CA3203426A1 - Calculation method and device for interval transit time, and storage medium - Google Patents
Calculation method and device for interval transit time, and storage mediumInfo
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Abstract
A calculation method for interval transit time. The method comprises: determining time domain boundaries of different types of wave signals in original signals of a pre-acquired target depth point (101); extracting target wave signals of the target depth point from the original signals of the target depth point according to the time domain boundaries of the different types of wave signals in the original signals of the target depth point (102); calculating frequency domain information and time domain information of the target wave signals (103); and calculating the interval transit time of the target wave signals at the target depth point by using the frequency domain information and the time domain information (104).
Description
Calculation Method and Device for Interval Transit Time, and Storage Medium Technical Field Embodiments of the present application relate to, but are not limited to, the field of logging data processing, in particular to a method for calculating an interval transit time, an apparatus, and a storage medium.
Background An interval transit time refers to a time of an acoustic wave signal to propagate per unit distance in the formation, and is an important parameter in logging interpretation, and is of great importance in use, it may be used for calculating porosity, identifying formation and rock layer, calculating rock mechanics characteristic parameter, indicating the overpressured formation, estimating formation strength, predicting petroleum and sand production pressure of formation, estimating reservoir permeability, evaluating formation anisotropy, analyzing .. borehole stability, etc., and it is a basis and a key point of array acoustic logging data processing and interpretation.
In a related technology, a Slowness Time Coherence (STC) method proposed by Kimball in 1984 is adopted to calculate the interval transit time. In this method, by correlating waveforms of different receivers, a time slowness correlation diagram is obtained, and then s a maximum value of a correlation coefficient is sought to obtain a transmit time of a target wave signal.
In a practical application, this method adopts a flow which includes artificial qualitative recognition, artificial stratification, stratification parameter artificial determination, and STC
method quantitative calculation, which has problems such as heavy workload, high technical difficulty, and relatively poor timeliness, etc.
Summary The following is a summary of the subject matter described in detail herein.
This Date regue/Date received 2023-05-26 summary is not intended to limit the protection scope of the claims.
An embodiment of the present application provides a method for calculating an interval transit time.
In order to achieve a purpose of the embodiments of the present application, an embodiment of the present application provides a method for calculating an interval transit time, which includes: determining a time domain boundary of different types of wave signals in an original signal of a target depth point acquired in advance; extracting a target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point; calculating frequency domain information and time domain information of the target wave signal; and calculating an interval transit time of the target wave signal at the target depth point by using the frequency domain information and the time domain information.
A storage medium is provided, having a computer program stored therein, wherein the computer program is configured to perform the method described above when running.
An electronic apparatus is provided, including a memory and a processor, wherein the memory has a computer program stored therein, and the processor is configured to run the computer program to perform the method described above.
Other aspects will become apparent after reading and understanding the drawings and detailed description.
Brief Description of Drawings Accompanying drawings are used to provide further understanding of technical solutions of embodiments of the present application, and constitute a part of the specification. They are used together with embodiments of the embodiments of the present application to explain the technical solutions of the embodiments of the present application, but do not constitute a restriction on the technical solutions of the embodiments of the present application.
FIG. 1 is a flowchart of a method for calculating an interval transit time provided according to an embodiment of the present application.
Background An interval transit time refers to a time of an acoustic wave signal to propagate per unit distance in the formation, and is an important parameter in logging interpretation, and is of great importance in use, it may be used for calculating porosity, identifying formation and rock layer, calculating rock mechanics characteristic parameter, indicating the overpressured formation, estimating formation strength, predicting petroleum and sand production pressure of formation, estimating reservoir permeability, evaluating formation anisotropy, analyzing .. borehole stability, etc., and it is a basis and a key point of array acoustic logging data processing and interpretation.
In a related technology, a Slowness Time Coherence (STC) method proposed by Kimball in 1984 is adopted to calculate the interval transit time. In this method, by correlating waveforms of different receivers, a time slowness correlation diagram is obtained, and then s a maximum value of a correlation coefficient is sought to obtain a transmit time of a target wave signal.
In a practical application, this method adopts a flow which includes artificial qualitative recognition, artificial stratification, stratification parameter artificial determination, and STC
method quantitative calculation, which has problems such as heavy workload, high technical difficulty, and relatively poor timeliness, etc.
Summary The following is a summary of the subject matter described in detail herein.
This Date regue/Date received 2023-05-26 summary is not intended to limit the protection scope of the claims.
An embodiment of the present application provides a method for calculating an interval transit time.
In order to achieve a purpose of the embodiments of the present application, an embodiment of the present application provides a method for calculating an interval transit time, which includes: determining a time domain boundary of different types of wave signals in an original signal of a target depth point acquired in advance; extracting a target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point; calculating frequency domain information and time domain information of the target wave signal; and calculating an interval transit time of the target wave signal at the target depth point by using the frequency domain information and the time domain information.
A storage medium is provided, having a computer program stored therein, wherein the computer program is configured to perform the method described above when running.
An electronic apparatus is provided, including a memory and a processor, wherein the memory has a computer program stored therein, and the processor is configured to run the computer program to perform the method described above.
Other aspects will become apparent after reading and understanding the drawings and detailed description.
Brief Description of Drawings Accompanying drawings are used to provide further understanding of technical solutions of embodiments of the present application, and constitute a part of the specification. They are used together with embodiments of the embodiments of the present application to explain the technical solutions of the embodiments of the present application, but do not constitute a restriction on the technical solutions of the embodiments of the present application.
FIG. 1 is a flowchart of a method for calculating an interval transit time provided according to an embodiment of the present application.
2 Date regue/Date received 2023-05-26 FIG. 2 is a schematic diagram of a signal image of a single receiver of an array acoustic logging tool provided according to an embodiment of the present application.
