Method and device for predicting transverse wave velocity of coal seam and coal-bearing reservoir

Abstract

The embodiment of the invention provides a method and device for predicting the shear wave velocity of a coal seam and a coal-bearing reservoir, and belongs to the technical field of oil and gas geophysical exploration. The prediction method comprises the following steps: acquiring a target logging curve of a characteristic well according to a correlation between a transverse wave velocity curve of the characteristic well of a coal seam and a plurality of logging curves; determining a regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well; and according to the target logging curve of the target well of the coal seam and the regression relationship, obtaining a shear wave velocity curve of the target well. According to the method, the regression relationship can be specifically established for the characteristic well of the coal seam to be researched by using the statistical regression mode, and is applied to the target well of the coal seam, so that the shear wave velocity of the coal seam can be simply and accurately predicted. Meanwhile, the shear wave velocity curve of the coal-bearing reservoir is jointly represented by integrating the rock physics theory model and the statistical regression model, so that the shear wave velocity of the coal-bearing reservoir can be simply and accurately predicted.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas geophysical exploration, and in particular to a method and a device for predicting the shear wave velocity of a coal seam or a coal-containing reservoir. Background Art

[0002] In current reservoir prediction work, both prestack seismic inversion and prestack seismic attribute analysis require accurate shear wave velocity curves. However, shear wave logging is currently missing in actual well logging, making it difficult to directly obtain shear wave velocity, and a rock physics model needs to be constructed for estimation.

[0003] At the same time, thin coal seams are often developed in offshore oil and gas reservoirs. Since the structure and properties of coal seams in different regions are often very different, it is difficult to accurately characterize the shear wave velocity of coal-bearing reservoirs using conventional physical models (for example, rock physics models), which makes it difficult to predict the shear wave velocity curve of coal-bearing reservoirs. Summary of the invention

[0004] The purpose of the embodiment of the present invention is to provide a method and device for predicting the shear wave velocity of coal seams and coal-containing reservoirs. By constructing a regression relationship and using logging curves to characterize the shear wave properties of the coal seams, the shear wave velocity of the coal seam to be studied can be accurately and simply predicted.

[0005] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a method for predicting the shear wave velocity of a coal seam, the prediction method comprising: obtaining a target logging curve of a characteristic well of the coal seam based on the correlation between the shear wave velocity curve of the characteristic well and multiple logging curves; determining the regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well; and obtaining the shear wave velocity curve of the target well of the coal seam based on the target logging curve of the target well of the coal seam and the regression relationship, wherein the target well and the characteristic well are within the same depth range of the coal seam.

[0006] Optionally, the correlation is a correlation degree, and obtaining the target logging curve of the characteristic well includes: determining a logging curve having a correlation degree greater than a set value among the multiple logging curves as the target logging curve of the characteristic well.

[0007] Optionally, the plurality of logging curves include at least two of the following: a gamma logging curve, a deep lateral resistivity curve, a neutron logging curve, and a compressional wave velocity curve.

[0008] Optionally, the regression relationship between the shear wave velocity and the multiple related logging curves includes: determining the regression relationship represented by the following formula according to the shear wave velocity curve vs of the characteristic well, the longitudinal wave velocity curve vp of the characteristic well, the gamma logging curve GR of the characteristic well, and the neutron logging curve CNL of the characteristic well:

[0009] vs=a×vp+b×GR+×CNL+,

[0010] Among them, a, b, d, and e are different fitting coefficients.

[0011] Through the above technical scheme, the beneficial effects of the present invention are as follows: the statistical relationship between the shear wave velocity curve of the characteristic well and multiple logging curves is used in the coal seam to determine the target logging curve of the characteristic well; then, by using statistical regression, a regression relationship between the shear wave velocity curve and the target logging curve can be established for the characteristic well in a targeted manner, and applied to the target well of the coal seam; finally, the precise fitting value of the shear wave velocity curve of the target well is obtained, thereby simply and accurately predicting the shear wave velocity of the coal seam.

[0012] In the second aspect, an embodiment of the present invention further provides a device for predicting the shear wave velocity of a coal seam, characterized in that the prediction device comprises: a first data acquisition module, used to acquire a target logging curve of the characteristic well according to the correlation between the shear wave velocity curve of the characteristic well of the coal seam and a plurality of logging curves; a first data processing module, used to determine the regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well; and a second data acquisition module, used to acquire the shear wave velocity curve of the target well of the coal seam according to the target logging curve of the target well of the coal seam and the regression relationship.

