Pseudo-acoustic wave curve reconstruction method, device and storage medium

Abstract

The present application discloses a pseudo-acoustic wave curve reconstruction method, device and storage medium. The method includes: calculating localized rock physical modeling parameters based on the rock physical modeling parameters of the area to be measured; substituting the localized rock physical modeling parameters into the rock physical model to obtain the bulk modulus and shear modulus of the first rock; obtaining a lithologic sensitivity curve based on the rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock; performing low-frequency filtering on the lithologic sensitivity curve to obtain medium- and high-frequency information; performing medium- and high-frequency filtering on the original acoustic wave curve to obtain low-frequency information; fusing the medium- and high-frequency information with the low-frequency information to obtain a pseudo-acoustic wave curve. This method constructs a lithologic sensitivity curve and reconstructs a pseudo-acoustic wave curve by fusing the constructed lithologic sensitivity curve with the original acoustic wave curve, which can highlight the difference between the reservoir and the surrounding rock and facilitate the identification of lithology.

Description

Technical Field

[0001] The present application relates to the field of petroleum exploration technology, and in particular to a pseudo-acoustic wave curve reconstruction method, device and storage medium. Background Art

[0002] When identifying reservoir lithology, such as sandstone and mudstone reservoirs, impedance overlap of different lithologies often occurs. Therefore, it is necessary to use other data to reconstruct lithology-sensitive pseudo-acoustic curves to improve the accuracy of seismic calibration and the resolution of inversion.

[0003] In related technologies, the pseudo-acoustic curve reconstruction method uses information fusion. This involves selecting a well logging response curve that is sensitive to lithology as a characteristic curve reflecting formation lithology changes. The high-frequency information of this characteristic curve is then re-fused with the low-frequency information of the original acoustic curve in the frequency domain to form a pseudo-acoustic curve. This curve can reflect the characteristics of lithologic formation velocity and wave impedance, as well as lithologic differences. However, the pseudo-acoustic curve derived from a single well logging response curve struggles to highlight differences between the reservoir and surrounding rock, making it difficult to identify lithologic properties. Summary of the Invention

[0004] In view of this, the present application provides a pseudo-acoustic wave curve reconstruction method, device and storage medium, which can highlight the differences between reservoirs and surrounding rocks and facilitate the identification of lithology.

[0005] Specifically, the following technical solutions are included:

[0006] In a first aspect, an embodiment of the present application provides a pseudo-acoustic wave curve reconstruction method, the method comprising:

[0007] Calculate localized rock physics modeling parameters based on the rock physics modeling parameters of the area to be measured;

[0008] Substituting the localized rock physics modeling parameters into the rock physics model to obtain the bulk modulus and shear modulus of the first rock;

[0009] Obtaining a lithologic sensitivity curve based on rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock;

[0010] Performing low-frequency filtering on the lithologic sensitivity curve to obtain medium- and high-frequency information;

[0011] Perform mid- and high-frequency filtering on the original sound wave curve to obtain low-frequency information;

[0012] The mid-high frequency information and the low frequency information are fused to obtain a pseudo-acoustic wave curve.

[0013] In some embodiments, the rock physical modeling parameters of the area to be measured include the volume fraction, shear modulus, bulk modulus, and density of each formation component, the volume fraction, bulk modulus, density, and saturation of each fluid component, and porosity and type. The localized rock physical modeling parameters calculated based on the rock physical modeling parameters of the area to be measured include:

[0014] Substituting the bulk modulus and saturation of each fluid component into the Brie model to obtain the bulk modulus of the mixed fluid;

[0015] Substituting the volume fraction, shear modulus and bulk modulus of each stratum component into the HS boundary model, the bulk modulus and shear modulus of the matrix are obtained;

[0016] Substituting the bulk modulus and shear modulus of the matrix, as well as the porosity and type thereof, into the rock physics model to obtain the bulk modulus and shear modulus of the second rock;

[0017] Substituting the bulk modulus of the mixed fluid, the bulk modulus of the matrix, and the bulk modulus and shear modulus of the second rock into the Gassmann model to obtain the bulk modulus and shear modulus of the fluid-saturated rock;

[0018] Obtaining the density of the fluid-saturated rock based on the volume fractions and densities of the various formation components and the volume fractions and densities of the various fluid components;

[0019] Obtaining a first predicted P-wave velocity and a first predicted S-wave velocity based on the bulk modulus, shear modulus, and density of the fluid-saturated rock;

[0020] Based on the first predicted longitudinal wave velocity and the first shear wave velocity and the measured acoustic wave velocity, rock physical modeling parameters of the area to be measured are inverted and calculated to obtain localized rock physical modeling parameters.