FIG. 3 is a schematic diagram of a result of acoustic wave segmentation on the signal image shown in FIG. 2.
FIG. 4 is a structure diagram of a full convolution neural network provided according to an embodiment of the present application.
FIG. 5 is a schematic diagram of segmenting different wave signals of a single depth point according to a time domain boundary according to an embodiment of the present application.
FIG. 6 is a schematic diagram of a time-frequency spectrum obtained after performing a spectrum calculation on a P wave in FIG. 5.
FIG. 7 is a schematic diagram of performing an extraction operation on a time-spectrum diagram in FIG. 6.
Detailed Description In order to make purposes, technical solutions, and advantages of embodiments of the present application clearer, embodiments of the present application will be described in detail below in conjunction with accompanying drawings. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily if there is no conflict.
An embodiment of the present application provides a method for calculating an interval transit time, wherein array acoustic logging data is use to accurately calculate transmit times of various wave signals.
FIG. 1 is a flowchart of a method for calculating an interval transit time provided by an embodiment of the present application. As shown in FIG. 1, the illustrated method includes the following acts 101 to 104.
In the act 101, a time domain boundary of different types of wave signals in an original signal of a target depth point acquired in advance is determined.
In an exemplary embodiment, the different types of wave signals may be longitudinal
FIG. 3 is a schematic diagram of a result of acoustic wave segmentation on the signal image shown in FIG. 2.
FIG. 4 is a structure diagram of a full convolution neural network provided according to an embodiment of the present application.
FIG. 5 is a schematic diagram of segmenting different wave signals of a single depth point according to a time domain boundary according to an embodiment of the present application.
FIG. 6 is a schematic diagram of a time-frequency spectrum obtained after performing a spectrum calculation on a P wave in FIG. 5.
FIG. 7 is a schematic diagram of performing an extraction operation on a time-spectrum diagram in FIG. 6.
Detailed Description In order to make purposes, technical solutions, and advantages of embodiments of the present application clearer, embodiments of the present application will be described in detail below in conjunction with accompanying drawings. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily if there is no conflict.
An embodiment of the present application provides a method for calculating an interval transit time, wherein array acoustic logging data is use to accurately calculate transmit times of various wave signals.
FIG. 1 is a flowchart of a method for calculating an interval transit time provided by an embodiment of the present application. As shown in FIG. 1, the illustrated method includes the following acts 101 to 104.
In the act 101, a time domain boundary of different types of wave signals in an original signal of a target depth point acquired in advance is determined.
In an exemplary embodiment, the different types of wave signals may be longitudinal
3 Date regue/Date received 2023-05-26 waves, transverse waves, or Stoneley waves under a monopole acoustic source;
or, they may be leakage longitudinal waves or transverse waves under a dipolar acoustic source.
In an exemplary embodiment, a time domain boundary is time information, wherein the time information is a demarcation point between a stop time of reception of one type of wave and a start time of reception of another type of wave, that is, a wave signal in the original signal in the time before reaching the time domain boundary is one type of wave signal, and a wave signal in the original signal in the time after the time domain boundary is another type of wave signal.
In the act 102, a target wave signal of the target depth point is extracted from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point.
In the act 103, frequency domain information and time domain information of the target wave signal are calculated.
In the act 104, an interval transit time of the target wave signal at the target depth point is calculated by using the frequency domain information and the time domain information.
The method for calculating an interval transit time in an embodiment of the present disclosure may be performed by a computer.
In the method provided according to an embodiment of the present application, by determining the time domain boundary of the different types of wave signals in the original signal of the target depth point acquired in advance, extracting the target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point, calculating the frequency domain information and the time domain information of the target wave signal, and then calculating the interval transit time of the target wave signal at the .. target depth point by using the frequency domain information and the time domain information, there are advantages of small workload and good calculation timeliness compared with a calculation method in a related technology.
The method provided according to an embodiment of the present application is described below.
or, they may be leakage longitudinal waves or transverse waves under a dipolar acoustic source.
In an exemplary embodiment, a time domain boundary is time information, wherein the time information is a demarcation point between a stop time of reception of one type of wave and a start time of reception of another type of wave, that is, a wave signal in the original signal in the time before reaching the time domain boundary is one type of wave signal, and a wave signal in the original signal in the time after the time domain boundary is another type of wave signal.
In the act 102, a target wave signal of the target depth point is extracted from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point.
In the act 103, frequency domain information and time domain information of the target wave signal are calculated.
In the act 104, an interval transit time of the target wave signal at the target depth point is calculated by using the frequency domain information and the time domain information.
The method for calculating an interval transit time in an embodiment of the present disclosure may be performed by a computer.
In the method provided according to an embodiment of the present application, by determining the time domain boundary of the different types of wave signals in the original signal of the target depth point acquired in advance, extracting the target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point, calculating the frequency domain information and the time domain information of the target wave signal, and then calculating the interval transit time of the target wave signal at the .. target depth point by using the frequency domain information and the time domain information, there are advantages of small workload and good calculation timeliness compared with a calculation method in a related technology.
The method provided according to an embodiment of the present application is described below.
4 Date regue/Date received 2023-05-26 In an exemplary embodiment, determining the time domain boundary of the different types of wave signals in the original signal of the target depth point acquired in advance includes: using two-dimensional matrix data received by a single receiver of an array acoustic logging tool as sample data, segmenting different types of wave signals in the sample data, and determining a segmenting line of the different types of wave signals in the sample data;
acquiring a time domain boundary of the different types of wave signals at each depth point by using a position of the segmenting line in the sample data; and determining the time domain boundary of the different types of wave signals in the original signal of the target depth point from the time domain boundary of the different types of wave signals at the each depth point.
