Method for predicting micro-fracture of tight sand gas reservoir based on elastic modulus dispersion inversion

Abstract

The present application is suitable for the oil and gas resource exploration technical field, and provides a tight sandstone gas reservoir micro-fracture prediction method based on elastic modulus dispersion inversion. The present application constructs a rock physics model based on an equivalent embedded body stress average theory, defines a dispersion attribute through the difference between the elastic modulus in the unrelaxed state and the relaxed state, reveals the micro-fracture parameter influence mechanism, drives inversion with the unrelaxed state model, and the calculated elastic modulus dispersion attribute is significantly positively correlated with the micro-fracture density. The quantitative correlation between the two is verified through measured data, the inversion precision of the well micro-fracture characteristics is improved, the L-BFGS algorithm is introduced to carry out seismic inversion, the dispersion attribute quantitative prediction of prestack seismic data is realized, the physical consistency between the well logging and the seismic data is ensured, the traditional limitations are broken through, the micro-fracture development zone of the target layer of the gas production well is successfully identified in the actual application, and an effective solution is provided for the regional scale high-precision prediction of the tight sandstone gas reservoir micro-fracture.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas resource exploration, and particularly relates to a micro-fracture prediction method for tight sandstone gas reservoirs based on elastic modulus dispersion inversion. BACKGROUND

[0002] The development degree of micro-fractures in a tight sandstone gas reservoir is a key factor for controlling the permeability thereof, and directly affects the reservoir permeability and oil and gas recovery. Therefore, it is of great significance to realize high-precision prediction of micro-fractures on a regional scale by using a seismic method for efficient development of the tight sandstone gas reservoir.

[0003] Rock physics experiments and theoretical researches show that the development of micro-fractures will cause the response characteristics of elastic parameters varying with frequency, which provides a reliable physical basis for identifying micro-fractures by using seismic dispersion. At present, many studies extract seismic dispersion attributes based on the frequency-variable AVO (Amplitude variation with offset) inversion method, but these studies mainly apply the method to the field of fluid identification, and the dispersion attribute inversion method for micro-fracture prediction is still in the exploratory stage.

[0004] The dispersion attribute calculated by the traditional frequency-variable AVO inversion depends on the simplified frequency gradient approximation, which greatly limits the accurate characterization ability of the rock dispersion response. In addition, although the existing researches improve the frequency-variable AVO inversion by introducing advanced time-frequency analysis techniques, the resolution of the inversion results is improved to a certain extent, but the inherent limitations of the dispersion attribute prediction are not fundamentally broken through. Therefore, it is imminent to develop a new seismic inversion method for quantitatively predicting the dispersion response induced by micro-fractures, so as to improve the identification accuracy of micro-fractures in tight sandstone gas reservoirs. For this purpose, the application provides a micro-fracture prediction method for tight sandstone gas reservoirs based on elastic modulus dispersion inversion. SUMMARY

[0005] The application aims to provide a micro-fracture prediction method for tight sandstone gas reservoirs based on elastic modulus dispersion inversion, and aims to solve the problems proposed in the background.

[0006] The purpose of the application is achieved by the following technical solutions.

[0007] The micro-fracture prediction method for tight sandstone gas reservoirs based on elastic modulus dispersion inversion comprises the following steps:

[0008] Step 1: rock physics model construction;

[0009] The rock physics model is constructed based on the equivalent embedded body stress average theory, and the elastic modulus response characteristics of the tight sandstone gas reservoir in the unrelaxed state under uneven pore pressure and in the relaxed state under even pore pressure are quantitatively described.

[0010] Step 2: Micro-fracture parameter petrophysical inversion in well;

[0011] With the petrophysical model in unrelaxed state as the driving, input the logging data to invert the micro-fracture parameter and calculate the micro-fracture density parameter;

[0012] Step 3: Prediction of elastic modulus in relaxed state in well;

[0013] Input the inverted micro-fracture parameter and logging data into the petrophysical model in relaxed state to predict the elastic modulus in relaxed state;

[0014] Step 4: Calculation and verification of elastic modulus dispersion attribute in well;

[0015] Based on the difference between the elastic modulus in relaxed state and unrelaxed state, the elastic modulus dispersion attribute in well is calculated to verify the effectiveness of the micro-fracture density identification;

[0016] Step 5: Calculation of elastic impedance curve and establishment of seismic inversion constraint;

[0017] Based on the elastic impedance equation, the elastic impedance curves in unrelaxed state and relaxed state are calculated as the seismic inversion constraint;

[0018] Step 6: Seismic inversion of elastic modulus dispersion attribute based on L-BFGS;

[0019] Spectrum decomposition is performed on the pre-stack seismic data, the elastic impedance curves in unrelaxed state and relaxed state are inverted by L-BFGS algorithm, the elastic modulus is converted to calculate the dispersion attribute;

[0020] Step 7: Verification of seismic inversion and micro-fracture prediction;

[0021] The inversion results are verified in combination with the micro-fracture density in well to realize the micro-fracture prediction.