[0021] In some embodiments, the rock physical modeling parameters of the area to be measured further include target lithology, and obtaining a lithology sensitivity curve based on the rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock includes:

[0022] Obtaining an equivalent bulk modulus and an equivalent shear modulus of the rock after solid filling based on the rock physical modeling parameters of the test area and the bulk modulus and shear modulus of the first rock, combined with the bulk modulus and shear modulus of the target lithology;

[0023] The lithology sensitivity curve is obtained based on the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling.

[0024] In some embodiments, obtaining the lithologic sensitivity curve based on the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling includes:

[0025] obtaining a second predicted longitudinal wave velocity based on an equivalent bulk modulus and an equivalent shear modulus of the rock after solid filling;

[0026] The lithology sensitivity curve is obtained based on the second predicted longitudinal wave velocity.

[0027] In some embodiments, before calculating the localized rock physical modeling parameters based on the rock physical modeling parameters of the area to be measured, the method further includes:

[0028] Obtaining well logging data, geological data and core data of the area to be tested;

[0029] Preprocessing the well logging data to obtain preprocessed well logging data;

[0030] The rock physical modeling parameters of the area to be measured are obtained based on the geological data, core data and the pre-processed logging data.

[0031] In some embodiments, the pre-processing of the well logging data of the area to be tested includes:

[0032] Perform well diameter correction on single wells with diameter expansion;

[0033] Based on at least three selected wells, a trend surface multi-well normalization process is performed on each well in the area to be measured.

[0034] In some embodiments, obtaining the rock physical modeling parameters of the area to be measured based on the geological data, core data, and the pre-processed logging data includes:

[0035] Based on geological data and core data, determine the composition of each formation component and its corresponding response characteristics, as well as the composition of each fluid component and its corresponding response characteristics;

[0036] According to the well logging optimization interpretation, a component volume model is established based on the composition of each formation component and its corresponding response characteristics and the composition of each fluid component and its corresponding response characteristics;

[0037] Based on the component volume model, the volume fraction of each formation component, the volume fraction and saturation of each fluid component, and the porosity are obtained.

[0038] In some embodiments, performing low-frequency filtering on the lithologic sensitivity curve to obtain medium- and high-frequency information includes:

[0039] Performing acoustic wave dimension conversion on the lithologic sensitivity curve to obtain a converted lithologic sensitivity curve;

[0040] The converted lithologic sensitivity curve is subjected to low-frequency filtering to obtain the medium- and high-frequency information.

[0041] In a second aspect, an embodiment of the present application provides a pseudo-acoustic wave curve reconstruction device, the device comprising:

[0042] A calculation module, configured to calculate localized rock physics modeling parameters based on the rock physics modeling parameters of the area to be measured;

[0043] a modulus obtaining module, configured to substitute the localized rock physics modeling parameters into the rock physics model to obtain the bulk modulus and shear modulus of the first rock;

[0044] A curve obtaining module, configured to obtain a lithologic sensitivity curve based on the rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock;

[0045] A first filtering module is used to perform low-frequency filtering on the lithologic sensitivity curve to obtain medium- and high-frequency information;

[0046] The second filtering module is used to perform medium and high frequency filtering on the original sound wave curve to obtain low frequency information;

[0047] The fusion module is used to fuse the mid-high frequency information and the low frequency information to obtain a pseudo-acoustic wave curve.

[0048] In a third aspect, an embodiment of the present application provides a non-volatile computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can perform the pseudo-acoustic wave curve reconstruction method described in the first aspect above.

[0049] The pseudo-acoustic curve reconstruction method provided in the embodiments of the present application utilizes the rock physics modeling parameters of the measured area to calculate localized rock physics modeling parameters. These localized rock physics modeling parameters are used to generate a lithologic sensitivity curve. The pseudo-acoustic curve is then fused with the mid- and high-frequency information obtained from the lithologic sensitivity curve and the low-frequency information obtained from the original acoustic curve. This method constructs a lithologic sensitivity curve and reconstructs the pseudo-acoustic curve by fusing this constructed lithologic sensitivity curve with the original acoustic curve. This method can highlight the differences between the reservoir and the surrounding rock, facilitating lithologic identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 A flowchart of a pseudo-acoustic wave curve reconstruction method provided in an embodiment of the present application;

[0052] Figure 2 A flowchart of calculating localized rock physics modeling parameters based on rock physics modeling parameters of a measured area in a pseudo-acoustic wave curve reconstruction method provided in an embodiment of the present application;

[0053] Figure 3 A flowchart of obtaining a lithologic sensitivity curve based on rock physical modeling parameters of a measured area and the bulk modulus and shear modulus of a first rock in a pseudo-acoustic wave curve reconstruction method provided in an embodiment of the present application;

[0054] Figure 4 A schematic diagram of the frequency histogram distribution of the wave impedance converted from the original acoustic wave velocity and density of a well in the test area provided in an embodiment of the present application;