FIG. 2 is a schematic diagram of a signal image of an array acoustic signal provided according to an embodiment of the present application. As shown in FIG. 2, taking logging data of a dipolar acoustic source in a soft formation as an example, different types of waves in the figure are visible and obvious leakage longitudinal waves (P) or transverse waves (S).
In FIG. 2, taking the logging data of the dipolar acoustic source in the soft formation as an example, in actual situations, there may be following combinations according to different acoustic source types and formation types:
1. dipolar acoustic source + soft formation, with visible and obvious leakage longitudinal waves (P) and transverse waves (S), see FIG. 3 for details;
2. dipolar acoustic source + hard formation, with visible and weak or invisible leakage longitudinal waves (P) and obvious transverse waves (S);
3. monopole acoustic source + soft formation, with visible and obvious longitudinal waves (P), weak or invisible transverse waves (S) and Stoneley waves; and 4. monopole acoustic source + hard formation, with visible and obvious longitudinal waves (P), transverse waves (S), and Stoneley waves.
Waveform signals of a single receiver may be resampled and cropped, and converted into several 512*256 data samples to build a prediction sample database. The sample database is predicted by using a full convolution neural network, and time domain boundaries of different wave signals in various samples are obtained. Then the time domain boundaries of the various sample are converted into times and spliced to obtain a complete wave signal boundary curve.
acquiring a time domain boundary of the different types of wave signals at each depth point by using a position of the segmenting line in the sample data; and determining the time domain boundary of the different types of wave signals in the original signal of the target depth point from the time domain boundary of the different types of wave signals at the each depth point.
FIG. 2 is a schematic diagram of a signal image of an array acoustic signal provided according to an embodiment of the present application. As shown in FIG. 2, taking logging data of a dipolar acoustic source in a soft formation as an example, different types of waves in the figure are visible and obvious leakage longitudinal waves (P) or transverse waves (S).
In FIG. 2, taking the logging data of the dipolar acoustic source in the soft formation as an example, in actual situations, there may be following combinations according to different acoustic source types and formation types:
1. dipolar acoustic source + soft formation, with visible and obvious leakage longitudinal waves (P) and transverse waves (S), see FIG. 3 for details;
2. dipolar acoustic source + hard formation, with visible and weak or invisible leakage longitudinal waves (P) and obvious transverse waves (S);
3. monopole acoustic source + soft formation, with visible and obvious longitudinal waves (P), weak or invisible transverse waves (S) and Stoneley waves; and 4. monopole acoustic source + hard formation, with visible and obvious longitudinal waves (P), transverse waves (S), and Stoneley waves.
Waveform signals of a single receiver may be resampled and cropped, and converted into several 512*256 data samples to build a prediction sample database. The sample database is predicted by using a full convolution neural network, and time domain boundaries of different wave signals in various samples are obtained. Then the time domain boundaries of the various sample are converted into times and spliced to obtain a complete wave signal boundary curve.
5 Date regue/Date received 2023-05-26 In an exemplary embodiment, the segmenting line of the different types of wave signals in the sample data is obtained by the following way, including: determining a probability that each element in the sample data belongs to a wave signal of a target type;
determining an element of which a probability is greater than a preset probability threshold value in the sample data as a target element according to the determined probability; determining a position of the wave signal of the target type in the sample data according to a position of the target element, and using a boundary of the wave signal of the target type as a segmenting line of the wave signal of the target type in the sample data.
FIG. 3 is a schematic diagram of a result of acoustic wave segmentation on the signal image shown in FIG. 2. As shown in FIG. 3, firstly, acoustic wave logging data are resampled and disassembled into several 256*512 fragments to form a prediction sample database; then each sample is inputted into the full convolution neural network for prediction, an edge of a wave signal is extracted from an output matrix, and an arrival time of a target wave signal of each sample is obtained, and finally, arrival times of target wave signals of various samples are spliced together to obtain a segmenting line of P wave and S wave in FIG. 3.
For details, refer to a gray line in a black box region in FIG. 3 as the segmenting line, that is, a time domain boundary. As can be seen from FIG. 3, the P wave is at a left side of the segmenting line, and the S wave is at a right side of the segmenting line, and each depth point corresponds to its own time domain boundary.
In an exemplary embodiment, the segmenting line of the different types of wave signals in the sample data is obtained by the following way, including: cutting the sample data into at least two segments; identifying a wave type of two-dimensional matrix data in each segment, and determining a boundary of wave signals in each segment; converting boundaries of wave signals in different segments into time data respectively; and splicing time data of each segment together to obtain the segmenting line of the different types of wave signals in the sample data.
When the volume of sample data is too large, that is, a judgment condition of a large amount of data is met, then the sample data is cut; and when the volume of sample data is relatively small, that is, the judgment condition of the large amount of data is not met, then the sample data is directly processed as a whole, identifying of the wave type is performed, and a
determining an element of which a probability is greater than a preset probability threshold value in the sample data as a target element according to the determined probability; determining a position of the wave signal of the target type in the sample data according to a position of the target element, and using a boundary of the wave signal of the target type as a segmenting line of the wave signal of the target type in the sample data.
FIG. 3 is a schematic diagram of a result of acoustic wave segmentation on the signal image shown in FIG. 2. As shown in FIG. 3, firstly, acoustic wave logging data are resampled and disassembled into several 256*512 fragments to form a prediction sample database; then each sample is inputted into the full convolution neural network for prediction, an edge of a wave signal is extracted from an output matrix, and an arrival time of a target wave signal of each sample is obtained, and finally, arrival times of target wave signals of various samples are spliced together to obtain a segmenting line of P wave and S wave in FIG. 3.
For details, refer to a gray line in a black box region in FIG. 3 as the segmenting line, that is, a time domain boundary. As can be seen from FIG. 3, the P wave is at a left side of the segmenting line, and the S wave is at a right side of the segmenting line, and each depth point corresponds to its own time domain boundary.
