A coal seam thickness prediction method, device and equipment based on well-seismic combination

Abstract

The present invention relates to the field of oil and gas exploration and development, and discloses a coal seam thickness prediction method, device and equipment based on well-seismic combination, which can obtain the real acoustic wave curve corresponding to the target layer segment and the constructed coal seam comprehensive identification factor curve. The real acoustic wave curve is subjected to wavelet transformation to extract the low-frequency component curve; the coal seam comprehensive identification factor curve is subjected to multivariate statistics to extract the high-frequency component curve. The low-frequency component curve and the high-frequency component curve are modulated to obtain a pseudo-acoustic wave curve. Based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method, the thickness distribution of the coal seam to be measured is determined. The present invention combines the coal seam comprehensive identification factor curve and the seismic wave impedance inversion method to predict the coal seam thickness, effectively solving the problem of the difficulty in predicting the coal seam thickness in the complex structural area of ​​the oil field with no wells or few wells, improving the efficiency and accuracy of coal seam thickness prediction in the complex structural area of ​​the oil field, and diversifying the coal seam thickness prediction method in the complex structural area of ​​the oil field.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration and development, and in particular to a method, device and equipment for predicting coal seam thickness based on well-seismic combination. Background Art

[0002] With the continuous deepening of oil and gas exploration, industrially valuable coal-derived gas and oil reservoirs have been discovered in more and more coal-bearing basins. Coal seams can serve not only as source rocks but also as reservoirs, opening up new areas for oil and gas exploration and development.

[0003] In seismic exploration, coal seams exhibit low-speed and low-density characteristics, and low-frequency strong reflection characteristics are formed during the propagation of seismic waves. However, they are superimposed in multiple phases vertically and superimposed in continuous pieces horizontally, making internal imaging complex.

[0004] The coal seam thickness prediction methods of related technologies are mainly aimed at conventional coal mining areas, which have flat terrain, simple structural style, shallow coal seams, continuous, stable and thick coal seams. There are fewer coal seam thickness prediction methods for complex structural areas of oil fields. Summary of the Invention

[0005] The present invention provides a coal seam thickness prediction method, device and equipment based on well-seismic combination, which is used to solve the defect of limited prediction methods for complex structural areas of oil fields in related technologies and diversify the coal seam thickness prediction methods in complex structural areas of oil fields.

[0006] In a first aspect, the present invention provides a method for predicting coal seam thickness based on well-seismic integration, comprising:

[0007] Obtaining a true acoustic wave curve corresponding to a target layer section in an oilfield exploration area and a constructed coal seam comprehensive identification factor curve; wherein the target layer section includes the coal seam to be measured;

[0008] Performing wavelet transformation on the real acoustic wave curve to extract a low-frequency component curve; and performing multivariate statistics on the coal seam comprehensive identification factor curve to extract a high-frequency component curve;

[0009] Modulating the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic wave curve;

[0010] Based on the pseudo-acoustic wave curve and seismic wave impedance inversion method, the thickness distribution of the coal seam to be measured is determined.

[0011] Optionally, the true acoustic wave curve is a curve showing how the acoustic wave time difference of the target layer varies with depth;

[0012] The performing wavelet transform on the real sound wave curve to extract the low-frequency component curve includes:

[0013] Sampling the real acoustic wave curve in order of depth from small to large to obtain a corresponding acoustic wave time difference sequence, and recording the depth corresponding to each acoustic wave time difference in the acoustic wave time difference sequence;

[0014] Reconstructing the acoustic wave time difference sequence using discrete wavelet transform to obtain a target reconstructed sequence; the target reconstructed sequence includes a plurality of reconstructed acoustic wave time differences corresponding one-to-one to each acoustic wave time difference in the acoustic wave time difference sequence;

[0015] For any of the reconstructed acoustic wave time differences in the target reconstruction sequence, determining a target acoustic wave time difference corresponding to the order in the acoustic wave time difference sequence according to the order of the reconstructed acoustic wave time differences in the target reconstruction sequence, and correlating the depth corresponding to the target acoustic wave time difference with the reconstructed acoustic wave time difference to obtain a reconstructed data point;

[0016] Using each of the obtained reconstructed data points, a curve showing how the reconstructed acoustic wave time difference changes with depth is drawn, and the curve is used as the low-frequency component curve.

[0017] Optionally, reconstructing the acoustic time difference sequence using discrete wavelet transform to obtain a target reconstructed sequence includes:

[0018] Performing a first wavelet decomposition on the acoustic time difference sequence using discrete wavelet transform to obtain a first approximate coefficient and a first detail coefficient; performing a second wavelet decomposition on the first approximate coefficient to obtain a second approximate coefficient and a second detail coefficient, until the Nth wavelet decomposition is completed to obtain an Nth approximate coefficient and an Nth detail coefficient; wherein N is an integer greater than 1;

[0019] Setting the first detail coefficient, the second detail coefficient, and finally the Nth detail coefficient to 0;

[0020] Based on the Nth detail coefficient set to 0, the Nth approximation coefficient is reconstructed to obtain a first reconstructed sequence; based on the N1th detail coefficient set to 0, the first reconstructed sequence is reconstructed to obtain a second reconstructed sequence; based on the N2th detail coefficient set to 0, the second reconstructed sequence is reconstructed until the latest reconstructed sequence is obtained, and the latest reconstructed sequence is used as the target reconstructed sequence; wherein N1 is the value obtained by subtracting 1 from N, and N2 is the value obtained by subtracting 2 from N.

[0021] Optionally, the dependent variable in the coal seam comprehensive identification factor curve is the coal seam comprehensive identification factor, and the independent variables include natural gamma ray variable, resistivity variable, acoustic wave time difference variable, neutron variable, total hydrocarbon value variable and drilling time variable;

[0022] The multivariate statistics of the coal seam comprehensive identification factor curve is performed to extract the high-frequency component curve, including:

[0023] Obtaining a mathematical expression corresponding to the coal seam comprehensive identification factor curve, and removing the contribution of acoustic wave time difference from the mathematical expression to obtain a pure non-acoustic wave factor;

[0024] Subtracting the acoustic wave time difference variable in the low-frequency component curve from the acoustic wave time difference variable in the real acoustic wave curve to obtain an acoustic wave high-frequency residual variable;

[0025] The acoustic high-frequency residual variable is used as a target dependent variable, and the pure non-acoustic factor, the natural gamma variable, the resistivity variable, the neutron variable, the total hydrocarbon value variable, and the drilling time variable are used as target independent variables;

[0026] Performing regression analysis on the target dependent variable and the target independent variable according to the parameter values ​​corresponding to the target dependent variable, the target independent variable and the depth to obtain a corresponding relationship;

[0027] A curve showing how the target dependent variable changes with depth is constructed according to the relationship and used as the high-frequency component curve.

