A method for identifying thin interbedded gas layers based on low-frequency acoustic signals

Abstract

The application provides a method for identifying thin interbedded gas layers based on low-frequency acoustic signals, belongs to the technical field of unconventional natural gas exploration, and solves the problem that the prior art cannot apply low-frequency acoustic signals of earthquakes to identify oil and gas reservoirs; the method comprises the following steps: S1, calculating the pore elastic parameters of a target layer, and constructing a pore elastic stratum model of the target layer; S2, calculating the pore elastic wave field response characteristics of a single interface or a stratum group of the target layer, and establishing a reflection amplitude and wave frequency relationship volume; S3, performing frequency division processing on seismic records, and determining the position of low-frequency amplitude anomalies; S4, calculating vertical pore elastic wave seismic records, combining the pore elastic wave field response characteristics and the reflection amplitude and wave frequency relationship volume, and interpreting the position of low-frequency amplitude anomalies to determine a region related to thin interbedded gas layers; the application can effectively determine the position relationship between low-frequency amplitude anomalies and gas layers in seismic records, fills the gap in the exploration technology, and improves the interpretation accuracy of seismic identification of gas layers.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unconventional natural gas exploration, and particularly relates to a method for identifying thin interbedded gas layers based on low-frequency acoustic signals. BACKGROUND

[0002] At present, seismic low-frequency acoustic signal information plays an important role in reservoir characterization and oil and gas exploration, and there are many successful examples of using P-wave low-frequency amplitude anomalies to identify gas layers; however, the generation mechanism of such seismic wave (or acoustic wave) low-frequency amplitude anomalies is still unclear. In some areas, the gas layers identified by using seismic wave low-frequency amplitude anomalies are not gas layers, which causes great economic losses. Therefore, in the process of oil and gas exploration and development, it is necessary to explore an effective discrimination method for the relationship between thin interbedded gas layers and seismic wave low-frequency amplitude anomalies, so as to realize the effect of accurately identifying the position of gas layers in thin interbedded layers by using seismic wave reflection records, and understand the relationship between seismic low-frequency response and the nature of oil and gas reservoirs.

[0003] The generation mechanism of seismic wave amplitude low-frequency anomalies is very complex, and the forms in seismic data are various. In some cases, the amplitude low-frequency anomaly signal appears in the reservoir position, and in other cases, the amplitude low-frequency anomaly signal occurs below the reservoir position, which is called low-frequency shadow (LFS). The person skilled in the art uses seismic wave attenuation theory to explain the reason why the low-frequency acoustic signal anomaly occurs in the gas reservoir; 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. The existing seismic exploration technology is mainly based on the theory of acoustic wave or elastic wave.

[0004] According to the poroelastic theory, when seismic waves propagate at different poroelastic layer interfaces, not only reflected and transmitted longitudinal and transverse waves are generated, but also reflected and transmitted slow longitudinal waves are generated. Slow waves can be observed in actual sandstone acoustic wave experiments saturated with air, and theoretical research also confirms that low-frequency acoustic signal anomalies may be caused by converted slow longitudinal waves in thin permeable fluid media. Based on the above understanding, it is necessary to discriminate and explain the seismic amplitude low-frequency anomaly signal of the gas layer in unconventional thin interbedded reservoirs; if the low-frequency acoustic signal enhancement analysis is to be applied to field data, it is necessary to study the influencing factors that may change the low-frequency acoustic signal enhancement characteristics, so as to identify oil and gas reservoirs. How to analyze and study the possible influencing factors has become the focus of attention of the person skilled in the art. SUMMARY

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

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

[0007] A method for identifying thin inter-gas layers based on low-frequency acoustic signals, the method comprising the following steps:

[0008] S1. Using logging data and core test data, calculating the poroelastic parameters of the target layer, and constructing a poroelastic stratigraphic model of the target layer;

[0009] S2. Using poroelastic wave theory, calculating the poroelastic wave field response characteristics of the single interface or stratigraphic group of the target layer, and establishing a reflection amplitude and wave frequency relationship volume;

[0010] S3. Frequency processing is performed on the post-stack amplitude-preserved seismic record to determine the low-frequency amplitude anomaly position in the seismic record;

[0011] S4. Using the poroelastic stratigraphic model, calculating the vertical poroelastic wave seismic record; combining the poroelastic wave field response characteristics, the reflection amplitude and wave frequency relationship volume, interpreting the low-frequency amplitude anomaly position in the single-component seismic record, and determining the low-frequency amplitude anomaly distribution area related to the thin inter-gas layer, thereby completing the identification of the thin inter-gas 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, and pore fluid bulk modulus, density and viscosity coefficient.

