A high-resolution P-wave time difference extraction method, device and equipment

Abstract

The application discloses a high-resolution P-wave time difference extraction method, device and equipment, and the method comprises the following steps: extracting a low-resolution P-wave time difference curve and a P-wave arrival time curve of a target well based on monopole array waveform data of the target well, and extracting a low-resolution S-wave time difference curve of the target well based on dipole array waveform data of the target well; constructing a borehole earth numerical model of the target well based on a caliper logging curve, a density logging curve and the low-resolution P-wave time difference curve and the low-resolution S-wave time difference curve of the target well, and determining a simulated monopole P-wave waveform curve of the target well based on the borehole earth numerical model; performing frequency-wave number domain analysis based on the simulated monopole P-wave waveform curve to determine a high-order P-wave frequency band distribution range; performing filtering processing on the measured monopole array waveform data based on the high-order P-wave frequency band distribution range to obtain high-order P-wave array waveform data; and determining a high-resolution P-wave time difference based on the high-order P-wave array waveform data and the P-wave arrival time curve.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unconventional reservoir oil and gas exploration, and particularly relates to a high-resolution P-wave time difference extraction method, device and equipment. BACKGROUND

[0002] With the continuous development and deepening of oil and gas field exploration and development, unconventional reservoir oil and gas exploration such as shale and tight sandstone has gradually deepened, making the requirements for logging technology and interpretation higher and higher. In the problem of thin layer division and thick layer subdivision, high-resolution acoustic logging plays an important role, and plays a key role in the evaluation of future logging data and the re-evaluation of old well logging data. When evaluating the formation, it is often necessary to estimate the acoustic parameters and petrophysical properties of horizontal thin layers in order to better understand the characteristics and reserves of oil reservoirs. The conventional processing of array acoustic logging data is limited by the structure of the instrument, and usually gives the average slowness curve in the range of the acceptance transducer array span, and the highest resolution is between the first acceptance transducer and the last acceptance transducer. This often ignores the formation characteristics of the span less than the array span, which is not conducive to the logging interpretation of thin layers.

[0003] In 1987, Hsu and Chang proposed using multi-source waveform similarity stacking method to process repeated sub-array data to improve the resolution of slowness curve. In 2000, Tang X M et al. proposed a signal processing method for evaluating thin layers using high-resolution acoustic slowness logging, which improved the resolution from 3.5ft to 0.5ft; then proposed a double waveform matching method using sub-array combination to obtain different formation spans, realizing 0.5ft resolution of P-wave time difference extraction. In 2005, Li Pengju et al. proposed a processing method combining correlation spectrum and multi-shot point, that is, using correlation spectrum method to extract single-pole P-wave time difference of the shortest common emission and common receiving sub-array of multi-pole array acoustic logging, and then averaging the P-wave time difference of all the shortest common emission or common receiving sub-arrays across the same depth formation to obtain common emission P-wave time difference or common receiving P-wave time difference, respectively. In 2019, T Lei et al. proposed a new interpretation algorithm based on a powerful downscaling technique, which extracts all the convolution relationships between different resolution acoustic logging curves to obtain an overdetermined matrix, and uses Moore-Penrose pseudo-inverse method to estimate the time difference to improve the vertical resolution of logging curves. In 2020, Zhou Haoyi proposed combining the bisection method, simulated annealing method and slowness-time method to extract component wave time difference. This method improves the traditional slowness-time correlation method in terms of processing efficiency and accuracy. SUMMARY

[0004] In order to realize high-resolution P-wave time difference extraction by using depth-offset multi-dimensional information and high-frequency components in waveform data, improve the identification accuracy of thin layer interpretation or thick layer subdivision, and further enrich the technical route and increase the selection space, the embodiments of the present application provide a high-resolution P-wave time difference extraction method, device and equipment.

[0005] In the first aspect, the embodiments of the present application provide a high-resolution P-wave time difference extraction method, which can include:

[0006] Extracting a low-resolution P-wave time difference curve and a P-wave arrival time curve of a target well based on monopole array waveform data of the target well, and extracting a low-resolution S-wave time difference curve of the target well based on dipole array waveform data of the target well;

[0007] Based on the caliper logging curve and the density logging curve of the target well and the low-resolution P-wave time difference curve and the low-resolution S-wave time difference curve, a borehole formation numerical model of the target well is constructed, and a simulated monopole P-wave waveform curve of the target well is determined based on the borehole formation numerical model.