[0028] Optionally, the mathematical expression corresponding to the coal seam comprehensive identification factor curve is:

[0029] ;

[0030] in, is the coal seam identification factor, is the natural gamma variable, is the resistivity variable, is the matrix resistivity, is the acoustic time difference variable, is the neutron variable, is the total hydrocarbon value variable, and Represent the minimum total hydrocarbon value and the maximum total hydrocarbon value, is the drilling time variable.

[0031] Optionally, the pure non-acoustic factor is obtained by dividing the coal seam identification factor by the acoustic wave time difference variable.

[0032] Optionally, the modulating the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic wave curve includes:

[0033] Detecting a target depth corresponding to the coal seam to be measured in the target layer section;

[0034] Determining a first acoustic wave time difference corresponding to the target depth in the real acoustic wave curve, determining a second acoustic wave time difference corresponding to the target depth in the low-frequency component curve, and determining a third acoustic wave time difference corresponding to the target depth in the high-frequency component curve;

[0035] Determine a sound wave difference value obtained by subtracting the second sound wave time difference from the first sound wave time difference, and divide the sound wave difference value by the third sound wave time difference to obtain a corresponding value as a scaling factor;

[0036] The high-frequency component curve is scaled based on the scaling factor to obtain a scaled curve, and the scaled curve is superimposed on the low-frequency component curve to obtain the pseudo-acoustic wave curve.

[0037] Optionally, determining the thickness of the coal seam to be measured based on the pseudo-acoustic wave curve and seismic wave impedance inversion method includes:

[0038] Performing sparse pulse inversion based on the pseudo-acoustic wave curve to determine the wave impedance inversion volume and wave impedance interval corresponding to the coal seam to be measured;

[0039] The wave impedance inversion body and wave impedance interval corresponding to the coal seam to be measured are input into the constructed coal seam thickness prediction model for prediction, so as to obtain the thickness distribution of the coal seam to be measured.

[0040] In a second aspect, the present invention provides a coal seam thickness prediction device based on well-seismic combination, comprising:

[0041] An acquisition unit is used to acquire a real acoustic wave curve corresponding to a target layer section in an oilfield exploration area, and a constructed coal seam comprehensive identification factor curve; wherein the target layer section includes a coal seam to be measured;

[0042] a first extraction unit, configured to perform wavelet transform on the real sound wave curve to extract a low-frequency component curve;

[0043] A second extraction unit is used to perform multivariate statistics on the coal seam comprehensive identification factor curve to extract a high-frequency component curve;

[0044] a modulation unit, configured to modulate the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic wave curve;

[0045] The determining unit is used to determine the thickness distribution of the coal seam to be measured based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method.

[0046] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions, thereby executing the coal seam thickness prediction method based on the combination of well and seismic.

[0047] The coal seam thickness prediction method, device and equipment based on well-seismic combination provided by the present invention can obtain the real acoustic wave curve corresponding to the target layer section in the oil field exploration area, as well as the constructed coal seam comprehensive identification factor curve; wherein, the target layer section includes the coal seam to be measured. The real acoustic wave curve is subjected to wavelet transformation to extract the low-frequency component curve; and the coal seam comprehensive identification factor curve is subjected to multivariate statistics to extract the high-frequency component curve. The low-frequency component curve and the high-frequency component curve are modulated to obtain a pseudo-acoustic wave curve. Based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method, the thickness distribution of the coal seam to be measured is determined. The present invention combines the coal seam comprehensive identification factor curve and the seismic wave impedance inversion method to predict the coal seam thickness, which can effectively solve the problem of the difficulty in predicting the coal seam thickness in the complex structural area of ​​the oil field with no wells or few wells, improve the efficiency and accuracy of coal seam thickness prediction in the complex structural area of ​​the oil field, and diversify the coal seam thickness prediction method in the complex structural area of ​​the oil field. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A flow chart of a coal seam thickness prediction method based on well-seismic integration provided by an embodiment of the present invention;

[0050] Figure 2 A comprehensive well logging interpretation result diagram provided by an embodiment of the present invention;

[0051] Figure 3 A block sparse pulse inversion profile provided by an embodiment of the present invention;

[0052] Figure 4 A block coal seam thickness contour map provided by an embodiment of the present invention;

[0053] Figure 5 A schematic diagram of the structure of a coal seam thickness prediction device based on well-seismic integration provided by an embodiment of the present invention;

[0054] Figure 6A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0056] In related technologies, the inversion of coal seam wave impedance based on logging data generally uses acoustic wave curves, which have a better reflection of strata with large differences in wave velocity or wave group impedance; however, the acoustic wave curves are easily disturbed by various reasons such as rock pores, mud and wellbore pollution during the logging process, resulting in the inversion results being unable to accurately invert the coal seam thickness.

[0057] In the related art, the main methods for predicting coal seam thickness are well logging curve prediction and seismic prediction. The well logging curve prediction method is relatively simple, and the coal seam thickness is generally predicted by the change in the shape of the well logging curve. The seismic prediction method mainly predicts through seismic attributes and seismic inversion methods. For example, the coal seam thickness is predicted by the linear correlation between spectral attributes and coal seam thickness. For another example, the sonic curve is reconstructed using a curve sensitive to the coal seam, and the coal seam thickness is predicted by logging constraint inversion. The related art prediction method for coal seam thickness is mainly aimed at conventional coal mining areas, which have flat terrain, simple structural style, shallow coal seams, continuous, stable and thick coal seams, while there are fewer prediction methods for complex structural areas of oil fields. Complex structural areas of oil fields have more developed faults, chaotic stratigraphic overlap, lateral lithologic mutations, and the superposition of multiple tectonic movements. As a result, the coal seams are affected by structure and sedimentation, and are often buried deep, discontinuous, and thin, making prediction more difficult.

[0058] The following combination Figures 1-4 The coal seam thickness prediction method based on well-seismic combination of the present invention is described.

[0059] like Figure 1 As shown, this embodiment proposes a first method, device, equipment and medium for predicting coal seam thickness based on well-seismic combination. The method may include the following steps:

[0060] S101. Obtain a real acoustic wave curve corresponding to a target layer segment in an oilfield exploration area, and a constructed coal seam comprehensive identification factor curve; wherein the target layer segment includes a coal seam to be measured.

[0061] Among them, oil field exploration areas are areas with oil and gas resource potential that require geological surveys and oil and gas exploration and development activities.

[0062] The target interval is a stratigraphic interval within a certain depth range within the oil and gas exploration area. The target interval may include coal seams and other strata. Other strata may include mudstone layers, sandstone layers, faults, fractures, and gas-water interfaces. Alternatively, the target interval may be a stratigraphic interval within a certain depth range within a single well within the oil and gas exploration area.