In an exemplary embodiment, the segmenting line of the different types of wave signals in the sample data is obtained by the following way, including: cutting the sample data into at least two segments; identifying a wave type of two-dimensional matrix data in each segment, and determining a boundary of wave signals in each segment; converting boundaries of wave signals in different segments into time data respectively; and splicing time data of each segment together to obtain the segmenting line of the different types of wave signals in the sample data.
When the volume of sample data is too large, that is, a judgment condition of a large amount of data is met, then the sample data is cut; and when the volume of sample data is relatively small, that is, the judgment condition of the large amount of data is not met, then the sample data is directly processed as a whole, identifying of the wave type is performed, and a
6 Date regue/Date received 2023-05-26 boundary of the wave signals in the sample data is determined without operations of switching into segments and splicing the segments.
FIG. 4 is a structure diagram of a full convolution neural network provided according to an embodiment of the present application. As shown in FIG. 4, an input of the model is a matrix with a size of 512*256, the matrix containing wave signals of 256 sampling points, five times of downsampling (a half branch at a left side) and five times of upsampling (a half branch at a right side) are performed in the middle, skip connection is adopted in the middle to ensure that spatial information is not lost, and a size of an output matrix is consistent with that of an input matrix, which is 512*256, each numerical value in the output matrix indicates a probability that each point in the input matrix belongs to the target wave signal, and a segmented wave signal may be obtained by binaryzation (a threshold value is 0.5).
A processing process of the above full convolution neural network is as follows: (1) acoustic wave logging data are resampled and disassembled into several 256*512 fragments to form a prediction sample database; (2) each sample is inputted into the full convolution neural network for prediction and the output matrix is obtained, wherein a value of 1 in the output matrix is a position of the target wave signal; (3) an edge of a wave signal is extracted from the output matrix, at this time an edge is represented by a column number in the matrix in which it is located, the edge of the signal may be converted from the column number to a time according to the measured start time, stop time, and the total number of columns, and then the arrival time of the target wave signal of each sample is obtained; and (4) finally, the arrival times of the target wave signals of the various samples are spliced together to obtain the segmenting line for the P wave and the S wave in FIG. 3.
In an exemplary embodiment, extracting a target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point includes: when the target wave signal is a signal received earlier in the original signal, indicating that the target wave signal is at a left side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, then extracting a signal of the original signal from a start time to the time domain boundary as the target wave signal; and when the target
FIG. 4 is a structure diagram of a full convolution neural network provided according to an embodiment of the present application. As shown in FIG. 4, an input of the model is a matrix with a size of 512*256, the matrix containing wave signals of 256 sampling points, five times of downsampling (a half branch at a left side) and five times of upsampling (a half branch at a right side) are performed in the middle, skip connection is adopted in the middle to ensure that spatial information is not lost, and a size of an output matrix is consistent with that of an input matrix, which is 512*256, each numerical value in the output matrix indicates a probability that each point in the input matrix belongs to the target wave signal, and a segmented wave signal may be obtained by binaryzation (a threshold value is 0.5).
A processing process of the above full convolution neural network is as follows: (1) acoustic wave logging data are resampled and disassembled into several 256*512 fragments to form a prediction sample database; (2) each sample is inputted into the full convolution neural network for prediction and the output matrix is obtained, wherein a value of 1 in the output matrix is a position of the target wave signal; (3) an edge of a wave signal is extracted from the output matrix, at this time an edge is represented by a column number in the matrix in which it is located, the edge of the signal may be converted from the column number to a time according to the measured start time, stop time, and the total number of columns, and then the arrival time of the target wave signal of each sample is obtained; and (4) finally, the arrival times of the target wave signals of the various samples are spliced together to obtain the segmenting line for the P wave and the S wave in FIG. 3.
In an exemplary embodiment, extracting a target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point includes: when the target wave signal is a signal received earlier in the original signal, indicating that the target wave signal is at a left side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, then extracting a signal of the original signal from a start time to the time domain boundary as the target wave signal; and when the target
7 Date regue/Date received 2023-05-26 wave signal is a signal received later in the original signal, indicating that a target wave is at a right side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, then extracting a signal of the original signal from the time domain boundary to a stop time as the target wave signal.
Taking FIG. 3 as an example for illustration, it can be seen from FIG. 3 that an arrival time of the P wave is at the left side of the segmenting line, an arrival time of the S wave is at the right side of the segmenting line, and each depth point corresponds to its own time domain boundary. The time domain boundary of the different types of wave signals in the original signal of the target depth point may be determined from FIG. 3.
FIG. 5 is a schematic diagram of segmenting different wave signals of a single depth point according to a time domain boundary according to an embodiment of the present application.
As shown in FIG. 5, in a coordinate diagram of the original signal, a horizontal coordinate is time, a unit is microseconds, and a longitudinal coordinate represents a signal strength, which is an amplitude of the signal. The time domain boundary of the original signal shown in FIG. 5 is 5000 microseconds, so a result after extracting is to split the original signal into two parts with the time domain boundary as a segmenting point.
In FIG. 5, an extraction operation under a single depth point in a case of a dipolar acoustic source and a soft formation is illustrated as an example. In FIG. 5, taking a leakage longitudinal wave (P wave) in the dipolar acoustic source as an example, a same operation mode will be adopted for a transverse wave of the dipolar acoustic source, a longitudinal wave, a transverse wave, or a Stoneley wave in a monopole acoustic source.
