Method for identifying thin mutual gas layer based on low-frequency sound wave signal

Abstract

The invention provides a method for identifying a thin mutual gas reservoir based on low-frequency sound wave signals, belongs to the technical field of unconventional natural gas exploration, and solves the problem that in the prior art, seismic low-frequency sound wave signals cannot be applied to identify oil and gas-containing reservoirs. Comprising the following steps: S1, calculating pore elasticity parameters of a target layer, and constructing a pore elasticity stratum model of the target layer; s2, calculating hole elastic wave field response characteristics of a target layer single interface or a stratum group, and establishing a reflection amplitude and fluctuation frequency relation quantity version; s3, performing frequency division processing on the seismic record, and determining a low-frequency amplitude abnormal position; s4, calculating a vertical hole elastic wave seismic record, explaining a low-frequency amplitude abnormal position in combination with a hole elastic wave field response characteristic and a reflection amplitude and fluctuation frequency relation quantity plate, and determining a region related to the thin intergas layer; the method can effectively discriminate the relationship between the low-frequency amplitude abnormity and the gas reservoir position in the seismic record, fills the blank of the exploration technology, and improves the interpretation precision of seismic recognition of the gas reservoir.

Description

Technical Field

[0001] The present invention belongs to the technical field of unconventional natural gas exploration, and in particular is a method for identifying thin intergas layers based on low-frequency acoustic wave signals. Background Art

[0002] At present, seismic low-frequency acoustic signal information plays an important role in reservoir characterization and oil and gas exploration. There are many successful examples of using low-frequency amplitude anomalies of longitudinal waves to identify gas layers; however, the generation mechanism of such low-frequency amplitude anomalies of seismic waves (or acoustic waves) is still unclear. In some areas, the gas layers identified by using low-frequency amplitude anomalies of seismic waves are not gas layers after drilling, which has caused great economic losses. Therefore, in the process of oil and gas exploration and development, it is necessary to explore an effective method for distinguishing the relationship between thin interbedded gas layers and low-frequency amplitude anomalies of seismic waves, so as to achieve the effect of accurately using seismic wave reflection records to identify the location of gas layers in thin interbedded strata, and thus understand the relationship between seismic low-frequency response and the properties of oil and gas reservoirs.

[0003] The mechanism of low-frequency anomalies in seismic wave amplitude is very complex, and its manifestations in seismic data vary. In some cases, the amplitude low-frequency anomaly signal appears at the reservoir location, while in other cases, the amplitude low-frequency anomaly signal occurs below the reservoir location, which is called low-frequency shadow (LFS). Technicians in this field use seismic wave attenuation theory to explain the reasons for the occurrence of low-frequency acoustic wave signal anomalies in gas-bearing reservoirs; these theories can be used to analyze the degree of low-frequency amplitude attenuation, but cannot explain the time delay characteristics of the amplitude low-frequency anomaly signal. Existing seismic exploration technology is mainly based on the theory of acoustic waves or elastic waves.

[0004] Poroelasticity theory holds that when seismic waves propagate at interfaces of different poroelastic formations, they not only generate reflected and transmitted longitudinal and transverse waves, but also reflect and transmit slow longitudinal waves. Slow waves are observed in the actual acoustic wave experimental results of air-saturated sandstones. Theoretical studies have also confirmed that the abnormal low-frequency acoustic wave signals may be caused by the conversion of slow longitudinal waves generated in thin permeable fluid media. Based on the above understanding, it is necessary to discriminate and interpret the low-frequency abnormal signals of seismic amplitude in gas layers in unconventional thin interbedded reservoirs; if the low-frequency acoustic wave signal enhancement analysis is to be applied to field data, it is necessary to study the influencing factors that may change the enhancement characteristics of the low-frequency acoustic wave signals, so as to identify oil and gas reservoirs. How to analyze and study the possible influencing factors has become the focus of continuous attention of technicians in this field. Summary of the invention

[0005] Since the abnormal low-frequency acoustic wave signal of earthquakes is closely related to the existence of oil and gas reservoirs, the present invention studies how to identify oil and gas reservoirs through low-frequency acoustic wave signals through practical technical means, and solves the application limitation problem caused by the complex formation mechanism of abnormal low-frequency acoustic wave signals; the present invention can effectively distinguish the relationship between the low-frequency amplitude anomaly and the gas layer position in the seismic record, and improve the interpretation accuracy of seismic identification of gas layers.

[0006] The present invention adopts the following technical solutions to achieve the purpose:

[0007] A method for identifying a thin intergas layer based on a low-frequency acoustic wave signal, the method comprising the following steps:

[0008] S1. Using well logging data and core test data, calculate the poroelastic parameters of the target layer and construct a poroelastic formation model of the target layer;

[0009] S2. Use the poroelastic wave theory to calculate the poroelastic wave field response characteristics of the single interface or formation group of the target layer, and establish a quantitative version of the relationship between the reflection amplitude and the wave frequency;

[0010] S3, performing frequency division processing on the post-stacked amplitude-preserved seismic records to determine the low-frequency amplitude anomaly position in the seismic records;

[0011] S4. Use the poroelastic formation model to calculate the vertical poroelastic wave seismic record; combine the poroelastic wave field response characteristics and the relationship between reflection amplitude and fluctuation frequency to interpret the low-frequency amplitude anomaly position in the single-component seismic record, and determine the low-frequency amplitude anomaly distribution area related to the thin intergas layer, thereby completing the identification of the thin intergas layer.