[0055] Figure 5 A schematic diagram of the frequency histogram distribution of wave impedance converted from density after pseudo-acoustic wave construction in a well in the test area provided in an embodiment of the present application;

[0056] Figure 6 This is a structural block diagram of a pseudo-acoustic wave curve reconstruction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] With the continuous advancement of oilfield exploration and development, thin reservoirs have gradually become a key focus of exploration and development. Using acoustic impedance to calibrate seismic horizons, construct initial inversion models, and perform impedance inversion is a key tool for seismic interpretation and reservoir prediction. However, due to factors such as drilling age and depth, the medium- and high-frequency information from acoustic logging often fails to reflect changes in the reservoir and surrounding rock. This leads to poor lithology identification and poor matching between well and seismic calibration, thus compromising the accuracy of reservoir prediction.

[0059] Sandstone-mudstone reservoirs, especially interbedded sandstone and mudstone, have well-developed porosity and are rich in fluids. This results in high acoustic time difference in the sandstone and frequent impedance overlap between sandstone and mudstone. Therefore, other data are needed to reconstruct lithologic-sensitive pseudo-acoustic curves to improve seismic calibration accuracy and inversion resolution.

[0060] The main methods for reconstructing pseudo-acoustic curves are (1) theoretical methods: such as the Faust formula, which converts other logging curves into acoustic logging curves, but this method does not consider the low-frequency information of the formation background; (2) multi-curve weighting: multiple lithologic sensitivity curves are fitted with acoustic curves according to given weights, but the weights are difficult to determine when using this method; (3) information fusion: the low-frequency information of the formation background velocity and the high-frequency information of the lithologic sensitivity curve are integrated into a pseudo-acoustic curve. This method is the current mainstream technology.

[0061] The information fusion method involves analyzing sensitive parameters to select a lithology-sensitive well logging response curve (such as natural gamma ray and spontaneous potential) as a characteristic curve reflecting formation lithologic variations. The high-frequency information of this characteristic curve is then re-fused in the frequency domain with the low-frequency information of the original acoustic wave curve. Combining the mid-frequency information from the seismic data with the low- and high-frequency information from the well logging data, a characteristic curve reflecting the vertical distribution of lithologic properties is reconstructed. This curve is capable of reflecting the characteristics of formation velocity and impedance, as well as lithologic differences. The key to this method is obtaining a lithology-sensitive characteristic curve; in other words, the selected characteristic curve must be capable of distinguishing lithologic properties. However, well logging response curves are influenced by multiple factors, including lithology, porosity, pore fluids, compaction, and cementation. Consequently, a single well logging response curve often fails to directly reflect lithologic properties. For example, if a gamma ray curve contains feldspar, it may not accurately reflect shale. Consequently, the pseudo-acoustic curve reconstructed from this curve still lacks clear reservoir characteristics, limiting the ability of impedance inversion to identify lithologic properties and assess oil content.

[0062] In order to solve the above-mentioned technical problems existing in the prior art, an embodiment of the present application provides a pseudo-acoustic wave curve reconstruction method. Figure 1 This is a flowchart of a pseudo-acoustic wave curve reconstruction method provided in an embodiment of the present application, see Figure 1 , the method comprises the following steps:

[0063] Step 101: Calculate localized rock physics modeling parameters based on the rock physics modeling parameters of the area to be measured.

[0064] Localized rock physical modeling parameters are obtained through calculation to facilitate the subsequent construction of lithologic sensitivity curves.

[0065] Among them, the rock physical modeling parameters of the area to be measured include the volume fraction, shear modulus, bulk modulus and density of each formation component, the volume fraction, bulk modulus, density and saturation of each fluid component, as well as porosity and its type.

[0066] It can be understood that the localized rock physics modeling parameters are of the same parameter type as the rock physics modeling parameters of the area to be measured.

[0067] See also Figure 2 , this step includes the following sub-steps:

[0068] Step 1011: Substitute the bulk modulus and saturation of each fluid component into the Brie model to obtain the bulk modulus of the mixed fluid.

[0069] Step 1012: Substitute the volume fraction, shear modulus, and bulk modulus of each formation component into the HS boundary model to obtain the bulk modulus and shear modulus of the matrix.

[0070] Step 1013 , substituting the bulk modulus and shear modulus of the matrix, as well as the porosity and type thereof, into the rock physics model to obtain the bulk modulus and shear modulus of the second rock.

[0071] In some embodiments, the rock physics model includes a Xu-White model or a Hertz-Mindlin model.

[0072] Step 1014: Substitute the bulk modulus of the mixed fluid, the bulk modulus of the matrix, and the bulk modulus and shear modulus of the second rock into the Gassmann model to obtain the bulk modulus and shear modulus of the fluid-saturated rock.

[0073] Step 1015: Based on the volume fraction and density of each formation component and the volume fraction and density of each fluid component, the density of the fluid-saturated rock is obtained.