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

[0014] S11. Obtain the logging curves of key wells in the study area, and normalize the logging curves to eliminate the consistency of data magnitudes in the logging curves;

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

[0016] S13. According to the volume proportion of different minerals at each depth point, and combining lithology logging data, perform lithofacies identification and division operations;

[0017] S14. According to the multi-mineral optimal decomposition processing results, calculate the rock matrix volume, shear modulus and density of each depth point;

[0018] S15. According to the porosity in the logging curves, calculate the permeability of each depth point;

[0019] S16, according to the temperature and pressure of each depth point stratum, combined with the salinity of the stratum water, the pore fluid volume modulus, density and viscosity coefficient of each depth point are calculated;

[0020] S17, according to the rock matrix shear modulus and porosity curve data of each depth point, the rock dry volume and shear modulus of each depth point are calculated;

[0021] S18, according to the calculation results of the above types, the model parameters of the poroelastic stratum model are determined.

[0022] Among them, the logging curve of the key well in the research area includes gamma, density, natural potential, formation resistivity and compensated neutron conventional curve data, and array acoustic logging data or element logging data and other related contents.

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

[0024] S21, the wave impedance of each stratum is calculated;

[0025] S22, the zero-order poroelastic reflection and transmission coefficients are calculated;

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

[0027] S24, the reflection and transmission coefficients of various poroelastic waves at the stratum interface are calculated;

[0028] S25, the total poroelastic wave reflection amplitude of the stratum interface is calculated;

[0029] Through the calculation of the poroelastic wave field response characteristics, the reflection amplitude variation characteristics of the seismic wave at the interface between different porosity media are simulated and calculated, and the total reflection amplitude variation characteristics of the seismic wave under different frequency conditions are analyzed to establish the reflection amplitude and wave frequency relationship volume.

[0030] Further, in step S3, the post-stack amplitude-preserved seismic record is processed by using the 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 by using the seismic interpretation software; the region appearing below the target layer with low frequency energy strong and high frequency energy weak is found to determine the low frequency amplitude anomaly position in the seismic record.

[0031] Further, in step S4, in the actual identification process, the borehole elastic wave field response characteristics of the actual formation are calculated, the frequency, amplitude and delay time of the low frequency energy anomaly in the seismic record are analyzed, it is judged whether the low frequency amplitude anomaly of the single frequency component is related to the existence of the thin inter-gas layer, and the identification of the thin inter-gas layer is completed; according to the distribution area of the low frequency amplitude anomaly in the seismic single frequency component profile, the distribution range of the thin inter-gas layer and the best position of the drillable target are predicted.

[0032] In summary, due to the adoption of the technical scheme, the application has the following beneficial effects:

[0033] 1. The borehole elastic formation model of the underground oil and gas reservoir and its surrounding rock is reasonably established by combining the conventional logging data with various rock physical models; the borehole elastic formation model is different from the existing conventional formation model, and the composition considers factors such as the formation pore, the permeability and the pore fluid properties, so that the model established is more in line with the physical properties of the actual rock.

[0034] 2. The propagation characteristics of the seismic wave in the pore medium are calculated by using the borehole elastic theory method; compared with the conventional elastic wave numerical simulation result, the calculation result of the theory is more in line with the propagation of the actual seismic wave; the reason is that the actual formation is a fluid-containing pore medium, and the borehole elastic wave field numerical simulation not only considers the propagation characteristics of the fast longitudinal wave, but also considers the slow wave reflection and projection caused by the pore fluid pressure diffusion. The relationship between the seismic wave energy attenuation and the wave frequency is studied by the method, which has higher precision and clear physical meaning.

[0035] 3. The borehole elastic formation model and the borehole elastic wave field numerical simulation established by the application can analyze the causes of various low frequency amplitude anomalies of the seismic wave, can analyze the delay time of the low frequency amplitude anomaly according to the formation thickness and other information, and further enhances the identification ability of whether the underground oil and gas reservoir exists, and improves the interpretation precision of directly detecting the oil and gas layer by the seismic record. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 It is the overall step flowchart of the method of the application;

[0037] Figure 2 It is the relationship diagram of the longitudinal wave velocity, the attenuation coefficient and the frequency in the pore medium;

[0038] Figure 3 It is the relationship diagram of the slow longitudinal wave velocity, the attenuation coefficient and the frequency of the rock containing different pore fluids;

