Method and device for obtaining near-surface quality factor based on dual-well micro-logging data

Abstract

An embodiment of the present application provides a method and device for obtaining the near-surface quality factor based on dual-well micro-logging data. The method includes: obtaining the near-surface velocity model and the first arrival time of the target area according to the dual-well micro-logging data and multiple shot point data of the target area; determining the waveform data corresponding to each trace of the shot points in the target layer based on the near-surface velocity model and the first arrival time; applying the waveform data corresponding to each trace to determine the quality factor, and determining the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model; fitting the near-surface quality factor of the target area according to the quality factor and the propagation time difference. The present application can effectively improve the accuracy and reliability of obtaining the near-surface quality factor, and further can effectively improve the accuracy and reliability of processing such as reservoir exploitation by applying the near-surface quality factor.

Description

Technical Field

[0001] The present application relates to the technical field of near-surface modeling in petroleum geophysical exploration, and particularly to a method and device for obtaining the near-surface quality factor based on dual-well micro-logging data. Background Art

[0002] With the deepening of exploration, the accuracy requirements for exploration targets are getting higher and higher. It is necessary to distinguish small geological target bodies such as thin interbeds, and seismic data with wide frequency band and high resolution is required. However, the absorption attenuation of the near-surface will seriously reduce the frequency band and resolution of seismic data. Therefore, it is extremely important to obtain the Q value (i.e., quality factor) of the near-surface medium and then perform inverse Q filtering. There are many commonly used methods for calculating the Q value at present. Among them, the time-domain methods include: rise-time method, amplitude attenuation method, wavelet simulation method, analytical signal method, etc.; the frequency-domain methods include: spectral simulation method, spectral ratio method, matching pursuit method, frequency shift method, etc. The accuracy of the Q value obtained by various methods depends on the accuracy of the collected data.

[0003] Among the existing Q value estimation methods, the spectral ratio method is the most widely used method. However, since the conventional method for calculating the quality factor of the first layer by dual-well micro-logging is to select the surface trace and bottom-hole trace data of a certain shot in the high-velocity layer and calculate it using the spectral ratio method (or other methods), the calculation of the quality factor is greatly affected by human factors. Different shots result in different results, and sometimes the results vary greatly. It is impossible to determine which shot's calculated quality factor should be used as the final result. Even using the method of averaging multiple shots cannot solve this problem well. Summary of the Invention

[0004] Aiming at the problems in the prior art, the present application provides a method and device for obtaining the near-surface quality factor based on dual-well micro-logging data, which can effectively improve the accuracy and reliability of obtaining the near-surface quality factor, and further can effectively improve the accuracy and reliability of oil reservoir exploitation and other processing using the near-surface quality factor.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for obtaining the near-surface quality factor based on dual-well micro-logging data, including:

[0007] Obtaining the near-surface velocity model and the first arrival time of the target area according to the dual-well micro-logging data and multiple shot point data of the target area;

[0008] Determining the waveform data corresponding to each trace of the shot points in the target layer based on the near-surface velocity model and the first arrival time;

[0009] Determine the quality factor using the waveform data corresponding to each trace, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model;

[0010] Fit the near-surface quality factor of the target area based on the quality factor and the propagation time difference.

[0011] Further, the obtaining of the near-surface velocity model and the first arrival time of the target area according to the crosshole micro-logging data and multiple shot point data of the target area includes:

[0012] Obtain the crosshole micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and the receiving point;

[0013] According to the crosshole micro-logging data of the target area and the relative coordinates between each shot point located in the target area and the receiving point, determine the near-surface velocity model of the target area by applying a preset seismic data interpretation method;

[0014] And, according to the crosshole micro-logging data of the target area and the relative coordinates between each shot point located in the target area and the receiving point, determine the first arrival time by applying a preset seismic data picking method.

[0015] Further, the determining of the waveform data corresponding to each trace of the shot point in the target layer based on the near-surface velocity model and the first arrival time includes:

[0016] Select the layer with a velocity value greater than the velocity threshold from the near-surface velocity model as the target layer;

[0017] Obtain the trace data of the ground receiving trace and the receiving well bottom trace of each shot point in the target layer;

[0018] Pick up the bottom cut of each trace data according to the preset waveform characteristics;

[0019] Based on the first arrival time, determine the waveform data for spectral analysis corresponding to the ground receiving trace and the receiving well bottom trace of each shot point in the target layer.

[0020] Further, the applying of the waveform data corresponding to each trace to determine the quality factor includes:

[0021] Apply the preset spectral ratio method to determine the quality factor according to the waveform data corresponding to the ground receiving trace and the receiving well bottom trace of each shot point in the target layer.

[0022] Further, the determining of the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model includes:

[0023] Perform ray tracing based on the near-surface velocity model to obtain the ray paths corresponding to the surface receiving channels and the bottom-hole receiving channels of each shot point, and the propagation time of each ray path.

[0024] Based on the ray paths corresponding to the surface receiving channels and the bottom-hole receiving channels of each shot point, and the propagation time of each ray path, respectively determine the propagation velocity differences of the surface receiving channels and the bottom-hole receiving channels of each shot point in the target layer.