[0013] Optionally, the correlation is a correlation degree, and obtaining the target logging curve of the characteristic well includes: determining a logging curve having a correlation degree greater than a set value among the multiple logging curves as the target logging curve of the characteristic well.

[0014] Optionally, the plurality of logging curves include at least two of the following: a gamma logging curve, a deep lateral resistivity curve, a neutron logging curve, and a compressional wave velocity curve.

[0015] Optionally, the regression relationship between the shear wave velocity and the plurality of related logging curves includes:

[0016] According to the shear wave velocity curve vs of the characteristic well, the longitudinal wave velocity curve vp of the characteristic well, the gamma logging curve GR of the characteristic well and the neutron logging curve CNL of the characteristic well, the regression relationship represented by the following formula is determined:

[0017] vs=a×vp+b×GR+×CNL+,

[0018] Among them, a, b, d, and e are different fitting coefficients.

[0019] The specific details and benefits of the device for predicting the shear wave velocity of a coal seam provided by an embodiment of the present invention can be found in the description of the above-mentioned method for predicting the shear wave velocity of the coal seam, which will not be repeated here.

[0020] In a third aspect, an embodiment of the present invention further provides a method for predicting the shear wave velocity of a coal-containing reservoir, characterized in that the prediction method comprises: obtaining the shear wave velocity of the coal seam of the coal-containing reservoir according to the above-mentioned prediction method; obtaining the shear wave velocity of the rock stratum of the coal-containing reservoir; and obtaining the shear wave velocity of the coal-containing reservoir based on the shear wave velocity of the coal seam and the shear wave velocity of the rock stratum.

[0021] Through the above technical solution, the beneficial effect of the present invention is that the reservoir section where coal seams are commonly developed cannot be ignored and needs to be processed specifically for the coal seams. However, due to the particularity of the properties of coal seams, it is difficult to accurately represent the coal seams using rock physics theoretical models. Therefore, on the basis of the sandstone rock physics modeling method based on the equivalent pore theory, the influence of the coal seam is added in a statistical regression manner. Therefore, the rock physics theoretical model and the statistical regression model can be combined to jointly characterize the shear wave velocity curve of the coal-bearing reservoir, and the shear wave velocity of the coal-bearing reservoir can be simply and accurately predicted.

[0022] In a fourth aspect, an embodiment of the present invention further provides a device for predicting the shear wave velocity of a coal-containing reservoir, characterized in that the prediction device comprises: a first shear wave velocity acquisition module, used to obtain the shear wave velocity of the coal seam of the coal-containing reservoir according to the above-mentioned prediction method; a second shear wave velocity acquisition module, used to obtain the shear wave velocity of the rock stratum of the coal-containing reservoir; and a second data processing module, used to obtain the shear wave velocity of the coal-containing reservoir based on the shear wave velocity of the coal seam and the shear wave velocity of the rock stratum.

[0023] The specific details and benefits of the device for predicting the shear wave velocity of coal-containing reservoirs provided by the embodiment of the present invention can be found in the above description of the method for predicting the shear wave velocity applicable to the above coal-containing reservoirs, which will not be repeated here.

[0024] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0026] Figure 1It is a schematic diagram of a flow chart of a method for predicting the shear wave velocity of a coal seam according to an embodiment of the present invention;

[0027] Figure 2a It is a schematic diagram showing the intersection of a shear wave velocity curve and a deep lateral resistivity curve of a characteristic well according to an embodiment of the present invention;

[0028] Figure 2b It is a schematic diagram showing the intersection of a shear wave velocity curve and a gamma logging curve of a characteristic well according to an embodiment of the present invention;

[0029] Figure 2c It is a schematic diagram showing the intersection of a shear wave velocity curve and a neutron logging curve of a characteristic well according to an embodiment of the present invention;

[0030] Figure 2d It is a schematic diagram showing the intersection of a shear wave velocity curve and a longitudinal wave velocity curve of a characteristic well according to an embodiment of the present invention;