In an exemplary embodiment, calculating frequency domain information and time domain information of the target wave signal includes: determining a time-based spectrum diagram of the target wave signal, wherein in a coordinate system in which the spectrum diagram is located, a horizontal coordinate is time, and a longitudinal coordinate is frequency;
acquiring horizontal coordinates of positions of left and right boundaries of the spectrum diagram and longitudinal coordinates of positions of upper and lower boundaries of the spectrum diagram; determining the horizontal coordinates of the positions of the left and right boundaries as a start time time start and a stop time time stop of the target wave signal respectively, and determining the
Taking FIG. 3 as an example for illustration, it can be seen from FIG. 3 that an arrival time of the P wave is at the left side of the segmenting line, an arrival time of the S wave is at the right side of the segmenting line, and each depth point corresponds to its own time domain boundary. The time domain boundary of the different types of wave signals in the original signal of the target depth point may be determined from FIG. 3.
FIG. 5 is a schematic diagram of segmenting different wave signals of a single depth point according to a time domain boundary according to an embodiment of the present application.
As shown in FIG. 5, in a coordinate diagram of the original signal, a horizontal coordinate is time, a unit is microseconds, and a longitudinal coordinate represents a signal strength, which is an amplitude of the signal. The time domain boundary of the original signal shown in FIG. 5 is 5000 microseconds, so a result after extracting is to split the original signal into two parts with the time domain boundary as a segmenting point.
In FIG. 5, an extraction operation under a single depth point in a case of a dipolar acoustic source and a soft formation is illustrated as an example. In FIG. 5, taking a leakage longitudinal wave (P wave) in the dipolar acoustic source as an example, a same operation mode will be adopted for a transverse wave of the dipolar acoustic source, a longitudinal wave, a transverse wave, or a Stoneley wave in a monopole acoustic source.
In an exemplary embodiment, calculating frequency domain information and time domain information of the target wave signal includes: determining a time-based spectrum diagram of the target wave signal, wherein in a coordinate system in which the spectrum diagram is located, a horizontal coordinate is time, and a longitudinal coordinate is frequency;
acquiring horizontal coordinates of positions of left and right boundaries of the spectrum diagram and longitudinal coordinates of positions of upper and lower boundaries of the spectrum diagram; determining the horizontal coordinates of the positions of the left and right boundaries as a start time time start and a stop time time stop of the target wave signal respectively, and determining the
8 Date regue/Date received 2023-05-26 longitudinal coordinates of the positions of the upper and lower boundaries as a minimum frequency freq_min and a maximum frequency freq_max of the target wave signal respectively.
FIG. 6 is a schematic diagram of a time-frequency spectrum obtained after performing a spectrum calculation on a P wave in FIG. 5. As shown in FIG. 6, a time-frequency analysis is performed on the target wave signal by using wavelet transform to obtain the time-frequency spectrum.
FIG. 7 is a schematic diagram of performing an extraction operation on a time-spectrum diagram in FIG. 6. As shown in FIG. 7, a wave signal region is segmented from the time-frequency spectrum, seeing a white region shown in FIG. 7. The start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max of the target wave signal may be calculated by identifying positions of upper, lower, left, and right boundary points of the wave signal region.
Different from a way of extracting frequency domain information in a related technology, the method provided according to an embodiment of the present application may automatically extract the frequency domain information by means of the time-spectrum diagram, which improves extraction efficiency.
Based on the above way, required numerical value information may be obtained quickly and accurately.
In an exemplary embodiment, calculating the interval transit time of the target wave signal at the target depth point by using the frequency domain information and the time domain information includes: determining a middle frequency freq_middle of the target wave signal;
when the target wave signal is a signal received earlier in the original signal, indicating that a target wave is at a left side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, then correcting the start time time start of the target wave signal by using the middle frequency freq_middle to obtain a corrected start time time start, and calculating the interval transit time of the target wave signal at the target depth point by using the corrected start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max; and when the target wave signal is a signal received later in the original signal, indicating that a target wave is at a right side of the
FIG. 6 is a schematic diagram of a time-frequency spectrum obtained after performing a spectrum calculation on a P wave in FIG. 5. As shown in FIG. 6, a time-frequency analysis is performed on the target wave signal by using wavelet transform to obtain the time-frequency spectrum.
FIG. 7 is a schematic diagram of performing an extraction operation on a time-spectrum diagram in FIG. 6. As shown in FIG. 7, a wave signal region is segmented from the time-frequency spectrum, seeing a white region shown in FIG. 7. The start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max of the target wave signal may be calculated by identifying positions of upper, lower, left, and right boundary points of the wave signal region.
Different from a way of extracting frequency domain information in a related technology, the method provided according to an embodiment of the present application may automatically extract the frequency domain information by means of the time-spectrum diagram, which improves extraction efficiency.
Based on the above way, required numerical value information may be obtained quickly and accurately.
In an exemplary embodiment, calculating the interval transit time of the target wave signal at the target depth point by using the frequency domain information and the time domain information includes: determining a middle frequency freq_middle of the target wave signal;
when the target wave signal is a signal received earlier in the original signal, indicating that a target wave is at a left side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, then correcting the start time time start of the target wave signal by using the middle frequency freq_middle to obtain a corrected start time time start, and calculating the interval transit time of the target wave signal at the target depth point by using the corrected start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max; and when the target wave signal is a signal received later in the original signal, indicating that a target wave is at a right side of the
9 Date regue/Date received 2023-05-26 time domain boundary of the different types of wave signals in the original signal of the target depth point, then correcting the stop time time stop of the target wave signal by using the middle frequency freq_middle to obtain a corrected start time time start, and calculating the interval transit time of the target wave signal at the target depth point by using the corrected start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max.
For a wave signal at the left side of the boundary, its stop time is relatively accurate, and its start time may be corrected; and for a wave signal at the right side of the boundary, its start time is relatively accurate, and its stop time may be corrected.
In an exemplary embodiment, when the target wave is at the left side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, the corrected start time time start is obtained by the following way, including: determining len coef according to the type of the target wave signal, wherein a value range of len coef is between 2 and 8 according to the different type of target wave signal; time start = time stop -106 / freq_ middle * len coef; when the target wave is at the right side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, the corrected stop time time stop is obtained by the following way, including:
time stop =
time start + 106 / freq_ middle * len coef .