[0008] Based on the simulated monopole P-wave waveform curve, frequency-wavenumber domain analysis is performed to determine a high-order P-wave frequency band distribution range.

[0009] Based on the high-order P-wave frequency band distribution range, the measured monopole array waveform data is filtered to obtain high-order P-wave array waveform data.

[0010] Based on the high-order P-wave array waveform data and the P-wave arrival time curve, the high-resolution P-wave time difference is determined.

[0011] Optionally, the construction of the borehole formation numerical model of the target well based on the caliper logging curve and the density logging curve of the target well and the low-resolution P-wave time difference curve and the low-resolution S-wave time difference curve can include:

[0012] Based on the caliper logging curve and the density logging curve of the target well and the low-resolution P-wave time difference curve and the low-resolution S-wave time difference curve, a uniform change layer section of each curve is determined.

[0013] Based on the uniform change layer section of each curve, the borehole formation numerical model of the target well is constructed.

[0014] Optionally, the determination of the simulated monopole P-wave waveform curve of the target well based on the borehole formation numerical model can include:

[0015] Based on the borehole formation numerical model, forward modeling is performed by using real axis integral method and / or finite difference method to obtain the simulated monopole P-wave waveform curve of the target well.

[0016] Optionally, the determining the high-resolution P-wave travel time difference based on the high-order P-wave array waveform data and the P-wave arrival time curve can comprise:

[0017] performing P-wave arrival time constrained correlation analysis on the high-order P-wave array waveform data based on the P-wave arrival time curve as a window starting time to determine the high-resolution P-wave travel time difference.

[0018] Optionally, the method can further comprise:

[0019] preprocessing the array sonic logging curve of the target well to obtain preprocessed monopole array waveform data and processed dipole array waveform data.

[0020] Optionally, the preprocessing the array sonic logging curve of the target well can comprise:

[0021] performing gain recovery on the array sonic raw waveform data of the target well based on the array sonic gain curve of the target well;

[0022] performing delay recovery on the gain-recovered array sonic raw waveform data based on the array sonic delay curve of the target well to pad the array sonic raw waveform data before zero time with zeros;

[0023] performing band-pass filtering processing on the delay-recovered array sonic raw waveform data to obtain the preprocessed monopole array waveform data and the processed dipole array waveform data.

[0024] In a second aspect, an embodiment of the present application provides an oil and gas exploration method, which can comprise: evaluating reservoir characteristics and reserves of an oil reservoir based on formation rock physical properties determined based on high-resolution P-wave travel time difference;

[0025] wherein the high-resolution P-wave travel time difference is determined based on the high-resolution P-wave travel time difference extraction method as described in the first aspect.

[0026] In a third aspect, an embodiment of the present application provides a high-resolution P-wave travel time difference extraction device, which can comprise:

[0027] an extraction module configured to extract a low-resolution P-wave travel time difference curve and a P-wave arrival time curve based on monopole array waveform data of a target well, and extract a low-resolution S-wave travel time difference curve based on dipole array waveform data of the target well.

[0028] The construction module is configured to construct a borehole formation numerical model of the target well based on a caliper logging curve, a density logging curve and the low-resolution P-wave slowness curve and the low-resolution S-wave slowness curve of the target well, and determine a simulated monopole P-wave waveform curve of the target well based on the borehole formation numerical model.

[0029] The analysis module is configured to perform frequency-wavenumber domain analysis based on the simulated monopole P-wave waveform curve to determine a high-order P-wave frequency band distribution range.

[0030] The filtering processing module is configured to perform filtering processing on the measured monopole array waveform data based on the high-order P-wave frequency band distribution range to obtain high-order P-wave array waveform data.

[0031] The determination module is configured to determine the high-resolution P-wave slowness based on the high-order P-wave array waveform data and the P-wave arrival time curve.

[0032] In a fourth aspect, an embodiment of the present application provides an oil and gas exploration device, which can include an evaluation module configured to evaluate reservoir characteristics and reserves of an oil reservoir based on formation rock physical properties determined based on the high-resolution P-wave slowness.