[0025] Further, the obtaining the near-surface quality factor of the target area by fitting according to the quality factor and the propagation time difference includes:

[0026] Perform polynomial fitting on the propagation velocity differences corresponding to each shot point and the quality factor to obtain corresponding fitting curves.

[0027] Based on the fitting curves, fit to obtain the near-surface quality factor of the target area.

[0028] In a second aspect, the present application provides an apparatus for obtaining a near-surface quality factor based on dual-well micro-logging data, including:

[0029] A near-surface velocity model acquisition module, configured to obtain a near-surface velocity model and a first arrival time of a target area according to dual-well micro-logging data of the target area and data of multiple shot points.

[0030] A waveform data determination module, configured to determine waveform data corresponding to each channel of a shot point in a target layer based on the near-surface velocity model and the first arrival time.

[0031] A propagation time difference determination module, configured to determine a quality factor by applying the waveform data corresponding to each channel, and determine the propagation time difference of each channel corresponding to each shot point in the target layer according to the near-surface velocity model.

[0032] A near-surface quality factor determination module, configured to obtain the near-surface quality factor of the target area by fitting according to the quality factor and the propagation time difference.

[0033] Further, the near-surface velocity model acquisition module is configured to perform the following:

[0034] Obtain dual-well micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and a receiving point.

[0035] According to the dual-well micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and a receiving point, apply a preset seismic data interpretation method to determine the near-surface velocity model of the target area.

[0036] Further, based on the crosshole micro-logging data of the target area and the relative coordinates between each shot point and the receiving point located within the target area, the first arrival time is determined by applying a preset seismic data picking method.

[0037] Furthermore, the waveform data determination module is configured to perform the following:

[0038] Select the layer with a velocity value greater than the velocity threshold from the near-surface velocity model as the target layer;

[0039] Obtain the trace data of the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer;

[0040] Pick up the bottom cut of each trace data according to the preset waveform characteristics;

[0041] Based on the first arrival time, determine the waveform data for spectral analysis corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer.

[0042] Furthermore, the propagation time difference determination module is configured to perform the following:

[0043] Apply the preset spectral ratio method to determine the quality factor according to the waveform data corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer.

[0044] Furthermore, the propagation time difference determination module is also configured to perform the following:

[0045] Perform ray tracing according to the near-surface velocity model to obtain the ray paths corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point, and the propagation time of each ray path;

[0046] Based on the ray paths corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point, and the propagation time of each ray path, respectively determine the propagation velocity difference of the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer.

[0047] Furthermore, the near-surface quality factor determination module is configured to perform the following:

[0048] Perform polynomial fitting on the propagation velocity difference and the quality factor corresponding to each shot point to obtain the corresponding fitting curve;

[0049] Based on the fitting curve, fit and obtain the near-surface quality factor of the target area.

[0050] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method for obtaining the near-surface quality factor based on the crosshole micro-logging data as described above is implemented.

[0051] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for obtaining the near-surface quality factor based on the crosshole micro-logging data as described above is implemented.

[0052] As can be seen from the above technical solutions, the present application provides a method and device for obtaining a near-surface quality factor based on crosshole micro-logging data. The method includes: obtaining a near-surface velocity model and first arrival times of a target area according to the crosshole micro-logging data and multiple shot point data of the target area; determining waveform data corresponding to each trace of the shot points in the target layer based on the near-surface velocity model and the first arrival times; determining the quality factor by applying the waveform data corresponding to each trace, and determining the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model; fitting the quality factor and the propagation time difference to obtain the near-surface quality factor of the target area, which can effectively improve the accuracy and reliability of obtaining the near-surface quality factor, and can effectively improve the processing efficiency, automation degree and accuracy of the process of obtaining the near-surface quality factor based on crosshole micro-logging data, can obtain an accurate near-surface Q value, makes more full use of the data of multiple shot points in the high-velocity layer compared with the calculation method of only selecting one shot data or averaging multiple shot data, makes the calculation result more statistically characteristic, this algorithm can avoid the uncertainty of the calculation result caused by artificial data selection, and further can effectively improve the accuracy and reliability of applying the obtained result of the near-surface quality factor for reservoir exploitation and other processing, and effectively save labor and material costs. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1 It is a schematic diagram of crosshole micro-logging ray propagation of a two-layer velocity model in an embodiment of the present application.

[0055] Figure 2 It is a schematic flowchart of the method for obtaining the near-surface quality factor based on crosshole micro-logging data in an embodiment of the present application.

[0056] Figure 3 It is a schematic flowchart of step 100 in the method for obtaining near-surface quality factor based on crosshole micro-logging data in an embodiment of the present application.

[0057] Figure 4 It is a schematic flowchart of step 200 in the method for obtaining near-surface quality factor based on crosshole micro-logging data in an embodiment of the present application.

[0058] Figure 5 It is a specific schematic flowchart of step 300 in the method for obtaining near-surface quality factor based on crosshole micro-logging data in an embodiment of the present application.

[0059] Figure 6 It is a specific schematic flowchart of step 400 in the method for obtaining near-surface quality factor based on crosshole micro-logging data in an embodiment of the present application.

[0060] Figure 7 It is a schematic structural diagram of the device for obtaining near-surface quality factor based on crosshole micro-logging data in an embodiment of the present application.

