismic inversion is constructed. The objective function is iteratively solved using the L-BFGS algorithm. By minimizing the difference between the synthetic seismic records and the spectral decomposition results, the elastic impedances in the unrelaxed and relaxed states are inverted. The elastic impedances are then converted into the corresponding elastic moduli in the unrelaxed and relaxed states, and the difference between the two is used to quantitatively predict the elastic modulus dispersion properties.

7. The method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion according to claim 6, characterized in that: In step 6, the reflection coefficient of the elastic impedance extended to the frequency domain is: ； in, for wave reflection coefficient; represents frequency; Δ and — represent the difference and average value of the elastic impedance of the upper and lower layers, respectively; Objective function of seismic inversion for: ； in, Elastic impedance model for unrelaxed / relaxed states ; is the spectral decomposition result of pre-stack seismic data; For time; Indicates that the current model Calculated reflection coefficient Synthetic seismic record obtained by convolution with seismic wavelet.

8. The method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion according to claim 1, characterized in that: In step 7, the elastic modulus dispersion attribute is positively correlated with the microcrack density. The stronger the abnormal response of the elastic modulus dispersion attribute, the higher the development degree of microcracks in the corresponding tight sandstone gas reservoir.

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