[0033] The high-resolution P-wave slowness is determined based on the high-resolution P-wave slowness extraction method of the first aspect.

[0034] In a fifth aspect, an embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the high-resolution P-wave slowness extraction method of the first aspect or the oil and gas exploration method of the second aspect.

[0035] In a sixth aspect, an embodiment of the present application provides a computer device including a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the high-resolution P-wave slowness extraction method of the first aspect or the oil and gas exploration method of the second aspect when executing the program.

[0036] The above technical solutions provided by the embodiments of the present application have at least the following beneficial effects:

[0037] The embodiments of the present application provide a high-resolution P-wave slowness extraction method, device and equipment, which determines a high-order P-wave frequency band distribution range by forward simulation, and then extracts high-resolution slowness based on the high-order P-wave frequency band, so that the longitudinal resolution of P-wave slowness is obviously improved. The high-resolution P-wave slowness curve shows more information about changes in formation details, and corresponds well with the depth changes of the electrical imaging diagram. Furthermore, the method lays a foundation for better analyzing oil and gas reservoir characteristics and oil and gas reserves in the later oil and gas exploration process.

[0038] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0039] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 This is a flow chart of a high-resolution longitudinal wave time difference extraction method provided in an embodiment of the present invention;

[0042] Figure 2 A flowchart of a detailed high-resolution longitudinal wave time difference extraction method provided in an embodiment of the present invention;

[0043] Figure 3 This is an example of original well logging data of a certain well provided in an embodiment of the present invention;

[0044] Figure 4 for Figure 3 An example of preprocessing of raw well logging data;

[0045] Figure 5 An example of a simulated monopole waveform corresponding to a forward model constructed for a certain well logging curve in the example provided in an embodiment of the present invention;

[0046] Figure 6 A two-dimensional spectrum in the frequency-wavenumber domain corresponding to the simulated monopole waveform provided in an embodiment of the present invention;

[0047] Figure 7 A comparison diagram of the high-resolution longitudinal wave time difference and the low-resolution longitudinal wave time difference provided in an embodiment of the present invention;

[0048] Figure 8 This is a schematic structural diagram of a high-resolution longitudinal wave time difference extraction device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be thoroughly understood and fully conveyed to those skilled in the art.

[0050] The inventors found that the existing research mainly focuses on how to use monopole array acoustic wave data to extract P-wave time difference, and does not consider how to use depth-offset multidimensional information and high-frequency components in waveform data to carry out high-resolution P-wave time difference extraction. In view of the above defects and problems, the present application is proposed in order to provide a high-resolution P-wave time difference extraction method, device and equipment which overcomes the above problems or at least partially solves the above problems.

[0051] The present application provides a high-resolution P-wave time difference extraction method, as shown in Figure 1 The method can include the following steps:

[0052] Step S11, based on the monopole array waveform data of the target well, extracting the low-resolution P-wave time difference curve and the P-wave arrival time curve of the target well, based on the dipole array waveform data of the target well, extracting the low-resolution S-wave time difference curve of the target well.

[0053] Step S12, based on the caliper logging curve, density logging curve and low-resolution P-wave time difference curve, low-resolution S-wave time difference curve of the target well, constructing a borehole earth numerical model of the target well, and determining a simulated monopole P-wave waveform curve of the target well based on the borehole earth numerical model.

[0054] Step S13, frequency-wavenumber domain analysis based on the simulated monopole P-wave waveform curve to determine the high-order P-wave frequency band distribution range.

[0055] Step S14, filtering the measured monopole array waveform data based on the high-order P-wave frequency band distribution range to obtain high-order P-wave array waveform data.

[0056] Step S15, based on the high-order P-wave array waveform data and the P-wave arrival time curve, determining the high-resolution P-wave time difference.

[0057] The high-resolution P-wave time difference extraction method provided in the embodiment of the application first determines the high-order P-wave frequency band distribution range (second-order P-wave frequency band distribution range) by using forward simulation, and then uses the second-order P-wave to carry out high-resolution time difference extraction on this basis. The longitudinal resolution of the P-wave time difference is obviously improved from 1 m in the traditional method to 0.3 m. The high-resolution P-wave time difference curve shows more information about the change of the formation details, and corresponds well to the depth change of the electrical imaging diagram. Furthermore, the method lays a foundation for better analyzing the characteristics of the oil and gas reservoir and the oil and gas reserves in the later oil and gas exploration process.