[0012] Specifically, in step S1, the poroelastic parameters include: reservoir porosity and permeability, rock matrix volume, shear modulus and density, rock dry volume and shear modulus, pore fluid volume modulus, density and viscosity coefficient.

[0013] Furthermore, the process of calculating the poroelastic parameters is as follows:

[0014] S11, obtaining the logging curves of the key wells in the study area, normalizing the logging curves, and eliminating the consistency of the data magnitude in the logging curves;

[0015] S12. Determine the mineral volume model of the study area based on the core X-ray diffraction experimental results of the reference wells in the study area; use quartz, feldspar, carbonate rock and clay with porosity to form the rock volume model; combine the well logging curve with the mineral volume model, perform multi-mineral optimization decomposition processing on the rock volume model, and determine the volume proportion of different minerals at each depth point;

[0016] S13. Perform lithofacies identification and division operations based on the volume proportion of different minerals at each depth point and in combination with lithology logging data;

[0017] S14. Calculate the rock matrix volume, shear modulus and density at each depth point based on the multi-mineral optimization decomposition processing results;

[0018] S15. Calculate the permeability at each depth point based on the porosity in the well logging curve;

[0019] S16. Calculate the bulk modulus, density and viscosity coefficient of the pore fluid at each depth point based on the temperature and pressure of the formation at each depth point and the salinity of the formation water;

[0020] S17, calculating the rock dry volume and shear modulus at each depth point based on the rock matrix shear modulus and porosity curve data at each depth point;

[0021] S18. Determine the model parameters of the poroelastic formation model based on the above-mentioned calculation results.

[0022] Among them, the logging curves of key wells in the study area include gamma, density, natural potential, formation resistivity and compensated neutron conventional curve data, as well as array acoustic logging data or element logging data and other related content.

[0023] Furthermore, in step S2, the process of calculating the poroelastic wave field response characteristics is specifically as follows:

[0024] S21, calculate the wave impedance of each layer;

[0025] S22, calculating the zero-order aperture elastic reflection and transmission coefficients;

[0026] S23, calculating the first-order poroelastic reflection and transmission coefficients;

[0027] S24. Calculate the reflection and transmission coefficients of elastic waves of various types of pores at the formation interface;

[0028] S25, calculating the sum of the amplitudes of the poroelastic waves reflected from the formation interface;

[0029] By calculating the pore elastic wave field response characteristics, the reflection amplitude variation characteristics of seismic waves on the interface between different porous media are simulated and calculated, and the total reflection amplitude variation characteristics of seismic waves under different frequency conditions are analyzed to establish a quantitative version of the relationship between reflection amplitude and fluctuation frequency.

[0030] Furthermore, in step S3, the post-stack amplitude-preserved seismic record is subjected to frequency division processing using a generalized S transform; the energy change of the target layer position in the single-frequency component profile of the seismic record is displayed and checked using seismic interpretation software; and the area with strong low-frequency energy and weak high-frequency energy under the target layer is searched to determine the low-frequency amplitude anomaly position in the seismic record.

[0031] Furthermore, in step S4, during the actual identification process, the poroelastic wave field response characteristics of the actual stratum are calculated, the frequency, amplitude and delay time of the low-frequency energy anomaly in the seismic record are analyzed, and it is determined whether the low-frequency amplitude anomaly of the single-frequency component is related to the existence of a thin intergas layer, thereby completing the identification of the thin intergas layer; based on the distribution area of ​​the low-frequency amplitude anomaly in the seismic single-frequency component profile, the distribution range of the thin intergas layer and the optimal position of the drillable target are predicted.

[0032] In summary, due to the adoption of this technical solution, the beneficial effects of the present invention are as follows:

[0033] 1. The present invention uses a variety of rock physics models and combines conventional logging data to reasonably establish a poroelastic formation model of underground oil and gas reservoirs and their surrounding rocks; the poroelastic formation model is different from the existing conventional formation model, and its composition takes into account factors such as formation porosity, permeability size and pore fluid properties, so the established model is more in line with the physical properties of actual rocks.

[0034] 2. The present invention adopts the poroelasticity theory method to calculate the propagation characteristics of seismic waves in porous media; compared with the conventional elastic wave numerical simulation results, the theoretical calculation results are more consistent with the actual propagation of seismic waves; the reason is that the actual stratum is a fluid-containing porous medium, and the poroelastic wave field numerical simulation not only considers the propagation characteristics of fast longitudinal waves, but also considers the slow wave reflection and projection caused by the diffusion of pore fluid pressure. The relationship between seismic wave energy attenuation and wave frequency is studied by the method of the present invention, which has higher accuracy and clear physical meaning.