[0031] Figure 3 It is a schematic diagram of a device for predicting the shear wave velocity of a coal seam according to an embodiment of the present invention;

[0032] Figure 4 It is a schematic flow chart of a method for predicting the shear wave velocity of a coal-bearing reservoir according to an embodiment of the present invention;

[0033] Figure 5 It is a schematic flow chart of a method for calculating the elastic modulus of a rock formation according to an embodiment of the present invention;

[0034] Figure 6 A schematic diagram showing a comparison between a predicted value of shear wave velocity and a measured value according to an embodiment of the present invention;

[0035] Figure 7 It is a schematic diagram of a device for predicting the shear wave velocity of a coal-containing reservoir according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0037] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The embodiment of the present invention provides a method for predicting the shear wave velocity of a coal seam. Figure 1 As shown, the prediction method includes the following steps S110-S130.

[0039] S110, obtaining a target logging curve of the characteristic well according to the correlation between the shear wave velocity curve of the characteristic well of the coal seam and a plurality of logging curves.

[0040] First, select the coal-bearing reservoir in the target work area, and select the well with better logging quality in the coal seam of the coal-bearing reservoir as the characteristic well of the coal seam. Here, "better quality" means that the selected characteristic well has relatively complete logging data, including shear wave velocity curve data and multiple logging curve data.

[0041] The plurality of logging curves may include at least two of the following: a gamma logging curve, a deep lateral resistivity curve, a neutron logging curve, and a longitudinal wave velocity curve, etc. Other types of logging curves may also be selected according to actual needs.

[0042] Then, the well logging data are used to analyze the correlation between the shear wave velocity curve of the characteristic well and multiple well logging curves. For example, the correlation can be expressed by correlation.

[0043] In one embodiment, the correlation can be characterized by the |r| value obtained by a simple correlation coefficient, ranging from 0 to 1. Generally speaking, the greater the correlation between a certain logging curve and the shear wave velocity curve, the higher the correlation between the logging curve and the shear wave velocity curve, and the more accurate the selection of the logging curve as the fitting parameter of the shear wave velocity curve will be. In order to select the logging curve more accurately, the logging curve with a correlation greater than a set value among multiple logging curves can be determined as the target logging curve of the characteristic well. The set value can be taken within a certain range according to actual needs. In a preferred embodiment, when four logging curves are selected, the set value of the correlation should not be less than 0.5. For example, the set value of the correlation can be between 0.6-0.8, or it can be taken within other ranges according to actual needs. In another preferred embodiment, the logging curve with a correlation greater than 0.8 can be determined as the target logging curve of the characteristic well.

[0044] In one embodiment, based on the multiple logging curve data of the selected characteristic well, an intersection diagram of the shear wave velocity curve of the characteristic well and the multiple logging curves can be drawn, such as Figure 2a , Figure 2b , Figure 2c , Figure 2d As shown. Among them, Figure 2a It is the intersection diagram of the shear wave velocity curve vs and the deep lateral resistivity curve Rd of the characteristic well; Figure 2b It is the intersection diagram of the shear wave velocity curve vs and the gamma logging curve GR of the characteristic well; Figure 2c is the intersection diagram of the shear wave velocity curve vs and the neutron logging curve CNL of the characteristic well; and Figure 2d It is the intersection diagram of the shear wave velocity curve vs and the longitudinal wave velocity curve vp of the characteristic well.

[0045] Through the intersection diagram of the shear wave velocity curve of the characteristic well and the above-mentioned multiple logging curves, the linear regression curves of each intersection diagram can be calculated, and the corresponding |r| value can be calculated. In one embodiment, if the set value of the correlation is taken as 0.6, and after measurement, the correlation between the above-mentioned multiple logging curves of the characteristic well and the shear wave velocity curve vs is greater than the set value. In this case, the target logging curves of the characteristic well are the longitudinal wave velocity curve vp, the gamma logging curve GR, the deep lateral resistivity curve Rd and the neutron logging curve CNL. Therefore, the longitudinal wave velocity curve vp, the gamma logging curve GR, the deep lateral resistivity curve Rd and the neutron logging curve CNL are selected to construct the regression relationship of the shear wave velocity vs of the coal seam.

