CN112415601B - Method and device for determining surface quality factor Q value - Google Patents
Method and device for determining surface quality factor Q value Download PDFInfo
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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Abstract
The invention provides a method and a device for determining a surface quality factor Q value, wherein the method comprises the following steps: establishing a surface layer horizontal lamellar geological model according to micro-logging data of a target area; correcting the low-speed stratum speed of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model; forward modeling direct wave recording by utilizing a ray tracing method on the corrected surface layer horizontal lamellar geological model; according to the forward wave record, identifying the direct wave position on the micro-well logging acquisition record, and removing the seismic trace of the first-arrival indirect wave on the micro-well logging acquisition record; removing interference waves by utilizing frequency wave number domain filtering to obtain a micro well logging after filtering; and determining the surface quality factor Q value of the target area according to the filtered micro well logging. The seismic channels of the first-arrival wave indirect wave are removed, the interference wave is removed by utilizing frequency wave number domain filtering, and the influence of the interference wave on the Q value calculation precision is reduced, so that the precision of the surface quality factor Q value is improved.
Description
Technical Field
The invention relates to the technical field of petroleum seismic exploration data processing, in particular to a method and a device for determining a surface quality factor Q value.
Background
As the seismic wave propagates in the formation, attenuation and dispersion of the seismic wave occurs as the seismic wave energy is absorbed by the medium. The quality factor Q is a basic parameter describing the absorption and attenuation characteristics of the stratum, and has important significance for improving the resolution of the seismic data. The quality factor Q may be calculated from laboratory, surface seismic, VSP, interwell seismic data, and micro-log data. The calculation method of the quality factor Q can be divided into two main methods of direct estimation and inversion. The direct estimation method can be divided into three categories of time domain, frequency domain and time-frequency domain according to different calculation domains. The method for calculating the quality factor Q in the time domain mainly comprises an amplitude attenuation method, a rise time method, an analysis signal method, a wavelet simulation method, a phase simulation method, an instantaneous frequency simulation method and the like. The method for calculating the quality factor Q in the frequency domain mainly comprises a spectrum simulation method, a spectrum ratio method, a centroid frequency offset method, a peak frequency method and the like. The theories of wavelet transformation, gabor transformation and the like are widely introduced into the exploration field, and the time-frequency domain calculation method can avoid the average effect in the frequency domain method and more accurately describe the absorption attenuation characteristics of the stratum. The Q value calculation method of inversion class mainly comprises Q tomography and Q waveform inversion.
However, the accuracy of the Q value calculation by the above method depends on the quality of the calculation record. The middle and deep Q value calculation data generally adopts zero offset VSP downstream data. The zero offset VSP downstream wave data is less interfered by the environment, and the wave detector records waveforms with different depths and different times, so that the zero offset VSP downstream wave data is ideal data for calculating the quality factor Q. A large number of practices prove that the influence of the surface layer Q value on the resolution of the seismic data is far greater than that of the deep layer Q value. Therefore, the accuracy of surface Q computation is particularly important for improving the resolution of seismic data.
The surface quality factor Q calculation depends on the variation of frequency components in the seismic wave propagation process, is influenced by a plurality of factors, and is sensitive to noise and interference of a complex wave field. Factors influencing the calculation of the Q value of the skin quality factor include: ① Exciting wavelet differences. In micro-logging, due to the difference of factors such as surrounding rock excitation, compaction degree and the like, the source wavelets generated by excitation at different depth positions are different; ② The difference in the coupling of the detector points. The coupling degree difference between the detector and the stratum in the embedding process causes the coupling response of the detector points to be different; ③ Near field effects. The near field component can generate a visual attenuation with the same dimension as the inherent attenuation, and the Q value calculation is seriously influenced; ④ Interference wave influence; interference waves such as surface waves, shallow refraction waves, virtual reflection and the like interfere direct waves (transmission waves), and the Q value calculation accuracy is affected. Therefore, the accuracy of determining the Q value of the skin quality factor in the prior art is not high.
Disclosure of Invention
The embodiment of the invention provides a method for determining a surface quality factor Q value, which is used for improving the precision of the surface quality factor Q value, and comprises the following steps:
Acquiring micro-logging data of a target area; the micro-well logging data comprises micro-well logging acquisition records;
establishing a surface layer horizontal lamellar geological model according to micro-logging data of a target area;
correcting the low-speed stratum speed of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model;
Forward modeling direct wave recording by utilizing a ray tracing method on the corrected surface layer horizontal lamellar geological model;
according to the forward wave record, identifying the direct wave position on the micro-well logging acquisition record, and removing the seismic trace of the first-arrival indirect wave on the micro-well logging acquisition record;
removing interference waves in the micro-well logging acquisition records after the seismic traces of the first arrival wave indirect waves by utilizing frequency wave number domain filtering to obtain the micro-well logging records after filtering;
determining the surface quality factor Q value of the target area according to the filtered micro-well logging;
correcting the low-speed stratum velocity of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model, comprising: carrying out ray tracing forward modeling according to different corrected stratum speeds, and counting simulation forward modeling data first arrival moments of all excitation points in a low-speed layer and a deceleration layer; determining the sum of forward wave recording first arrival time and actual recording first arrival time difference values corresponding to each corrected stratum speed according to forward wave mixed recording first arrival wave crest time and the simulated forward wave data first arrival time; and determining the corrected stratum speed corresponding to the minimum value in the sum of the forward record first arrival time and the actual record first arrival time difference as the stratum speed of the first stratum in the surface layer horizontal lamellar geological model, so as to correct the low-speed stratum speed of the surface layer horizontal lamellar geological model, and obtaining the corrected surface layer horizontal lamellar geological model.