[0061] Figure 8 It is a schematic diagram of the crosshole micro-logging observation system and the near-surface structure diagram of a certain work area in an application example of the present application.

[0062] Figure 9 It is a schematic diagram of the first arrival and bottom cut of the surface trace data and the bottom hole trace data of the shot point with an excitation depth of 15 m in an application example of the present application.

[0063] Figure 10 It is an amplitude spectrum and a logarithmic diagram of the amplitude spectrum ratio in an application example of the present application.

[0064] Figure 11 It is a schematic diagram of the curve corresponding to the calculated Q value and the depth in an application example of the present application.

[0065] Figure 12 It is a schematic diagram of the curve corresponding to the calculated Q value and DT and the fitting line in an application example of the present application.

[0066] Figure 13 It is a schematic structural diagram of the electronic device in an embodiment of the present application. Specific embodiments

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0068] Among the existing Q-value estimation methods, the spectral ratio method is the most widely used method. The principle of the spectral ratio method is briefly introduced below.

[0069] According to the Futterman attenuation model, the amplitude spectrum of seismic waves in a viscoelastic medium can be expressed as:

[0070]

[0071] Where: A(f,t) is the amplitude spectrum of seismic waves at time t, S(f) is the amplitude spectrum of the source wavelet, G(f) is the geophone response, P(t) is an operator independent of frequency including geometric spreading, transmission loss, etc., f is the frequency, and Q is the quality factor. Taking the logarithm of the ratio of the amplitude spectra at times t2 and t1, we have:

[0072]

[0073] Assuming that two receiving points have the same source wavelet and geophone response, the above equation can be simplified to:

[0074]

[0075] Where: is a constant independent of frequency.

[0076] Obviously, Equation (3) is a linear function of frequency. Let the slope of the fitted line be k, then the quality factor Q can be calculated by the following equation:

[0077]

[0078] Using dual-well micro-log data to obtain the near-surface Q value is a method generally considered reliable in the industry. Figure 1 Figure 1 is a schematic diagram of ray propagation of dual-well micro-log for a two-layer velocity model, where the velocity of the first layer is V0, the thickness is H0, the quality factor is Q0, the velocity of the second layer is V1, the quality factor is Q1, R1, R2, and R3 are receiving points on the ground, R4 is the receiving point at the bottom of the well, and S i is the excitation point. According to the Futterman attenuation model, when the shot point S i excites, the amplitude spectrum at the receiving point R1 is:

[0079]

[0080] Where: A(f,t1+t2) is the amplitude spectrum received at the receiving point R3, S i (f) is the amplitude spectrum of the source wavelet, G3(f) is the geophone response, P(t1+t2) is an operator independent of frequency including geometric spreading, transmission loss, etc., f is the frequency, Q0 is the quality factor of the first layer, and Q1 is the quality factor of the second layer.

[0081] Shot point S i When excited, the amplitude spectrum at the receiving point R4 is as follows:

[0082]

[0083] Where: A(f,t3) is the amplitude spectrum received at the receiving point R4, S i (f) is the source wavelet amplitude spectrum, G4(f) is the geophone response, P(t3) is an operator independent of frequency including geometric spreading, transmission loss, etc., f is the frequency, and Q1 is the quality factor of the second layer.

[0084] Taking the logarithm of the ratio of the amplitude spectra at the receiving points R3 and R4, assuming the geophone responses are the same, we have:

[0085]

[0086] In equation (7), if t1 - t3 = 0, then the Q0 value of the first layer can be obtained by fitting a straight line.

[0087] The conventional method for calculating Q0 in cross - hole micro - logging is to select the surface trace and the bottom - hole trace data of a certain shot in the high - velocity layer and obtain it using the spectral ratio method (or other methods). In this way, the calculation of Q0 is greatly affected by human factors. Different shots yield different results, and sometimes the differences are significant. It is impossible to determine which Q0 value calculated from which shot should be used as the final result. Even using the method of averaging the values from multiple shots cannot solve this problem well.

[0088] Therefore, the present application provides a method for obtaining the near - surface quality factor based on cross - hole micro - logging data, and the implementation steps are as follows:

[0089] 1) Obtain cross - hole micro - logging data and the relative coordinates of the shot point and the receiving point using common methods, pick the first - arrival time using common seismic data picking methods, and obtain the near - surface velocity model through conventional interpretation methods.

[0090] 2) According to the near - surface velocity model, select the shot points in the high - velocity layer, and for each shot, select two traces of data, one surface receiving trace and the bottom - hole trace data of the receiving well.

[0091] 3) According to the waveform characteristics of the selected data, manually pick the bottom cut of each trace of data, and combine the first - arrival time to determine the waveform data for spectral analysis of each trace.

[0092] 4) Calculate the Q value using the conventional spectral ratio method with the surface trace waveform data and the bottom - hole trace waveform data of each shot.

[0093] 5) Perform ray tracing according to the near - surface velocity model to obtain the ray paths of the surface trace and the bottom - hole trace of each shot, and the propagation time of each segment of the ray, as Figure 1 shown.