Window length calculation: window length =106/freq_ middle * a; wherein a value of a is between 1 and 2, and a specific numerical value is determined according to experience and may be set to 1.5.
The following parameters obtained are used as parameters to be brought into the STC
method for calculating a transmit time, including: when the target wave signal is a signal received earlier in the original signal, indicating that a target wave is at a left side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, then performing the calculation by using the corrected start time time start and the corrected stop time time stop, the minimum frequency freq_min, the maximum frequency freq_max, and the window length calculation window length; and when the target wave signal is a signal received later in the original signal, indicating that a target wave is at a right side of Date regue/Date received 2023-05-26 the time domain boundary of the different types of wave signals in the original signal of the target depth point, then performing the calculation by using the start time time start and the corrected stop time time stop, the minimum frequency freq_min, the maximum frequency freq_max, and the window length calculation window length.
The method provided according to an embodiment of the present application, in combination with a deep learning technology and a signal time-frequency analysis method, achieves an intelligent calculation of all interpretation parameters in the STC method, and finally achieves the calculation of the transmit time by using the STC method, which has low workload of data analysis, good timeliness, and low operation difficulty. The method provided according to an embodiment of the present application achieves, based on a wave signal segmentation technology, accurate segmentation of wave signals obtained by different measurement modes and different formation types, and performs, based on a result of a wave signal segmentation operation, operations of target wave signal extraction, target wave signal time-frequency analysis, target wave signal time domain and frequency domain information extraction to automatically complete analysis parameters of the interval transit time, and finally achieves the calculation of the transmit time by using a time-slowness correlation method.
An embodiment of the present application provides a storage medium, having a computer program stored therein, wherein the computer program is configured to perform the method described in the above when being run.
An embodiment of the present application provides an electronic apparatus, including a memory and a processor, wherein the memory has a computer program stored therein, and the processor is configured to run the computer program to perform the method described in the above.
Those of ordinary skill in the art can understand that all or some of acts in methods, systems, functional modules/units in apparatuses disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, a division between functional modules/units mentioned in the above description does not necessarily correspond to a division of physical components; for example, one physical component may have multiple functions, or one function or act may be performed Date regue/Date received 2023-05-26 cooperatively by several physical components. Some or all of components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transient medium) and a communication medium (or a transient medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (such as computer-readable instructions, data structures, program modules, or other data). The computer storage medium includes, but is not limited to, RAM, ROM, EEPROM, a flash memory or another memory technology, CD-ROM, a digital versatile disk (DVD) or another optical disk storage, a magnetic cal tlidge, a magnetic tape, a magnetic disk storage or another magnetic storage apparatus, or any other medium that may be configured to store desired information and may be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that a communication medium typically contains computer readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or another transmission mechanism, and may include any information delivery medium.
Date regue/Date received 2023-05-26
For a wave signal at the left side of the boundary, its stop time is relatively accurate, and its start time may be corrected; and for a wave signal at the right side of the boundary, its start time is relatively accurate, and its stop time may be corrected.
In an exemplary embodiment, when the target wave is at the left side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, the corrected start time time start is obtained by the following way, including: determining len coef according to the type of the target wave signal, wherein a value range of len coef is between 2 and 8 according to the different type of target wave signal; time start = time stop -106 / freq_ middle * len coef; when the target wave is at the right side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, the corrected stop time time stop is obtained by the following way, including:
time stop =
time start + 106 / freq_ middle * len coef .
Window length calculation: window length =106/freq_ middle * a; wherein a value of a is between 1 and 2, and a specific numerical value is determined according to experience and may be set to 1.5.
The following parameters obtained are used as parameters to be brought into the STC
method for calculating a transmit time, including: when the target wave signal is a signal received earlier in the original signal, indicating that a target wave is at a left side of the time domain boundary of the different types of wave signals in the original signal of the target depth point, then performing the calculation by using the corrected start time time start and the corrected stop time time stop, the minimum frequency freq_min, the maximum frequency freq_max, and the window length calculation window length; and when the target wave signal is a signal received later in the original signal, indicating that a target wave is at a right side of Date regue/Date received 2023-05-26 the time domain boundary of the different types of wave signals in the original signal of the target depth point, then performing the calculation by using the start time time start and the corrected stop time time stop, the minimum frequency freq_min, the maximum frequency freq_max, and the window length calculation window length.
The method provided according to an embodiment of the present application, in combination with a deep learning technology and a signal time-frequency analysis method, achieves an intelligent calculation of all interpretation parameters in the STC method, and finally achieves the calculation of the transmit time by using the STC method, which has low workload of data analysis, good timeliness, and low operation difficulty. The method provided according to an embodiment of the present application achieves, based on a wave signal segmentation technology, accurate segmentation of wave signals obtained by different measurement modes and different formation types, and performs, based on a result of a wave signal segmentation operation, operations of target wave signal extraction, target wave signal time-frequency analysis, target wave signal time domain and frequency domain information extraction to automatically complete analysis parameters of the interval transit time, and finally achieves the calculation of the transmit time by using a time-slowness correlation method.
An embodiment of the present application provides a storage medium, having a computer program stored therein, wherein the computer program is configured to perform the method described in the above when being run.
An embodiment of the present application provides an electronic apparatus, including a memory and a processor, wherein the memory has a computer program stored therein, and the processor is configured to run the computer program to perform the method described in the above.