[0035] 3. The poroelastic formation model and poroelastic wave field numerical simulation established by the present invention can analyze the causes of various low-frequency amplitude anomalies of seismic waves, and can analyze the delay time of the occurrence of low-frequency amplitude anomalies based on information such as formation thickness, thereby enhancing the ability to identify the existence of underground oil and gas reservoirs and improving the interpretation accuracy of directly detecting oil and gas layers with seismic records. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the overall steps of the method of the present invention;

[0037] Figure 2 It is a schematic diagram of the relationship between the longitudinal wave velocity, attenuation coefficient and frequency in porous media;

[0038] Figure 3 Schematic diagram of the relationship between slow P-wave velocity, attenuation coefficient and frequency of rocks containing different pore fluids;

[0039] Figure 4 Schematic diagram of simulation of the effect of conversion slow wave on reflection and transmission coefficient of fluid-containing porous medium;

[0040] Figure 5 It is a schematic diagram of the interpretation of pore fluid and fast and slow wave reflection coefficients;

[0041] Figure 6 This is a schematic diagram of the interpretation of formation thickness and fast and slow wave reflection coefficients;

[0042] Figure 7 It is a schematic diagram of the interpretation of formation permeability and fast and slow wave reflection coefficients;

[0043] Figure 8 This is a schematic diagram of the physical simulation experiment of seismic wave fields in multi-layer media;

[0044] Fig. 9 Schematic diagram of the seismic record profile of the oil-bearing and gas-bearing models collected for the experiment;

[0045] Fig.10 This is a schematic diagram of the 20 Hz frequency-division profile after time-frequency processing of the seismic record;

[0046] Fig.11 This is a schematic diagram of the simulation results of the poroelastic wave field;

[0047] Fig.12 This is a schematic diagram of the seismic record section in a certain exploration block;

[0048] Fig.13 for Fig.12 Comprehensive histogram of target layer section through the well in the middle section;

[0049] Fig.14 It is a schematic diagram of the actually established poroelastic parameter formation model;

[0050] Fig.15 It is the reflection coefficient diagram of the numerically simulated poroelastic formation model;

[0051] Fig.16 Schematic diagram of the amplitude distribution of earthquake records at different frequencies. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] A method for identifying thin intergas layers based on low-frequency acoustic wave signals. The overall steps of the method are as follows. Figure 1 The hint:

[0056] S1. Using well logging data and core test data, calculate the poroelastic parameters of the target layer and construct a poroelastic formation model of the target layer;

[0057] S2. Use the poroelastic wave theory to calculate the poroelastic wave field response characteristics of the single interface or formation group of the target layer, and establish a quantitative version of the relationship between the reflection amplitude and the wave frequency;

[0058] S3, performing frequency division processing on the post-stacked amplitude-preserved seismic records to determine the low-frequency amplitude anomaly position in the seismic records;

[0059] S4. Use the poroelastic formation model to calculate the vertical poroelastic wave seismic record; combine the poroelastic wave field response characteristics and the relationship between reflection amplitude and fluctuation frequency to interpret the low-frequency amplitude anomaly position in the single-component seismic record, and determine the low-frequency amplitude anomaly distribution area related to the thin intergas layer, thereby completing the identification of the thin intergas layer.

[0060] This embodiment will introduce the details of each step in detail according to the execution order of the method.

[0061] 1. Construction of poroelastic formation model

[0062] 1-1. Obtain the logging curves of key wells in the study area (the logging curves mainly include conventional curve data such as gamma, density, natural potential, formation resistivity, compensated neutron, array acoustic logging data or element logging data), lithology logging, top and bottom depths of the target layer; seismic pure wave data volume, top and bottom reflection time data of the target layer, etc.

[0063] 1-2. Normalize the logging curve to eliminate the consistency of data magnitude in the logging curve.

[0064] 1-3. The mineral volume model of the study area is determined based on the core X-ray diffraction experimental results of the reference well core in the study area. Quartz, feldspar, carbonate rock and clay with porosity are used to construct the rock volume model. The rock volume model is subjected to multi-mineral optimization decomposition processing by combining the logging curve with the mineral volume model to determine the volume proportion of different minerals at each depth point.

[0065] 1-4. According to the volume proportion of different minerals at each depth point, combined with the lithology logging data, fine processing operations such as lithofacies identification and division are carried out.

[0066] 1-5. Based on the results of multi-mineral optimization decomposition, the HILL equation is used to calculate the rock matrix volume and shear modulus at each depth. The calculation formula is as follows:

[0067]

[0068] Where i represents the i-th mineral; N represents the total number of rock mineral components; f i represents the volume fraction of the i-th mineral; M i represents the volume or shear modulus of the i-th mineral; M s represents the corresponding rock matrix bulk or shear modulus;

[0069] Calculate the rock matrix density at each depth point using the following formula:

[0070]

[0071] Where ρ represents the density of the i-th mineral; ρ s Represents the rock matrix density.

[0072] 1-6. Calculate the permeability at each depth point based on the porosity in the logging curve. The calculation formula is:

[0073] κ=aφ c

[0074] Where φ is the porosity of the formation at each depth point; κ is the permeability of the formation at each depth point; a and c are the fitting parameters of the corresponding formation in the core X-ray diffraction experiment.