[0094] 6) Calculate the propagation time difference of the surface traces and the bottom-hole traces of each shot in the high-velocity layer, that is, t1 - t3, denoted as DT. Fit DT and the Q value (obtained in step 4) with a polynomial to obtain a fitting curve. From formula (7), it can be seen that the Q value when DT = 0 is the desired Q0.

[0095] Based on the above, an embodiment of a method for obtaining the near-surface quality factor based on dual-well micro-logging data is provided in this application. Refer to Figure 2 The method for obtaining the near-surface quality factor based on dual-well micro-logging data specifically includes the following content:

[0096] Step 100: Obtain the near-surface velocity model and the first arrival time of the target area according to the dual-well micro-logging data and multiple shot-point data of the target area.

[0097] Step 200: Based on the near-surface velocity model and the first arrival time, determine the waveform data corresponding to each trace of the shot points in the target layer.

[0098] Step 300: Use the waveform data corresponding to each trace to determine the quality factor, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model.

[0099] Step 400: Fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference.

[0100] As can be seen from the above description, the method for obtaining the near-surface quality factor based on dual-well micro-logging data provided in the embodiment of this application obtains the near-surface velocity model and the first arrival time of the target area according to the dual-well micro-logging data and multiple shot-point data of the target area; based on the near-surface velocity model and the first arrival time, determine the waveform data corresponding to each trace of the shot points in the target layer; use the waveform data corresponding to each trace to determine the quality factor, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model; fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference, which can effectively improve the accuracy and reliability of obtaining the near-surface quality factor, and can effectively improve the processing efficiency, automation degree and accuracy of the process of obtaining the near-surface quality factor based on dual-well micro-logging data, can obtain an accurate near-surface Q value, makes more full use of the data of multiple shot points in the high-velocity layer compared with the calculation method of only selecting one-shot data or averaging multiple-shot data, makes the calculation result more statistically characteristic, this algorithm can avoid the uncertainty of the calculation result caused by artificial data selection, and then can effectively improve the accuracy and reliability of applying the obtained result of the near-surface quality factor for reservoir exploitation and other processing, and effectively save labor and material costs.

[0101] In an embodiment of a method for obtaining near-surface quality factor based on dual-well micro-logging data, refer to Figure 3 , step 100 in the method for obtaining near-surface quality factor based on dual-well micro-logging data specifically includes the following content:

[0102] Step 110: Obtain the dual-well micro-logging data of the target area, and the relative coordinates between each shot point and the receiving point located within the target area.

[0103] Step 120: According to the dual-well micro-logging data of the target area, and the relative coordinates between each shot point and the receiving point located within the target area, apply a preset seismic data interpretation method to determine the near-surface velocity model of the target area.

[0104] Step 130: According to the dual-well micro-logging data of the target area, and the relative coordinates between each shot point and the receiving point located within the target area, apply a preset seismic data picking method to determine the first arrival time.

[0105] In an embodiment of a method for obtaining near-surface quality factor based on dual-well micro-logging data, refer to Figure 4 , step 200 in the method for obtaining near-surface quality factor based on dual-well micro-logging data can specifically include the following content:

[0106] Step 210: Select the layer with a velocity value greater than the velocity threshold from the near-surface velocity model as the target layer.

[0107] Step 220: Obtain the trace data of the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer.

[0108] Step 230: Pick the bottom cut of each trace data according to the preset waveform characteristics.

[0109] Step 240: Based on the first arrival time, determine the waveform data for spectral analysis corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer.

[0110] In an embodiment of a method for obtaining near-surface quality factor based on dual-well micro-logging data, refer to Figure 5 , step 300 in the method for obtaining near-surface quality factor based on dual-well micro-logging data can specifically include the following content:

[0111] Step 310: Apply the preset spectral ratio method to determine the quality factor according to the waveform data corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer.

[0112] In an embodiment of a method for obtaining near-surface quality factor based on crosshole micro-logging data, refer to Figure 5 Step 300 in the method for obtaining near-surface quality factor based on crosshole micro-logging data may further specifically include the following content:

[0113] Step 320: Perform ray tracing according to the near-surface velocity model to obtain the ray paths corresponding to the surface receiving channels and the bottom channels of the receiving wells for each shot point, and the propagation time of each ray path.

[0114] Step 330: Based on the ray paths corresponding to the surface receiving channels and the bottom channels of the receiving wells for each shot point, and the propagation time of each ray path, respectively determine the propagation velocity differences of the surface receiving channels and the bottom channels of the receiving wells for each shot point in the target layer.

[0115] In an embodiment of a method for obtaining near-surface quality factor based on crosshole micro-logging data, refer to Figure 6 Step 400 in the method for obtaining near-surface quality factor based on crosshole micro-logging data specifically includes the following content:

[0116] Step 410: Perform polynomial fitting on the propagation velocity differences corresponding to each shot point and the quality factor to obtain a corresponding fitting curve.

[0117] Step 420: Based on the fitting curve, fit to obtain the near-surface quality factor of the target area.

[0118] In summary, the present application can obtain an accurate near-surface Q value, makes more full use of the data of multiple shot points in the high-velocity layer compared with the calculation method of only selecting one-shot data or averaging multiple-shot data, and makes the calculation result more statistically characteristic. This algorithm can avoid the uncertainty of the calculation result caused by artificial data selection.