The embodiment of the invention also provides a device for determining the Q value of the surface quality factor, which is used for improving the precision of the Q value of the surface quality factor, and comprises the following steps:
the data acquisition module is used for acquiring micro-logging data of the target area; the micro-well logging data comprises micro-well logging acquisition records;
the geological model building module is used for building a surface layer horizontal lamellar geological model according to micro-logging data of the target area;
the geological model correction module is used for correcting the low-speed stratum speed of the surface layer horizontal lamellar geological model to obtain a corrected surface layer horizontal lamellar geological model;
The direct wave identification module is used for forward modeling direct wave records on the corrected surface layer horizontal lamellar geological model by utilizing a ray tracing method, identifying the direct wave positions on the micro-logging acquisition records according to the forward modeling direct wave records, and removing seismic traces of first-arrival indirect waves on the micro-logging acquisition records;
the interference wave suppression module is used for removing interference waves in the micro-well logging acquisition records after the seismic traces of the first arrival wave indirect waves by utilizing frequency wave number domain filtering to obtain the micro-well logging records after filtering;
the Q value calculation module is used for determining the Q value of the surface quality factor of the target area according to the filtered micro-well logging;
The geologic model correction module is specifically configured to: carrying out ray tracing forward modeling according to different corrected stratum speeds, and counting simulation forward modeling data first arrival moments of all excitation points in a low-speed layer and a deceleration layer; determining the sum of forward wave recording first arrival time and actual recording first arrival time difference values corresponding to each corrected stratum speed according to forward wave mixed recording first arrival wave crest time and the simulated forward wave data first arrival time; and determining the corrected stratum speed corresponding to the minimum value in the sum of the forward record first arrival time and the actual record first arrival time difference as the stratum speed of the first stratum in the surface layer horizontal lamellar geological model, so as to correct the low-speed stratum speed of the surface layer horizontal lamellar geological model, and obtaining the corrected surface layer horizontal lamellar geological model.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for determining the quality factor Q value of the surface layer when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the method for determining the Q value of the surface quality factor when being executed by a processor.
In the embodiment of the invention, micro-logging data of a target area are acquired; the micro-logging data comprises micro-logging acquisition records; establishing a surface layer horizontal lamellar geological model according to micro-logging data of a target area; correcting the low-speed stratum speed of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model; forward modeling direct wave recording by utilizing a ray tracing method on the corrected surface layer horizontal lamellar geological model; according to the forward wave record, identifying the direct wave position on the micro-well logging acquisition record, and removing the seismic trace of the first-arrival indirect wave on the micro-well logging acquisition record; removing interference waves in the micro-well logging acquisition records after the seismic traces of the first arrival wave indirect waves by utilizing frequency wave number domain filtering to obtain the micro-well logging records after filtering; and determining the surface quality factor Q value of the target area according to the filtered micro well logging. The influence of excitation wavelet difference, wave detection point coupling difference and near field influence on the Q value precision in the calculation process is reduced by correcting the surface layer horizontal lamellar geological model. The seismic channels of the first-arrival wave indirect wave are removed, the interference wave is removed by utilizing frequency wave number domain filtering, and the influence of the interference wave on the Q value calculation precision is reduced, so that the precision of the surface quality factor Q value is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a method for determining a quality factor Q value of a middle layer according to an embodiment of the invention.
Fig. 2 is a schematic diagram of a specific implementation method of step 103 in an embodiment of the present invention.
Fig. 3 is a schematic diagram of a specific implementation method of step 202 in an embodiment of the present invention.
Fig. 4 is a schematic diagram of a specific implementation method of step 105 in an embodiment of the present invention.
Fig. 5 is a schematic diagram of a specific implementation method of step 106 in an embodiment of the present invention.
FIG. 6 is a schematic diagram showing a method for implementing step 107 in an embodiment of the present invention.
FIG. 7 is a schematic representation of a surface level layered geologic model constructed and modified in accordance with an embodiment of the invention.
FIG. 8 is a schematic diagram of a direct wave identification process in a microlog according to an embodiment of the present invention.
Fig. 9 is a schematic diagram of suppressing interference waves by using the frequency-wave number domain in an embodiment of the present invention.
FIG. 10 is a graph showing the results of filtering the frequency wavenumber domain of a microlog in accordance with an embodiment of the present invention.
FIG. 11 is a diagram showing the comparison of the filtered front and back of the acquired microlog using the frequency wavenumber domain for different depth excitations in an embodiment of the present invention.
FIG. 12 is a schematic diagram of a micro log for Q calculation in accordance with an embodiment of the present invention.
FIG. 13 is a graph showing the results of calculating the quality factor Q from the frequency-wavenumber domain filtered front-to-back micro log records in accordance with one embodiment of the present invention.
Fig. 14 is a schematic diagram of a device for determining a quality factor Q value of a surface layer according to an embodiment of the present invention.