[0039] Figure 4 It is the simulation diagram of the influence of the converted slow wave on the reflection and transmission coefficients of the fluid-containing pore medium;

[0040] Figure 5 The schematic diagram of the interpretation volume of the pore fluid and the fast and slow wave reflection coefficients;

[0041] Figure 6 The schematic diagram of the interpretation volume of the formation thickness and the fast and slow wave reflection coefficients;

[0042] Figure 7 The schematic diagram of the interpretation volume of the formation permeability and the fast and slow wave reflection coefficients;

[0043] Figure 8 The schematic diagram of the physical simulation experiment of the seismic wave field of the multi-layer medium;

[0044] Figure 9 The schematic diagram of the seismic record profile of the oil-bearing and gas-bearing model collected by the experiment;

[0045] Figure 10 The schematic diagram of the 20Hz frequency profile after the time-frequency processing of the seismic record;

[0046] Figure 11 The schematic diagram of the Borehole elastic wave field simulation result;

[0047] Figure 12 The schematic diagram of the seismic record profile in a certain exploration block;

[0048] Figure 13 The schematic diagram of the comprehensive columnar chart of the target layer section through the well in the middle profile; Figure 12

[0049] The schematic diagram of the Borehole elastic parameter formation model actually established; Figure 14

[0050] The reflection coefficient diagram of the numerical simulation Borehole elastic formation model; Figure 15

[0051] The schematic diagram of the amplitude distribution of the seismic record under different frequencies. Figure 16 DETAILED DESCRIPTION

[0052] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0053] ​Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based upon the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0054] Embodiment 1

[0055] A method for identifying thin inter-gas layers based on low-frequency acoustic wave signals, the overall steps of the method are as follows, which can be referred to the schematic diagram of Figure 1 .

[0056] S1, using logging data and core test data, calculating the poroelastic parameters of the target layer, and constructing a poroelastic stratum model of the target layer;

[0057] S2, using poroelastic wave motion theory, calculating the poroelastic wave field response characteristics of the single interface or stratum group of the target layer, and establishing a reflection amplitude and wave frequency relationship volume;

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

[0059] S4, using the poroelastic stratum model, calculating the vertical poroelastic wave seismic record; combining the poroelastic wave field response characteristics and the reflection amplitude and wave frequency relationship volume, interpreting the low-frequency amplitude anomaly position in the single-component seismic record, and determining the low-frequency amplitude anomaly distribution area related to the thin inter-gas layer, thereby completing the identification of the thin inter-gas layer.

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

[0061] I. Construction of Poroelastic Stratum Model

[0062] 1-1. Obtain the logging curves (mainly including conventional curve data such as gamma, density, natural potential, formation resistivity, compensated neutron, array acoustic logging data or elemental logging data) of key wells in the study area, lithology logging, top and bottom depth 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 curves to eliminate the consistency of the data magnitude in the logging curves.

[0064] 1-3. According to the core X-ray diffraction experimental results of the reference well core in the research area, the mineral volume model of the research area is determined; quartz, feldspar, carbonate rock and clay with porosity are used to constitute the rock volume model; combined with the logging curve and the mineral volume model, the rock volume model is subjected to multi-mineral optimization decomposition treatment, and the volume proportion of different minerals at each depth point is determined.

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

[0066] 1-5. According to the multi-mineral optimization decomposition treatment results, the rock matrix volume and shear modulus at each depth point are calculated by using HILL equation, and the calculation formula is as follows:

[0067]

[0068] In the formula, 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 volume or shear modulus;

[0069] The rock matrix density at each depth point is calculated, and the calculation formula is as follows:

[0070]

[0071] In the formula, p represents the density of the i th mineral; p s represents the rock matrix density.

[0072] 1-6. According to the porosity in the logging curve, the permeability at each depth point is calculated, and the calculation formula is:

[0073] κ=aφ c

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

[0075] 1-7. According to the temperature and pressure of each depth point formation, combined with the salinity of formation water, the pore fluid bulk modulus, density and viscosity coefficient at each depth point are calculated; 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 the formula, T and P represent formation temperature and pressure; S represents formation water salinity; w ij is an empirical coefficient; p f represents pore fluid density; K f represents pore fluid bulk modulus; and η represents viscosity coefficient.