[0119] From the software level, the present application provides an embodiment of a device for obtaining near-surface quality factor based on crosshole micro-logging data for implementing all or part of the content in the method for obtaining near-surface quality factor based on crosshole micro-logging data. Refer to Figure 7 The device for obtaining near-surface quality factor based on crosshole micro-logging data specifically includes the following content:

[0120] Near-surface velocity model acquisition module 10, configured to obtain the near-surface velocity model and the first arrival time of the target area according to the crosshole micro-logging data and multiple shot point data of the target area;

[0121] Waveform data determination module 20, configured to determine the waveform data corresponding to each channel of the shot point in the target layer based on the near-surface velocity model and the first arrival time;

[0122] The propagation time difference determination module 30 is configured to determine the quality factor by applying the waveform data corresponding to each trace, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model;

[0123] The near-surface quality factor determination module 40 is configured to fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference.

[0124] The embodiment of the near-surface quality factor acquisition device based on the crosshole micro-logging data provided by the present application can specifically be used to execute the processing flow of the embodiment of the near-surface quality factor acquisition method based on the crosshole micro-logging data in the above embodiment, and its functions will not be elaborated here. For details, reference can be made to the detailed description of the above method embodiment.

[0125] As can be seen from the above description, the near-surface quality factor acquisition device based on the crosshole micro-logging data provided by the embodiment of the present application acquires the near-surface velocity model and the first arrival time of the target area according to the crosshole micro-logging data and multiple shot point data of the target area; based on the near-surface velocity model and the first arrival time, determine the waveform data corresponding to each trace of the shot points in the target layer; determine the quality factor by applying the waveform data corresponding to each trace, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model; fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference, which can effectively improve the acquisition accuracy and reliability of the near-surface quality factor, and can effectively improve the processing efficiency, automation degree and accuracy of the near-surface quality factor acquisition process based on the crosshole micro-logging data, and can obtain an accurate near-surface Q value. Compared with the calculation method of only selecting one shot of data or averaging multiple shots of data, it makes more full use of the data of multiple shot points in the high-velocity layer, making the calculation result more statistically characteristic. This algorithm can avoid the uncertainty of the calculation result caused by artificial data selection, and thus can effectively improve the accuracy and reliability of applying the near-surface quality factor acquisition result for reservoir exploitation and other processing, and effectively save labor and material costs.

[0126] In an embodiment of the near-surface quality factor acquisition device based on the crosshole micro-logging data, the near-surface velocity model acquisition module 10 in the near-surface quality factor acquisition device based on the crosshole micro-logging data is configured to perform the following:

[0127] Step 110: Acquire the crosshole micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and the receiving point.

[0128] Step 120: Determine the near-surface velocity model of the target area by applying a preset seismic data interpretation method based on the crosshole micro-logging data of the target area and the relative coordinates between each shot point and the receiving point located within the target area.

[0129] Step 130: Determine the first arrival time by applying a preset seismic data picking method based on the crosshole micro-logging data of the target area and the relative coordinates between each shot point and the receiving point located within the target area.

[0130] In an embodiment of a near-surface quality factor acquisition device based on crosshole micro-logging data, the waveform data determination module 20 in the near-surface quality factor acquisition device based on crosshole micro-logging data is used to perform the following:

[0131] Step 210: Select the layer with a velocity value greater than the velocity threshold from the near-surface velocity model as the target layer.

[0132] Step 220: Obtain the trace data of the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer.

[0133] Step 230: Pick the bottom cut of each trace data according to the preset waveform characteristics.

[0134] Step 240: Determine the waveform data for spectral analysis corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer based on the first arrival time.

[0135] In an embodiment of a near-surface quality factor acquisition device based on crosshole micro-logging data, the propagation time difference determination module 30 in the near-surface quality factor acquisition device based on crosshole micro-logging data is used to perform the following:

[0136] Step 310: Apply the preset spectral ratio method to determine the quality factor according to the waveform data corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point in the target layer.

[0137] In an embodiment of a near-surface quality factor acquisition device based on crosshole micro-logging data, the propagation time difference determination module 30 in the near-surface quality factor acquisition device based on crosshole micro-logging data is further used to perform the following:

[0138] Step 320: Perform ray tracing according to the near-surface velocity model to obtain the ray paths corresponding to the surface receiving trace and the bottom trace of the receiving well for each shot point, and the propagation time of each ray path segment.

[0139] Step 330: Based on the ray paths corresponding to the surface receiving channels and the bottom-hole receiving channels of each shot point, and the propagation time of each ray path segment, respectively determine the propagation velocity difference of the surface receiving channels and the bottom-hole receiving channels of each shot point in the target layer.

[0140] In an embodiment of a near-surface quality factor acquisition device based on dual-well micro-logging data, the near-surface quality factor determination module 40 in the near-surface quality factor acquisition device based on dual-well micro-logging data is used to perform the following:

[0141] Step 410: Perform polynomial fitting on the propagation velocity difference corresponding to each shot point and the quality factor to obtain a corresponding fitting curve.

[0142] Step 420: Based on the fitting curve, fit to obtain the near-surface quality factor of the target area.