Those of ordinary skill in the art can understand that all or some of acts in methods, systems, functional modules/units in apparatuses disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, a division between functional modules/units mentioned in the above description does not necessarily correspond to a division of physical components; for example, one physical component may have multiple functions, or one function or act may be performed Date regue/Date received 2023-05-26 cooperatively by several physical components. Some or all of components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transient medium) and a communication medium (or a transient medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (such as computer-readable instructions, data structures, program modules, or other data). The computer storage medium includes, but is not limited to, RAM, ROM, EEPROM, a flash memory or another memory technology, CD-ROM, a digital versatile disk (DVD) or another optical disk storage, a magnetic cal tlidge, a magnetic tape, a magnetic disk storage or another magnetic storage apparatus, or any other medium that may be configured to store desired information and may be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that a communication medium typically contains computer readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or another transmission mechanism, and may include any information delivery medium.
Date regue/Date received 2023-05-26
Claims (10)
1. A method for calculating an interval transit time, comprising:
determining a time domain boundary of different types of wave signals in an original signal of a target depth point acquired in advance;
extracting a target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point;
calculating frequency domain information and time domain information of the target wave signal; and 1 0 calculating an interval transit time of the target wave signal at the target depth point by using the frequency domain information and the time domain information.
determining a time domain boundary of different types of wave signals in an original signal of a target depth point acquired in advance;
extracting a target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point;
calculating frequency domain information and time domain information of the target wave signal; and 1 0 calculating an interval transit time of the target wave signal at the target depth point by using the frequency domain information and the time domain information.
2. The method of claim 1, wherein determining the time domain boundary of the different types of wave signals in the original signal of the target depth point acquired in advance comprises:
1 5 using two-dimensional matrix data received by a single receiver of an array acoustic logging tool as sample data, segmenting different types of wave signals in the sample data, and determining a segmenting line of the different types of wave signals in the sample data;
acquiring a time domain boundary of different types of wave signals at each depth point by using a position of the segmenting line in the sample data; and 20 determining the time domain boundary of the different types of wave signals in the original signal of the target depth point from the time domain boundary of the different types of wave signals at the each depth point.
1 5 using two-dimensional matrix data received by a single receiver of an array acoustic logging tool as sample data, segmenting different types of wave signals in the sample data, and determining a segmenting line of the different types of wave signals in the sample data;
acquiring a time domain boundary of different types of wave signals at each depth point by using a position of the segmenting line in the sample data; and 20 determining the time domain boundary of the different types of wave signals in the original signal of the target depth point from the time domain boundary of the different types of wave signals at the each depth point.
3. The method of claim 2, wherein the segmenting line of the different types of wave signals in the sample data is obtained by a following way, comprising:
25 determining a probability that each element in the sample data belongs to a wave signal of a target type;
determining an element of which a probability is greater than a preset probability Date regue/Date received 2023-05-26 threshold value in the sample data as a target element according to the determined probability;
and determining a position of the wave signal of the target type in the sample data according to a position of the target element, and using a boundary of the wave signal of the target type as a segmenting line of the wave signal of the target type in the sample data.
25 determining a probability that each element in the sample data belongs to a wave signal of a target type;
determining an element of which a probability is greater than a preset probability Date regue/Date received 2023-05-26 threshold value in the sample data as a target element according to the determined probability;
and determining a position of the wave signal of the target type in the sample data according to a position of the target element, and using a boundary of the wave signal of the target type as a segmenting line of the wave signal of the target type in the sample data.
4. The method of claim 2, wherein the segmenting line of the different types of wave signals in the sample data is obtained by a following way, comprising:
cutting the sample data into at least two segments;
identifying a wave type of two-dimensional matrix data in each segment, and determining a boundary of wave signals in each segment;
converting boundaries of wave signals in different segments into time data respectively;
and splicing time data of each segment together to obtain the segmenting line of the different types of wave signals in the sample data.
cutting the sample data into at least two segments;
identifying a wave type of two-dimensional matrix data in each segment, and determining a boundary of wave signals in each segment;
converting boundaries of wave signals in different segments into time data respectively;
and splicing time data of each segment together to obtain the segmenting line of the different types of wave signals in the sample data.
1 5 5. The method of claim 1, wherein extracting the target wave signal of the target depth point from the original signal of the target depth point according to the time domain boundary of the different types of wave signals in the original signal of the target depth point comprises:
when the target wave signal is a signal received earlier in the original signal, extracting a signal of the original signal from a start time to the time domain boundary as the target wave signal; and when the target wave signal is a signal received later in the original signal, extracting a signal of the original signal from the time domain boundary to a stop time as the target wave signal.
when the target wave signal is a signal received earlier in the original signal, extracting a signal of the original signal from a start time to the time domain boundary as the target wave signal; and when the target wave signal is a signal received later in the original signal, extracting a signal of the original signal from the time domain boundary to a stop time as the target wave signal.
6. The method of claim 1, wherein calculating the frequency domain information and the time domain information of the target wave signal comprises:
determining a time-based spectrum diagram of the target wave signal, wherein a horizontal coordinate of a coordinate system in which the spectrum diagram is located is time, and a longitudinal coordinate of the coordinate system in which the spectrum diagram is located Date regue/Date received 2023-05-26 is frequency;
acquiring horizontal coordinates of positions of left and right boundaries of the spectrum diagram and longitudinal coordinates of positions of upper and lower boundaries of the spectrum diagram;
determining the horizontal coordinates of the positions of the left and right boundaries as a start time time start and a stop time time stop of the target wave signal respectively, and determining the longitudinal coordinates of the positions of the upper and lower boundaries as a minimum frequency freq_min and a maximum frequency freq_max of the target wave signal respectively.
determining a time-based spectrum diagram of the target wave signal, wherein a horizontal coordinate of a coordinate system in which the spectrum diagram is located is time, and a longitudinal coordinate of the coordinate system in which the spectrum diagram is located Date regue/Date received 2023-05-26 is frequency;
acquiring horizontal coordinates of positions of left and right boundaries of the spectrum diagram and longitudinal coordinates of positions of upper and lower boundaries of the spectrum diagram;
determining the horizontal coordinates of the positions of the left and right boundaries as a start time time start and a stop time time stop of the target wave signal respectively, and determining the longitudinal coordinates of the positions of the upper and lower boundaries as a minimum frequency freq_min and a maximum frequency freq_max of the target wave signal respectively.