[0046] In another embodiment, if the set value of the correlation is 0.8, and after measurement, only the correlation between the deep lateral resistivity curve Rd and the shear wave velocity curve vs is less than the set value. In this case, the target logging curves of the characteristic well are the longitudinal wave velocity curve vp, the gamma logging curve GR and the neutron logging curve CNL. Therefore, only the longitudinal wave velocity curve vp, the gamma logging curve GR and the neutron logging curve CNL are selected to construct the regression relationship of the coal seam shear wave velocity vs.

[0047] S120, determining a regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well.

[0048] In this step, it is necessary to collect historical data of the shear wave velocity curve of the characteristic well and the target logging curve, and then determine the regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well. For example, in one embodiment, if the correlation between multiple logging curves of the characteristic well and the shear wave velocity curve vs all meet the requirements (that is, they are all greater than the set value of the correlation), the following formula (1) can be used to establish the regression relationship model:

[0049] vs = a × vp + b × GR + c × Rd + d × CNL + e, Formula (1)

[0050] Where vs is the shear wave velocity curve of the characteristic well;

[0051] vp is the P-wave velocity curve of the characteristic well;

[0052] GR is the gamma logging curve of the characteristic well;

[0053] Rd is the deep lateral resistivity curve of the characteristic well;

[0054] CNL is the neutron logging curve of the characteristic well; and

[0055] a, b, c, d, and e are different fitting coefficients.

[0056] In another embodiment, if the correlation between the deep lateral resistivity curve Rd and the shear wave velocity curve vs of the characteristic well does not meet the requirement (i.e., is less than the set value of the correlation), the following formula (2) can be used to establish a regression relationship model:

[0057] vs = a × vp + b × GR + d × CNL + e, Formula (2)

[0058] Where vs is the shear wave velocity curve of the characteristic well;

[0059] vp is the P-wave velocity curve of the characteristic well;

[0060] GR is the gamma logging curve of the characteristic well;

[0061] CNL is the neutron logging curve of the characteristic well; and

[0062] a, b, d, and e are different fitting coefficients.

[0063] It is worth noting that this step can also adopt other regression relationship fitting models that meet actual needs.

[0064] S130, obtaining a shear wave velocity curve of the target well according to the target logging curve of the target well in the coal seam and the regression relationship.

[0065] Among them, the target well and the characteristic well are in the same depth range of the coal seam.

[0066] First, in the same coal seam in the target work area, a well in the same depth range (for example, h1-h2) as the characteristic well is selected as the target well of the coal seam. Generally speaking, the target well has less logging data, especially the shear wave velocity curve. Therefore, it is necessary to obtain the shear wave velocity curve of the target well based on the above steps according to the multiple logging curve data of the target well.

[0067] Specifically, in one embodiment, if the target logging curve of the characteristic well is the P-wave velocity curve vp, the gamma logging curve GR, the deep lateral resistivity curve Rd and the neutron logging curve CNL, then the target logging curve of the target well is also the P-wave velocity curve vp, the gamma logging curve GR, the deep lateral resistivity curve Rd and the neutron logging curve CNL. Then, the target logging curve of the target well (P-wave velocity curve vp, the gamma logging curve GR, the deep lateral resistivity curve Rd and the neutron logging curve CNL) is substituted into the regression relationship model established in step S120 to obtain the S-wave velocity curve vs of the target well.

[0068] In another embodiment, if the target logging curve of the characteristic well is the P-wave velocity curve vp, the gamma logging curve GR and the neutron logging curve CNL, then the target logging curve of the target well is also the P-wave velocity curve vp, the gamma logging curve GR and the neutron logging curve CNL. Then, the target logging curve of the target well (P-wave velocity curve vp, the gamma logging curve GR and the neutron logging curve CNL) is substituted into the regression relationship model established in step S120 to obtain the S-wave velocity curve vs of the target well.

[0069] For example, in one embodiment, a regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well is established as shown in formula (3):

[0070] vs=0.384vp-1.724GR-564.965CNL+756.208, Formula (3)

[0071] Then, the P-wave velocity curve vp of the target well, the gamma logging curve GR of the target well, and the neutron logging curve CNL of the target well are substituted into the regression relationship model to obtain the S-wave velocity curve vs of the target well.