[0074] The first density is obtained by multiplying the volume fraction of each formation component by the corresponding density and then adding them together; the second density is obtained by multiplying the volume fraction of each fluid component by the corresponding density and then adding them together; and the density of the fluid-saturated rock is obtained by adding the first density and the second density.

[0075] Step 1016: Obtain a first predicted P-wave velocity and a first predicted S-wave velocity based on the bulk modulus, shear modulus, and density of the fluid-saturated rock.

[0076] The first predicted longitudinal wave velocity and the first predicted shear wave velocity are calculated according to the following formula:

[0077]

[0078]

[0079] Where: Vs sat is the first predicted shear wave velocity, Vp sat is the first predicted longitudinal wave velocity, μ sat is the shear modulus of fluid-saturated rock, K sat is the bulk modulus of fluid-saturated rock, ρ sat is the density of the fluid-saturated rock.

[0080] Step 1017 : Based on the first predicted P-wave velocity, the first S-wave velocity, and the measured acoustic wave velocity, inversely calculate the rock physics modeling parameters of the area to be measured to obtain localized rock physics modeling parameters.

[0081] The measured sound wave velocity includes the measured shear wave velocity and the measured longitudinal wave velocity.

[0082] In this step, the method for inversely calculating the rock physical modeling parameters of the area to be measured can be obtained by setting the objective function and combining it with the particle swarm optimization algorithm.

[0083] In some embodiments, the objective function may be min{(Vp-Vp sat ) 2 +∑(Vs-Vs sat ) 2}, where Vp is the measured longitudinal wave velocity and Vs is the measured shear wave velocity.

[0084] Prior to this step, the pseudo-acoustic wave curve reconstruction method provided in the embodiment of the present application also includes: obtaining logging data, geological data and core data of the area to be tested; preprocessing the logging data to obtain preprocessed logging data; and obtaining rock physical modeling parameters of the area to be tested based on the geological data, core data and preprocessed logging data.

[0085] Among them, logging data include compressional wave velocity, shear wave velocity, density, natural gamma, neutron, mud content, porosity, etc.

[0086] In some embodiments, preprocessing the logging data for the target area includes: performing caliper correction on individual wells with caliper expansion; and performing multi-well normalization of trend surfaces for each well in the target area based on at least three selected wells. Preprocessing the logging data for the target area can eliminate systematic errors and prepare for localization of petrophysical modeling parameters.

[0087] In some embodiments, there are multiple ways to obtain the rock physical modeling parameters of the area to be measured based on the geological data, core data and the pre-processed logging data.

[0088] In one possible implementation, the rock physical modeling parameters of the area to be measured can be obtained in the following manner: based on geological data and core data, the composition of each formation component and its corresponding response characteristics and the composition of each fluid component and its corresponding response characteristics are determined; based on the composition of each formation component and its corresponding response characteristics and the composition of each fluid component and its corresponding response characteristics according to the optimized interpretation of well logging, a component volume model is established; based on the component volume model, the volume fraction of each formation component, the volume fraction and saturation of each fluid component, and the porosity are obtained.

[0089] In another possible implementation, the rock physical modeling parameters of the area to be measured can be directly obtained based on geological data and core data.

[0090] Step 102: Substitute the localized rock physics modeling parameters into the rock physics model to obtain the bulk modulus and shear modulus of the first rock.

[0091] By incorporating the localized rock physics modeling parameter bands into the rock physics model, the bulk modulus and shear modulus of the first rock can be calculated to construct a lithologic sensitivity curve.

[0092] It can be understood that the rock physics model used in this step is the same as the rock physics model used in step 1013.

[0093] Step 103: Obtain a lithologic sensitivity curve based on the rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock.

[0094] Among them, the rock physical modeling parameters of the area to be measured also include target lithology.

[0095] See also Figure 3 , this step includes the following sub-steps:

[0096] Step 1031 , based on the rock physics modeling parameters of the test area and the bulk modulus and shear modulus of the first rock, combined with the bulk modulus and shear modulus of the target lithology, obtain the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling.

[0097] This step may specifically include: obtaining an equivalent bulk modulus and an equivalent shear modulus of the rock after solid filling based on the bulk modulus and shear modulus of the matrix and the bulk modulus and shear modulus of the first rock, combined with the bulk modulus and shear modulus of the target lithology.

[0098] In some embodiments, the target lithology may be sandstone.

[0099] In the embodiment of the present application, the generalized fluid substitution equation can be used to obtain the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling.

[0100] Furthermore, the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling are calculated according to the following formula:

[0101]

[0102]

[0103] Where: K s is the equivalent bulk modulus of the rock after solid filling, μ s is the equivalent shear modulus of rock after solid filling, K dry is the bulk modulus of the matrix, μ dry is the shear modulus of the matrix, K if is the bulk modulus of the target lithology, μ if is the shear modulus of the target lithology, K0 is the bulk modulus of the first rock, μ0 is the shear modulus of the first rock, and φ is the porosity.