1 0 7. The method of claim 6, wherein calculating the interval transit time of the target wave signal at the target depth point by using the frequency domain information and the time domain information comprises:
determining a middle frequency freq_middle of the target wave signal;
when the target wave signal is a signal received earlier in the original signal, correcting the start time time start of the target wave signal by using the middle frequency freq_middle to obtain a corrected start time time start, and calculating the interval transit time of the target wave signal at the target depth point by using the corrected start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max; and when the target wave signal is a signal received later in the original signal, correcting the stop time time stop of the target wave signal by using the middle frequency freq_middle to obtain a corrected start time time start, and calculating the interval transit time of the target wave signal at the target depth point by using the corrected start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max.
determining a middle frequency freq_middle of the target wave signal;
when the target wave signal is a signal received earlier in the original signal, correcting the start time time start of the target wave signal by using the middle frequency freq_middle to obtain a corrected start time time start, and calculating the interval transit time of the target wave signal at the target depth point by using the corrected start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max; and when the target wave signal is a signal received later in the original signal, correcting the stop time time stop of the target wave signal by using the middle frequency freq_middle to obtain a corrected start time time start, and calculating the interval transit time of the target wave signal at the target depth point by using the corrected start time time start and the stop time time stop, the minimum frequency freq_min, and the maximum frequency freq_max.
8. The method of claim 7, further comprising:
determining, according to a target wave type, a coefficient len coef of a different target wave type, wherein, a value range of len coef is between 2 and 8;
wherein when the target wave signal is the signal received earlier in the original signal, the corrected start time time start is obtained by a following way, comprising:
Date regue/Date received 2023-05-26 time start = time stop - 106 / freq_ middle * len coef; and when the target wave signal is the signal received later in the original signal, the corrected stop time time stop is obtained by a following wayõ comprising:
time stop = time start + 106 / freq_ middle * len coef.
determining, according to a target wave type, a coefficient len coef of a different target wave type, wherein, a value range of len coef is between 2 and 8;
wherein when the target wave signal is the signal received earlier in the original signal, the corrected start time time start is obtained by a following way, comprising:
Date regue/Date received 2023-05-26 time start = time stop - 106 / freq_ middle * len coef; and when the target wave signal is the signal received later in the original signal, the corrected stop time time stop is obtained by a following wayõ comprising:
time stop = time start + 106 / freq_ middle * len coef.
9. A storage medium, having a computer program stored therein, wherein the computer program is configured to perform the method of any one of claims 1 to 8 when running.
10. An electronic apparatus, comprising a memory and a processor, wherein the memory has a computer program stored therein, and the processor is configured to run the computer program to perform the method of any one of claims 1 to 8.
Date regue/Date received 2023-05-26
Date regue/Date received 2023-05-26
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| CN202011584687.XA CN112817049B (en) | 2020-12-28 | 2020-12-28 | Method for calculating sound wave time difference |
| PCT/CN2021/129860 WO2022142755A1 (en) | 2020-12-28 | 2021-11-10 | Calculation method and device for interval transit time, and storage medium |
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| US5278805A (en) * | 1992-10-26 | 1994-01-11 | Schlumberger Technology Corporation | Sonic well logging methods and apparatus utilizing dispersive wave processing |
| US6453240B1 (en) * | 1999-04-12 | 2002-09-17 | Joakim O. Blanch | Processing for sonic waveforms |
| US6845325B2 (en) * | 2001-11-08 | 2005-01-18 | Schlumberger Technology Corporation | Global classification of sonic logs |
| US7764572B2 (en) * | 2004-12-08 | 2010-07-27 | Schlumberger Technology Corporation | Methods and systems for acoustic waveform processing |
| CN103726836B (en) * | 2012-10-12 | 2021-03-16 | 中国石油集团长城钻探工程有限公司 | Method for extracting mode wave slowness based on acoustic logging data |
| CN104295293B (en) * | 2014-10-23 | 2017-04-12 | 中国石油天然气股份有限公司 | Method for obtaining logging density curve |
| CN104833952B (en) * | 2015-04-24 | 2017-10-17 | 电子科技大学 | It is a kind of determine it is multiple when frequency aliasing signal step-out time method |
| CN107678064B (en) * | 2016-08-02 | 2020-07-17 | 中石化石油工程技术服务有限公司 | Real-time extraction method for sound wave time difference |
| GB201700399D0 (en) * | 2017-01-10 | 2017-02-22 | Reeves Wireline Tech Ltd | Improved method of and apparatus for carrying out acoustic well logging |
| WO2020076308A1 (en) * | 2018-10-09 | 2020-04-16 | Halliburton Energy Services, Inc. | Methods and systems for processing slowness values from borehole sonic data |
| CN110320285A (en) * | 2019-07-08 | 2019-10-11 | 创新奇智(青岛)科技有限公司 | A kind of steel rail defect recognition methods, system and electronic equipment based on ultrasonic signal |
| CN110456418A (en) * | 2019-09-12 | 2019-11-15 | 西南石油大学 | The processing and means of interpretation of array acoustic Image Logging Data |
| CN110782915A (en) * | 2019-10-31 | 2020-02-11 | 广州艾颂智能科技有限公司 | Waveform music component separation method based on deep learning |
| CN110916645A (en) * | 2019-12-10 | 2020-03-27 | 电子科技大学 | QRS wave identification method combining wavelet transformation and image segmentation network |
| CN112817049B (en) * | 2020-12-28 | 2022-08-02 | 中海油田服务股份有限公司 | Method for calculating sound wave time difference |
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