[0143] To further illustrate the present solution, the present application also provides a specific application example of a method for obtaining a near-surface quality factor based on dual-well micro-logging data by applying a near-surface quality factor acquisition device based on dual-well micro-logging data, which is specifically described as follows:

[0144] Figure 8 is a schematic diagram of a dual-well micro-logging observation system for a certain work area. Figure 8 The dots in the figure are shot points, and the triangle points are receiving points. The well depth is 20m, the distance between the two wells is 5m, 22 shots are fired in the excitation well, and there are a total of 13 receiving channels. The offset of channels 1-4 is 1m, the offset of channels 5-8 is 2m, the offset of channels 9-12 is 3m, and channel 13 is the bottom-hole receiving channel with an offset of 5m. The steps for calculating the near-surface Q0 by this method are as follows:

[0145] 1) Obtain the dual-well micro-logging data and the relative coordinates of the shot points and receiving points by the usual method, and obtain the near-surface velocity model through conventional interpretation methods. Refer to Figure 8 , and obtain the first arrival time by using the usual seismic data picking method.

[0146] 2) According to the near-surface velocity model, select the shot points in the high-velocity layer. In this example, select the shot points with an excitation depth of 6-16 meters, a total of 11 shots. For each shot, select the data of one surface receiving channel and one bottom-hole receiving channel. In this example, the surface channel number 12 is selected for the surface channel, and the bottom-hole channel number 13 is selected for the bottom-hole channel.

[0147] 3) According to the waveform characteristics of the selected data, manually pick the bottom cut of each channel of data. As Figure 9 shown, it is the data of the surface receiving channel (channel number 12) and the bottom-hole receiving channel (channel number 13) excited at a depth of 15 meters in the dual-well micro-logging of a certain work area. The red short line in the figure is the first arrival, and the blue short line is the bottom cut. Combine the first arrival to determine the waveform data of each channel of data for spectral analysis.

[0148] 4) For each shot, the Q value of the surface trace and the bottom-hole trace is calculated using the conventional spectral ratio method. The amplitude spectrum and the logarithm of the amplitude spectrum ratio are as Figure 10 shown. Figure 10 For Figure 9 the amplitude spectrum diagram and the logarithm diagram of the amplitude spectrum ratio of the two traces in Figure 11 it, the blue curve in the upper left of the figure is the amplitude spectrum of the surface trace, the red curve in the lower left is the amplitude spectrum of the bottom-hole trace, the black curve on the right is the logarithm curve of the amplitude spectrum ratio of the two traces, and the green line on the right is the fitting line of the spectral ratio method. The curve corresponding to the calculated Q value and the depth is as Figure 11 shown. Figure 11 In it, the abscissa is the depth and the ordinate is the Q value. The Q value obtained at the position of the excitation depth of 11 meters is an abnormal value and has been removed in

[0149] 5) According to Figure 8 the near-surface velocity model shown, ray tracing is performed to obtain the ray paths of the surface trace and the bottom-hole trace of each shot, and the propagation time of each segment of the ray.

[0150] 6) Calculate the propagation time difference of the surface trace and the bottom-hole trace of each shot in the high-velocity layer, that is, t1 - t3, denoted as DT. As Figure 12 shown, linearly fit DT and the Q value (obtained in step 4). The Q value when DT = 0 is the desired Q0. Therefore, for this micro-logging well point, Q0 = 1.657. Figure 12 In it, the abscissa is DT (time difference) and the ordinate is the Q value. The red dashed line is the fitting curve. In this example, linear fitting is used, and the fitting line formula is Y = 222.8X + 1.657. Therefore, the surface layer Q0 of this micro-logging well point is 1.657.

[0151] Based on this, the multi-shot fitting near-surface Q value calculation method provided by this application uses the shot points in the high-velocity layer of the excitation well to fit the relationship curve between the propagation time difference of the surface trace and the bottom-hole trace in the high-velocity layer and the Q value calculated from the data of these two traces, and obtains the accurate surface layer Q value. The propagation time difference of the surface trace and the bottom-hole trace in the high-velocity layer is obtained through ray tracing. The Q value when this time difference is 0 is the desired surface layer Q value.

[0152] From the hardware level, this application provides an embodiment of an electronic device for implementing all or part of the content in the method for obtaining the near-surface quality factor based on dual-well micro-logging data. The electronic device specifically includes the following content:

[0153] Figure 13 For the schematic block diagram of the device composition of the electronic device 9600 in the embodiment of this application. As Figure 13 shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It should be noted that thisFigure 13 is exemplary; other types of structures can also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0154] In one embodiment, the function of obtaining the near-surface quality factor based on the crosshole micro-logging data can be integrated into the central processing unit. Among them, the central processing unit can be configured to perform the following controls:

[0155] Step 100: Obtain the near-surface velocity model and the first arrival time of the target area according to the crosshole micro-logging data and multiple shot point data of the target area.

[0156] Step 200: Based on the near-surface velocity model and the first arrival time, determine the waveform data corresponding to each trace of the shot points in the target layer.

[0157] Step 300: Apply the waveform data corresponding to each trace to determine the quality factor, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model.

[0158] Step 400: Fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference.