[0022] Further, in the step 1, the elastic modulus in unrelaxed state includes bulk modulus and shear modulus , and the calculation formula is:

[0023] ;

[0024] ;

[0025] The elastic modulus in relaxed state includes bulk modulus and shear modulus , and the calculation formula is:

[0026] ;

[0027] ;

[0028] where, and are the bulk and shear moduli of the rock solid matrix, respectively; is the fluid bulk modulus; is the total porosity of the rock, equal to the sum of the spherical pore porosity and the microfracture porosity ; and are parameters related to the microfracture content and the microfracture aspect ratio ; and correspond to the case when ;

[0029] Further, in said step 2, the objective function to be inverted is

[0030] ;

[0031] where, and are the microfracture content and the microfracture aspect ratio, respectively; and are the logging bulk and shear moduli, respectively;

[0032] The microfracture parameters and obtained from the inversion are further used to calculate the microfracture density :

[0033] .

[0034] Further, in said step 4, the formula to quantitatively calculate the elastic modulus dispersion attribute in the well is as follows:

[0035] ;

[0036] ;

[0037] where, is the bulk modulus dispersion attribute; is the shear modulus dispersion attribute.

[0038] Further, in said step 5, the elastic impedance equation is:

[0039] ;

[0040] where, 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;

[0041] ,in is the ratio of the longitudinal and transverse wave velocities; ; .

[0042] Furthermore, the specific process of step 6 is as follows:

[0043] 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.

[0044] Furthermore, in step 6, the reflection coefficient of the elastic impedance extended to the frequency domain is:

[0045] ;

[0046] 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;

[0047] Objective function of seismic inversion for:

[0048] ;

[0049] 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.

[0050] Further, in step 7, the elastic modulus dispersion attribute is positively correlated with the microcrack density, and the stronger the abnormal response of the elastic modulus dispersion attribute is, the higher the development degree of the microcrack in the tight sand gas reservoir is.

[0051] Compared with the prior art, the present application has the following beneficial effects:

[0052] The present application constructs a rock physics model based on the equivalent embedded body stress average theory, quantitatively describes the elastic response characteristics corresponding to the unrelaxed state of the tight sandstone under the unbalanced pore pressure and the relaxed state under the balanced pore pressure, defines the dispersion attribute through the difference between the elastic modulus in the unrelaxed state and the relaxed state, accurately reveals the influence mechanism of the microcrack parameter on the elastic modulus dispersion, calculates the elastic modulus dispersion attribute through the microcrack parameter inversion in the well driven by the unrelaxed state model, and verifies the quantitative correlation between the two through the measured data, thereby improving the inversion accuracy of the microcrack characteristics in the well. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The present application is a method flowchart.

[0054] Figure 2 The present application is a schematic diagram of the relationship between the elastic modulus dispersion attribute simulated based on the EIAS model and the microcrack density; wherein (a) is the bulk modulus dispersion attribute D K with the microcrack density ε c (b) is the shear modulus dispersion attribute D μ with the microcrack density ε c .

[0055] Figure 3 The present application is a result diagram of the microcrack parameter inversion in the well driven by the EIAS model and the elastic modulus dispersion attribute prediction; wherein (a) is the bulk modulus K , including the logging bulk modulus K Log , the unrelaxed state bulk modulus K Unrelaxed predicted by the rock physics model and the relaxed state bulk modulusK Relaxed ; (b) is a shear modulus μ , including a well logging shear modulus μ Log , a rock physics model predicted unrelaxed state shear modulus μ Unrelaxed and a relaxed state shear modulus μ Relaxed ; (c) is a bulk modulus dispersion attribute D K ; (d) is a shear modulus dispersion attribute D μ ; (e) is a microfracture content obtained by wellbore rock physics inversion f c ; (f) is a microfracture aspect ratio obtained by wellbore rock physics inversion α ; (g) is a microfracture density calculated by the microfracture content f c and the microfracture aspect ratio α ε c .

[0056] Figure 4 is a crossplot analysis diagram between the wellbore elastic modulus dispersion attribute and the microfracture density; wherein (a) is a bulk modulus dispersion attribute D K and the microfracture density ε c , (b) is a shear modulus dispersion attribute D μ and the microfracture density ε c .

[0057] Figure 5 is an angle elastic impedance profile in unrelaxed state, relaxed state; wherein (a) is an unrelaxed state elastic impedance EI Unrelaxed , (b) is a relaxed state elastic impedance EI Relaxed .