[0075] 1-7. Based on the temperature and pressure of the formation at each depth point and the mineralization of the formation water, calculate the bulk modulus, density and viscosity coefficient of the pore fluid at each depth point; the calculation formula is as follows:

[0076]

[0077]

[0078] η=0.1+0.333S+(1.65+91.9S3 )exp{-[0.42(S 0.8 -0.17) 2 +0.045]T 0.8}

[0079] In each formula, T and P represent the formation temperature and pressure; S represents the mineralization of formation water; w ij is the empirical coefficient; ρ f represents the pore fluid density; K f represents the bulk modulus of the pore fluid; η represents the viscosity coefficient.

[0080] 1-8. Based on the rock matrix shear modulus and porosity curve data at each depth point, calculate the rock dry volume and shear modulus at each depth point; the calculation formula is as follows:

[0081]

[0082]

[0083] In the formula, K s , μ s represents the rock matrix volume and shear modulus respectively, φ represents the formation porosity at each depth point; q and h are parameters related to the rock pore structure, and h = 1.5q; K d , μ d represent the dry volume and shear moduli of rock, respectively.

[0084] Based on the above calculation results, the model parameters of the poroelastic formation model are determined as follows:

[0085]

[0086] φ n = <φ>; μ n =<η>

[0087] Wherein, n represents the nth layer of the formation in the poroelastic formation model; the construction of the poroelastic formation model can be completed through the above equation.

[0088] 2. Calculation of poroelastic wave field response characteristics of a single interface or formation group in the target layer

[0089] This part is based on the constructed poroelastic formation model, and conducts numerical simulation to study the propagation of poroelastic seismic waves in the target layer, and establishes the interpretation scale of different factors, namely the scale of the relationship between reflection amplitude and fluctuation frequency.

[0090] 2-1. Calculate the wave impedance of each layer as follows:

[0091]

[0092] Where Z represents the formation wave impedance; V p represents the longitudinal wave velocity; ρ b Indicates the stratum density; the superscript n and subscript n indicate the nth layer of the stratum.

[0093] 2-2. Calculate the zero-order hole elastic reflection and transmission coefficients as follows:

[0094]

[0095]

[0096] In the formula, and They represent the zero-order reflection and projection coefficients of fast longitudinal waves, respectively.

[0097] 2-3. Calculate the first-order poroelastic reflection and transmission coefficients as follows:

[0098]

[0099]

[0100] In the formula, and They represent the first-order reflection and projection coefficients of fast longitudinal waves, respectively.

[0101] This embodiment provides a preferred example of the relevant calculation method in the calculation here, that is, the calculation method of the reflection and transmission coefficients of fast-slow longitudinal waves, which are respectively and as follows:

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] In the above calculation formulas, φ is the formation porosity; K represents the bulk modulus of the formation, and its subscripts g, d, and f represent the rock matrix, rock dryness, and pore fluid, respectively; ρ represents the density, and its subscripts s, b, and f represent the rock matrix, formation volume, and pore fluid, respectively.

[0110] 2-4. Calculate the reflection and transmission coefficients of various types of pore elastic waves at the formation interface as follows:

[0111] Poroelastic fast wave incident-fast wave reflection coefficient:

[0112] Poroelastic fast wave incident-fast wave transmission coefficient:

[0113] Poroelastic fast wave incident-slow wave reflection coefficient:

[0114] Poroelastic fast wave incident-slow wave transmission coefficient:

[0115] The B(ε) parameters involved in each formula are as follows:

[0116]

[0117] ε=(iρ f ωκ) / η

[0118] Where ω represents the rounding frequency; i is an imaginary number; κ represents the formation permeability; η represents the viscosity coefficient; ρ f represents the pore fluid density

[0119] 2-5. Calculate the sum of the poroelastic wave reflection amplitudes A at the formation interface F , the calculation formula is as follows:

[0120]

[0121] In the formula, A f is the initial fast longitudinal wave reflection coefficient; V s (ω) and α s (ω) represent the phase velocity and attenuation coefficient of slow P-wave respectively; h represents the formation thickness.

[0122] In this embodiment, by calculating the above-mentioned pore elastic wave field response characteristics, the reflection amplitude variation characteristics of seismic waves on the interface between different porous media are simulated and calculated, and the total reflection amplitude variation characteristics of seismic waves under different frequency conditions are analyzed to establish a quantitative version of the relationship between reflection amplitude and fluctuation frequency.

[0123] According to the calculation results of this section, the relationship between the reflection amplitude and frequency change of the formation under the condition of continuous formation is calculated. The calculation formula is as follows:

[0124] r=M r0 d;t=M t0 d

[0125] Where d represents the incident longitudinal wave matrix, d = [d fast ,d slow] T ; r represents the complex reflection coefficient matrix of the top interface, r = [R FF ,R FS ] T ; t represents the complex projection coefficient matrix of the top interface, t = [T FF ,T FS ] T ;M r0 and M t0 The reflection matrix and transmission matrix are recursively calculated and established.

[0126] 3. Frequency division processing of seismic record profiles and calculation and analysis of actual formation poroelastic wave field records

[0127] The amplitude-preserving post-stack seismic data after seismic data processing is subjected to frequency division processing, especially the time-frequency analysis of the through-well seismic records. Combined with the analysis and calculation results of the second part, the low-frequency amplitude change characteristics are studied to determine the location of abnormally high low-frequency amplitude.