[0159] As can be seen from the above description, the electronic device provided in the embodiment of the present application obtains the near-surface velocity model and the first arrival time of the target area according to the crosshole micro-logging data and multiple shot point data of the target area; based on the near-surface velocity model and the first arrival time, determine the waveform data corresponding to each trace of the shot points in the target layer; apply the waveform data corresponding to each trace to determine the quality factor, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model; fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference, which can effectively improve the accuracy and reliability of obtaining the near-surface quality factor, and can effectively improve the processing efficiency, automation degree and accuracy of the process of obtaining the near-surface quality factor based on the crosshole micro-logging data, and can obtain an accurate near-surface Q value. Compared with the calculation method of only selecting one shot data or averaging multiple shot data, it makes more full use of the data of multiple shot points in the high-velocity layer, making the calculation result more statistically characteristic. This algorithm can avoid the uncertainty of the calculation result caused by artificial data selection, and thus can effectively improve the accuracy and reliability of applying the result of obtaining the near-surface quality factor for reservoir exploitation and other processes, and effectively save the labor and material costs.

[0160] In another embodiment, the near-surface quality factor acquisition device based on crosshole micro-logging data can be separately configured from the central processing unit 9100. For example, the near-surface quality factor acquisition device based on crosshole micro-logging data can be configured as a chip connected to the central processing unit 9100, and the function of acquiring the near-surface quality factor based on crosshole micro-logging data can be realized through the control of the central processing unit.

[0161] As Figure 13 shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily have to include Figure 13 all the components shown in Figure 13 ; in addition, the electronic device 9600 may further include

[0162] As Figure 13 shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processing unit 9100 receives inputs and controls the operations of the various components of the electronic device 9600.

[0163] Among them, the memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. The above information related to failures can be stored, and in addition, programs for executing relevant information can also be stored. And the central processing unit 9100 can execute the programs stored in the memory 9140 to implement information storage or processing, etc.

[0164] The input unit 9120 provides inputs to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to supply power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display can be, for example, an LCD display, but is not limited thereto.

[0165] The memory 9140 can be a solid-state memory, for example, a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It can also be a memory that stores information even when power is off, can be selectively erased and has more data. Examples of such a memory are sometimes referred to as EPROMs, etc. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage unit 9142 that is used to store application programs and function programs or the processes for operating the electronic device 9600 by the central processing unit 9100.

[0166] The memory 9140 can also include a data storage unit 9143 that is used to store data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 can include various drivers of the electronic device for communication functions and / or for performing other functions of the electronic device (such as a messaging application, an address book application, etc.).

[0167] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal.

[0168] Based on different communication technologies, multiple communication modules 9110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc. The communication module (transmitter / receiver) 9110 is also coupled to the speaker 9131 and the microphone 9132 via the audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby implementing normal telecommunication functions. The audio processor 9130 can include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is also coupled to the central processing unit 9100, so that recording can be performed on the local machine through the microphone 9132 and the sound stored on the local machine can be played through the speaker 9131.

[0169] Embodiments of the present application further provide a computer-readable storage medium capable of implementing all steps in the method for obtaining the near-surface quality factor based on dual-well micro-logging data in the above embodiments. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, all steps in the method for obtaining the near-surface quality factor based on dual-well micro-logging data with the execution subject being a server or a client in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0170] Step 100: Obtain the near-surface velocity model and the first arrival time of the target area according to the dual-well micro-logging data and multiple shot point data of the target area.

[0171] Step 200: Based on the near-surface velocity model and the first arrival time, determine the waveform data corresponding to each trace of the shot points in the target layer.

[0172] Step 300: Determine the quality factor by applying the waveform data corresponding to each trace, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model.

[0173] Step 400: Fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference.

[0174] As can be seen from the above description, the computer-readable storage medium provided by the embodiments of the present application obtains the near-surface velocity model and the first arrival time of the target area according to the dual-well micro-logging data and multiple shot point data of the target area; based on the near-surface velocity model and the first arrival time, determines the waveform data corresponding to each trace of the shot points in the target layer; determines the quality factor by applying the waveform data corresponding to each trace, and determines the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model; fits the near-surface quality factor of the target area according to the quality factor and the propagation time difference, which can effectively improve the accuracy and reliability of obtaining the near-surface quality factor, and can effectively improve the processing efficiency, automation degree and accuracy of the process of obtaining the near-surface quality factor based on dual-well micro-logging data, can obtain an accurate near-surface Q value, makes more full use of the data of multiple shot points in the high-velocity layer compared with the calculation method of only selecting one shot data or averaging multiple shot data, makes the calculation result more statistically characteristic, this algorithm can avoid the uncertainty of the calculation result caused by artificial data selection, and thus can effectively improve the accuracy and reliability of applying the obtained result of the near-surface quality factor for reservoir exploitation and other processing, and effectively save labor and material costs.