[0080] 1-8. According to the rock matrix shear modulus and porosity curve data of each depth point, the dry volume and shear modulus of the rock of each depth point are calculated; the calculation formula is as follows:

[0081]

[0082]

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

[0084] According to the calculation results of the above types, the model parameters of the poroelastic formation model are determined as follows:

[0085]

[0086] φ n =<φ>; μ n =<η>

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

[0088] II. Calculation of poroelastic wave field response characteristics of a single interface or a group of formations in a target layer

[0089] This part is based on the constructed poroelastic formation model, and the numerical simulation research is carried out to study the poroelastic seismic wave propagation in the target layer, and different element interpretation versions are established, i.e. reflection amplitude and wave frequency relationship version.

[0090] 2-1. The wave impedance of each formation is calculated, as follows:

[0091]

[0092] where Z represents the formation wave impedance; V p represents the P-wave velocity; p b represents the formation density; the subscripts n represent the nth layer of the formation.

[0093] 2-2. The zero-order borehole elastic reflection and transmission coefficients are calculated as follows:

[0094]

[0095]

[0096] where and represent the zero-order reflection and transmission coefficients of the fast P-wave, respectively.

[0097] 2-3. The first-order borehole elastic reflection and transmission coefficients are calculated as follows:

[0098]

[0099]

[0100] where and represent the first-order reflection and transmission coefficients of the fast P-wave, respectively.

[0101] This example gives a preferred example of the calculation method related to the calculation herein, i.e., the calculation method of the reflection and transmission coefficients of the fast-slow P-wave, which are 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, the dry rock, and the pore fluid, respectively; p represents the density, and its subscripts s, b, and f represent the rock matrix, the formation bulk, and the pore fluid, respectively.

[0110] 2-4. The reflection and transmission coefficients of various types of borehole elastic waves at the interface of the stratum are calculated as follows:

[0111] Borehole elastic fast wave incidence-fast wave reflection coefficient:

[0112] Borehole elastic fast wave incidence-fast wave transmission coefficient:

[0113] Borehole elastic fast wave incidence-slow wave reflection coefficient:

[0114] Borehole elastic fast wave incidence-slow wave transmission coefficient:

[0115] The B(ε) parameter involved in each formula is as follows:

[0116]

[0117] ε = (iρ f ωκ) / η

[0118] In the formula, ω represents the circular frequency; i is an imaginary number; κ represents the permeability of the stratum; η represents the viscosity coefficient; ρ f represents the density of the pore fluid

[0119] 2-5. The sum A of the reflection amplitudes of borehole elastic waves at the interface of the stratum is calculated as follows: F

[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 the slow longitudinal wave respectively; and h represents the thickness of the stratum.

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

[0123] According to the calculation results of this part, the relationship between the reflection amplitude of the stratum and the frequency variation under the condition of continuous stratum is calculated as follows:

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

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

[0126] Three, frequency division processing of seismic record section and record calculation and analysis of actual formation hole elastic wave field

[0127] The frequency division processing is performed on the amplitude-preserved post-stack seismic data after the seismic data processing, especially the time-frequency analysis is performed on the through-well seismic record, the low-frequency amplitude variation characteristics are studied in combination with the analysis and calculation results of the second part, and the position of the low-frequency amplitude anomaly is determined.

[0128] In this embodiment, according to the analysis and calculation results of the second part, the wave field response characteristics of different thin inter-gas layers generating low-frequency amplitude anomalies are analyzed, the relationship between the low-frequency amplitude anomaly and the strength and delay size of the low-frequency amplitude anomaly is analyzed, and the reasonable relationship between the low-frequency amplitude anomaly and the gas layer position is determined. The horizon calibration and structure interpretation are performed on the post-stack amplitude-preserved seismic record, the reflection time of the target layer is determined, and the frequency division processing is performed on the seismic record to obtain the reflection amplitude data of the target layer under different frequency conditions.

[0129] The hole elastic wave field record of the continuous formation is calculated, the reflection amplitude variation characteristics of the target layer are studied, the low-frequency strong amplitude anomaly position in the seismic frequency division section and the relationship with the gas layer are compared and analyzed by comparing the corresponding seismic record time-frequency section. According to the frequency-reflection amplitude-travel time relationship determined in the foregoing, the low-frequency amplitude variation characteristics in the seismic record, whether the low-frequency amplitude anomaly position is caused by the gas layer, and the indication function of the low-frequency amplitude anomaly are studied and judged, so as to complete the identification of the thin inter-gas layer, and the distribution range and the best position of the drillable target of the thin inter-gas layer can be predicted accordingly.