[0128] In this embodiment, based on the analysis and calculation results of the second part, the wave field response characteristics of low-frequency amplitude anomalies generated by different thin intergas layers are analyzed, the relationship between the strength of low-frequency amplitude anomalies and the delay size is analyzed, and the reasonable relationship between low-frequency amplitude anomalies and gas layer positions is determined. The post-stack amplitude-preserving seismic records are calibrated and structurally interpreted to determine the reflection time of the target layer; and the seismic records are processed by frequency division to obtain the reflection amplitude data of the target layer under different frequency conditions.

[0129] Calculate the poroelastic wave field records of continuous formations, study the reflection amplitude variation characteristics of the target layer, compare the corresponding seismic record time-frequency profiles, and determine the relationship between the low-frequency strong amplitude anomaly position and the gas layer in the seismic frequency profile. Based on the frequency-reflection amplitude-travel time relationship determined above, study and determine the low-frequency amplitude variation characteristics in the seismic record, whether the low-frequency amplitude anomaly position is caused by the gas layer, and reasonably explain the indication function of the low-frequency amplitude anomaly, so as to complete the identification of the thin intergas layer, and predict the distribution range of the thin intergas layer and the best location of the drillable target.

[0130] The following content is some descriptions and introductions of the aforementioned calculation process in this embodiment in conjunction with the accompanying drawings.

[0131] Figure 2 The figure is a graph showing the change of the longitudinal wave velocity and attenuation coefficient of the porous medium calculated by the above formula with the frequency. The hollow origin in the figure is the result of the experiment in the prior art, which shows that the calculation method of this embodiment also has a high calculation accuracy.

[0132] Figure 3This is a graph showing the variation of the slow P-wave velocity and attenuation coefficient of the porous medium calculated using the above formula with frequency. The figure shows that the slow P-wave velocity and attenuation coefficient of the porous medium containing fluids of different properties are different. The slow wave velocity of the gas layer is higher than that of the water layer, but the quality factor is lower than the quality factor value of the water layer.

[0133] Figure 4 is the reflection coefficient of seismic waves between media with different porosity calculated using the above formula, Figure 4 a shows the conventional longitudinal wave reflection coefficient and the comprehensive reflection coefficient considering the effect of the converted slow wave; Figure 4 b shows the difference in fast-slow wave reflection coefficients under different frequency conditions, which is different from the reflection coefficient calculated by elastic wave theory in the prior art.

[0134] Figure 5 This is the interpretation plate of the longitudinal wave reflection coefficient-frequency relationship and the conversion slow wave reflection coefficient-frequency relationship of different porous fluid formations calculated by the above formula. According to this plate, we can analyze the frequency range in which the reflection coefficient of the gas layer and the water layer differs, and the range in which the conversion slow wave has a greater impact on the reflection coefficient-frequency. The analysis helps to correctly explain the reasons for the abnormal low-frequency amplitude of the gas layer.

[0135] Figure 6 The above formula is used to calculate the P-wave reflection coefficient-frequency relationship interpretation and the conversion slow wave reflection coefficient-frequency relationship interpretation under different reservoir thickness conditions. According to this version, the dominant frequency of the low-frequency amplitude anomaly in the gas layer and the length of the delay time can be analyzed.

[0136] Figure 7 This is the interpretation version of the P-wave reflection coefficient-frequency relationship and the conversion slow wave reflection coefficient-frequency relationship of reservoirs with different permeabilities calculated by the above formula. According to this version, the energy changes of low-frequency amplitude anomalies in reservoirs of different properties (with different permeabilities) can be analyzed, which is helpful to correctly interpret the geological reasons for the formation of low-frequency amplitude anomalies and provide evidence for determining the relationship between the location of low-frequency amplitude anomalies and thin intergas layers.

[0137] Example 2

[0138] On the basis of Example 1, this example verifies the calculation involved in the technical solution of the method of the present invention by physically simulating seismic records of porous layered media, and demonstrates the performance effect of the technical solution.

[0139] This verification process is also divided into several steps, as follows:

[0140] (1) Based on the principle of physical similarity, a thin interbed geological model is designed to measure the P-wave velocity, S-wave velocity, density, porosity, permeability and other parameters of each small layer. First, the reservoir pores in the thin interbed model are filled with water to form a saturated thin interbed model. A large physical model acquisition device is used to collect the reflection wave field records of the saturated model. The working principle of wave field acquisition is shown in Figure 8 shown.

[0141] (2) After the reflection wave field of the water-saturated model is collected, the model is taken out of the water tank, the water in the reservoir pores in the model is drained, gas is injected, the model is sealed around, and the gas-saturated model is placed in the water tank to collect the reflection wave field; finally, the necessary pre-stack processing is performed on the collected seismic data to obtain the post-stack amplitude-preserved seismic record sections of the water-saturated model and the gas-saturated model, respectively. Fig. 9 The advantage of physical model acquisition of reflected wave fields is that the parameters of the studied formation model are completely known, and the only factor that causes the difference between the seismic records of the water-saturated model and the gas-saturated model is the different properties of the pore fluid. Therefore, factors such as low-frequency amplitude anomalies caused by differences in formation skeletons are eliminated.