FIG. 15 is a schematic diagram of a geologic model modification module 1403 in accordance with an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a method for determining a surface quality factor Q value, which is used for improving the accuracy of the surface quality factor Q value, as shown in fig. 1, and comprises the following steps:
step 101: acquiring micro-logging data of a target area; the micro-well logging data comprises micro-well logging acquisition records;
step 102: establishing a surface layer horizontal lamellar geological model according to micro-logging data of a target area;
Step 103: correcting the low-speed stratum speed of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model;
step 104: forward modeling direct wave recording by utilizing a ray tracing method on the corrected surface layer horizontal lamellar geological model;
Step 105: according to the forward wave record, identifying the direct wave position on the micro-well logging acquisition record, and removing the seismic trace of the first-arrival indirect wave on the micro-well logging acquisition record;
step 106: removing interference waves in the micro-well logging acquisition records after the seismic traces of the first arrival wave indirect waves by utilizing frequency wave number domain filtering to obtain the micro-well logging records after filtering;
Step 107: and determining the surface quality factor Q value of the target area according to the filtered micro well logging.
As can be seen from the flow chart shown in fig. 1, in the embodiment of the present invention, the micro-logging data of the target area is obtained; the micro-logging data comprises micro-logging acquisition records; establishing a surface layer horizontal lamellar geological model according to micro-logging data of a target area; correcting the low-speed stratum speed of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model; forward modeling direct wave recording by utilizing a ray tracing method on the corrected surface layer horizontal lamellar geological model; according to the forward wave record, identifying the direct wave position on the micro-well logging acquisition record, and removing the seismic trace of the first-arrival indirect wave on the micro-well logging acquisition record; removing interference waves in the micro-well logging acquisition records after the seismic traces of the first arrival wave indirect waves by utilizing frequency wave number domain filtering to obtain the micro-well logging records after filtering; and determining the surface quality factor Q value of the target area according to the filtered micro well logging. The influence of excitation wavelet difference, wave detection point coupling difference and near field influence on the Q value precision in the calculation process is reduced by correcting the surface layer horizontal lamellar geological model. The seismic channels of the first-arrival wave indirect wave are removed, the interference wave is removed by utilizing frequency wave number domain filtering, and the influence of the interference wave on the Q value calculation precision is reduced, so that the precision of the surface quality factor Q value is improved.
In particular, first micro-log data of a target area is acquired. The micro-well logging data comprises micro-well logging acquisition records. In a specific embodiment, acquiring micro-log data of a target area includes: and acquiring micro-logging observation parameters, micro-logging acquisition records and micro-logging interpretation results of the target area. Wherein the micro-logging observation parameters include: the depth of the underground excitation point and the distance between the ground detection point and the wellhead; micro-well interpretation effort includes: horizon speed and thickness are explained.
After micro-logging data of the target area are acquired, a surface layer horizontal lamellar geological model is established according to the micro-logging data of the target area. In the concrete implementation, a surface layer horizontal lamellar geological model is established mainly according to micro-logging interpretation results.
And correcting the low-speed stratum speed of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model. The specific implementation process, as shown in fig. 2, includes:
step 201: establishing a forward method for underground excitation and ground reception according to micro-logging observation parameters, and adopting a ray tracing method to forward the mixed wave record of direct wave, refraction wave, reflection wave and multiple wave;
Step 202: and (3) picking up the first arrival wave crest moment of forward wave mixing record, and correcting the low-speed stratum velocity of the surface layer horizontal lamellar geological model according to the first arrival wave crest moment of forward wave mixing record to obtain the corrected surface layer horizontal lamellar geological model.
Step 202 is a specific implementation process, as shown in fig. 3, including:
step 301: carrying out ray tracing forward modeling according to different corrected stratum speeds, and counting simulation forward modeling data first arrival moments of all excitation points in a low-speed layer and a deceleration layer;
step 302: determining the sum of forward wave recording first arrival time and actual recording first arrival time difference values corresponding to each corrected stratum speed according to forward wave mixed wave recording first arrival wave crest time and simulated forward wave data first arrival time;
Step 303: and determining the corrected stratum speed corresponding to the minimum value in the sum of the forward record first arrival time and the actual record first arrival time difference as the stratum speed of the first stratum in the surface layer horizontal lamellar geological model, so as to correct the low-speed stratum speed of the surface layer horizontal lamellar geological model, and obtaining the corrected surface layer horizontal lamellar geological model.
The low-speed layer (low velocity zone) is also called a low-speed zone, is a deep geophysical layer with the propagation speed of the seismic P wave and the S wave lower than that of the overlying and underlying layers, and the surface medium near the surface is divided into a low-speed layer (with the speed less than 1000 m/S), a speed-reducing layer (with the speed between 1000m/S and 2000 m/S) and a high-speed layer (with the speed higher than 2000 m/S), namely a diagenetic stratum.
Specifically, because of human interpretation errors in the surface layer low-speed layer speed, the surface layer horizontal lamellar geologic model low-speed layer needs to be corrected, and the method comprises the following steps:
and (3) calculating the intersection point position x 0 of the fitting line of the first layer L 1 and the time axis in the micro-well logging interpretation result according to the formula (1). If x 0 is not equal to 0, adding a layer thickness H 0 of layer L 0,L0, a layer thickness of speeds V 0 and L 1 to the surface layer horizontal lamellar geological model, and calculating according to a formula (2).
Wherein D 1 represents the first excitation point depth; t 1 represents the vertical propagation time at the depth of the first excitation point; v 1 represents the first layer velocity in the micro-log interpretation.
Wherein X 1 represents the distance from the first receiving point to the wellhead; FB 1,1 represents the first arrival time of the first receipt of the first excitation point in the actual recording.