[0058] Figure 6 is a bulk modulus profile in unrelaxed state, relaxed state; wherein (a) is an unrelaxed state bulk modulus K Unrelaxed , (b) is a relaxed state bulk modulus K Relaxed .

[0059] Figure 7 is a shear modulus profile in unrelaxed state, relaxed state; wherein (a) is an unrelaxed state shear modulus μ Unrelaxed ​, (b) is the shear modulus in the relaxed state μ Relaxed .

[0060] Figure 8 is the quantitative prediction profile of the 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

[0061] In order to have a clearer understanding of the technical features, objectives and beneficial effects of the present application, the technical solutions of the present application will be described in detail below, but it should not be understood as limiting the scope of the present application.

[0062] The specific implementation of the present application will be described in detail below in combination with specific embodiments.

[0063] One embodiment of the present application provides a tight sand gas reservoir micro-fracture prediction method based on elastic modulus dispersion inversion, a flow chart as shown in Figure 1 , comprising the following steps:

[0064] Step 1: rock physics model construction;

[0065] Based on the equivalent inclusion-average stress (EIAS: Equivalent inclusion-average stress) theory, a rock physics model suitable for wellbore tight sand gas reservoir (i.e. EIAS model) is constructed, which is used to quantitatively describe the elastic modulus response characteristics of the tight sand gas reservoir with complex pore-fracture structure in the unrelaxed state when the pore pressure is not balanced and in the relaxed state when the pore pressure is balanced (equations 1-4), and the elastic modulus dispersion is defined by the difference of the elastic modulus in the two states, and the influence mechanism of micro-fracture parameters on the elastic modulus dispersion is studied.

[0066] The elastic modulus corresponding to the unrelaxed state when the pore pressure is not balanced is represented as:

[0067] Equation 1: ;

[0068] Equation 2: ;

[0069] The elastic modulus corresponding to the relaxed state when the pore pressure is balanced is represented as:

[0070] Equation 3: ;

[0071] Equation 4: ;

[0072] wherein the subscript and denote the unrelaxed and relaxed states, respectively; and are the bulk and shear moduli of the saturated rock; and are the bulk and shear moduli of the rock solid matrix; is the fluid bulk modulus; is the total porosity of the rock, equal to the sum of the spherical pore porosity microfracture porosity ; and are parameters related to the microfracture content and the microfracture aspect ratio ; and correspond to the case when .

[0073] Step 2: Well microfracture parameter petrophysical inversion

[0074] A well microfracture parameter petrophysical inversion method driven by the EIAS model in unrelaxed state is established. The logging data (such as mineral composition, porosity, gas saturation, etc.) are input, and the microfracture parameters (microfracture content and microfracture aspect ratio) are taken as the inversion target parameters. The best fitting of the logging elastic modulus (E, G) is found through the objective function (equation 5), so as to obtain the microfracture content and microfracture aspect ratio parameters. Based on the inversion results, the microfracture density parameter (equation 6) which comprehensively represents the microfracture characteristics is further calculated. and .

[0075] The objective function is:

[0076] Equation 5: ;

[0077] wherein, is the objective function of inversion; and denote the logging bulk modulus and shear modulus, respectively.

[0078] The microfracture parameters and obtained by inversion are further used to calculate the microfracture density :

[0079] Equation 6: .

[0080] The iteration process of the best fitting is as follows:

[0081] Given and initial value, based on the EIAS model of unrelaxed state (equation 1, equation 2), substitute the current assumed and , calculate the unrelaxed state elastic modulus and ; use the objective function to quantify the difference between the unrelaxed state elastic modulus and the logging elastic modulus 、 , adjust and until the objective function converges to a minimum value (or below a preset threshold), at which time and are the optimal parameters. Substitute the optimal parameters obtained by inversion into equation 6 to calculate the microfracture density, completing the quantitative characterization of microfracture characteristics.

[0082] Step 3: Prediction of in-well unrelaxed state elastic modulus;

[0083] Input the microfracture parameters obtained by inversion and logging data (such as mineral composition, porosity, gas saturation, etc.) into the EIAS model of unrelaxed state (equation 3, equation 4) to predict the unrelaxed state elastic modulus of the tight sand gas reservoir in the well and ).

[0084] Step 4: Calculation and verification of in-well elastic modulus dispersion attribute;

[0085] Based on the difference between the predicted unrelaxed state elastic modulus and the unrelaxed state elastic modulus, quantitatively calculate the in-well elastic modulus dispersion attribute (equation 7, equation 8), and verify the effectiveness of the elastic modulus dispersion attribute in identifying the microfracture density by comparing it with the microfracture density inversion results, and establish a quantitative correlation between the two. The stronger the abnormal response of the elastic modulus dispersion attribute, the higher the development degree of the microfracture in the tight sand gas reservoir.