[0063] Specifically, the true acoustic wave curve corresponding to the target layer is a true acoustic wave transit time curve. This curve is constructed by transmitting an acoustic wave signal to the target layer using an acoustic logging tool and measuring its propagation time in the rock to calculate the acoustic wave velocity of the formation. This curve shows how the acoustic wave transit time value changes with depth.

[0064] The coal seam comprehensive identification factor curve can be constructed by observing core and thin sections of known wells in the region or surrounding areas, studying drilling and logging information, and analyzing the logging and logging response characteristics of the coal seams. In terms of well logging, the most representative factors reflecting the lithology, electrical properties, physical properties, and gas content of the coal seams are constructed based on the relationship between the four properties of the coal seams. Specifically, this embodiment can analyze the logging response characteristics of the coal seams based on the relationship between the four properties of the coal seams and combine them with the logging information to create a coal seam comprehensive identification factor curve that is highly sensitive to the coal seams.

[0065] Optionally, the mathematical expression corresponding to the coal seam comprehensive identification factor curve can be:

[0066] ;

[0067] in, is the coal seam identification factor, is the natural gamma variable, is the resistivity variable, is the matrix resistivity, is the acoustic time difference variable, is the neutron variable, is the total hydrocarbon value variable, and Represent the minimum total hydrocarbon value and the maximum total hydrocarbon value, is the drilling time variable.

[0068] Specifically, such as Figure 2 As shown in the figure, this example constructs identification factor variation curves for different lithologic layers, demonstrating significant differences in the comprehensive identification factors of coal seams and other surrounding rocks. The comprehensive identification factor values ​​for coal seams are mostly between 500 and 2000, while those for sandstone are mostly between 50 and 500, and those for mudstone are within 0 to 150. These significant differences allow for accurate lithologic boundary definition, laying the foundation for the subsequent inversion.

[0069] S102: Perform wavelet transform on the real sound wave curve to extract the low-frequency component curve.

[0070] Specifically, this embodiment can extract a low-frequency component curve from a real acoustic wave curve corresponding to the target layer segment based on wavelet transform.

[0071] It should be noted that the low-frequency component curve is used to characterize the slowly changing, large-scale background trend in the real acoustic wave curve. It can reflect the overall direction of the curve, as well as the overall compaction trend of the stratum, large sedimentary cycles and regional geological background. Its geological significance is to represent the basic skeleton of the stratum and the sedimentary environment background.

[0072] Optionally, the true acoustic wave curve is a curve showing how the acoustic wave time difference of the target layer varies with depth. Step S102 may include:

[0073] Sampling is performed on the real acoustic wave curve in order of depth from small to large to obtain the corresponding acoustic wave time difference sequence, and the depth corresponding to each acoustic wave time difference in the acoustic wave time difference sequence is recorded;

[0074] Reconstructing the acoustic wave time difference sequence using discrete wavelet transform to obtain a target reconstructed sequence; the target reconstructed sequence includes a plurality of reconstructed acoustic wave time differences corresponding one to one to each acoustic wave time difference in the acoustic wave time difference sequence;

[0075] For any reconstructed acoustic wave time difference in the target reconstruction sequence, determine the target acoustic wave time difference corresponding to the order in the acoustic wave time difference sequence according to the order of the reconstructed acoustic wave time difference in the target reconstruction sequence, and associate the depth corresponding to the target acoustic wave time difference with the reconstructed acoustic wave time difference to obtain a reconstructed data point;

[0076] Using each reconstructed data point obtained, a curve showing how the reconstructed acoustic wave time difference changes with depth is plotted and used as a low-frequency component curve.

[0077] Optionally, the above-mentioned use of discrete wavelet transform to reconstruct the acoustic time difference sequence to obtain a target reconstructed sequence includes:

[0078] Performing a first wavelet decomposition on the acoustic time difference sequence using discrete wavelet transform to obtain a first approximate coefficient and a first detail coefficient; performing a second wavelet decomposition on the first approximate coefficient to obtain a second approximate coefficient and a second detail coefficient, until the Nth wavelet decomposition is completed to obtain an Nth approximate coefficient and an Nth detail coefficient; wherein N is an integer greater than 1;

[0079] Set the first detail coefficient, the second detail coefficient, and the Nth detail coefficient to 0;

[0080] Based on the Nth detail coefficient set to 0, the Nth approximation coefficient is reconstructed to obtain a first reconstructed sequence; based on the N1th detail coefficient set to 0, the first reconstructed sequence is reconstructed to obtain a second reconstructed sequence; based on the N2th detail coefficient set to 0, the second reconstructed sequence is reconstructed until the latest reconstructed sequence is obtained, and the latest reconstructed sequence is used as the target reconstructed sequence; where N1 is the value obtained by subtracting 1 from N, and N2 is the value obtained by subtracting 2 from N.

[0081] Specifically, an example is given below. This embodiment can perform acoustic wave sequence sampling on a real acoustic wave curve. First, by sampling the data points, five data points (1000.0, 80.0), (1000.5, 82.0), (1001.0, 78.0), (1001.5, 85.0), and (1002.0, 79.0) with data format (depth, acoustic wave time difference) are obtained. Then, the acoustic wave time difference in each data point is extracted and sorted in sequence to obtain the acoustic wave time difference sequence S = [80.0, 82.0, 78.0, 85.0, 79.0]. The acoustic wave time difference sequence S is reconstructed using discrete wavelet transform. First, the acoustic wave time difference sequence S is subjected to the first wavelet decomposition to obtain the first approximation coefficient A1 = [81.2, 80.8, 82.3] and the first detail coefficient [-0.5, 1.2, -2.1]. Performing a second wavelet decomposition on the first approximate coefficient yields the second approximate coefficient A2 = [80.9, 81.5] and the second detail coefficient D2 = [0.3, -0.7]. Performing a third wavelet decomposition on the second approximate coefficient A2 yields the third approximate coefficient A3 = [81.2] and the third detail coefficient D3 = [-0.3].

[0082] Subsequently, in this embodiment, the first detail coefficient D1, the second detail coefficient D2, and the third detail coefficient D3 can all be set to 0, and the third approximation coefficient A3 can be reconstructed based on the third detail coefficient D3 to obtain a first reconstructed sequence P1 = [80.9, 81.5]. The first reconstructed sequence is reconstructed based on the second detail coefficient D2 to obtain a second reconstructed sequence P2 = [81.2, 80.8, 82.3]. The second reconstructed sequence P2 is reconstructed based on the third detail coefficient to obtain a third reconstructed sequence P3 = [80.5, 81.2, 81.6, 82.4, 80.1]. The third reconstructed sequence P3 is then determined as the target reconstructed sequence.