Given the L 1 formation speed correction range [ V 1-ΔV V1 +DeltaV ], the surface level layered geologic model L 1 formation speed V' k, k=1, 2,3, …, NV, NV is the total number of formation speed correction speeds of L 1 in 1m/s increments.
And (3) carrying out ray tracing forward modeling at different correction speeds V' k, counting forward modeling data arrival moments of all excitation points in the low-speed layer and the deceleration layer, and calculating the sum of differences between forward modeling record arrival moments and actual record arrival moments according to a formula (3).
In the method, in the process of the invention,Representing the sum of the differences between the forward recording first arrival time and the actual recording first arrival time under the kth correction speed condition; Representing the first arrival time of forward record of the ith excitation point and the jth receiving point under the condition of the correction speed V' k; FB i,j represents the actual recording first arrival time of the ith excitation point and the jth receiving point; n is the total number of excitation points in the low-speed reducing layer; m is the total number of ground receiving channels.
Will beAnd determining the correction speed corresponding to the minimum value as the stratum speed of the surface layer geological model L 1 to obtain a corrected surface layer horizontal lamellar geological model.
And forward modeling the direct wave record on the corrected surface layer horizontal lamellar geological model by using a ray tracing method, and picking up the wave crest moment of the direct wave. And identifying the direct wave position on the micro-well logging acquisition record according to the forward wave record, and removing the seismic trace of the first-arrival indirect wave on the micro-well logging acquisition record. In the specific implementation, as shown in fig. 4, the method includes:
Step 401: picking up the wave crest moment of the direct wave recorded by the forward direct wave;
Step 402: calculating the difference value between the first arrival time of exciting different receiving channels at different depths in the micro-logging acquisition record and the corresponding forward direct wave time according to the forward direct wave record direct wave crest time;
Step 403: if the difference is less than one fourth of the first arrival wave period of the receiving channel, determining that the first arrival wave of the receiving channel is a direct wave.
In a specific embodiment, the forward direct wave time corresponding to the first arrival time FB i,j of the different receiving channels excited at different depths is calculated according to the formula (4)Is set to be equal to the difference Δfb i,j:
Wherein i=1, 2,3, …, N represents excitation points of different depths, and N represents total excitation points; j=1, 2,3, …, M represents the different reception channels, M represents the total number of reception channels on the ground.
If the difference value delta FB i,j is smaller than one fourth of the period of the first arrival wave recorded by the track, the first arrival wave recorded by the track is a direct wave; on the contrary, the first arrival wave recorded in the track is not a direct wave.
Removing the seismic traces of the first-arrival indirect wave on the micro-logging acquisition records, and removing the interference waves in the micro-logging acquisition records after the seismic traces of the first-arrival indirect wave by utilizing frequency wave number domain filtering to obtain the filtered micro-logging records. The specific process, as shown in fig. 5, includes:
step 501: splicing the residual seismic traces in the micro-logging acquisition records after removing the seismic traces of the first-arrival indirect wave into a data body, and correcting the first-arrival wave jump time of all the seismic traces in the data body to the same time to obtain the micro-logging acquisition records after the first-arrival wave is leveled;
Step 502: calculating energy balance factors of all sample point values in the micro-well logging acquisition records after the first arrival wave is leveled, and applying the energy balance factors of all sample point values to the micro-well logging acquisition records after the first arrival wave is leveled to obtain micro-well logging records after the energy balance;
step 503: and carrying out frequency wave number domain filtering operation on the micro-well logging subjected to energy balance, removing interference waves, and obtaining the micro-well logging subjected to filtering.
In the specific embodiment, removing the seismic traces of the first arrival wave indirect wave in the micro-logging record, splicing the rest seismic traces into a data body according to a formula (5), and correcting the first arrival wave jump time of all the seismic traces to the same time;
A(i-1)*M+j＝Si,j (5)
Wherein S i,j represents an ith excitation point and a jth receiving point micro-log; a (i-1)*M+j represents a spliced data body; m is the total number of ground receiving channels.
And calculating and applying energy balance factors of all recorded sample point values after the first arrival wave is leveled.
① Calculating an energy balance factor recorded in a given time window on a single-channel record by adopting a formula (6), placing the energy balance factor at the middle point of the time window, and then sliding half the length of the time window to obtain the energy balance factor of the next time window;
Wherein W i represents the equalization coefficient of the ith time window; ampLevel denotes the desired amplitude level; n represents the number of samples in the calculation time window; x k represents the sample value at time k.
② Linear interpolation is adopted to obtain energy balance factors of all sample points on the single-channel record by adopting a formula (7);
Wk＝W2+(W2-W1)(T2-Tk)/(T2-T1)k＝1,2,...NS (7)
Wherein W k represents an equalization coefficient corresponding to the kth sample; t k represents a time value corresponding to the kth sample; t 1 and T 2 represent the corresponding time values of the center points of the two time windows closest to the kth sample; w 1 and W 2 represent energy balance factors corresponding to the center points of the two time windows closest to the kth sample; NS represents the number of single pass recorded samples.
③ The energy balance factor for all samples is applied to the microlog according to equation (8).
A'i＝Ai*Wii＝1,2,3,…,NX (8)
Wherein A' i is a micro-logging single-pass record after energy balance; a i is a micro-logging single-pass record before energy balance; and NX is the total number of the data bodies after splicing.