[0104] Step 1032: Obtain a lithologic sensitivity curve based on the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling.

[0105] In an embodiment of the present application, this step may specifically include: obtaining a second predicted P-wave velocity based on the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling; and obtaining a lithologic sensitivity curve based on the second predicted P-wave velocity.

[0106] The calculation formula for the second predicted P-wave velocity is similar to the calculation formula for the first predicted P-wave velocity, wherein the density of the rock after solid filling is equal to the sum of the product of the first density and (1-porosity) and the product of the porosity and the density of the target lithology.

[0107] It can be understood that the lithologic sensitivity curve is a curve with depth as the independent variable and velocity as the dependent variable. Since the well logging data used as the known input information represents the different physical responses of the reservoir at different depths, each depth point corresponds to a set of well logging responses. After the above steps, a second P-wave velocity is obtained for each depth point. The multiple second predicted P-wave velocities obtained based on multiple depth points can be used as the lithologic sensitivity curve.

[0108] Step 104: Perform low-frequency filtering on the lithologic sensitivity curve to obtain medium- and high-frequency information.

[0109] By performing low-frequency filtering on the lithologic sensitivity curve, low-frequency information is filtered out, so that medium and high-frequency information based on the lithologic sensitivity curve can be obtained.

[0110] In some embodiments, this step may include: performing acoustic wave dimension conversion on the lithologic sensitivity curve to obtain a converted lithologic sensitivity curve; and performing low-frequency filtering on the converted lithologic sensitivity curve to obtain medium- and high-frequency information.

[0111] The lithologic sensitivity curve is converted into an acoustic wave dimension to obtain a converted lithologic sensitivity curve, so that the shape of the converted lithologic sensitivity curve is more similar to the acoustic wave curve.

[0112] Step 105 , performing mid- and high-frequency filtering on the original sound wave curve to obtain low-frequency information.

[0113] By performing mid- and high-frequency filtering on the original sound wave curve, the mid- and high-frequency information is filtered out, so that the low-frequency information based on the original sound wave curve can be obtained.

[0114] Step 106: Fusing the mid- and high-frequency information with the low-frequency information to obtain a pseudo-acoustic wave curve.

[0115] By fusing the mid- and high-frequency information obtained in step 104 and the low-frequency information obtained in step 105 , a pseudo-acoustic wave curve can be obtained.

[0116] The fusion method may be to add the frequencies in the frequency domain, then transform the added result into the depth domain, and then denormalize the result to the original sound wave dimension to obtain the final pseudo-sound wave curve.

[0117] Therefore, the pseudo-acoustic curve reconstruction method provided in the embodiments of the present application utilizes the rock physics modeling parameters of the measured area to calculate localized rock physics modeling parameters, uses these localized rock physics modeling parameters to obtain a lithologic sensitivity curve, and then fuses the mid- and high-frequency information obtained from the lithologic sensitivity curve with the low-frequency information obtained from the original acoustic curve to obtain a pseudo-acoustic curve. This method constructs a lithologic sensitivity curve and reconstructs the pseudo-acoustic curve by fusing this constructed lithologic sensitivity curve with the original acoustic curve, thereby highlighting the differences between the reservoir and the surrounding rock, facilitating lithologic identification.

[0118] In one possible example, for a clastic reservoir, analysis revealed that the well logging acoustic wave and density of the sandstone reservoir in the work area were not significantly different from those of the surrounding rocks above and below, making it impossible to distinguish between sandstone and mudstone. Natural gamma ray logging showed some reflection of the sandstone, but it was not obvious. Relying solely on natural gamma ray, acoustic wave, or density curves was unable to effectively distinguish the reservoir lithology. Therefore, the pseudo-acoustic wave curve reconstruction method provided in the embodiments of this application can be applied to this clastic reservoir to highlight the differences between the reservoir and the surrounding rock, facilitating lithology identification.

[0119] Figure 4This is a schematic diagram of the frequency histogram distribution of the wave impedance converted from the original acoustic wave velocity and density of a well in the test area provided in an embodiment of the present application, wherein the horizontal axis PP_IMP represents the wave impedance, the vertical axis Sample Number represents the frequency of occurrence of different wave impedance values, the label "Shale" represents mudstone, and the label "Medium sandstone" represents sandstone; Figure 5 This is a diagram showing the frequency histogram distribution of the wave impedance after the pseudo-acoustic wave is constructed and converted to density in a well in the test area provided in the embodiment of the present application, where the horizontal axis PP_IMP represents the wave impedance and the vertical axis Sample Number represents the frequency of occurrence of different wave impedance values. The label Shale represents mudstone and Medium sandstone represents sandstone. Figure 4 and Figure 5 It can be seen that the original wave impedance calculated using the measured longitudinal waves shows that both sandstone and mudstone are concentrated in the range of 5000-7000, so sandstone and mudstone cannot be distinguished based on the wave impedance value. The wave impedance calculated using the pseudo-acoustic wave curve reconstruction method provided in the embodiment of the present application shows that the wave impedance of sandstone is mainly concentrated in the range of 6500, and that of mudstone is mainly concentrated in the range of 8500. At this time, sandstone and mudstone can be better distinguished based on the wave impedance value.