[0058] In one detailed embodiment, referring to Figure 2 The high-resolution P-wave time difference extraction method can include the following steps:

[0059] Step S21, acquiring conventional logging data and array acoustic logging data of a target well.

[0060] This step is to precollect and acquire the data of the target well. The conventional logging data can mainly include a natural gamma ray logging curve (GR), a caliper logging curve, a density logging curve, etc. The array acoustic logging data can include a monopole array acoustic original waveform curve, a monopole array acoustic gain curve, a monopole array acoustic delay curve, and a dipole array acoustic original waveform curve, a dipole array acoustic gain curve, and a dipole array acoustic delay curve. The subsequent natural gamma ray logging curve can be used to verify the results, so as to verify the accuracy of the method provided in the embodiment of the application.

[0061] In one specific example, a well logging is taken as an example for illustration, referring to Figure 3 The first track in the figure is a natural gamma ray logging curve, the second track is a caliper logging curve, the third track is a depth track, the fourth track is a density logging curve, the fifth track is a monopole array acoustic original waveform curve from which P-wave information can be observed, and the sixth track is a dipole array acoustic original waveform curve from which S-wave information can be observed.

[0062] Step S22, pre-processing the array acoustic logging curve of the target well to obtain pre-processed monopole array waveform data and processed dipole array waveform data.

[0063] This step is to pre-process the acquired monopole array acoustic original waveform and dipole array acoustic original waveform. The specific processing process is as follows: first, gain recovery is performed, then delay recovery is performed, and finally band-pass filtering is performed.

[0064] That is, the array acoustic logging curve of the target well is pre-processed, specifically including the following steps: first, gain recovery is performed on the array acoustic original waveform data of the target well based on the array acoustic gain curve of the target well.

[0065] In order to ensure the amplitude of the waveform to be in the highest precision, the automatic gain control is used in the acquisition of the monopole array acoustic original waveform, so that the waveform amplitude is amplified from a fraction to an integer, thereby facilitating the storage and recording. Therefore, the gain recovery is first performed on the acquired waveform, and the gain parameter AGN is shown in equation (1).

[0066] AGN = 10 GN*0.05 Equation (1)

[0067] In the gain recovery, the monopole array waveform is divided by AGN.

[0068] Then, the array acoustic original waveform data after the gain recovery is delayed and recovered based on the array acoustic delay curve of the target well, so as to zero fill before the zero time of the array acoustic original waveform data; that is, in the waveform acquisition process, in order to reduce the storage amount of the recorded data, the waveform data of a period of time before the arrival of the first wave is not acquired. The purpose of the delay recovery is to zero fill before the zero time of the monopole array waveform, so as to obtain the waveform data of the accurate time.

[0069] Finally, the array acoustic original waveform data after the delay recovery is subjected to the band-pass filter processing, so as to obtain the monopole array waveform data and the dipole array waveform data after the preprocessing. In the execution of this step, the purpose of the band-pass filter is to extract the low-frequency Stoneley wave in the monopole waveform, and the frequency band range is usually selected as 600-3000 Hz. Similarly, the dipole array acoustic data preprocessing mainly includes the gain recovery, the delay recovery and the band-pass filter. The purpose of the dipole array acoustic data preprocessing is to obtain the dipole transverse wave waveform data, and the frequency band range of the band-pass filter is selected as 3000-5500 Hz.

[0070] Referring to Figure 4 Fig. 2, wherein the first track is the depth track; the second track is the monopole array waveform data after the preprocessing, from which it can be observed that the longitudinal wave signal becomes clearer after the waveform preprocessing; and the fifth track is the dipole array waveform data after the preprocessing, from which it can be observed that the transverse wave signal becomes clearer after the waveform preprocessing.

[0071] In step S23, the low-resolution longitudinal wave moveout curve and the longitudinal wave arrival time curve of the target well are extracted based on the monopole array waveform data of the target well, and the low-resolution transverse wave moveout curve of the target well is extracted based on the dipole array waveform data of the target well.