[0083] Specifically, this embodiment can associate each reconstructed acoustic wave time difference in P3 with the depth of the five sampled data points to construct the corresponding five reconstructed data points (1000.0, 80.5), (1000.5, 81.2), (1001.0, 81.6), (1001.5, 82.4), and (1002.0, 80.1). This embodiment can use the five reconstructed data points to draw a low-frequency component curve.

[0084] It is understandable that this embodiment can obtain a large number of reconstructed data points by sampling a large number of data points and using the large number of reconstructed data points to draw the corresponding low-frequency component curve, thereby enhancing the accuracy of the low-frequency component curve.

[0085] S103. Perform multivariate statistics on the coal seam comprehensive identification factor curve to extract the high-frequency component curve.

[0086] It should be noted that the high-frequency component is the rapidly changing detail feature in the curve (high frequency and short period), which can reflect small-scale lithologic changes (such as thin coal seams, cracks and pores) and short-period fluctuations in the signal.

[0087] Specifically, this embodiment can extract a high-frequency component curve from the coal seam comprehensive identification factor curve based on a multivariate statistical method.

[0088] Optionally, the dependent variable in the coal seam comprehensive identification factor curve is the coal seam comprehensive identification factor, and the independent variables include natural gamma ray variable, resistivity variable, acoustic wave time difference variable, neutron variable, total hydrocarbon value variable and drilling time variable. Step S103 may include:

[0089] Obtaining the mathematical expression corresponding to the coal seam comprehensive identification factor curve, and eliminating the contribution of acoustic wave time difference from the mathematical expression to obtain a pure non-acoustic factor;

[0090] Subtract the sound wave time difference variable in the low-frequency component curve from the sound wave time difference variable in the real sound wave curve to obtain the sound wave high-frequency residual variable;

[0091] The acoustic high-frequency residual variable is used as the target dependent variable, and the pure non-acoustic factor, natural gamma variable, resistivity variable, neutron variable, total hydrocarbon value variable and drilling time variable are used as the target independent variables;

[0092] According to the corresponding parameter values ​​between the target dependent variable, the target independent variable and the depth, regression analysis is performed on the target dependent variable and the target independent variable to obtain the corresponding relationship formula;

[0093] A curve showing how the target dependent variable changes with depth is constructed based on the relationship and used as a high-frequency component curve.

[0094] Optionally, the pure non-acoustic factor is obtained by dividing the coal seam identification factor by the acoustic time difference variable.

[0095] Specifically, this embodiment can remove the contribution of acoustic wave time difference from the coal seam comprehensive identification factor curve. For example, for the above formula (1), this embodiment can remove the contribution of acoustic wave time difference from the formula and construct a pure non-acoustic factor:

[0096] .

[0097] Specifically, this embodiment can construct an acoustic high-frequency residual variable by subtracting the acoustic time difference variable in the low-frequency component curve from the acoustic time difference variable in the true acoustic wave curve. Using the acoustic high-frequency residual variable as the target dependent variable, and using the pure non-acoustic factor, natural gamma ray variable, resistivity variable, neutron variable, total hydrocarbon value variable, and drilling time variable as the target independent variables, the corresponding parameter values ​​of the target dependent variable and target independent variable at different depths are determined, and regression analysis is performed on the corresponding parameter values ​​of the target dependent variable and target independent variable at different depths to obtain the corresponding relationship. It can be understood that there is a one-to-one correspondence between the parameter values ​​of the target dependent variable and the depth, and there is also a one-to-one correspondence between the parameter values ​​of the target independent variable and the depth.

[0098] Specifically, this embodiment can substitute different depths in the target layer segment into the obtained relationship formula for calculation to calculate the values ​​of the above-mentioned target independent variables corresponding to different depths in the target layer segment, that is, calculate the high-frequency residual values ​​of the acoustic waves corresponding to different depths in the target layer segment, and take each depth and the corresponding high-frequency residual value of the acoustic wave as a data point, and use each data point to draw a curve of the target dependent variable changing with depth, and use it as a high-frequency component curve.

[0099] S104 , modulating the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic wave curve.

[0100] Specifically, this embodiment can modulate the low-frequency component curve extracted from the real acoustic wave curve and the high-frequency component curve extracted from the coal seam comprehensive identification factor curve to obtain a pseudo-acoustic wave curve.

[0101] Optionally, step S104 may include:

[0102] Detect the target depth corresponding to the coal seam to be measured in the target layer;

[0103] Determining a first acoustic wave time difference corresponding to the target depth in the real acoustic wave curve, determining a second acoustic wave time difference corresponding to the target depth in the low-frequency component curve, and determining a third acoustic wave time difference corresponding to the target depth in the high-frequency component curve;

[0104] Determine a sound wave difference obtained by subtracting the second sound wave time difference from the first sound wave time difference, and divide the sound wave difference by the third sound wave time difference to obtain a corresponding value as a scaling factor;

[0105] The high-frequency component curve is scaled based on the scaling factor to obtain a scaled curve, and the scaled curve is superimposed on the low-frequency component curve to obtain a pseudo-acoustic wave curve.

[0106] Specifically, the target depth is the depth at which the coal seam to be measured is located in the target interval. The target depth can be a specific depth, such as 1025 meters, or can include multiple depths, such as 1025.2 and 1025.5.

[0107] Specifically, this embodiment can determine the first, second, and third acoustic wave time differences corresponding to the target depth from the true acoustic wave curve, the low-frequency component curve, and the high-frequency component curve, respectively. The second acoustic wave time difference is subtracted from the first acoustic wave time difference to obtain a corresponding acoustic wave difference, which is then divided by the third acoustic wave time difference to obtain a scaling factor corresponding to the target depth.

[0108] It should be noted that when the target depth includes multiple depths, this embodiment may determine the corresponding scaling factor according to each depth in the target depth, and then calculate the average value of each scaling factor as the scaling factor corresponding to the target depth.

[0109] Specifically, this embodiment can scale the high-frequency component curve based on the scaling factor corresponding to the target depth to obtain a scaled curve, and superimpose the scaled curve with the low-frequency component curve to obtain a pseudo-acoustic wave curve. It will be understood that this embodiment can regard the scaling factor corresponding to the target depth as the weight coefficient of the high-frequency component curve, regard 1 as the weight coefficient of the low-frequency component curve, and perform weighted superposition of the high-frequency component curve and the low-frequency component curve based on the weight coefficient of the high-frequency component curve and the weight coefficient of the low-frequency component curve to obtain the pseudo-acoustic wave curve.