[0130] The following is some description and introduction of the foregoing calculation process of this embodiment in combination with the accompanying drawings.

[0131] Figure 2 The curve diagram in the figure is the curve diagram of the pore medium longitudinal wave velocity and the attenuation coefficient changing with frequency calculated by using the foregoing formula, and the hollow origin point in the figure is the experimental result in the prior art, which shows that the calculation method of this embodiment also has high calculation precision.

[0132] Figure 3The schematic diagram of the variation of the slow longitudinal wave velocity and attenuation coefficient of the porous medium with frequency calculated by the above formula is shown in the figure, which shows that the slow wave velocity of the gas layer is higher than that of the water layer, but the quality factor is smaller than that of the water layer after the porous medium contains different properties of fluid.

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

[0134] Figure 5 The longitudinal wave reflection coefficient-frequency relationship interpretation version and the converted slow wave reflection coefficient-frequency relationship interpretation version of the layer containing different pore fluids calculated by the above formula. According to the interpretation version, the difference of the reflection coefficient of the gas layer and the water layer in the frequency range, and the range in which the converted slow wave has a greater influence on the reflection coefficient-frequency can be analyzed, which helps to correctly explain the reason for the low-frequency amplitude anomaly of the gas layer.

[0135] Figure 6 The longitudinal wave reflection coefficient-frequency relationship interpretation version and the converted slow wave reflection coefficient-frequency relationship interpretation version under the condition of different reservoir thickness calculated by the above formula. According to the interpretation version, the dominant frequency of the low-frequency amplitude anomaly of the gas layer and the length of the delay time can be analyzed.

[0136] Figure 7 The longitudinal wave reflection coefficient-frequency relationship interpretation version and the converted slow wave reflection coefficient-frequency relationship interpretation version of the reservoir with different permeability calculated by the above formula. According to the interpretation version, the energy size change of the low-frequency amplitude anomaly of the reservoir with different properties (having permeability difference) can be analyzed, which helps to correctly explain the geological reason for the formation of the low-frequency amplitude anomaly, and provides evidence for judging the relationship between the low-frequency amplitude anomaly position and the thin interlayer.

[0137] Example 2

[0138] On the basis of example 1, this example verifies the calculation involved in the technical scheme of the method of the application by physical simulation of the seismic record of the porous layered medium, and shows the performance effect of the technical scheme.

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

[0140] (1) Based on the principle of physical similarity, a thin interbedded geological model was designed, and parameters such as P-wave velocity, S-wave velocity, density, porosity, and permeability of each sublayer were measured. First, water was filled into the reservoir pores of the thin interbedded model to form a water-saturated thin interbedded model. A large-scale physical model acquisition device was used to collect the reflected wave field records of the water-saturated model. The working principle of wave field acquisition is described in [reference needed]. Figure 8 As shown.

[0141] (2) After the reflected wave field acquisition of the water-saturated model is completed, the model is removed from the water tank, the water in the reservoir pores of the model is drained, gas is injected, the model is sealed, and then the gas-saturated model is placed back into the water tank for reflected wave field acquisition. Finally, the acquired seismic data are processed according to necessary pre-stack processing to obtain the post-stack amplitude-preserving seismic record profiles of the water-saturated model and the gas-saturated model, respectively. Figure 9 As shown. The advantage of acquiring reflected wave fields using physical models is that the parameters of the studied stratigraphic model are completely known, and the only factor causing the difference between the seismic records of the water-saturated model and the gas-saturated model is the difference in pore fluid properties. Therefore, factors such as low-frequency amplitude anomalies caused by differences in the stratigraphic framework are eliminated.

[0142] (3) Figure 9 The processed seismic records were subjected to S-transform processing, and time-frequency processing was performed separately to obtain time-frequency profiles for different frequency components, as shown in the figure. Figure 10 As shown. Figure 10 a is the 20Hz single-frequency amplitude profile of the gas layer model. The area circled by the ellipse in the figure shows a region with a significantly high low-frequency amplitude. This region corresponds to the non-reservoir section at the bottom of the actual model. Figure 10 b represents the 20Hz single-frequency amplitude profile of the water-saturated model. No low-frequency amplitude anomalies were observed at the corresponding location, consistent with the actual model. (Explanation) Figure 10 The low-frequency amplitude anomaly observed in a is likely related to the presence of the upper air layer (this phenomenon is also known as low-frequency shadowing feature).