[0086] The formula for quantitatively calculating the in-well elastic modulus dispersion attribute is as follows:

[0087] Equation 7: ;

[0088] Equation 8: ;

[0089] Wherein, is the bulk modulus dispersion attribute; is the shear modulus dispersion attribute.

[0090] Step 5: Calculation of elastic impedance curve and establishment of seismic inversion constraint;

[0091] Based on the elastic impedance equation (equation 14), according to the logging unrelaxed state elastic modulus 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.

[0092] Step 6: Seismic inversion of elastic modulus dispersion properties based on L-BFGS;

[0093] 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 expression for the reflection coefficient 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 impedances are converted to 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).

[0094] The formulas involved in steps 5 and 6 are as follows:

[0095] The AVO equation for elastic impedance (EI) is:

[0096] Formula 9: ;

[0097] 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.

[0098] Expand Equation 9 to the frequency domain to construct a frequency-dependent AVO expression:

[0099] Formula 10: ;

[0100] in, Represents frequency.

[0101] Objective function of seismic inversion Defined as:

[0102] Formula 11: ;

[0103] 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.

[0104] The objective function gradient required in the L-BFGS algorithm is:

[0105] Formula 12: ;

[0106] Formula 13: ;

[0107] in, Represents seismic wavelet.

[0108] The elastic impedance equation is:

[0109] Equation 14: ;

[0110] 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; ; .

[0111] Taking the logarithm of Equation 14 and expanding it to the frequency domain, we obtain:

[0112] Equation 15: .

[0113] Combined reference frequency f 0, eliminating the influence of density and constructing the inverse equation of elastic modulus in the unrelaxed and relaxed states:

[0114] Equation 16: .

[0115] Finally, the elastic modulus dispersion attribute is quantitatively calculated by the difference between the elastic modulus of the unrelaxed state and the relaxed state according to formula 7 and formula 8 D K and D μ , which is used for microfracture prediction of the tight sand gas reservoir.

[0116] Step 7: Seismic inversion verification and microfracture prediction

[0117] In combination with the microfracture density prediction result in the well, the effectiveness of the seismic inversion result of the elastic modulus dispersion attribute on microfracture identification is verified, and is applied to microfracture prediction of the tight sand gas reservoir. Specifically, the stronger the abnormal response of the elastic modulus dispersion attribute is, the higher the development degree of the microfracture in the tight sand gas reservoir is, and thus the quantitative prediction of the reservoir microfracture is realized.

[0118] In the embodiment of the application, first, the elastic response characteristics corresponding to the unrelaxed state and the relaxed state of the tight sand gas reservoir under the conditions of the unbalanced pore pressure and the balanced pore pressure are quantitatively described by the EIAS model of formula 1-4, and the unrelaxed state EIAS model defined by formula 1-2 is used as the driving force, the logging data is used to invert the microfracture content and the microfracture aspect ratio in the well by formula 5, and then the microfracture density comprehensively representing the microfracture characteristics is calculated by formula 6 using the microfracture parameters inverted. Meanwhile, the relaxed state elastic modulus is calculated by using the relaxed state EIAS model defined by formula 3-4, the elastic modulus dispersion attribute in the well is quantitatively predicted by the difference between the unrelaxed state and the relaxed state elastic modulus according to formula 7-8, and the quantitative relationship between the microfracture density in the well and the elastic modulus dispersion attribute is determined. Subsequently, the seismic inversion of the elastic modulus dispersion attribute is carried out, the unrelaxed state and the relaxed state elastic impedances are obtained by taking the unrelaxed state and the relaxed state elastic impedances in the well based on the EIAS model as the constraints of the seismic inversion, the L-BFGS algorithm is used to solve the inversion objective function shown in formula 11 to minimize, the unrelaxed state and the relaxed state elastic impedances are obtained, the unrelaxed state and the relaxed state elastic impedances are converted into the unrelaxed state and the relaxed state elastic modulus by formula 14-16, and finally the elastic modulus dispersion attribute is quantitatively predicted by the difference between the unrelaxed state and the relaxed state elastic modulus according to formula 7-8, so that the microfracture in the tight sand gas reservoir is identified.

[0119] 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.

[0120] 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 c The 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.

[0121] 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 propertiesD 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.

[0122] 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 that D K and D μ It can effectively predict the development degree of microcracks in tight sandstone.

[0123] 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.

[0124] 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.

[0125] Figure 7The 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.

[0126] 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.

[0127] 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 2, 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 the microcrack density : 。 4. The method for predicting microfractures in tight sandstone gas reservoirs based on elastic modulus dispersion inversion according to claim 3, 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 5, 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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