[0072] The beneficial effect of this prediction method is that the statistical relationship between the shear wave velocity curve of the characteristic well and multiple logging curves is used in the coal seam to determine the target logging curve of the characteristic well; then, by using statistical regression, a regression relationship between the shear wave velocity curve and the target logging curve can be established for the characteristic well in a targeted manner, and applied to the target well of the coal seam; finally, the accurate fitting value of the shear wave velocity curve of the target well is obtained, thereby simply and accurately predicting the shear wave velocity of the coal seam.

[0073] In a second aspect, the present invention also discloses a device for predicting the shear wave velocity of a coal seam. Figure 3 As shown, the prediction device includes the following modules.

[0074] The first data acquisition module 210 is used to acquire a target logging curve of the characteristic well according to the correlation between the shear wave velocity curve of the characteristic well of the coal seam and a plurality of logging curves.

[0075] The first data processing module 220 is used to determine the regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well.

[0076] The second data acquisition module 230 is used to acquire the shear wave velocity curve of the target well according to the target logging curve of the target well of the coal seam and the regression relationship.

[0077] The specific details and benefits of the device for predicting the shear wave velocity of a coal seam provided by an embodiment of the present invention can be found in the description of the above-mentioned method for predicting the shear wave velocity of the coal seam, which will not be repeated here.

[0078] In a third aspect, the present invention also discloses a method for predicting the shear wave velocity of a coal-bearing reservoir, such as Figure 4 As shown, the prediction method includes steps S310-S330.

[0079] S310, obtaining the shear wave velocity of the coal seam containing the coal reservoir.

[0080] Specifically, according to the above-mentioned method for predicting the shear wave velocity of a coal seam, the statistical relationship between the shear wave velocity curve of a characteristic well and multiple logging curves is used to determine the target logging curve of the characteristic well in the coal seam; then, by using statistical regression, a regression relationship between the shear wave velocity curve and the target logging curve is established for the characteristic well, and applied to the target well of the coal seam; finally, the accurate fitting value of the shear wave velocity curve of the target well is obtained, thereby obtaining the shear wave velocity of the coal seam containing the coal reservoir.

[0081] S320, obtaining the shear wave velocity of the rock formation containing the coal reservoir.

[0082] In coal-bearing reservoirs, the non-coal layer is muddy sandstone. The pores of muddy sandstone are usually divided into mudstone pores and sandstone pores. For example, the pore aspect ratio can be used to describe the pores of muddy sandstone: mudstone pores have a smaller pore aspect ratio, while sandstone pores have a larger pore aspect ratio. Muddy sandstone can be collectively referred to as a rock formation, so a rock physics model needs to be constructed to calculate the shear wave curve of the rock formation.

[0083] The rock physics theoretical model usually divides rocks into three components: mixed mineral matrix, pore structure, and fluid. The relationship between velocity parameters and minerals, porosity, fluid and other physical parameters can be constructed through corresponding equations. In addition, the shear wave velocity of the rock formation is usually related to the elastic modulus and density of the rock formation, so the elastic modulus of the rock formation can be solved first.

[0084] Specifically, Figure 5 As shown, obtaining the elastic modulus of the rock formation may include steps S321-S324.

[0085] S321, obtaining the elastic modulus of the mixed mineral matrix.

[0086] Specifically, the mixed mineral matrix is ​​obtained according to the well logging interpretation data, and the elastic modulus of the mixed mineral matrix is ​​calculated by the following formulas (4) to (6):

[0087]

[0088]

[0089]

[0090] Among them, K VRH is the elastic modulus of the mixed mineral matrix, including the bulk modulus K of the mixed mineral matrix m and shear modulus G m ;

[0091] M V is the Voigt boundary modulus, M R is the Reuss boundary modulus;

[0092] f i is the volume content of the i-th mineral, M i is the modulus of the ith mineral; and

[0093] N is the number of different mineral types.

[0094] S322, obtaining the elastic modulus of the rock skeleton.

[0095] Specifically, pore structures (including mudstone pores and sandstone pores) are added to the mixed mineral matrix to construct a rock skeleton, and the elastic modulus of the rock skeleton is calculated using the following equations (7) and (8):

[0096]

[0097]

[0098] Among them, K d is the bulk modulus of the rock skeleton, K m is the bulk modulus of the mixed mineral matrix, G d is the shear modulus of the rock skeleton, G m is the shear modulus of the mixed mineral matrix;

[0099] p and q are coefficients related to the pore aspect ratio; and

[0100] is the porosity.