[0143] (4) Based on the model parameters and the method described in Example 1, the pore elastic parameters of each layer in the physical model were calculated, a pore elastic parameter model was established, and the pore elastic wave seismic record of this model was calculated simultaneously. See Figure 11 As shown. The calculation results show that in the seismic record of the gas-saturated model, there is a significant increase in low-frequency amplitude at a position approximately 0.3 ms below the gas layer, which is consistent with... Figure 10 The characteristics of mid-atmosphere frequency-division seismic records are consistent; while Figure 11 No low-frequency strong amplitude phenomenon was found at the corresponding location in the seismic record of the water-saturated model on the right. Therefore, it is inferred that the increased low-frequency amplitude below the gas layer is caused by the presence of a gas layer in the thin interbedded layers. Based on this, it is inferred that... Figure 10The enhanced amplitude at low and medium frequencies is due to the gas filling the porous medium in the model, which is consistent with the actual saturated fluid conditions in the physical model. This embodiment thus verifies the effectiveness of the technical solution and method of the present invention.

[0144] Example 2

[0145] Based on Embodiment 1 or Embodiment 2, this embodiment uses a working area of ​​an actual gas field as an example to supplement the description of the technical solution of the present invention with reference to the accompanying drawings. The description proceeds in the following order:

[0146] (1) The target area is located in the Sulige Gas Field of the Ordos Basin. The gas reservoirs in this area are mainly lithologic gas reservoirs, with the main target layers being the H8 and S1 layers. The structure of the target layers is mainly a slowly changing monocline, and the seismic records show strong westward-dipping reflection phase axes, such as... Figure 12 As shown. The seismic record passes through three wells in an east-west direction on the ground: well A, well B, and well C. Figure 12 As shown in the diagram, well D is a verification well for the seismic record prediction results. Drilling results show that wells A and C both obtained industrial gas flow in layer H8, while well B encountered a water layer in the corresponding section. Figure 12 The seismic response characteristics of the air layer are shown to be of high energy, with continuous reflection phase axes (see...). Figure 12 (As shown by solid circles A / C / D). The water layer reflects energy weakly, and the continuity of the phase axis is poor. Figure 12 (B is shown as a solid circle). The gas layer in well A is located in the lower part of the H8 structure, while C is located in the higher part. The target layer in well B lies between the structures of wells A and C. Clearly, the structural variations in the target layer are not the primary factor controlling the formation of the gas reservoir. The gas reservoir in this area is influenced by various factors such as stratigraphic lithology, water saturation, and gas source configuration, making it a typical lithologic gas reservoir.

[0147] (2) Based on the known drilling data of wells A, B, and C, the target interval mainly consists of river deltaic sediments. The gas layer lithology is quartz sandstone, forming a thin interbedded lithological assemblage with the underlying and surrounding mudstones. See Figure 13 As shown, vertically, water-bearing sandstone, gas-bearing sandstone, and mudstone layers are superimposed on each other, and there is no unified gas-water interface in the sandstone reservoir of layer H8.

[0148] (3) The well logging and core testing data of the wells 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 was obtained in H8 layer during drilling process, and 35 columnar rock samples were prepared for H8 sandstone. The basic parameters of all samples were determined through experimental testing: the mineral component content of the samples was determined by X-ray diffraction method; the porosity, density and permeability of the core samples were determined by using a porosity and permeability tester; the longitudinal and transverse wave velocities of the samples in dry state and saturated water state were determined by using a pulse transmission method.

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

[0150] (5) According to the calculation method of example 1, combined with the above poro-elastic formation model, the seismic record response of the poro-elastic formation model is calculated (see Figure 15 ). When calculating the seismic record response, the method described in the present application can control a single or multiple factors affecting the seismic response. For example, the seismic response of the model containing different fluid layers can be calculated, and the seismic response of a single frequency can also be calculated. Figure 15 a shows the 12 Hz low-frequency seismic record results of the model in the state of the formation pore filling natural gas and formation water. Because the thickness of the formation is relatively thin compared with the wavelength of the seismic wave, the seismic record waveform actually reflects the wave field response of the rock body superimposed by the gas layer and the surrounding rock. Note that there is obvious delay energy (marked position in the ellipse in the figure) below the reflection time (about 0.185 s) of the target layer, and this reflection feature is a typical low-frequency amplitude anomaly phenomenon caused by the fast-slow longitudinal wave conversion caused by the superposition of the gas layer and the surrounding rock layer. As a comparison, Figure 15 b shows the seismic record waveform in the case of completely saturated water in the poro-elastic formation pores. It can be seen that there is no low-frequency amplitude anomaly phenomenon in the seismic response of the completely water-saturated formation; Figure 15 c shows that the formation model is the same as Figure 15 a, but the frequency of the wave field response is 45 Hz (equivalent to high-frequency seismic record), and there is no amplitude anomaly delay phenomenon in the high-frequency record waveform of the formation model;Figure 15 d The ground model shown is the same as Figure 15 a, but the formation thickness is set to 3m, and no low-frequency amplitude anomaly phenomenon is found in the calculated seismic record waveform, which is mainly because when the formation thickness is large, the converted wave energy decays rapidly and does not produce a low-frequency strong amplitude delay phenomenon. Through the hole elastic theory calculation, it can be known that the gas layer in the area is thin and easy to appear strong reflection energy delay phenomenon at low frequency, and the frequency of the strong amplitude delay can be observed about 12Hz.