[0142] (3) Fig. 9 The processed seismic records are processed by S transform method, and the time-frequency processing is performed respectively to obtain the time-frequency profiles of different frequency components, see Fig.10 shown. Fig.10 a is the 20 Hz single-frequency amplitude profile of the gas layer model. The area circled by the ellipse in the figure shows an obvious area with abnormally high low-frequency amplitude, which corresponds to the non-reservoir section at the bottom of the actual model; Fig.10 b is the 20 Hz single-frequency amplitude profile of the saturated model. At the corresponding position, there is no low-frequency amplitude anomaly, which is consistent with the actual model. Fig.10 The low-frequency amplitude anomaly in a is probably related to the existence of the upper air layer (this phenomenon is also called the low-frequency shadow feature).

[0143] (4) Based on the model parameters and in combination with the method in Example 1, the poroelastic parameters of each layer in the physical model are calculated to establish a poroelastic parameter model, and the poroelastic wave seismic record of the model is calculated at the same time. Fig.11 The calculation results show that in the seismic records of the saturated model, there is an obvious increase in low-frequency amplitude at about 0.3ms below the gas layer, which is consistent with Fig.10 The characteristics of the mid-atmosphere frequency-divided seismic records are consistent; Fig.11 No low-frequency strong amplitude phenomenon was found at the corresponding position in the seismic record of the saturated model on the right side. It can be inferred that the increase in low-frequency amplitude under the gas layer is caused by the existence of gas layers in the thin interlayers. Fig.10The low-frequency amplitude enhancement phenomenon in a is caused by the filling of gas in the porous medium in the model, which is consistent with the actual saturated fluid situation of the physical model. This embodiment thus verifies the effectiveness of the technical solution method of the present invention.

[0144] Example 2

[0145] Based on Example 1 or Example 2, this example takes a work area of ​​a certain actual gas field as an example, and combines the accompanying drawings to supplement the technical solution of the present invention. The introduction is in the following order:

[0146] (1) The target area is located in the Sulige gas field in the Ordos Basin. The gas reservoirs in this area are mainly lithologic gas reservoirs. The main target layers are the H8 layer and the S1 layer. The structure of the target layer is mainly a slowly changing monocline, which is manifested as a strong west-dipping reflection phase axis in the seismic record, such as Fig.12 As shown. The seismic record passes through three wells in the east-west direction on the ground, namely Well A, Well B and Well C ( Fig.12 Well D is a verification well for the prediction results of seismic records. The drilling results show that both Well A and Well C obtained industrial gas flow in the H8 layer, and Well B encountered water layers in the corresponding layer. Fig.12 In the display, the seismic response characteristics of the gas layer are strong energy and continuous reflection phase axis (see Fig.12 A / C / D are shown as solid circles. However, the water layer has weaker reflection energy and poor continuity of the event axis ( Fig.12 B is shown as a solid circle). The gas layer of well A is located in the low part of the H8 structure, and C is located in the high part of the structure. The target layer of well B is between the structures of wells A and C. Obviously, the structural change of the target layer is not the main factor controlling the formation of gas reservoirs. The gas reservoirs in this area are affected by many factors such as stratum lithology, water saturation, gas source configuration, etc., and are typical lithologic gas reservoirs.

[0147] (2) According to the known drilling data of wells A, B and C, the target layer mainly develops river delta sediments. The gas layer lithology is quartz sandstone, which forms a thin interbedded lithology combination with the upper and lower mudstones. Fig.13 As shown in Figure 2, vertically, water-bearing sandstone, gas-bearing sandstone, and mudstone layers overlap each other, and there is no uniform gas-water interface in the sandstone reservoir of the H8 layer.

[0148] (3) The well logging and core test data of each well in the area are relatively complete, including conventional logging curves (gamma, natural potential, density, supplementary neutron, acoustic wave, and deep and shallow resistivity curves, etc.) and array acoustic logging data; more than 20 meters of core were obtained in the H8 layer during the drilling process, and 35 columnar rock samples were made for the H8 sandstone. The basic parameters of all samples were determined through experimental tests: the mineral component content of the samples was determined by X-ray diffraction; the porosity, density and permeability of the core samples were determined by using a pore permeability tester; the longitudinal and transverse wave velocities of the samples in the dry state and the saturated water state were determined by the pulse transmission method.

[0149] (4) According to the mineral components of the core, a reservoir mineral volume model (including quartz, feldspar, calcite, clay and pores) is established. Combined with conventional logging curves, the optimal calculation method is used to determine the mineral component content, porosity and other parameters of the formation at each depth of the target layer; according to the calculation method of Example 1, the rock matrix bulk modulus, rock dry bulk modulus, rock dry shear modulus, rock matrix density, porosity and permeability at each depth point are determined; according to the burial depth, temperature and pressure of the formation, the formation water (pore fluid) bulk modulus, density and viscosity coefficient are determined. A complete poroelastic formation model of the target layer is established, and the calculation results are shown in Fig.14 The total thickness of the model is 31m, with a depth interval of 0.125m.