[0110] It should be noted that the principle of reconstructing the pseudo-acoustic curve is to use wavelet transform to separate the low-frequency component curve of the real acoustic curve, and at the same time extract the high-frequency component curve of the non-acoustic curve, that is, the coal seam comprehensive identification factor curve, through multiple regression analysis and geological statistics. The low-frequency component curve of the real acoustic curve and the high-frequency component curve of the non-acoustic curve are modulated together to synthesize the pseudo-acoustic curve, with the purpose of obtaining a new curve that conforms to geological laws and can well distinguish lithology. In the original inversion, the wave impedance difference between the coal seam and other surrounding rocks is not large and difficult to distinguish. This embodiment uses the coal seam comprehensive identification factor that is highly sensitive to the coal seam to reconstruct the acoustic curve for inversion, which can better distinguish the coal.

[0111] It's also important to note that the pseudo-acoustic curve is a sonic time-difference curve reconstructed by integrating multiple logging data. It aims to overcome the limitations of traditional sonic logging in complex reservoir prediction. Its core principle is to mathematically integrate non-sonic logging information sensitive to the reservoir to generate a synthetic curve capable of distinguishing lithologic characteristics. Since the coal seam comprehensive identification factor curve can accurately identify coal seam thickness, reconstructing the pseudo-acoustic curve from the pseudo-acoustic curve can also improve the accuracy of coal seam thickness identification.

[0112] S105. Determine the thickness distribution of the coal seam to be measured based on the pseudo-acoustic wave curve and seismic wave impedance inversion method.

[0113] Specifically, this embodiment may use a seismic wave impedance inversion method to invert the pseudo-acoustic wave curve, and determine the thickness of the coal seam to be measured based on the inversion result.

[0114] Optionally, step S105 may include:

[0115] Based on the pseudo-acoustic wave curve, sparse pulse inversion is performed to determine the wave impedance inversion volume and wave impedance interval corresponding to the coal seam to be measured;

[0116] The wave impedance inversion volume and wave impedance interval corresponding to the coal seam to be measured are input into the constructed coal seam thickness prediction model for prediction, and the thickness distribution of the coal seam to be measured is obtained.

[0117] Specifically, after determining the pseudo-acoustic wave curve corresponding to a certain layer segment, this embodiment can predict the coal seam by applying the pseudo-acoustic wave curve to perform sparse pulse inversion, and obtain a coal seam wave impedance value between 2000 and 3800 kg / (m²·s). Figure 3 As shown, the red area represents the coal seam; the sandstone impedance value is between 6000 and 11000 kg / (m²·s). Figure 3 The blue and green areas represent sandstone; the mudstone impedance value is between 4400 and 8300 kg / (m²·s). Figure 3 The yellow and green areas in the figure represent mudstone.

[0118] Specifically, this embodiment can apply the coal seam thickness prediction model to calculate the coal seam thickness. Specifically, this embodiment can input the wave impedance inversion body obtained in the previous step into the model, and load the above-mentioned coal seam wave impedance range of 4400 to 8300, and the model automatically calculates the coal seam thickness. The principle is to extract the thickness corresponding to the coal rock wave impedance value range in the inversion data body corresponding to the step three. For each channel, within the analysis time window, if the given wave impedance is greater than zero, the wave impedance value is greater than the product of the number of wave impedance threshold values ​​and the sampling rate. If the given wave impedance value is less than zero, the wave impedance value is less than the product of the number of wave impedance threshold values ​​and the sampling rate. It should be noted that the seismic data body is in the time domain, and the obtained inversion data body is also in the time domain. Therefore, it is necessary to use the formation velocity and time conversion to form a depth data body, so as to realize the depth domain thickness extraction. This calculation method is used because the coal seams in the complex structural belts of the oil field are mostly thin and unstable, with large changes in thickness in the horizontal and vertical directions. This method is calculated based on the sampling rate to maximize the accuracy of lithologic resolution and can better reflect the changes in the lithologic properties of the target layer. Therefore, it can be used to predict changes in coal seam thickness.

[0119] It can be understood that in this embodiment, the range of the calculated layer interval is first limited, and then the interval range of the coal seam wave impedance value and the coal seam velocity in the inversion volume are input to calculate the coal seam thickness, and the result is as follows: Figure 4 A map of regional coal seam thickness is shown.

[0120] It should be noted that the main advantage of using sparse pulse inversion in this embodiment is that it can obtain broadband reflection coefficients, which can effectively solve the underdetermination problem of seismic records, thereby making the data obtained from wave impedance inversion more realistic. Moreover, because it directly uses seismic data for inversion and is less dependent on the initial geological model, it is particularly suitable for reservoir prediction in areas with few wells and meets the needs of coal seam development in these areas. Its limitation is that the vertical resolution is limited by the seismic frequency band, but the application of pseudo-acoustic wave curve reconstruction of the comprehensive coal seam identification factor compensates for the accuracy of the vertical resolution. Therefore, the combination of the two, logging-constrained sparse pulse inversion, is a more advantageous technical means for identifying coal seams.

[0121] This embodiment predicts coal seam thickness using the logging constraint inversion technology based on the coal seam comprehensive identification factor curve fused with logging and well logging information, which can effectively solve the problem of difficulty in predicting coal seam thickness in areas with no wells or few wells in complex oil field structures.

[0122] This embodiment is a method for predicting coal seam thickness based on combined well and seismic data, which can solve the difficult problem of predicting coal seams in complex structural oil fields, thereby effectively improving the efficiency and accuracy of determining such exploration and development targets.

[0123] The coal seam thickness prediction method based on well-seismic combination proposed in this embodiment can obtain the real acoustic wave curve corresponding to the target layer segment and the constructed coal seam comprehensive identification factor curve; wherein, the target layer segment includes the coal seam to be measured. The real acoustic wave curve is subjected to wavelet transform to extract the low-frequency component curve; and the coal seam comprehensive identification factor curve is subjected to multivariate statistics to extract the high-frequency component curve. The low-frequency component curve and the high-frequency component curve are modulated to obtain a pseudo-acoustic wave curve. Based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method, the thickness distribution of the coal seam to be measured is determined. This embodiment combines the coal seam comprehensive identification factor curve and the seismic wave impedance inversion method to predict the coal seam thickness, which can effectively solve the problem of the difficulty in predicting the coal seam thickness in the complex structural area of ​​the oil field with no wells or few wells, improve the efficiency and accuracy of coal seam thickness prediction in the complex structural area of ​​the oil field, and diversify the coal seam thickness prediction method in the complex structural area of ​​the oil field.

[0124] based on Figure 1 In the second coal seam thickness prediction method based on the combination of well and seismic data proposed in this embodiment, in the process of constructing the comprehensive identification factor curve of the coal seam, this embodiment can construct the most representative factors that can reflect the lithology, electrical properties, physical properties, and gas content of the coal seam from the perspective of the relationship between the four properties of the coal seam in terms of well logging.