[0120] It should be noted that the wave impedance at a certain depth is equal to the product of the sound wave velocity and density at the current position.

[0121] Figure 6 This is a structural block diagram of a pseudo-acoustic wave curve reconstruction device provided in an embodiment of the present application. Figure 6 , the device comprises:

[0122] A calculation module 601 is used to calculate localized rock physical modeling parameters based on the rock physical modeling parameters of the area to be measured;

[0123] A modulus obtaining module 602 is configured to substitute the localized rock physics modeling parameters into the rock physics model to obtain the bulk modulus and shear modulus of the first rock;

[0124] A curve obtaining module 603 is configured to obtain a lithologic sensitivity curve based on the rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock;

[0125] The first filtering module 604 is used to perform low-frequency filtering on the lithologic sensitivity curve to obtain medium- and high-frequency information;

[0126] The second filtering module 605 is used to perform medium and high frequency filtering on the original sound wave curve to obtain low frequency information;

[0127] The fusion module 606 is used to fuse the mid-high frequency information and the low frequency information to obtain a pseudo-acoustic wave curve.

[0128] In some embodiments, the rock physical modeling parameters of the measured area include the volume fraction, shear modulus, bulk modulus, and density of each formation component, the volume fraction, bulk modulus, density, and saturation of each fluid component, and the porosity and type. The calculation module 601 includes:

[0129] The first model calculation unit is used to substitute the bulk modulus and saturation of each fluid component into the Brie model to obtain the bulk modulus of the mixed fluid;

[0130] The second model calculation unit is used to substitute the volume fraction, shear modulus and bulk modulus of each stratum component into the HS boundary model to obtain the bulk modulus and shear modulus of the matrix;

[0131] a third model calculation unit, for substituting the bulk modulus and shear modulus of the matrix, and the porosity and type thereof into the rock physics model to obtain the bulk modulus and shear modulus of the second rock;

[0132] a fourth model calculation unit, for substituting the bulk modulus of the mixed fluid, the bulk modulus of the matrix, the bulk modulus and the shear modulus of the second rock into the Gassmann model to obtain the bulk modulus and the shear modulus of the fluid-saturated rock;

[0133] A density calculation unit for obtaining the density of the fluid-saturated rock based on the volume fraction and density of each formation component and the volume fraction and density of each fluid component;

[0134] a velocity calculation unit, configured to obtain a first predicted longitudinal wave velocity and a first predicted shear wave velocity based on a bulk modulus, a shear modulus, and a density of the fluid-saturated rock;

[0135] The inversion unit is used to inversely calculate the rock physical modeling parameters of the area to be measured based on the first predicted longitudinal wave velocity, the first shear wave velocity and the measured acoustic wave velocity to obtain localized rock physical modeling parameters.

[0136] In some embodiments, the rock physical modeling parameters of the area to be measured also include target lithology, and the curve obtaining module 603 includes:

[0137] An equivalent modulus unit is used to obtain an equivalent bulk modulus and an equivalent shear modulus of the rock after solid filling based on the rock physical modeling parameters of the test area and the bulk modulus and shear modulus of the first rock, combined with the bulk modulus and shear modulus of the target lithology;

[0138] The curve obtaining unit is used to obtain the lithology sensitivity curve based on the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling.

[0139] In some embodiments, the curve obtaining unit includes:

[0140] a velocity subunit, for obtaining a second predicted longitudinal wave velocity based on an equivalent bulk modulus and an equivalent shear modulus of the rock after solid filling;

[0141] The curve subunit is used to obtain a lithologic sensitivity curve based on the second predicted P-wave velocity.

[0142] In some embodiments, the pseudo-acoustic wave curve reconstruction device provided in the embodiments of the present application further includes:

[0143] Acquisition module, used to obtain logging data, geological data and core data of the area to be tested;

[0144] A preprocessing module is used to preprocess the logging data to obtain preprocessed logging data;

[0145] The parameter obtaining unit is used to obtain the rock physical modeling parameters of the area to be measured based on geological data, core data and pre-processed logging data.

[0146] In some embodiments, the pre-processing module includes:

[0147] Single well correction unit, used to perform well diameter correction on single wells with diameter expansion;

[0148] The multi-well normalization unit is used to perform multi-well normalization processing on the trend surface of each well in the test area based on at least three selected wells.