The indirect wave in the first arrival wave is suppressed by adopting a amplitude-preserving frequency wave number domain filtering method, and the method specifically comprises the following steps:
① Intercepting a first arrival wave in a micro-logging record as a calculation time window, and generating a frequency wave number spectrum by adopting two-dimensional Fourier transform;
② Manually extracting energy values of energy cluster edges near zero wave numbers on a frequency wave number spectrum, and designing a filter for suppressing interference waves on the frequency wave number spectrum according to the energy values;
③ And performing two-dimensional inverse Fourier transform to obtain the first arrival wave of the micro-well logging after eliminating the interference such as indirect waves.
The energy balance factor is removed.
And (3) obtaining the micro well log after interference rejection finally by adopting a formula (9).
S”k,j＝FK(A'i)/Wii＝1,2,3,…,NX (9)
Wherein FK represents a frequency-wave number domain filtering operation; s' k,j represents a micro-logging single-channel record of a jth receiving point of a kth excitation point after removing an energy balance factor, k=floor (i/M), j=i-k.M, floor represents an upward rounding operation, and M represents the total number of ground receiving channels.
And after the filtered micro-well logging is obtained, determining the surface quality factor Q value of the target area according to the filtered micro-well logging. In the specific implementation, as shown in fig. 6, the method includes:
Step 601: picking up a complete cycle time of the first arrival wave on the filtered micro-logging record, reserving sample point amplitude values in a complete cycle of the first arrival wave, and enabling the residual sample point amplitude values to be zero to obtain micro-logging data for calculating a Q value;
Step 602: and determining the surface quality factor Q value of the target area according to the micro-logging data for calculating the Q value.
Those skilled in the art can understand that the calculation of the surface quality factor Q value of the target area by using the micro-log data belongs to a mature technology in the art, so that the description is omitted in the embodiment of the present invention.
According to the invention, direct waves in the micro-logging acquisition records are identified by forward-modeling record first-arrival time mapping, seismic channels of the first-arrival indirect waves on the micro-logging acquisition records are removed, interference waves such as shallow refraction waves, virtual reflection and the like interfered in the first-arrival waves (direct waves) are suppressed by a amplitude-preserving frequency wave number domain filtering method, the precision of quality factors Q calculated according to the micro-logging records is improved, and a high-precision surface layer Q model is provided for inverse Q filtering processing.
A specific example is given below to illustrate how embodiments of the present invention may be implemented. The method is applied to the quality factor Q calculation of a micro-logging acquisition record in a western desert area of China.
FIG. 7 is a top level layered geologic model of the position of the microlog constructed from the interpretation of the microlog. Part (a) in fig. 7 is a micro-well interpretation chart, and part (c) in fig. 7 is a surface layer horizontal lamellar geologic model built according to the micro-well interpretation result. As can be seen from part (a) of fig. 7, the intersection point of the first layer fitting line and the time axis in the interpretation result is 1.34ms, which is not zero. Correcting the surface layer horizontal lamellar geologic model: adding a horizon L 0 at the shallowest layer, wherein the speed is 230m/s, and the thickness is 0.52m; the L 1 horizon thickness was modified to 2.78m and the speed was modified to 490m/s (as shown in part (d) of FIG. 7). The maximum error between the first arrival time of the forward record of the corrected model and the first arrival time of the actual data is reduced from 8.25ms to 1.5ms (as shown in part (b) of fig. 7).
On the corrected surface layer horizontal lamellar geological model, the forward wave record is tracked by rays, and the first arrival time of the forward wave record is picked up and mapped onto the micro-well logging (shown in fig. 8). Judging whether the first arrival wave in the micro-well logging is a direct wave or not, and eliminating the indirect wave channel of the first arrival wave in the micro-well logging. And suppressing indirect waves in the first-arrival wave by adopting a amplitude-preserving frequency wave number domain filtering method.
Fig. 9 is a schematic diagram of suppressing interference waves using a frequency-wave number domain method. The left plot of fig. 9 is the frequency wavenumber spectrum after forward direct wave recording is leveled. When the direct waves are in phase horizontally, the direct waves appear as energy clusters concentrated near the zero value on the frequency-wave number domain spectrum. The right graph of fig. 9 shows the frequency wave number spectrum after the initial arrival wave is leveled, and the energy values outside the oval frame in the right graph are removed by designing a filter in the frequency wave number domain, so as to suppress the indirect wave in the initial arrival wave. The effect of suppressing indirect waves is shown in fig. 10. If the frequency wave number domain filtering method is adopted, amplitude preservation is not carried out, the amplitude of the seismic channel is greatly increased after filtering, the amplitude spectrum energy is also greatly increased, the absorption attenuation rule of the seismic channel before filtering is changed, and the method cannot be used for subsequent Q value calculation. And only the indirect wave of interference is suppressed after the amplitude-preserving frequency wave number domain filtering, the original absorption attenuation rule of the seismic channel is not changed, and the method can be used for subsequent Q value calculation. FIG. 11 shows the results of the wave number domain filtering front-back comparison of the frequency domain of the micro-logs acquired by different depth excitation. After suppression in the frequency-wave number domain, indirect waves interfering in the first arrival wave of the microlog are suppressed (shown in the rectangular box of the figure).