[0125] For lithology, natural gamma ray logging (GR) is determined using nuclear logging and spectral logging. Sedimentary rocks generally do not contain radioactive minerals; their radioactivity is primarily due to adsorption of radioactive substances. Igneous and metamorphic rocks, on the other hand, contain a higher concentration of radioactive minerals. Furthermore, the greater the argillaceous content of sedimentary rocks, the higher the natural radioactivity. Coal seams have a low argillaceous content, resulting in low gamma ray values, which can be used as a representative factor in reflecting lithology.

[0126] For electrical properties, resistivity logging is used within electrical logging to obtain the resistivity logging value RT. Formation conductivity types mainly include ionic conductivity and electronic conductivity. Ionic conductivity is the conduction of salt ions in connected pores. Sedimentary rocks have strong conductivity and low resistivity, which depends on porosity, formation water resistivity, oil saturation, and other factors. Electronic conductivity is the conduction of free electrons within the mineral itself. Volcanic rocks have only a small number of free electrons and high resistivity; metallic minerals have many free electrons, strong conductivity, and low resistivity; mudstone has strong conductivity due to the presence of an ionic double layer on the surface of clay minerals. Coal seams have a low mud content and are relatively dense, resulting in extremely weak conductivity and high resistivity, which is significantly different from the resistivity of other surrounding rocks. The resistivity ratio of a coal seam to its matrix sandstone can be used as a representative factor reflecting its electrical properties.

[0127] For physical properties, neutron logging, a combination of sonic and radioactive logging, is used to obtain sonic logging values ​​(AC) and neutron logging values ​​(CN). Acoustic logging measures the propagation velocity of sound waves in the formation, determining formation porosity, lithology, and pore fluid properties. Neutron logging uses fast neutrons emitted by an artificial neutron source to generate epithermal or thermal neutrons, which are slowed down by hydrogen in the rock pores. These neutrons reflect the porosity of the formation. Because coal seams are porous and fractured, their acoustic transit time and neutron values ​​are significantly higher than those of other surrounding rocks. Therefore, these two factors can be used as representative factors for their physical properties.

[0128] To determine gas content, total hydrocarbon logging within gas logging is used to obtain the total hydrocarbon value (QT). Total hydrocarbons are based on the property of hydrocarbon gases released from the formation during drilling and returned to the surface with the drilling fluid. By measuring the gas concentration in the drilling fluid, it reflects the formation's oil and gas content. Coal seams are organic-rich formations with high gas content, developed fractures, and coalbed methane adsorption and desorption. Their high total hydrocarbon values ​​significantly distinguish them from other surrounding rocks. The ratio of the total hydrocarbon value of a coal seam to that of other surrounding rocks can be used as a representative factor to reflect the gas content of the coal seam.

[0129] The typical sensitive logging factor for coal seams is drilling time logging. Drilling time logging is a method of recording the time T required for the drill bit to drill into the unit thickness of the formation. drill A logging technology that reflects the lithology, hardness, and drillability of the formation in real time. Coal seams are soft formations with well-developed fractures, low density, and brittleness. Their drilling time is lower than that of other surrounding rocks, and this logging information can be used as significant logging information to distinguish coal seams.

[0130] It should be noted that, although the above information reflecting the coal seam sometimes overlaps with the information of other surrounding rocks in a single piece of information, the coal seam comprehensive identification factor curve Q shown in the above formula (1) is obtained by combining multiple information, and integrating the logging and well logging information. coal It can more accurately define the boundaries of coal seams, remove the interference of single information identification, and calculate the thickness of coal seams more accurately, laying the foundation for the subsequent prediction of coal seam thickness in areas with few wells or no wells.

[0131] Specifically, this embodiment can create a highly sensitive comprehensive coal seam identification factor by fusing logging and well logging information. The high-frequency components of the comprehensive coal seam identification factor curve and the low-frequency components of the acoustic wave curve are fused to construct a pseudo-acoustic wave curve. This pseudo-acoustic wave curve is used for sparse pulse inversion, and the amplitude-thickness attribute is used to calculate coal seam thickness.

[0132] like Figure 5 As shown, this embodiment proposes a coal seam thickness prediction device based on well-seismic combination, which may include:

[0133] The acquisition unit 501 is used to acquire a real acoustic wave curve corresponding to a target layer section in an oilfield exploration area, and a constructed coal seam comprehensive identification factor curve; wherein the target layer section includes the coal seam to be measured;

[0134] A first extraction unit 502 is configured to perform wavelet transformation on the real sound wave curve to extract a low-frequency component curve;

[0135] The second extraction unit 503 is used to perform multivariate statistics on the coal seam comprehensive identification factor curve to extract a high-frequency component curve;

[0136] a modulation unit 504, configured to modulate the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic wave curve;

[0137] The determination unit 505 is configured to determine the thickness distribution of the coal seam to be measured based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method.

[0138] It should be noted that the processing of the acquisition unit 501, the first extraction unit 502, the second extraction unit 503, the modulation unit 504 and the determination unit 505 and the beneficial effects thereof can be referred to in detail. Figure 1 Steps S101 to S105 in the above are not described in detail.

[0139] Optionally, the true acoustic wave curve is a curve showing how the acoustic wave time difference of the target layer varies with depth;

[0140] The first extraction unit 502 is further configured to:

[0141] Sampling the real acoustic wave curve in order of depth from small to large to obtain a corresponding acoustic wave time difference sequence, and recording the depth corresponding to each acoustic wave time difference in the acoustic wave time difference sequence;

[0142] Reconstructing the acoustic wave time difference sequence using discrete wavelet transform to obtain a target reconstructed sequence; the target reconstructed sequence includes a plurality of reconstructed acoustic wave time differences corresponding one-to-one to each acoustic wave time difference in the acoustic wave time difference sequence;

[0143] For any of the reconstructed acoustic wave time differences in the target reconstruction sequence, determining a target acoustic wave time difference corresponding to the order in the acoustic wave time difference sequence according to the order of the reconstructed acoustic wave time differences in the target reconstruction sequence, and correlating the depth corresponding to the target acoustic wave time difference with the reconstructed acoustic wave time difference to obtain a reconstructed data point;

[0144] Using each of the obtained reconstructed data points, a curve showing how the reconstructed acoustic wave time difference changes with depth is drawn, and the curve is used as the low-frequency component curve.