[0101] S323, obtaining the elastic modulus of the fluid-saturated rock.

[0102] Specifically, fluid is added to the rock skeleton to construct a fluid-saturated rock, and the elastic modulus of the fluid-saturated rock is the elastic modulus of the rock formation. Specifically, the elastic modulus of the fluid-saturated rock is calculated using the following formulas (9) to (11):

[0103]

[0104] G s =G d , formula (10)

[0105]

[0106] Among them, K s is the bulk modulus of fluid-saturated rock, K d is the bulk modulus of the rock skeleton, K m is the bulk modulus of the mixed mineral matrix, K f is the bulk modulus of the fluid;

[0107] G s is the shear modulus of fluid-saturated rock, G d is the shear modulus of the rock skeleton;

[0108] is the porosity; and

[0109] S w is water saturation, K w is the bulk modulus of water, K g is the bulk modulus of gas.

[0110] S324, obtaining the shear wave velocity of the rock formation.

[0111] Specifically, the elastic modulus of rock includes bulk modulus and shear modulus. Based on the bulk modulus, shear modulus and rock density, the shear wave velocity of the rock formation can be calculated using the following formula (12):

[0112]

[0113] Among them, vs is the shear wave velocity of the coal seam, G is the shear modulus of the rock, and ρ is the rock density.

[0114] S330, obtaining the shear wave velocity of the coal-bearing reservoir according to the shear wave velocity of the coal seam and the shear wave velocity of the rock formation.

[0115] The calculated values ​​of the shear wave velocity of the rock layer and the coal layer are combined to obtain the final predicted value of the shear wave velocity of the coal-bearing reservoir. Specifically, according to the lithology category (rock layer or coal layer) of the coal-bearing reservoir at different depths, the shear wave velocity of the coal layer at different depths and the shear wave velocity of the rock layer at different depths can be plotted in sequence according to the depth, so as to obtain the final predicted value of the shear wave velocity of the coal-bearing reservoir. It is worth noting that other superposition methods can also be selected according to actual needs.

[0116] At the same time, in order to further verify the accuracy of the prediction method of the present invention, the predicted value is compared with the actual value, for example, Figure 6 A plotted curve graph of the predicted and measured values ​​of the shear wave velocity of a coal-bearing reservoir is shown. Among them, the lithology of the coal-bearing reservoir at different depths (4240-4300) is distinguished, the line area represents the coal seam, and the blank area (area without lines) represents the rock layer. In addition, the solid line in the figure represents the measured shear wave velocity, and the dotted line represents the fitted shear wave velocity. Among them, the range of the shear wave velocity is 1263-3723m / s. It can be seen from the figure that the fitted shear wave velocity curve of the coal-bearing reservoir has a high degree of match with the measured shear wave velocity curve, which shows that the prediction method has a good accuracy, and further proves that the prediction method of the present invention can accurately predict the shear wave velocity of the coal-bearing reservoir.

[0117] The beneficial effect of the prediction method of the shear wave velocity of the coal-bearing reservoir is that the reservoir section where the coal seam is generally developed cannot be ignored and needs to be processed specifically for the coal seam. However, due to the particularity of the coal seam properties, it is difficult to accurately represent the coal seam using the rock physics theoretical model. Therefore, on the basis of the sandstone rock physics modeling method based on the equivalent pore theory, the influence of the coal seam is added in the form of statistical regression. Therefore, the rock physics theoretical model and the statistical regression model can be combined to jointly characterize the shear wave velocity curve of the coal-bearing reservoir, and the shear wave velocity of the coal-bearing reservoir can be simply and accurately predicted.

[0118] In a fourth aspect, the present invention also discloses a device for predicting the shear wave velocity of a coal-bearing reservoir, such as Figure 7 As shown, the prediction device may include:

[0119] The first shear wave velocity acquisition module 410 is used to acquire the shear wave velocity of the coal seam containing the coal reservoir according to the above-mentioned method for predicting the shear wave velocity of the coal seam.