Suppressing indirect waves in the first-arrival wave, picking up a complete cycle time of the first-arrival wave on the record, reserving amplitude values in a complete cycle of the first-arrival wave, and giving the rest amplitude values to zero values for calculating a quality factor Q (shown in figure 12). FIG. 13 is a graph showing the result of calculating the quality factor Q from the frequency-wavenumber domain filtered front-to-back micro logs. The quality factor Q is calculated by selecting seismic traces that are excited at a well depth of 8 meters and excited at a well depth of 42 meters and received 7 meters from the wellhead. The Q value is-13.7 calculated before the frequency wave number domain filtering. The quality factor Q is a parameter describing the absorption and attenuation characteristics of the seismic wave, and should be greater than zero, so that the calculation result is erroneous. The Q value calculated by the seismic channel after the frequency wave number domain filtering is 36.5, and the Q value is larger than zero, accords with the absorption attenuation rule of the seismic wave and is close to the Q value calculated by an empirical formula.
The implementation of the above specific application is only an example, and the rest of the embodiments are not described in detail.
Based on the same inventive concept, the embodiment of the present invention further provides a device for determining a Q value of a surface layer quality factor, and since the principle of the problem solved by the device for determining a Q value of a surface layer quality factor is similar to that of the method for determining a Q value of a surface layer quality factor, the implementation of the device for determining a Q value of a surface layer quality factor can be referred to the implementation of the method for determining a Q value of a surface layer quality factor, and the repetition is omitted, and the specific structure is shown in fig. 14:
a data acquisition module 1401, configured to acquire micro-log data of a target area; the micro-logging data comprises micro-logging acquisition records;
A geological model building module 1402, configured to build a surface layer horizontal lamellar geological model according to micro-logging data of a target area;
A geologic model correction module 1403, configured to correct a low-speed stratum velocity of the surface layer horizontal lamellar geologic model, to obtain a corrected surface layer horizontal lamellar geologic model;
The direct wave identification module 1404 is configured to forward-act the direct wave record on the corrected surface layer horizontal lamellar geological model by using a ray tracing method, identify a direct wave position on the microlog acquisition record according to the forward-act direct wave record, and reject a seismic trace of a first-arrival indirect wave on the microlog acquisition record;
The interference wave pressing module 1405 is configured to filter and reject interference waves in the microlog acquisition record after the seismic trace of the first arrival wave indirect wave by using the frequency wave number domain, so as to obtain a filtered microlog;
and the Q value calculation module 1406 is configured to determine a surface quality factor Q value of the target area according to the filtered micro log.
In a specific embodiment, the data acquisition module 1401 is specifically configured to:
acquiring micro-logging observation parameters, micro-logging acquisition records and micro-logging interpretation results of a target area;
wherein the micro-logging observation parameters include: the depth of the underground excitation point and the distance between the ground detection point and the wellhead;
Micro-well interpretation effort includes: horizon speed and thickness are explained.
In a specific embodiment, the structure of geologic model modification module 1403, as shown in FIG. 15, includes:
The forward wave full-wave field recording unit 1501 is used for establishing a forward wave method of underground excitation and ground reception according to micro logging observation parameters, and adopting a ray tracing method to forward wave direct wave, refraction wave, reflection wave and multiple wave mixed wave records;
The geologic model correcting unit 1502 is configured to pick up a forward mixed wave recorded first arrival wave crest moment, correct a low-speed stratum velocity of the surface layer horizontal lamellar geologic model according to the forward mixed wave recorded first arrival wave crest moment, and obtain a corrected surface layer horizontal lamellar geologic model.
In particular, the geologic model correction unit 1502 is specifically configured to:
Carrying out ray tracing forward modeling according to different corrected stratum speeds, and counting simulation forward modeling data first arrival moments of all excitation points in a low-speed layer and a deceleration layer;
determining the sum of forward wave recording first arrival time and actual recording first arrival time difference values corresponding to each corrected stratum speed according to forward wave mixed wave recording first arrival wave crest time and simulated forward wave data first arrival time;
and determining the corrected stratum speed corresponding to the minimum value in the sum of the forward record first arrival time and the actual record first arrival time difference as the stratum speed of the first stratum in the surface layer horizontal lamellar geological model, so as to correct the low-speed stratum speed of the surface layer horizontal lamellar geological model, and obtaining the corrected surface layer horizontal lamellar geological model.
In a specific embodiment, the direct wave identification module 1404 is specifically configured to:
picking up the wave crest moment of the direct wave recorded by the forward direct wave;
Calculating the difference value between the first arrival time of exciting different receiving channels at different depths in the micro-logging acquisition record and the corresponding forward direct wave time according to the forward direct wave record direct wave crest time;
If the difference is less than one fourth of the first arrival wave period of the receiving channel, determining that the first arrival wave of the receiving channel is a direct wave.
In a specific embodiment, the interference wave compressing module 1405 is specifically configured to:
Splicing the residual seismic traces in the micro-logging acquisition records after removing the seismic traces of the first-arrival indirect wave into a data body, and correcting the first-arrival wave jump time of all the seismic traces in the data body to the same time to obtain the micro-logging acquisition records after the first-arrival wave is leveled;
Calculating energy balance factors of all sample point values in the micro-well logging acquisition records after the first arrival wave is leveled, and applying the energy balance factors of all sample point values to the micro-well logging acquisition records after the first arrival wave is leveled to obtain micro-well logging records after the energy balance;
And carrying out frequency wave number domain filtering operation on the micro-well logging subjected to energy balance, removing interference waves, and obtaining the micro-well logging subjected to filtering.