[0145] Optionally, the first extraction unit 502 is further configured to:

[0146] Performing a first wavelet decomposition on the acoustic time difference sequence using discrete wavelet transform to obtain a first approximate coefficient and a first detail coefficient; performing a second wavelet decomposition on the first approximate coefficient to obtain a second approximate coefficient and a second detail coefficient, until the Nth wavelet decomposition is completed to obtain an Nth approximate coefficient and an Nth detail coefficient; wherein N is an integer greater than 1;

[0147] Setting the first detail coefficient, the second detail coefficient, and finally the Nth detail coefficient to 0;

[0148] Based on the Nth detail coefficient set to 0, the Nth approximation coefficient is reconstructed to obtain a first reconstructed sequence; based on the N1th detail coefficient set to 0, the first reconstructed sequence is reconstructed to obtain a second reconstructed sequence; based on the N2th detail coefficient set to 0, the second reconstructed sequence is reconstructed until the latest reconstructed sequence is obtained, and the latest reconstructed sequence is used as the target reconstructed sequence; wherein N1 is the value obtained by subtracting 1 from N, and N2 is the value obtained by subtracting 2 from N.

[0149] Optionally, the dependent variable in the coal seam comprehensive identification factor curve is the coal seam comprehensive identification factor, and the independent variables include natural gamma ray variable, resistivity variable, acoustic wave time difference variable, neutron variable, total hydrocarbon value variable and drilling time variable;

[0150] The second extraction unit 503 is further configured to:

[0151] Obtaining a mathematical expression corresponding to the coal seam comprehensive identification factor curve, and removing the contribution of acoustic wave time difference from the mathematical expression to obtain a pure non-acoustic wave factor;

[0152] Subtracting the acoustic wave time difference variable in the low-frequency component curve from the acoustic wave time difference variable in the real acoustic wave curve to obtain an acoustic wave high-frequency residual variable;

[0153] The acoustic high-frequency residual variable is used as a target dependent variable, and the pure non-acoustic factor, the natural gamma variable, the resistivity variable, the neutron variable, the total hydrocarbon value variable, and the drilling time variable are used as target independent variables;

[0154] Performing regression analysis on the target dependent variable and the target independent variable according to the parameter values ​​corresponding to the target dependent variable, the target independent variable and the depth to obtain a corresponding relationship;

[0155] A curve showing how the target dependent variable changes with depth is constructed according to the relationship and used as the high-frequency component curve.

[0156] Optionally, the mathematical expression corresponding to the coal seam comprehensive identification factor curve is:

[0157] ;

[0158] in, is the coal seam identification factor, is the natural gamma variable, is the resistivity variable, is the matrix resistivity, is the acoustic time difference variable, is the neutron variable, is the total hydrocarbon value variable, and Represent the minimum total hydrocarbon value and the maximum total hydrocarbon value, is the drilling time variable.

[0159] Optionally, the pure non-acoustic factor is obtained by dividing the coal seam identification factor by the acoustic wave time difference variable.

[0160] Optionally, the modulation unit 504 is further configured to:

[0161] Detecting a target depth corresponding to the coal seam to be measured in the target layer section;

[0162] Determining a first acoustic wave time difference corresponding to the target depth in the real acoustic wave curve, determining a second acoustic wave time difference corresponding to the target depth in the low-frequency component curve, and determining a third acoustic wave time difference corresponding to the target depth in the high-frequency component curve;

[0163] Determine a sound wave difference value obtained by subtracting the second sound wave time difference from the first sound wave time difference, and divide the sound wave difference value by the third sound wave time difference to obtain a corresponding value as a scaling factor;

[0164] The high-frequency component curve is scaled based on the scaling factor to obtain a scaled curve, and the scaled curve is superimposed on the low-frequency component curve to obtain the pseudo-acoustic wave curve.

[0165] Optionally, the determining unit 505 is further configured to:

[0166] Performing sparse pulse inversion based on the pseudo-acoustic wave curve to determine the wave impedance inversion volume and wave impedance interval corresponding to the coal seam to be measured;

[0167] The wave impedance inversion body and wave impedance interval corresponding to the coal seam to be measured are input into the constructed coal seam thickness prediction model for prediction, so as to obtain the thickness distribution of the coal seam to be measured.

[0168] The coal seam thickness prediction device based on well-seismic combination proposed in this embodiment can obtain the real acoustic wave curve corresponding to the target layer section in the oil field exploration area, as well as the constructed coal seam comprehensive identification factor curve. The real acoustic wave curve is subjected to wavelet transform to extract the low-frequency component curve; the coal seam comprehensive identification factor curve is subjected to multivariate statistics to extract the high-frequency component curve. The low-frequency component curve and the high-frequency component curve are modulated to obtain a pseudo-acoustic wave curve. Based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method, the thickness distribution of the coal seam to be measured is determined. This embodiment combines the coal seam comprehensive identification factor curve and the seismic wave impedance inversion method to predict the coal seam thickness, effectively solving the problem of the difficulty in predicting the coal seam thickness in the complex structural area of ​​the oil field with no wells or few wells, improving the efficiency and accuracy of coal seam thickness prediction in the complex structural area of ​​the oil field, and diversifying the coal seam thickness prediction method in the complex structural area of ​​the oil field.

[0169] The coal seam thickness prediction device based on combined well and seismic analysis in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0170] The embodiment of the present invention also provides a computer device having the above Figure 5 The coal seam thickness prediction device based on the combination of well and seismic data is shown.

[0171] See also Figure 6 , a structural diagram of a computer device provided by an optional embodiment of the present invention, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0172] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0173] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0174] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0175] The memory 20 may include volatile memory, such as random access memory. The memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive. The memory 20 may also include a combination of the above types of memory.

[0176] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0177] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A coal seam thickness prediction method based on well-seismic combination, characterized in that: include: Obtaining a true acoustic wave curve corresponding to a target layer section in an oilfield exploration area and a constructed coal seam comprehensive identification factor curve; wherein the target layer section includes the coal seam to be measured; Performing wavelet transformation on the real acoustic wave curve to extract a low-frequency component curve; and performing multivariate statistics on the coal seam comprehensive identification factor curve to extract a high-frequency component curve; Modulating the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic wave curve; Determining the thickness distribution of the coal seam to be measured based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method; The dependent variable in the coal seam comprehensive identification factor curve is the coal seam comprehensive identification factor, and the independent variables include natural gamma ray variable, resistivity variable, acoustic wave time difference variable, neutron variable, total hydrocarbon value variable and drilling time variable; The multivariate statistics of the coal seam comprehensive identification factor curve is performed to extract the high-frequency component curve, including: Obtaining a mathematical expression corresponding to the coal seam comprehensive identification factor curve, and removing the contribution of acoustic wave time difference from the mathematical expression to obtain a pure non-acoustic wave factor; Subtract the acoustic wave time difference variable in the low-frequency component curve from the acoustic wave time difference variable in the real acoustic wave curve to obtain an acoustic wave high-frequency residual variable; The acoustic high-frequency residual variable is used as a target dependent variable, and the pure non-acoustic factor, the natural gamma variable, the resistivity variable, the neutron variable, the total hydrocarbon value variable, and the drilling time variable are used as target independent variables; Performing regression analysis on the target dependent variable and the target independent variable according to the parameter values ​​corresponding to the target dependent variable, the target independent variable and the depth to obtain a corresponding relationship; A curve showing how the target dependent variable changes with depth is constructed according to the relationship and used as the high-frequency component curve.