[0072] In this step, the correlation coefficient method can be used to extract the low-resolution P-wave slowness curve, P-wave arrival time curve, and low-resolution S-wave slowness curve of the target well. In specific implementation, the correlation method is selected to extract the P-wave slowness curve from the preprocessed monopole array waveform data. This method calculates the correlation function of the array waveform in the time and slowness dimensions, and the slowness at which the maximum function value is located is the low-resolution P-wave slowness. Based on the low-resolution P-wave slowness, the P-wave arrival time curve can be directly calculated by considering the well diameter and the length of the logging instrument. Similarly, the correlation method is selected to extract the S-wave slowness curve from the preprocessed dipole array acoustic waveform data. This method calculates the correlation function of the array waveform in the time and slowness dimensions, and the slowness at which the maximum function value is located is the low-resolution S-wave slowness.

[0073] Still parameters Figure 4 are shown, wherein the third trace is a low-resolution P-wave slowness curve, which is the result of processing the monopole array waveform data by using the conventional slowness extraction method (correlation coefficient method); the fourth trace is a P-wave arrival time curve, which is the result of joint calculation of the low-resolution P-wave slowness curve, the logging instrument parameters, and the well diameter curve; and the sixth trace is a low-resolution S-wave slowness curve, which is the result of processing the dipole array waveform data by using the conventional slowness extraction method.

[0074] Step S24, based on the well diameter logging curve, the density logging curve, and the low-resolution P-wave slowness curve and the low-resolution S-wave slowness curve of the target well, a borehole earth numerical model of the target well is constructed.

[0075] In implementation of this step, first, the uniform variation layer section of each curve is determined based on the well diameter logging curve, the density logging curve, and the low-resolution P-wave slowness curve and the low-resolution S-wave slowness curve of the target well; then, based on the uniform variation layer section of each curve, the borehole earth numerical model of the target well is constructed.

[0076] That is, the well diameter logging curve, the density logging curve, the P-wave slowness curve, and the S-wave slowness curve of the well are comprehensively analyzed, and the layer section in which the well diameter has no collapse, the density value, the P-wave slowness, and the S-wave slowness have no mutation is found out. The density, the well diameter, the P-wave slowness, and the S-wave slowness of the layer section are taken as input parameters to construct the borehole earth numerical model of the target well. Also refer to the above Figure 4 are shown. In combination with the above example, the well diameter, the density, the P-wave slowness, and the S-wave slowness at the depth of 2491m are selected by comprehensively considering various conditions such as the well diameter having no expansion, the density value, the P-wave slowness, and the S-wave slowness having no mutation, and the borehole earth numerical model is established.

[0077] Step S25, based on the borehole earth numerical model, a simulated monopole P-wave waveform curve of the target well is determined.

[0078] The step is based on the borehole ground numerical model, and the forward modeling is carried out by using the real axis integral method and / or the finite difference method to obtain the simulated monopole longitudinal wave waveform curve of the target well. Generally, the real monopole array acoustic logging instrument measures eight monopole longitudinal wave waveforms at each depth position, which have different source distances, and these source distances should also be input parameters in the numerical simulation process. Referring to Figure 5 , the forward modeling result is shown in Figure 5 , in which the horizontal coordinate is time, and the vertical coordinate is the source distance corresponding to the 8-channel monopole array waveform, the minimum source distance is 12ft, and the maximum source distance is 15.5ft. It can be observed from the figure that the first arrival is the longitudinal wave waveform (P), and the second arrival is the shear wave waveform (S) with larger amplitude.

[0079] Step S26, frequency-wavenumber domain analysis is carried out based on the simulated monopole longitudinal wave waveform curve to determine the high-order longitudinal wave frequency band distribution range.

[0080] In this step, the frequency-wavenumber domain analysis of the simulated monopole longitudinal wave waveform curve is mainly to perform double Fourier transform on the waveform data to obtain the frequency-wavenumber two-dimensional spectrum corresponding to the simulated monopole array waveform as shown in Figure 6 . In the two-dimensional spectrum, the horizontal axis is set as the wavenumber, and the vertical axis is set as the frequency. From the figure, the first-order longitudinal wave mode with lower frequency and the second-order longitudinal wave mode with higher frequency can be observed, and the frequency band distribution range of the second-order longitudinal wave mode is the frequency selected for the next band-pass filtering. Among them, the wavenumber less than 20m -1 mainly represents the longitudinal wave mode, and a low-frequency band with a frequency distribution range of 8-10kHz can be observed; a high-frequency band, i.e. the high-order longitudinal wave mode, with a frequency distribution range of 16-17.5kHz can also be observed.