In a specific embodiment, the Q value calculation module 1406 is specifically configured to:
Picking up a complete cycle time of the first arrival wave on the filtered micro-logging record, reserving sample point amplitude values in a complete cycle of the first arrival wave, and enabling the residual sample point amplitude values to be zero to obtain micro-logging data for calculating a Q value;
And determining the surface quality factor Q value of the target area according to the micro-logging data for calculating the Q value.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for determining the quality factor Q value of the surface layer when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program for executing the method for determining the Q value of the surface quality factor.
In summary, the method and the device for determining the Q value of the surface quality factor provided by the embodiment of the invention have the following advantages:
Acquiring micro-logging data of a target area; the micro-logging data comprises micro-logging acquisition records; establishing a surface layer horizontal lamellar geological model according to micro-logging data of a target area; correcting the low-speed stratum speed of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model; forward modeling direct wave recording by utilizing a ray tracing method on the corrected surface layer horizontal lamellar geological model; according to the forward wave record, identifying the direct wave position on the micro-well logging acquisition record, and removing the seismic trace of the first-arrival indirect wave on the micro-well logging acquisition record; removing interference waves in the micro-well logging acquisition records after the seismic traces of the first arrival wave indirect waves by utilizing frequency wave number domain filtering to obtain the micro-well logging records after filtering; and determining the surface quality factor Q value of the target area according to the filtered micro well logging. The influence of excitation wavelet difference, wave detection point coupling difference and near field influence on the Q value precision in the calculation process is reduced by correcting the surface layer horizontal lamellar geological model. The seismic channels of the first-arrival wave indirect wave are removed, the interference wave is removed by utilizing frequency wave number domain filtering, and the influence of the interference wave on the Q value calculation precision is reduced, so that the precision of the surface quality factor Q value is improved.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (14)
1. A method for determining a Q value of a skin quality factor, comprising:
Acquiring micro-logging data of a target area; the micro-well logging data comprises micro-well logging acquisition records;
establishing a surface layer horizontal lamellar geological model according to micro-logging data of a target area;
correcting the low-speed stratum speed of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model;
Forward modeling direct wave recording by utilizing a ray tracing method on the corrected surface layer horizontal lamellar geological model;
according to the forward wave record, identifying the direct wave position on the micro-well logging acquisition record, and removing the seismic trace of the first-arrival indirect wave on the micro-well logging acquisition record;
removing interference waves in the micro-well logging acquisition records after the seismic traces of the first arrival wave indirect waves by utilizing frequency wave number domain filtering to obtain the micro-well logging records after filtering;
determining the surface quality factor Q value of the target area according to the filtered micro-well logging;
correcting the low-speed stratum velocity of the surface layer horizontal lamellar geologic model to obtain a corrected surface layer horizontal lamellar geologic model, comprising: carrying out ray tracing forward modeling according to different corrected stratum speeds, and counting simulation forward modeling data first arrival moments of all excitation points in a low-speed layer and a deceleration layer; determining the sum of forward wave recording first arrival time and actual recording first arrival time difference values corresponding to each corrected stratum speed according to forward wave mixed recording first arrival wave crest time and the simulated forward wave data first arrival time; and determining the corrected stratum speed corresponding to the minimum value in the sum of the forward record first arrival time and the actual record first arrival time difference as the stratum speed of the first stratum in the surface layer horizontal lamellar geological model, so as to correct the low-speed stratum speed of the surface layer horizontal lamellar geological model, and obtaining the corrected surface layer horizontal lamellar geological model.
2. The method of claim 1, wherein acquiring micro-log data of the target area comprises:
acquiring micro-logging observation parameters, micro-logging acquisition records and micro-logging interpretation results of a target area;
Wherein the micro-logging observation parameters include: the depth of the underground excitation point and the distance between the ground detection point and the wellhead;
the micro-logging interpretation comprises: horizon speed and thickness are explained.
3. The method of claim 2, wherein modifying the low-velocity horizon speed of the subsurface horizontal lamellar geologic model to obtain a modified subsurface horizontal lamellar geologic model comprises:
Establishing a forward method for underground excitation and ground reception according to micro-logging observation parameters, and adopting a ray tracing method to forward the mixed wave record of direct wave, refraction wave, reflection wave and multiple wave;
And (3) picking up the first arrival wave crest moment of forward wave mixing record, and correcting the low-speed stratum velocity of the surface layer horizontal lamellar geological model according to the first arrival wave crest moment of forward wave mixing record to obtain the corrected surface layer horizontal lamellar geological model.
4. The method of claim 1, wherein identifying a direct wave location on a microlog acquisition record from a forward direct wave record comprises:
picking up the wave crest moment of the direct wave recorded by the forward direct wave;
Calculating the difference value between the first arrival time of exciting different receiving channels at different depths in the micro-logging acquisition record and the corresponding forward direct wave time according to the forward direct wave record direct wave crest time;
and if the difference value is smaller than one fourth of the period of the first arrival wave of the receiving channel, determining that the first arrival wave of the receiving channel is a direct wave.