[0151] (6) The seismic record shown in Figure 12 The generalized S transform method is used for frequency division processing to obtain the seismic record of different harmonic components, Figure 16 The 8Hz, 12Hz and 45Hz single frequency seismic records are respectively shown. From the frequency division profile, it can be seen that the single frequency harmonic energy of the known gas layer position is stronger than the water layer reflection energy, and the 12Hz component seismic record appears obvious strong amplitude band under the gas layer, and this phenomenon is not obvious in the 8Hz single frequency record and the 45Hz single frequency record. Through the analysis of the theoretical calculation results of the hole elastic formation model ( Figure 15 a), in the 12Hz single frequency record, the strong amplitude delay time is about 30ms. According to the theoretical model results, it can be inferred that the low-frequency amplitude anomaly under the reflection phase axis of the H8 target layer of the A well and the C well is a landmark phenomenon of the existence of the gas layer. According to this analysis, it is inferred that the low-frequency amplitude anomaly area (D well position) east of the C well is caused by the existence of the gas layer. This inference is verified by the drilling of the D well, and high natural gas output is obtained in the low-frequency strong amplitude area, which shows the practical application effect and value of the present application.

Claims

1. A method for identifying a thin interbedded gas layer based on low-frequency acoustic signals, characterized in that, The method comprises the following steps: S1, using logging data and core test data, calculating the poroelastic parameters of the target layer, and constructing a poroelastic stratum model of the target layer; S2, using poroelastic wave motion theory, calculating the poroelastic wave field response characteristics of the single interface or stratum group of the target layer, and establishing a reflection amplitude and wave frequency relationship volume plate; S3, performing frequency division processing on the post-stack amplitude-preserved seismic record to determine the low-frequency amplitude anomaly position in the seismic record; S4, using the poroelastic stratum model, calculating the vertical poroelastic wave seismic record; combining the poroelastic wave field response characteristics and the reflection amplitude and wave frequency relationship volume plate, interpreting the low-frequency amplitude anomaly position in the single-component seismic record to determine the low-frequency amplitude anomaly distribution area related to the thin inter-gas layer, thereby completing the identification of the thin inter-gas layer; In step S1, the process of calculating the poroelastic parameters is specifically as follows: S11, obtaining the logging curves of key wells in the study area, and performing normalization processing on the logging curves to eliminate the consistency of the data magnitude in the logging curves; S12, determining the mineral volume model of the study area according to the core X-ray diffraction experimental results of the reference wells in the study area; adopting quartz, feldspar, carbonate rock and clay with porosity to constitute the rock volume model; combining the logging curves and the mineral volume model, performing multi-mineral optimal decomposition processing on the rock volume model to determine the volume proportion of different minerals at each depth point; S13, according to the volume proportion of different minerals at each depth point, combining lithology logging data, performing lithofacies identification and division operation; S14, according to the multi-mineral optimal decomposition processing result, calculating the rock matrix volume, shear modulus and density of each depth point; S15, according to the porosity in the logging curve, calculating the permeability of each depth point; S16, according to the temperature and pressure of the stratum at each depth point, combining the formation water salinity, calculating the bulk modulus, density and viscosity coefficient of the pore fluid at each depth point; S17, according to the rock matrix shear modulus and porosity curve data at each depth point, calculating the rock dry volume and shear modulus at each depth point; S18, according to the calculation results of the above types, determining the model parameters of the poroelastic stratum model.

2. The method according to claim 1, wherein: In step S1, the poroelastic parameters include: reservoir porosity and permeability, rock matrix volume, shear modulus and density, rock dry volume and shear modulus, and bulk modulus, density and viscosity coefficient of the pore fluid.