[0081] Step S27, the measured monopole array waveform data is filtered based on the high-order longitudinal wave frequency band distribution range to obtain the high-order longitudinal wave array waveform data.

[0082] In this step, the measured monopole array waveform data is filtered based on the high-order longitudinal wave frequency band distribution range to obtain the high-order longitudinal wave array waveform data. In the specific implementation, a digital band-pass filter is set, and the frequency band passing range is the high-order longitudinal wave frequency band distribution range calculated in step S26. The measured monopole array waveform data is processed by using the digital band-pass filter, so that the low-order longitudinal wave information is filtered out, and the remaining waveform data is the high-order longitudinal wave information.

[0083] Referring to Figure 7The result of the filtering processing is shown, wherein the first trace is a depth trace; the second trace is a pre-processed wideband monopole waveform; and the third trace is a result of processing the wideband monopole waveform by selecting a high-order longitudinal wave frequency band, which represents a high-order longitudinal wave waveform signal. By comparing the third trace with the second trace, it can be found that the high-order longitudinal wave waveform frequency is indeed higher, and the waveform oscillation period is more in the same time.

[0084] In step S28, high-resolution longitudinal wave moveout is determined based on the high-order longitudinal wave array waveform data and the longitudinal wave arrival time curve.

[0085] In the implementation of the present step, the correlation analysis method with arrival time constraint is improved on the basis of the existing correlation method. Specifically, the window starting time is no longer a fixed value set by the user, but is based on the longitudinal wave arrival time curve as the window starting time. The high-order longitudinal wave array waveform data is subjected to the correlation analysis with longitudinal wave arrival time constraint to determine the high-resolution longitudinal wave moveout, which aims to reduce the influence of noise.

[0086] The correlation analysis with longitudinal wave arrival time constraint on the high-order longitudinal wave array waveform data obtains the high-resolution longitudinal wave moveout curve, which is shown in the fifth trace of Figure 7 Compared with the low-resolution longitudinal wave moveout curve shown in the fourth trace, the high-resolution longitudinal wave moveout curve exhibits more information about the change of the formation details. Figure 7 The sixth trace in the figure is an electrical imaging diagram, in which the light color represents high resistance and the dark color represents low resistance. It can be seen that the high-resolution longitudinal wave moveout curve corresponds well to the light and dark changes of the electrical imaging diagram, while the low-resolution longitudinal wave moveout curve cannot exhibit the corresponding relationship.

[0087] The high-resolution longitudinal wave moveout extraction method provided in the embodiments of the present application determines the measured high-order longitudinal wave frequency band through forward modeling waveform, and then extracts the high-resolution longitudinal wave moveout based on the high-order longitudinal wave frequency band. Compared with the conventional longitudinal wave moveout extraction method, the present method can improve the longitudinal resolution of the longitudinal wave moveout from the conventional 1 m to 0.3 m. Further, the arrival time constraint is performed by taking the longitudinal wave arrival time curve as the window starting time to reduce the influence of noise.

[0088] Based on the same inventive concept, the embodiments of the present application further provide a high-resolution longitudinal wave moveout extraction device, which is shown in Figure 8 The device can include an extraction module 12, a construction module 13, an analysis module 14, a filtering processing module 15 and a determination module 16, and the working principles are as follows:

[0089] The extraction module 12 is configured to extract a low-resolution longitudinal wave moveout curve and a longitudinal wave arrival time curve of a target well based on monopole array waveform data of the target well, and extract a low-resolution shear wave moveout curve of the target well based on dipole array waveform data of the target well.

[0090] The constructing module 13 is configured to construct a borehole earth numerical model of the target well based on a caliper logging curve, a density logging curve, a low-resolution compressional wave slowness curve and a low-resolution shear wave slowness curve of the target well, and determine a simulated monopole compressional wave waveform curve of the target well based on the borehole earth numerical model.

[0091] The analyzing module 14 is configured to perform frequency-wavenumber domain analysis based on the simulated monopole compressional wave waveform curve to determine a high-order compressional wave frequency band distribution range.