[0120] The second shear wave velocity acquisition module 420 is used to acquire the shear wave velocity of the coal-bearing reservoir according to the shear wave velocity passing through the coal seam and the shear wave velocity of the rock layer.

[0121] The specific details and benefits of the device for predicting the shear wave velocity of coal-containing reservoirs provided by the embodiment of the present invention can be found in the above description of the method for predicting the shear wave velocity applicable to the above coal-containing reservoirs, which will not be repeated here.

[0122] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0123] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for predicting the shear wave velocity of a coal seam, characterized in that: The prediction method comprises: According to the correlation between the shear wave velocity curve of the characteristic well of the coal seam and a plurality of logging curves, a target logging curve of the characteristic well is obtained; Determining a regression relationship between a shear wave velocity curve of the characteristic well and a target logging curve of the characteristic well; and According to the target logging curve of the target well of the coal seam and the regression relationship, the shear wave velocity curve of the target well is obtained, wherein the target well and the characteristic well are within the same depth range of the coal seam.

2. The prediction method according to claim 1, characterized in that: The correlation is the correlation degree, The acquiring the target logging curve of the characteristic well comprises: determining a logging curve whose correlation is greater than a set value among the plurality of logging curves as the target logging curve of the characteristic well.

3. The prediction method according to claim 1, characterized in that: The plurality of well logs include at least two of the following: a gamma well log, a deep lateral resistivity curve, a neutron well log, and a compressional wave velocity curve.

4. The prediction method according to claim 3, characterized in that: The step of determining the regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well comprises: According to the shear wave velocity curve vs of the characteristic well, the longitudinal wave velocity curve vp of the characteristic well, the gamma logging curve GR of the characteristic well and the neutron logging curve CNL of the characteristic well, the regression relationship represented by the following formula is determined: vs=a×vp+b×GR+×CNL+, Among them, a, b, d, and e are different fitting coefficients.

5. A device for predicting the shear wave velocity of a coal seam, characterized in that: The prediction device comprises: A first data acquisition module is used to acquire a target logging curve of the characteristic well according to the correlation between the shear wave velocity curve of the characteristic well of the coal seam and a plurality of logging curves; A first data processing module is used to determine the regression relationship between the shear wave velocity curve of the characteristic well and the target logging curve of the characteristic well; The second data acquisition module is used to acquire the shear wave velocity curve of the target well according to the target logging curve of the target well of the coal seam and the regression relationship.

6. The prediction device according to claim 5, characterized in that The correlation is a correlation degree, and obtaining the target logging curve of the characteristic well includes: determining a logging curve having a correlation degree greater than a set value among the multiple logging curves as the target logging curve of the characteristic well.

7. The prediction device according to claim 5, characterized in that The plurality of well logs include at least two of the following: a gamma well log, a deep lateral resistivity curve, a neutron well log, and a compressional wave velocity curve.

8. The prediction device according to claim 7, characterized in that The regression relationship between the shear wave velocity and the plurality of related logging curves includes: According to the shear wave velocity curve vs of the characteristic well, the longitudinal wave velocity curve vp of the characteristic well, the gamma logging curve GR of the characteristic well and the neutron logging curve CNL of the characteristic well, the regression relationship represented by the following formula is determined: vs=a×vp+b×GR+×CNL+, Among them, a, b, d, and e are different fitting coefficients.

9. A method for predicting shear wave velocity of a coal-bearing reservoir, characterized in that: The prediction method comprises: Obtaining the shear wave velocity of the coal seam of the coal-bearing reservoir according to the prediction method described in claims 1-4; Obtaining the shear wave velocity of the rock formation containing the coal reservoir; and The shear wave velocity of the coal-bearing reservoir is obtained according to the shear wave velocity of the coal seam and the shear wave velocity of the rock formation.

10. A device for predicting shear wave velocity of a coal-bearing reservoir, characterized in that: The prediction device comprises: A first shear wave velocity acquisition module, used for acquiring the shear wave velocity of the coal seam containing the coal reservoir by the prediction method according to claims 1-4; A second shear wave velocity acquisition module, used to acquire the shear wave velocity of the rock formation containing the coal reservoir; and The second data processing module is used to obtain the shear wave velocity of the coal-bearing reservoir according to the shear wave velocity of the coal seam and the shear wave velocity of the rock formation.

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