3. The method according to claim 2, wherein: In step S14, the HILL equation is used to calculate the rock matrix volume and shear modulus of each depth point, and the calculation formula is as follows: wherein, represents the number of minerals of the first kind; represents the total number of mineral components of the rock; represents the volume fraction of the minerals of the first kind; represents the volume or shear modulus of the minerals of the first kind; represents the corresponding volume or shear modulus of the rock matrix; The calculation formula for calculating the rock matrix density of each depth point is as follows: wherein represents the density of the mineral; represents the density of the rock matrix; In step S17, the calculation formula for rock dry volume and shear modulus is as follows: wherein, , respectively represent the rock matrix volume and the shear modulus, represents the formation porosity at each depth point; , is a rock pore structure related parameter, and ; , respectively represent the rock dry volume and the shear modulus.

4. The method of claim 3, wherein: In step S15, the calculation formula for the permeability of each depth point is as follows: wherein, is the formation porosity at each depth point; is the formation permeability at each depth point; , is the fitting parameter corresponding to the 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: Among the various types, , Indicates formation temperature and pressure; Indicates the mineralization degree of formation water; This is an empirical coefficient; Indicates pore fluid density; Indicates the bulk modulus of pore fluids; This represents the viscosity coefficient.

5. The method according to claim 4, wherein: In step S18, the model parameter calculation equation is as follows: In the formula, represents the first layer of the formation in the poroelastic formation model layer; the construction of the poroelastic formation model can be completed through the above equation.

6. The method of claim 1, wherein: In step S2, the process of calculating the poroelastic wave field response characteristics is specifically as follows: S21, calculating the wave impedance of each stratum, as follows: wherein represents formation wave impedance; represents P-wave velocity; represents formation density; Subscripts first layer; S22, calculating the zero-order poroelastic reflection and transmission coefficients, as follows: wherein with respectively denote the zeroth order reflection and transmission coefficients for the fast longitudinal wave; S23, calculating the first-order hole elastic reflection and transmission coefficients, as follows: wherein represents the first order reflection and transmission coefficients of the fast longitudinal wave, respectively; represents the first order reflection and transmission coefficients of the fast longitudinal wave, respectively; represents the reflection coefficient of the fast-slow longitudinal wave, represents the transmission coefficient of the fast-slow longitudinal wave; S24, calculating the reflection and transmission coefficients of various hole elastic waves at the formation interface, as follows: Pore elastic fast wave incidence-fast wave reflection coefficient: Pore elastic fast wave incidence - fast wave transmission coefficient: Hole elastic fast wave incidence-slow wave reflection coefficient: Pore elastic fast wave incidence-slow wave transmission coefficient: each of which is directed to The parameters are as follows: wherein denotes the corner frequency; is imaginary; denotes the formation permeability; denotes the viscosity coefficient; denotes the pore fluid density; S25, calculating the sum of the borehole elastic wave reflection amplitudes of the formation interface The calculation formula is as follows: wherein is the initial P-wave reflection coefficient; and represent the phase velocity and attenuation coefficient of the S-wave, respectively; denotes the formation thickness.

7. The method of claim 6, wherein: Through the calculation of the hole elastic wave field response characteristics, the reflection amplitude variation characteristics of the seismic wave at the interface between different porous media are simulated and calculated, the total reflection amplitude variation characteristics of the seismic wave under different frequency conditions are analyzed, and the reflection amplitude and wave frequency relationship volume are established.

8. The method of claim 1, wherein: In step S3, the post-stack amplitude-preserved seismic record is processed by using the generalized S transform for frequency division; The energy variation of the target layer position in the single-frequency component profile of the seismic record is displayed and checked by using the seismic interpretation software. The region with strong low-frequency energy and weak high-frequency energy appearing below the target layer is searched to determine the low-frequency amplitude anomaly position in the seismic record.

9. The method for identifying thin interbedded gas reservoirs based on low frequency acoustic signals according to claim 8, characterized in that: In step S4, in the actual identification process, the hole elastic wave field response characteristics of the actual formation are calculated, the frequency, amplitude and delay time of the low-frequency energy anomaly in the seismic record are analyzed, it is judged whether the low-frequency amplitude anomaly of the single-frequency component is related to the existence of the thin inter-gas layer, and the identification of the thin inter-gas layer is completed; According to the distribution region of the low-frequency amplitude anomaly in the seismic single-frequency component profile, the distribution range of the thin inter-gas layer and the best position of the drillable target are predicted.

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