[0150] (5) According to the calculation method of Example 1, in combination with the above-mentioned poroelastic formation model, the seismic record response of the poroelastic formation model is calculated (see Fig.15 The method of the present invention can control a single or multiple factors that affect the seismic response when calculating the seismic record response. For example, the seismic response of different fluid strata in the model can be calculated, and the seismic response of a single frequency can also be calculated. Fig.15 a shows the 12Hz low-frequency seismic recording results of the model when the pores in the poroelastic formation model are filled with natural gas and formation water. Because the formation thickness is relatively thin relative to the seismic wavelength, the seismic recording waveform actually reflects the wave field response of the rock mass superimposed by the gas layer and the surrounding rock. Note that there is obvious delayed energy (the position marked by the ellipse in the figure) below the reflection time of the target layer (about 0.185s). This reflection feature is a typical low-frequency amplitude anomaly caused by the fast-slow longitudinal wave conversion caused by the superposition of the gas layer and the surrounding rock layer. For comparison, Fig.15 b shows the seismic waveform recorded when the pores of the poroelastic formation are completely saturated with water. It can be seen that there is no low-frequency amplitude anomaly in the seismic response of the fully saturated formation; Fig.15 c shows the stratigraphic model and Fig.15 a is the same, but the frequency of the wave field response is 45 Hz (equivalent to high-frequency seismic recording). There is no abnormal amplitude delay in the high-frequency recording waveform of this formation model; Fig.15 d shows the ground model and Fig.15 a is the same, but the formation thickness is set to 3m. There is no low-frequency amplitude anomaly in the calculated seismic record waveform. This is mainly because when the formation thickness is large, the conversion wave energy decays rapidly and no low-frequency strong amplitude delay occurs. Through the calculation of poroelastic theory, it can be seen that the thin gas layer in this area is prone to strong reflection energy delay at low frequency. The frequency of strong amplitude delay can be observed to be about 12Hz.

[0151] (6) Yes Fig.12 The seismic records shown are processed by generalized S transform method to obtain seismic records of different harmonic components. Fig.16 The 8Hz, 12Hz and 45Hz single-frequency seismic records are shown respectively. It can be seen from the frequency-divided profile that the single-frequency harmonic energy at the known gas layer position is stronger than the water layer reflection energy. In the 12Hz component seismic record, an obvious strong amplitude band appears below the gas layer, which is not obvious in the 8Hz single-frequency record and the 45Hz single-frequency record. By analyzing the theoretical calculation results of the poroelastic formation model ( Fig.15 a), in the 12Hz single-frequency record, the strong amplitude delay time is about 30ms. Based on the results of the theoretical model, it can be inferred that the low-frequency amplitude anomaly below the reflection phase axis of the H8 target layer of Well A and Well C is a sign of the existence of a gas layer. Based on this analysis, it is inferred that the low-frequency amplitude anomaly area on the east side of Well C (D well location) is caused by the existence of a gas layer. This speculation was confirmed by the drilling of Well D, and a higher natural gas output was obtained in the low-frequency strong amplitude area, showing the practical application effect and value of the present invention.

Claims

1. A method for identifying thin intergas layers based on low-frequency acoustic wave signals, characterized in that: The method comprises the following steps: S1. Using well logging data and core test data, calculate the poroelastic parameters of the target layer and construct a poroelastic formation model of the target layer; S2. Use the poroelastic wave theory to calculate the poroelastic wave field response characteristics of the single interface or formation group of the target layer, and establish a quantitative version of the relationship between the reflection amplitude and the wave frequency; S3, performing frequency division processing on the post-stacked amplitude-preserved seismic records to determine the low-frequency amplitude anomaly position in the seismic records; S4. Use the poroelastic formation model to calculate the vertical poroelastic wave seismic record; combine the poroelastic wave field response characteristics and the relationship between reflection amplitude and fluctuation frequency to interpret the low-frequency amplitude anomaly position in the single-component seismic record, and determine the low-frequency amplitude anomaly distribution area related to the thin intergas layer, thereby completing the identification of the thin intergas layer.

2. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 1, characterized in that: In step S1, the poroelastic parameters include: reservoir porosity and permeability, rock matrix volume, shear modulus and density, rock dry volume and shear modulus, pore fluid volume modulus, density and viscosity coefficient.

3. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 2, characterized in that: The process of calculating the poroelastic parameters is as follows: S11, obtaining the logging curves of the key wells in the study area, normalizing the logging curves, and eliminating the consistency of the data magnitude in the logging curves; S12. Determine the mineral volume model of the study area based on the core X-ray diffraction experimental results of the reference wells in the study area; use quartz, feldspar, carbonate rock and clay with porosity to form the rock volume model; combine the well logging curve with the mineral volume model, perform multi-mineral optimization decomposition processing on the rock volume model, and determine the volume proportion of different minerals at each depth point; S13. Perform lithofacies identification and division operations based on the volume proportion of different minerals at each depth point and in combination with lithology logging data; S14. Calculate the rock matrix volume, shear modulus and density at each depth point based on the multi-mineral optimization decomposition processing results; S15, calculating the permeability at each depth point based on the porosity in the well logging curve; S16. Calculate the bulk modulus, density and viscosity coefficient of the pore fluid at each depth point based on the temperature and pressure of the formation at each depth point and the salinity of the formation water; S17, calculating the rock dry volume and shear modulus at each depth point based on the rock matrix shear modulus and porosity curve data at each depth point; S18. Determine the model parameters of the poroelastic formation model based on the above-mentioned calculation results.

4. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 3, characterized in that: In step S14, the HILL equation is used to calculate the rock matrix volume and shear modulus at each depth point. The calculation formula is as follows: Where i represents the i-th mineral; N represents the total number of rock mineral components; f i represents the volume fraction of the i-th mineral; M i represents the volume or shear modulus of the i-th mineral; M s represents the corresponding rock matrix bulk or shear modulus; Calculate the rock matrix density at each depth point using the following formula: Where ρ represents the density of the i-th mineral; ρ s represents the rock matrix density; In step S17, the calculation formulas for the rock dry volume and shear modulus are as follows: In the formula, K s , μ s represent the rock matrix volume and shear modulus respectively, φ represents the formation porosity at each depth point; g and h are parameters related to the rock pore structure, and h = 1.5g; K d , μ d represent the dry volume and shear moduli of rock, respectively.

5. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 4, characterized in that: In step S15, the permeability calculation formula at each depth point is as follows: k = aφ c Where, φ is the porosity of the formation at each depth point; κ is the permeability of the formation at each depth point; a and c are the fitting parameters of the corresponding formation in the core X-ray diffraction experiment; In step S16, the calculation formulas for the bulk modulus, density and viscosity coefficient of the pore fluid at each depth point are as follows: η=0.1+0.333S+(1.65+91.9S 3 )exp{-[0.42(S 0.8 -0.17) 2 +0.045]T 0.8 } In each formula, T and P represent the formation temperature and pressure; S represents the mineralization of formation water; w ij is the empirical coefficient; ρ f represents the pore fluid density; K f represents the bulk modulus of the pore fluid; η represents the viscosity coefficient.

6. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 5, characterized in that: In step S18, the model parameter calculation equation is as follows: f n =<φ>; m n =<η> Wherein, n represents the nth layer of the formation in the poroelastic formation model; the construction of the poroelastic formation model can be completed through the above equation.

7. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 1, characterized in that: In step S2, the process of calculating the poroelastic wave field response characteristics is as follows: S21. Calculate the wave impedance of each layer as follows: Where Z represents the formation wave impedance; V p represents the longitudinal wave velocity; ρ b Indicates the stratum density; the subscript n and subscript n indicate the nth layer of the stratum; S22. Calculate the zero-order hole elastic reflection and transmission coefficients as follows: In the formula, and represent the zero-order reflection and projection coefficients of fast longitudinal waves, respectively; S23. Calculate the first-order poroelastic reflection and transmission coefficients as follows: In the formula, and represent the first-order reflection and projection coefficients of fast longitudinal waves, respectively; S24. Calculate the reflection and transmission coefficients of various types of pore elastic waves at the formation interface as follows: Poroelastic fast wave incident-fast wave reflection coefficient: Poroelastic fast wave incident-fast wave transmission coefficient: Poroelastic fast wave incident-slow wave reflection coefficient: Poroelastic fast wave incident-slow wave transmission coefficient: The B(ε) parameters involved in each formula are as follows: e=(iρ f ok) / h Where ω represents the rounding frequency; i is an imaginary number; κ represents the formation permeability; η is the viscosity coefficient; ρ f represents the pore fluid density; S25. Calculate the total amplitude of the pore elastic wave reflection at the formation interface A F , the calculation formula is as follows: In the formula, A f is the initial fast longitudinal wave reflection coefficient; V s (ω) and α s (ω) represent the phase velocity and attenuation coefficient of slow P-wave respectively; h represents the formation thickness.

8. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 7, characterized in that: By calculating the pore elastic wave field response characteristics, the reflection amplitude variation characteristics of seismic waves on the interface between different porous media are simulated and calculated, and the total reflection amplitude variation characteristics of seismic waves under different frequency conditions are analyzed to establish a quantitative version of the relationship between reflection amplitude and fluctuation frequency.

9. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 1, characterized in that: In step S3, generalized S transform is used to perform frequency division processing on the post-stack amplitude-preserved seismic records; Use seismic interpretation software to display and check the energy changes of the target layer position in the single frequency component section of the seismic record; Look for areas with strong low-frequency energy and weak high-frequency energy below the target layer to determine the location of low-frequency amplitude anomalies in seismic records.

10. The method for identifying thin intergas layers based on low-frequency acoustic wave signals according to claim 9, characterized in that: In step S4, during the actual identification process, the poroelastic wave field response characteristics of the actual stratum are calculated, the frequency, amplitude and delay time of the low-frequency energy anomaly in the seismic record are analyzed, and it is determined whether the low-frequency amplitude anomaly of the single-frequency component is related to the existence of the thin intergas layer, so as to complete the identification of the thin intergas layer; Based on the abnormal distribution area of ​​low-frequency amplitude in the seismic single-frequency component profile, the distribution range of the thin intergas layer and the optimal location of the drillable target are predicted.

Images