[0175] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, apparatus, or computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0176] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (apparatuses), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0177] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0178] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0179] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for obtaining near-surface quality factor based on dual-well micro-log data, characterized in that, Including: Obtain the near-surface velocity model and the first arrival time of the target area according to the crosshole micro-logging data and multiple shot point data of the target area; Based on the near-surface velocity model and the first arrival time, determine the waveform data corresponding to each trace of the shot points in the target layer; Apply the waveform data corresponding to each trace to determine the quality factor, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model; Fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference; The determining the waveform data corresponding to each trace of the shot points in the target layer based on the near-surface velocity model and the first arrival time includes: Select the layer with a velocity value greater than the velocity threshold from the near-surface velocity model as the target layer; the target layer is the high-velocity layer corresponding to the shot points with an excitation depth of 6-16 meters; Obtain the trace data of the surface receiving trace and the bottom of the receiving well of each shot point in the target layer; Pick up the bottom cut of each trace data according to the preset waveform characteristics; Based on the first arrival time, determine the waveform data for spectral analysis corresponding to the surface receiving trace and the bottom of the receiving well of each shot point in the target layer; The applying the waveform data corresponding to each trace to determine the quality factor includes: Apply the preset spectral ratio method to determine the quality factor according to the waveform data corresponding to the surface receiving trace and the bottom of the receiving well of each shot point in the target layer, and eliminate the abnormal quality factors; The determining the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model includes: Perform ray tracing according to the near-surface velocity model to obtain the ray paths corresponding to the surface receiving trace and the bottom of the receiving well of each shot point, and the propagation time of each ray path segment; Based on the ray paths corresponding to the surface receiving trace and the bottom of the receiving well of each shot point, and the propagation time of each ray path segment, respectively determine the propagation time difference of the surface receiving trace and the bottom of the receiving well of each shot point in the target layer.

2. The method for obtaining near-surface quality factor based on dual-well micro-log data according to claim 1, wherein The obtaining the near-surface velocity model and the first arrival time of the target area according to the crosshole micro-logging data and multiple shot point data of the target area includes: Obtain the crosshole micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and the receiving point; According to the crosshole micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and the receiving point, apply the preset seismic data interpretation method to determine the near-surface velocity model of the target area; And, according to the crosshole micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and the receiving point, apply the preset seismic data picking method to determine the first arrival time.

3. The method for obtaining near-surface quality factor based on dual-well micro-logging data according to claim 1, characterized in that, The fitting the near-surface quality factor of the target area according to the quality factor and the propagation time difference includes: Perform polynomial fitting on the propagation time difference and the quality factor corresponding to each shot point to obtain the corresponding fitting curve; The near-surface quality factor of the target area is obtained by fitting based on the fitting curve.

4. A device for obtaining near-surface quality factor based on dual-well micro-logging data, characterized in that, It includes: A near-surface velocity model acquisition module, configured to obtain the near-surface velocity model and the first arrival time of the target area according to the crosshole micro-logging data and multiple shot point data of the target area; A waveform data determination module, configured to determine the waveform data corresponding to each trace of the shot points in the target layer based on the near-surface velocity model and the first arrival time; A propagation time difference determination module, configured to determine the quality factor by applying the waveform data corresponding to each trace, and determine the propagation time difference of each trace corresponding to each shot point in the target layer according to the near-surface velocity model; A near-surface quality factor determination module, configured to fit the near-surface quality factor of the target area according to the quality factor and the propagation time difference; The waveform data determination module is configured to perform the following: Select the layer with a velocity value greater than the velocity threshold from the near-surface velocity model as the target layer; the target layer is the high-velocity layer corresponding to the shot points with an excitation depth of 6-16 meters; Obtain the trace data of the surface receiving trace and the bottom trace of the receiving well of each shot point in the target layer; Pick up the bottom cut of each trace data according to the preset waveform characteristics; Based on the first arrival time, determine the waveform data for spectral analysis corresponding to the surface receiving trace and the bottom trace of the receiving well of each shot point in the target layer; The propagation time difference determination module is configured to perform the following: Apply the preset spectral ratio method to determine the quality factor according to the waveform data corresponding to the surface receiving trace and the bottom trace of the receiving well of each shot point in the target layer, and eliminate the abnormal quality factors; The propagation time difference determination module is further configured to perform the following: Perform ray tracing according to the near-surface velocity model to obtain the ray paths corresponding to the surface receiving trace and the bottom trace of the receiving well of each shot point, and the propagation time of each ray path segment; Based on the ray paths corresponding to the surface receiving trace and the bottom trace of the receiving well of each shot point, and the propagation time of each ray path segment, respectively determine the propagation time difference of the surface receiving trace and the bottom trace of the receiving well of each shot point in the target layer.

5. The near-surface quality factor acquisition device based on dual-well micro-logging data according to claim 4, characterized in that, The near-surface velocity model acquisition module is configured to perform the following: Obtain the crosshole micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and the receiving point; According to the crosshole micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and the receiving point, apply the preset seismic data interpretation method to determine the near-surface velocity model of the target area; And, according to the crosshole micro-logging data of the target area, and the relative coordinates between each shot point located in the target area and the receiving point, apply the preset seismic data picking method to determine the first arrival time.

6. The near-surface quality factor acquisition device based on dual-well micro-logging data according to claim 4, characterized in that The near-surface quality factor determination module is configured to perform the following: Perform polynomial fitting on the propagation time difference and the quality factor corresponding to each shot point to obtain the corresponding fitting curve; Based on the fitting curve, fit the near-surface quality factor of the target area.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for obtaining near-surface quality factor based on dual-well micro-logging data according to any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for obtaining near-surface quality factor based on dual-well micro-logging data according to any one of claims 1 to 3.

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