5. The method of claim 1, wherein removing interfering waves from the microlog acquisition records after the seismic traces of the first-arrival indirect wave using frequency-wave number domain filtering to obtain filtered microlog records comprises:
splicing the residual seismic traces in the micro-logging acquisition records after removing the seismic traces of the first-arrival indirect wave into a data body, and correcting the first-arrival jump time of all the seismic traces in the data body to the same time to obtain the micro-logging acquisition records after the first-arrival leveling;
Calculating energy balance factors of all sample point values in the micro-well logging acquisition records after the first arrival wave is leveled, and applying the energy balance factors of all sample point values to the micro-well logging acquisition records after the first arrival wave is leveled to obtain micro-well logging records after the energy balance;
And carrying out frequency wave number domain filtering operation on the micro-well logging subjected to energy balance, removing interference waves, and obtaining the micro-well logging subjected to filtering.
6. The method of claim 1, wherein determining a skin quality factor Q value for the target zone from the filtered microlog comprises:
Picking up a complete cycle time of the first arrival wave on the filtered micro-logging record, reserving sample point amplitude values in a complete cycle of the first arrival wave, and enabling the residual sample point amplitude values to be zero to obtain micro-logging data for calculating a Q value;
And determining the surface quality factor Q value of the target area according to the micro-logging data for calculating the Q value.
7. A surface quality factor Q value determining apparatus, comprising:
the data acquisition module is used for acquiring micro-logging data of the target area; the micro-well logging data comprises micro-well logging acquisition records;
the geological model building module is used for building a surface layer horizontal lamellar geological model according to micro-logging data of the target area;
the geological model correction module is used for correcting the low-speed stratum speed of the surface layer horizontal lamellar geological model to obtain a corrected surface layer horizontal lamellar geological model;
The direct wave identification module is used for forward modeling direct wave records on the corrected surface layer horizontal lamellar geological model by utilizing a ray tracing method, identifying the direct wave positions on the micro-logging acquisition records according to the forward modeling direct wave records, and removing seismic traces of first-arrival indirect waves on the micro-logging acquisition records;
the interference wave suppression module is used for removing interference waves in the micro-well logging acquisition records after the seismic traces of the first arrival wave indirect waves by utilizing frequency wave number domain filtering to obtain the micro-well logging records after filtering;
the Q value calculation module is used for determining the Q value of the surface quality factor of the target area according to the filtered micro-well logging;
The geologic model correction module is specifically configured to: carrying out ray tracing forward modeling according to different corrected stratum speeds, and counting simulation forward modeling data first arrival moments of all excitation points in a low-speed layer and a deceleration layer; determining the sum of forward wave recording first arrival time and actual recording first arrival time difference values corresponding to each corrected stratum speed according to forward wave mixed recording first arrival wave crest time and the simulated forward wave data first arrival time; and determining the corrected stratum speed corresponding to the minimum value in the sum of the forward record first arrival time and the actual record first arrival time difference as the stratum speed of the first stratum in the surface layer horizontal lamellar geological model, so as to correct the low-speed stratum speed of the surface layer horizontal lamellar geological model, and obtaining the corrected surface layer horizontal lamellar geological model.
8. The apparatus of claim 7, wherein the data acquisition module is specifically configured to:
acquiring micro-logging observation parameters, micro-logging acquisition records and micro-logging interpretation results of a target area;
Wherein the micro-logging observation parameters include: the depth of the underground excitation point and the distance between the ground detection point and the wellhead;
the micro-logging interpretation comprises: horizon speed and thickness are explained.
9. The apparatus of claim 8, wherein the geologic model modification module comprises:
the forward wave full-wave field recording unit is used for establishing a forward wave method of underground excitation and ground reception according to micro-logging observation parameters, and adopting a ray tracing method to forward wave direct wave, refraction wave, reflection wave and multiple wave mixed wave records;
The geological model correction unit is used for picking up the first arrival wave crest moment of forward mixed wave recording, correcting the low-speed stratum velocity of the surface layer horizontal lamellar geological model according to the first arrival wave crest moment of forward mixed wave recording, and obtaining the corrected surface layer horizontal lamellar geological model.
10. The apparatus of claim 7, wherein the direct wave identification module is specifically configured to:
picking up the wave crest moment of the direct wave recorded by the forward direct wave;
Calculating the difference value between the first arrival time of exciting different receiving channels at different depths in the micro-logging acquisition record and the corresponding forward direct wave time according to the forward direct wave record direct wave crest time;
and if the difference value is smaller than one fourth of the period of the first arrival wave of the receiving channel, determining that the first arrival wave of the receiving channel is a direct wave.
11. The apparatus of claim 7, wherein the interference wave compaction module is specifically configured to:
splicing the residual seismic traces in the micro-logging acquisition records after removing the seismic traces of the first-arrival indirect wave into a data body, and correcting the first-arrival jump time of all the seismic traces in the data body to the same time to obtain the micro-logging acquisition records after the first-arrival leveling;
Calculating energy balance factors of all sample point values in the micro-well logging acquisition records after the first arrival wave is leveled, and applying the energy balance factors of all sample point values to the micro-well logging acquisition records after the first arrival wave is leveled to obtain micro-well logging records after the energy balance;
And carrying out frequency wave number domain filtering operation on the micro-well logging subjected to energy balance, removing interference waves, and obtaining the micro-well logging subjected to filtering.
12. The apparatus of claim 7, wherein the Q value calculation module is specifically configured to:
Picking up a complete cycle time of the first arrival wave on the filtered micro-logging record, reserving sample point amplitude values in a complete cycle of the first arrival wave, and enabling the residual sample point amplitude values to be zero to obtain micro-logging data for calculating a Q value;
And determining the surface quality factor Q value of the target area according to the micro-logging data for calculating the Q value.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 6 when executing the computer program.
14. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 6.
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