[0092] The filtering processing module 15 is configured to perform filtering processing on the measured monopole array waveform data based on the high-order compressional wave frequency band distribution range to obtain high-order compressional wave array waveform data.

[0093] The determining module 16 is configured to determine a high-resolution compressional wave slowness based on the high-order compressional wave array waveform data and a compressional wave arrival time curve.

[0094] In an optional embodiment, the constructing module 13 is specifically configured to:

[0095] determine a uniform variation layer section of each curve based on the caliper logging curve, the density logging curve, the low-resolution compressional wave slowness curve and the low-resolution shear wave slowness curve of the target well.

[0096] construct the borehole earth numerical model of the target well based on the uniform variation layer section of each curve.

[0097] In another optional embodiment, the constructing module 13 is specifically further configured to perform forward modeling in a real axis integral method and / or a finite difference method based on the borehole earth numerical model to obtain the simulated monopole compressional wave waveform curve of the target well.

[0098] In another optional embodiment, the determining module 16 is specifically configured to perform compressional wave arrival time constrained correlation analysis on the high-order compressional wave array waveform data based on the compressional wave arrival time curve as a window starting time to determine the high-resolution compressional wave slowness.

[0099] In another optional embodiment, referring to Figure 8 the device can further include an obtaining module 10, and the obtaining module 11 is configured to obtain conventional logging data and array sonic logging data of a target well.

[0100] In another optional embodiment, referring to Figure 8 the device can further include a preprocessing module 11, and the preprocessing module 11 is configured to pre-process array sonic logging curves of the target well to obtain pre-processed monopole array waveform data and processed dipole array waveform data.

[0101] In another optional embodiment, the preprocessing module 11 is specifically configured to:

[0102] Gain recovery is performed on the array acoustic wave original waveform data of the target well based on the array acoustic wave gain curve of the target well;

[0103] Delay recovery is performed on the array acoustic wave original waveform data after gain recovery based on the array acoustic wave delay curve of the target well, so that the array acoustic wave original waveform data is zero-padded before zero time.

[0104] Band-pass filtering is performed on the array acoustic wave original waveform data after delay recovery, to obtain preprocessed monopole array waveform data and dipole array waveform data.

[0105] Based on the same inventive concept, the embodiments of the application further provide an oil and gas exploration method, which can include: evaluating reservoir characteristics and reserves of an oil reservoir based on formation rock physical properties determined based on high-resolution P-wave time difference; wherein the high-resolution P-wave time difference is determined based on the high-resolution P-wave time difference extraction method described above.

[0106] Based on the same inventive concept, the embodiments of the application further provide an oil and gas exploration device, which includes: an evaluation module configured to evaluate reservoir characteristics and reserves of an oil reservoir based on formation rock physical properties determined based on high-resolution P-wave time difference; wherein the high-resolution P-wave time difference is determined based on the high-resolution P-wave time difference extraction method described above.

[0107] Based on the same inventive concept, the embodiments of the application further provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the high-resolution P-wave time difference extraction method described above, or implements the oil and gas exploration method described above.

[0108] Based on the same inventive concept, the embodiments of the application further provide a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the high-resolution P-wave time difference extraction method described above, or implements the oil and gas exploration method described above.

[0109] The above-mentioned devices, media, and related equipment in the embodiments of the application solve the problems in the same way as the above-mentioned methods, so the implementation can refer to the implementation of the above-mentioned methods, and the repeated parts will not be described again.

[0110] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may 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, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0114] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A high-resolution longitudinal wave time difference extraction method, characterized in that: include: Extracting a low-resolution P-wave time difference curve and a P-wave arrival time curve of the target well based on the monopole array waveform data of the target well, and extracting a low-resolution S-wave time difference curve of the target well based on the dipole array waveform data of the target well; constructing a wellbore formation numerical model of the target well based on the caliper logging curve, density logging curve, the low-resolution P-wave time difference curve, and the low-resolution S-wave time difference curve of the target well, and determining a simulated monopolar P-wave waveform curve of the target well based on the wellbore formation numerical model; Performing frequency-wavenumber domain analysis based on the simulated monopole longitudinal wave waveform curve to determine the distribution range of high-order longitudinal wave frequency bands; Performing filtering processing on the measured monopole array waveform data based on the high-order longitudinal wave frequency band distribution range to obtain high-order longitudinal wave array waveform data; The high-resolution longitudinal wave time difference is determined based on the high-order longitudinal wave array waveform data and the longitudinal wave arrival time curve.