[0149] In some embodiments, the parameter obtaining unit includes:

[0150] Determine subunits for determining the composition of each formation component and its corresponding response characteristics and the composition of each fluid component and its corresponding response characteristics based on geological data and core data;

[0151] The model building subunit is used to build a component volume model based on the composition of each formation component and its corresponding response characteristics and the composition of each fluid component and its corresponding response characteristics according to the optimized interpretation of well logging;

[0152] The parameter acquisition subunit is used to obtain the volume fraction of each formation component, the volume fraction and saturation of each fluid component, and the porosity based on the component volume model.

[0153] In some embodiments, the first filtering module 604 includes:

[0154] A conversion unit is used to convert the lithologic sensitivity curve into an acoustic dimension to obtain a converted lithologic sensitivity curve;

[0155] The information acquisition module is used to perform low-frequency filtering on the converted lithologic sensitivity curve to obtain medium and high-frequency information.

[0156] Therefore, the pseudo-acoustic curve reconstruction device provided in the embodiments of the present application utilizes the rock physics modeling parameters of the measured area to calculate localized rock physics modeling parameters, uses these localized rock physics modeling parameters to obtain a lithologic sensitivity curve, and fuses the mid- and high-frequency information obtained from the lithologic sensitivity curve with the low-frequency information obtained from the original acoustic curve to obtain a pseudo-acoustic curve. This method constructs a lithologic sensitivity curve and reconstructs a pseudo-acoustic curve by fusing this constructed lithologic sensitivity curve with the original acoustic curve, thereby highlighting the differences between the reservoir and the surrounding rock, facilitating lithologic identification.

[0157] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory including program code. The program code can be executed by a processor in an electronic device to perform the pseudo-acoustic wave curve reconstruction method in the above embodiment. For example, the non-volatile computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0158] Those skilled in the art will understand that all or part of the steps of implementing the above embodiments can be completed by hardware or by instructing related hardware through a program. The above program can be stored in a computer non-volatile computer-readable storage medium. The above storage medium can be a read-only memory, a disk or an optical disk, etc.

[0159] In this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The term "plurality" refers to two or more than two, unless expressly limited otherwise.

[0160] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the present invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only.

[0161] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A pseudo-acoustic wave curve reconstruction method, characterized in that: The method comprises: Calculate localized rock physics modeling parameters based on the rock physics modeling parameters of the area to be measured; Substituting the localized rock physics modeling parameters into the rock physics model to obtain the bulk modulus and shear modulus of the first rock; Obtaining a lithologic sensitivity curve based on rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock; Performing low-frequency filtering on the lithologic sensitivity curve to obtain medium- and high-frequency information; Perform mid- and high-frequency filtering on the original sound wave curve to obtain low-frequency information; fusing the mid- and high-frequency information with the low-frequency information to obtain a pseudo-acoustic wave curve; The rock physical modeling parameters of the area to be measured include the volume fraction, shear modulus, bulk modulus and density of each formation component, the volume fraction, bulk modulus, density and saturation of each fluid component, and porosity and type. The localized rock physical modeling parameters calculated based on the rock physical modeling parameters of the area to be measured include: Substituting the bulk modulus and saturation of each fluid component into the Brie model to obtain the bulk modulus of the mixed fluid; Substituting the volume fraction, shear modulus and bulk modulus of each stratum component into the HS boundary model, the bulk modulus and shear modulus of the matrix are obtained; Substituting the bulk modulus and shear modulus of the matrix, as well as the porosity and type thereof, into the rock physics model to obtain the bulk modulus and shear modulus of the second rock; Substituting the bulk modulus of the mixed fluid, the bulk modulus of the matrix, and the bulk modulus and shear modulus of the second rock into the Gassmann model to obtain the bulk modulus and shear modulus of the fluid-saturated rock; Obtaining the density of the fluid-saturated rock based on the volume fractions and densities of the various formation components and the volume fractions and densities of the various fluid components; Obtaining a first predicted P-wave velocity and a first predicted S-wave velocity based on the bulk modulus, shear modulus, and density of the fluid-saturated rock; Based on the first predicted longitudinal wave velocity and the first shear wave velocity and the measured acoustic wave velocity, rock physical modeling parameters of the area to be measured are inverted and calculated to obtain localized rock physical modeling parameters.

2. The pseudo-acoustic wave curve reconstruction method according to claim 1, characterized in that: The rock physical modeling parameters of the area to be measured also include target lithology, and obtaining a lithology sensitivity curve based on the rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock includes: Obtaining an equivalent bulk modulus and an equivalent shear modulus of the rock after solid filling based on the rock physical modeling parameters of the test area and the bulk modulus and shear modulus of the first rock, combined with the bulk modulus and shear modulus of the target lithology; The lithology sensitivity curve is obtained based on the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling.