2. The method according to claim 1, characterized in that The true acoustic wave curve is a curve showing how the acoustic wave time difference of the target layer changes with depth; The performing wavelet transform on the real sound wave curve to extract the low-frequency component curve includes: Sampling the real acoustic wave curve in order of depth from small to large to obtain a corresponding acoustic wave time difference sequence, and recording the depth corresponding to each acoustic wave time difference in the acoustic wave time difference sequence; Reconstructing the acoustic wave time difference sequence using discrete wavelet transform to obtain a target reconstructed sequence; the target reconstructed sequence includes a plurality of reconstructed acoustic wave time differences corresponding one-to-one to each acoustic wave time difference in the acoustic wave time difference sequence; For any of the reconstructed acoustic wave time differences in the target reconstruction sequence, determining a target acoustic wave time difference corresponding to the order in the acoustic wave time difference sequence according to the order of the reconstructed acoustic wave time differences in the target reconstruction sequence, and correlating the depth corresponding to the target acoustic wave time difference with the reconstructed acoustic wave time difference to obtain a reconstructed data point; Using each of the obtained reconstructed data points, a curve showing how the reconstructed acoustic wave time difference changes with depth is drawn, and the curve is used as the low-frequency component curve.

3. The method according to claim 2, characterized in that The method of reconstructing the acoustic time difference sequence using discrete wavelet transform to obtain a target reconstructed sequence includes: Performing a first wavelet decomposition on the acoustic time difference sequence using discrete wavelet transform to obtain a first approximate coefficient and a first detail coefficient; performing a second wavelet decomposition on the first approximate coefficient to obtain a second approximate coefficient and a second detail coefficient, until the Nth wavelet decomposition is completed to obtain an Nth approximate coefficient and an Nth detail coefficient; wherein N is an integer greater than 1; Setting the first detail coefficient, the second detail coefficient, and finally the Nth detail coefficient to 0; Based on the Nth detail coefficient set to 0, the Nth approximation coefficient is reconstructed to obtain a first reconstructed sequence; based on the N1th detail coefficient set to 0, the first reconstructed sequence is reconstructed to obtain a second reconstructed sequence; based on the N2th detail coefficient set to 0, the second reconstructed sequence is reconstructed until the latest reconstructed sequence is obtained, and the latest reconstructed sequence is used as the target reconstructed sequence; wherein N1 is the value obtained by subtracting 1 from N, and N2 is the value obtained by subtracting 2 from N.

4. The method according to claim 1, wherein The mathematical expression corresponding to the coal seam comprehensive identification factor curve is: ； in, is the coal seam identification factor, is the natural gamma variable, is the resistivity variable, is the matrix resistivity, is the acoustic time difference variable, is the neutron variable, is the total hydrocarbon value variable, and Represent the minimum total hydrocarbon value and the maximum total hydrocarbon value, is the drilling time variable.

5. The method according to claim 4, characterized in that The pure non-acoustic factor is obtained by dividing the coal seam identification factor by the acoustic wave time difference variable.

6. The method according to claim 1, characterized in that The step of modulating the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic wave curve includes: Detecting a target depth corresponding to the coal seam to be measured in the target layer section; Determining a first acoustic wave time difference corresponding to the target depth in the real acoustic wave curve, determining a second acoustic wave time difference corresponding to the target depth in the low-frequency component curve, and determining a third acoustic wave time difference corresponding to the target depth in the high-frequency component curve; Determine a sound wave difference value obtained by subtracting the second sound wave time difference from the first sound wave time difference, and divide the sound wave difference value by the third sound wave time difference to obtain a corresponding value as a scaling factor; The high-frequency component curve is scaled based on the scaling factor to obtain a scaled curve, and the scaled curve is superimposed on the low-frequency component curve to obtain the pseudo-acoustic wave curve.

7. The method according to claim 1, characterized in that The method of determining the thickness of the coal seam to be measured based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method includes: Performing sparse pulse inversion based on the pseudo-acoustic wave curve to determine the wave impedance inversion volume and wave impedance interval corresponding to the coal seam to be measured; The wave impedance inversion body and wave impedance interval corresponding to the coal seam to be measured are input into the constructed coal seam thickness prediction model for prediction, so as to obtain the thickness distribution of the coal seam to be measured.

8. A coal seam thickness prediction device based on well-seismic combination, characterized in that: include: An acquisition unit is used to acquire a real acoustic wave curve corresponding to a target layer section in an oilfield exploration area, and a constructed coal seam comprehensive identification factor curve; wherein the target layer section includes a coal seam to be measured; a first extraction unit, configured to perform wavelet transform on the real sound wave curve to extract a low-frequency component curve; A second extraction unit is used to perform multivariate statistics on the coal seam comprehensive identification factor curve to extract a high-frequency component curve; a modulation unit, configured to modulate the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic wave curve; a determination unit, configured to determine the thickness distribution of the coal seam to be measured based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method; The dependent variable in the coal seam comprehensive identification factor curve is the coal seam comprehensive identification factor, and the independent variables include natural gamma ray variable, resistivity variable, acoustic wave time difference variable, neutron variable, total hydrocarbon value variable and drilling time variable; The second extraction unit is further used for: Obtaining a mathematical expression corresponding to the coal seam comprehensive identification factor curve, and removing the contribution of acoustic wave time difference from the mathematical expression to obtain a pure non-acoustic wave factor; Subtract the acoustic wave time difference variable in the low-frequency component curve from the acoustic wave time difference variable in the real acoustic wave curve to obtain an acoustic wave high-frequency residual variable; The acoustic high-frequency residual variable is used as a target dependent variable, and the pure non-acoustic factor, the natural gamma variable, the resistivity variable, the neutron variable, the total hydrocarbon value variable, and the drilling time variable are used as target independent variables; Performing regression analysis on the target dependent variable and the target independent variable according to the parameter values ​​corresponding to the target dependent variable, the target independent variable and the depth to obtain a corresponding relationship; A curve showing how the target dependent variable changes with depth is constructed according to the relationship and used as the high-frequency component curve.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the coal seam thickness prediction method based on well-seismic combination as described in any one of claims 1 to 7 by executing the computer instructions.

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