2. The method according to claim 1, characterized in that The method of constructing a wellbore formation numerical model of the target well based on the caliper logging curve, the density logging curve, the low-resolution P-wave time difference curve, and the low-resolution S-wave time difference curve of the target well comprises: Based on the caliper logging curve, the density logging curve, the low-resolution P-wave time difference curve, and the low-resolution S-wave time difference curve of the target well, determining a layer section where each curve has a uniform change; Based on the uniformly changing layers of each curve, a wellbore formation numerical model of the target well is constructed.

3. The method according to claim 1, characterized in that The step of determining a simulated monopolar longitudinal wave waveform curve of the target well based on the wellbore formation numerical model includes: Based on the wellbore formation numerical model, forward simulation is performed using a real axis integration method and / or a finite difference method to obtain a simulated monopolar P-wave waveform curve of the target well.

4. The method according to claim 1, wherein The determining of the high-resolution longitudinal wave time difference based on the high-order longitudinal wave array waveform data and the longitudinal wave arrival time curve comprises: Based on the longitudinal wave arrival time curve as the windowing start time, the longitudinal wave arrival time constraint correlation analysis is performed on the high-order longitudinal wave array waveform data to determine the high-resolution longitudinal wave time difference.

5. The method according to any one of claims 1 to 4, characterized in that Also includes: The array acoustic logging curve of the target well is preprocessed to obtain preprocessed monopole array waveform data and processed dipole array waveform data.

6. The method according to claim 5, characterized in that The pre-processing of the array acoustic logging curve of the target well includes: Performing gain recovery on the original waveform data of the array acoustic wave of the target well based on the array acoustic wave gain curve of the target well; Performing delay recovery on the array acoustic wave original waveform data after gain recovery based on the array acoustic wave delay curve of the target well, so as to fill zeros before time zero of the array acoustic wave original waveform data; The original waveform data of the array acoustic wave after delay recovery is subjected to bandpass filtering to obtain pre-processed monopole array waveform data and dipole array waveform data.

7. A method for oil and gas exploration, characterized in that: include: Evaluate reservoir characteristics and reserves based on formation rock physical properties determined by high-resolution P-wave time difference; Wherein, the high-resolution longitudinal wave time difference is determined based on the high-resolution longitudinal wave time difference extraction method according to any one of claims 1 to 6.

8. A high-resolution longitudinal wave time difference extraction device, characterized in that: include: an extraction module, configured to extract a low-resolution P-wave time difference curve and a P-wave arrival time curve of the target well based on the monopole array waveform data of the target well, and to extract a low-resolution S-wave time difference curve of the target well based on the dipole array waveform data of the target well; a construction module, configured to construct a wellbore formation numerical model of the target well based on a caliper logging curve, a density logging curve, the low-resolution P-wave time difference curve, and the low-resolution S-wave time difference curve of the target well, and determine a simulated monopolar P-wave waveform curve of the target well based on the wellbore formation numerical model; An analysis module, configured to perform frequency-wavenumber domain analysis based on the simulated monopole longitudinal wave waveform curve to determine a frequency band distribution range of high-order longitudinal waves; A filtering processing module, configured to perform filtering processing on the measured monopole array waveform data based on the high-order longitudinal wave frequency band distribution range to obtain high-order longitudinal wave array waveform data; A determination module is used to determine the high-resolution longitudinal wave time difference based on the high-order longitudinal wave array waveform data and the longitudinal wave arrival time curve.

9. An oil and gas exploration device, characterized in that: include: An evaluation module for evaluating reservoir characteristics and reserves based on formation rock physical properties determined by high-resolution P-wave time difference; Wherein, the high-resolution longitudinal wave time difference is determined based on the high-resolution longitudinal wave time difference extraction method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the high-resolution longitudinal wave time difference extraction method according to any one of claims 1 to 6 is implemented, or the oil and gas exploration method according to claim 7 is implemented.

11. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the high-resolution longitudinal wave time difference extraction method according to any one of claims 1 to 6 is implemented, or the oil and gas exploration method according to claim 7 is implemented.

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