3. The pseudo-acoustic wave curve reconstruction method according to claim 2, characterized in that: The obtaining of the lithologic sensitivity curve based on the equivalent bulk modulus and equivalent shear modulus of the rock after solid filling includes: obtaining a second predicted longitudinal wave velocity based on an equivalent bulk modulus and an equivalent shear modulus of the rock after solid filling; The lithology sensitivity curve is obtained based on the second predicted longitudinal wave velocity.

4. The pseudo-acoustic wave curve reconstruction method according to claim 1, characterized in that: Before calculating the localized rock physics modeling parameters based on the rock physics modeling parameters of the area to be measured, the method further includes: Obtaining well logging data, geological data and core data of the area to be tested; Preprocessing the well logging data to obtain preprocessed well logging data; The rock physical modeling parameters of the area to be measured are obtained based on the geological data, core data and the pre-processed logging data.

5. The pseudo-acoustic wave curve reconstruction method according to claim 4, characterized in that: The pre-processing of the logging data of the area to be tested includes: Perform well diameter correction on single wells with diameter expansion; Based on at least three selected wells, a trend surface multi-well normalization process is performed on each well in the area to be measured.

6. The pseudo-acoustic wave curve reconstruction method according to claim 4, characterized in that: Obtaining the rock physical modeling parameters of the area to be measured based on the geological data, core data and the pre-processed well logging data includes: Based on geological data and core data, determine the composition of each formation component and its corresponding response characteristics, as well as the composition of each fluid component and its corresponding response characteristics; According to the well logging optimization interpretation, a component volume model is established based on the composition of each formation component and its corresponding response characteristics and the composition of each fluid component and its corresponding response characteristics; Based on the component volume model, the volume fraction of each formation component, the volume fraction and saturation of each fluid component, and the porosity are obtained.

7. The pseudo-acoustic wave curve reconstruction method according to claim 1, characterized in that: The low-frequency filtering of the lithologic sensitivity curve to obtain medium- and high-frequency information includes: Performing acoustic wave dimension conversion on the lithologic sensitivity curve to obtain a converted lithologic sensitivity curve; The converted lithologic sensitivity curve is subjected to low-frequency filtering to obtain the medium- and high-frequency information.

8. A pseudo-acoustic wave curve reconstruction device, characterized in that: The device comprises: A calculation module, configured to calculate localized rock physics modeling parameters based on the rock physics modeling parameters of the area to be measured; a modulus obtaining module, configured to substitute the localized rock physics modeling parameters into the rock physics model to obtain the bulk modulus and shear modulus of the first rock; A curve obtaining module, configured to obtain a lithologic sensitivity curve based on the rock physical modeling parameters of the area to be measured and the bulk modulus and shear modulus of the first rock; A first filtering module is used to perform low-frequency filtering on the lithologic sensitivity curve to obtain medium- and high-frequency information; The second filtering module is used to perform medium and high frequency filtering on the original sound wave curve to obtain low frequency information; A fusion module, configured to fuse the mid- and high-frequency information with the low-frequency information to obtain a pseudo-acoustic wave curve; The rock physics modeling parameters of the measured area include the volume fraction, shear modulus, bulk modulus and density of each formation component, the volume fraction, bulk modulus, density and saturation of each fluid component, and porosity and its type. The calculation module includes: A first model calculation unit is used to substitute the bulk modulus and saturation of each fluid component into the Brie model to obtain the bulk modulus of the mixed fluid; The second model calculation unit is used to substitute the volume fraction, shear modulus and bulk modulus of each formation component into the HS boundary model to obtain the bulk modulus and shear modulus of the matrix; a third model calculation unit, configured to substitute the bulk modulus and shear modulus of the matrix, as well as the porosity and type thereof, into the rock physics model to obtain the bulk modulus and shear modulus of the second rock; a fourth model calculation unit, configured to substitute the bulk modulus of the mixed fluid, the bulk modulus of the matrix, and the bulk modulus and shear modulus of the second rock into a Gassmann model to obtain the bulk modulus and shear modulus of the fluid-saturated rock; a density calculation unit, configured to obtain the density of the fluid-saturated rock based on the volume fraction and density of each formation component and the volume fraction and density of each fluid component; a velocity calculation unit, configured to obtain a first predicted longitudinal wave velocity and a first predicted shear wave velocity based on the bulk modulus, shear modulus, and density of the fluid-saturated rock; The inversion unit is used to inversely calculate the rock physical modeling parameters of the test area based on the first predicted longitudinal wave velocity and the first shear wave velocity and the measured acoustic wave velocity to obtain localized rock physical modeling parameters.

9. A non-volatile computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the pseudo-acoustic wave curve reconstruction method according to any one of claims 1 to 7.

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