CN113093274A - Method, device, terminal and storage medium for low-level sequence fault recognition - Google Patents
Method, device, terminal and storage medium for low-level sequence fault recognition Download PDFInfo
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Abstract
The application discloses a low-order fault identification method, device, terminal and storage medium, and belongs to the technical field of oil and gas field development. The method comprises the following steps: the method comprises the steps of performing azimuth stacking processing on an original seismic data body, determining a target azimuth stacking data body, performing guiding filtering processing based on multiple filtering algorithms, determining a target guiding filter body, performing edge detection smoothing filtering processing respectively to obtain a smoothing filter body, performing coherent body construction processing on the original seismic data body, the smoothing filter body and the corresponding target guiding filter body, determining a target coherent body, performing fault tracking on the target coherent body to obtain ant bodies in two directions, determining a fusion ant body according to the ant bodies in the two directions, performing horizontal cross-section analysis and fracture combination processing on the fusion ant body, and determining the spatial position and the geometric form of a low-level fault. By the method and the device, the spatial position and the geometric form of the low-order fault can be obtained, and the low-order fault with smaller fault distance can be identified.
Description
Technical Field
The application relates to the technical field of oil and gas field development, in particular to a low-order fault identification method, device, terminal and storage medium.
Background
The low-order fault is a small fault and has the characteristics of small fault distance, short extension length, few disconnected layers and the like. When the residual oil is exploited, the residual oil is highly dispersed, most of which is concentrated near low-order faults, and the low-order faults have great influence on fine waterflood and chemical flooding development. Therefore, it is important to identify low-order faults.
In the related art, the low-order fault identification technology mainly comprises the following steps: technicians observe physical phenomena on the crust of a target region through various ground instruments, and perform low-order fault identification by using methods such as seismic profile interpretation, dip analysis, variance analysis and the like, so that low-order faults with large fault distances, such as low-order faults with the fault distances of more than 10m, can be identified.
In the course of implementing the present application, the inventors found that the related art has at least the following problems:
in the method for identifying low-level sequence faults in the related art, only low-level sequence faults with large fault distances can be identified, and for low-level sequence faults with small fault distances, such as low-level sequence faults with fault distances below 10m, the same-phase axes are weak in distortion and dislocation, and low-level sequence faults with small fault distances cannot be identified.
Disclosure of Invention
The embodiment of the application provides a low-order fault identification method, a low-order fault identification device, a terminal and a storage medium, and can solve the problem that a low-order fault with a smaller fault distance cannot be identified. The technical scheme is as follows:
in a first aspect, there is provided a method of low order fault identification, the method comprising:
performing azimuth-based stacking processing on an original seismic data volume of a target area to obtain two target azimuth-based stacking data volumes with vertical azimuths;
respectively carrying out multi-window dip angle scanning processing on the two target azimuth-based superposed data volumes to obtain two direction dip angle volumes;
respectively carrying out guiding filtering processing on the two direction inclination angle bodies to obtain two target guiding filtering bodies;
respectively carrying out edge detection smoothing filtering processing on the two target guide filtering bodies to obtain two smoothing filtering bodies;
for each smooth filter, respectively carrying out coherent body construction processing on the original seismic data body, the smooth filter and a target guide filter body corresponding to the smooth filter, and determining a target coherent body with the most obvious fault enhancement effect in the three sets of coherent bodies;
respectively carrying out fault tracking on the two target coherent bodies in different directions to obtain ant bodies in two directions, and determining a fusion ant body based on the ant bodies in the two directions;
and performing flat-section interaction analysis and fracture combination treatment based on the ant bodies in the two directions and the fused ant body, and determining the spatial position and the geometric form of the low-order fault of the target region on the fused ant body.
Optionally, the performing azimuth-separated stacking processing on the original seismic data volume of the target area to obtain two target azimuth-separated stacked data volumes with perpendicular azimuths includes:
performing azimuth-based stacking processing on an original seismic data volume of a target area to obtain azimuth-based stacking data volumes of a plurality of azimuths, and extracting a time slice of each azimuth-based stacking data volume;
and determining two target sub-position superposed data volumes with vertical positions in the sub-position superposed data volumes of the plurality of positions based on the time slice of each sub-position superposed data volume.
Optionally, the performing multi-window tilt scanning on the two target azimuth-based superimposed data volumes respectively to obtain two direction tilt volumes includes:
and respectively carrying out multi-window dip angle scanning processing on the two target azimuth-divided superposed data volumes by using 9 scanning windows to obtain two direction dip angle volumes.
Optionally, the performing guided filtering processing on the two direction inclination angle bodies respectively to obtain two target guided filtering bodies includes:
and for each direction inclination angle body, carrying out mean value filtering processing, median value filtering processing and main component filtering processing on the direction inclination angle body to obtain a mean value filter body, a median value filter body and a main component filter body, and determining the filter body with the most obvious fault enhancement effect as a target guide filter body in the mean value filter body, the median value filter body and the main component filter body.
Optionally, the determining, as a target-oriented filter, a filter with the most significant fault enhancement effect among the mean filter, the median filter, and the main component filter includes:
respectively extracting sections of the mean filter, the median filter and the main component filter at the same structural position;
and determining a filter body with the most obvious fault enhancement effect as a target guide filter body in the mean filter body, the median filter body and the main component filter body based on the section of the mean filter body, the section of the median filter body and the section of the main component filter body.
Optionally, after performing edge detection smoothing filtering processing on the two target-oriented filtering volumes respectively to obtain two smoothing filtering volumes, the method further includes:
for each smooth filter body, carrying out construction curvature body processing by using a set fractional derivative index to obtain a most positive curvature body and a most negative curvature body of the smooth filter body, and determining a construction curvature body with a clearer fault in the most positive curvature body and the most negative curvature body;
for each structure curvature body with clearer faults, performing parameter optimization processing on the structure curvature body with clearer faults by using a fractional derivative index different from the set fractional derivative index to obtain one or more new structure curvature bodies, and determining the structure curvature body with the clearest faults in the one or more new structure curvature bodies and the structure curvature body with clearer faults;
and carrying out comparative analysis on the two construction curvature bodies with the clearest faults and the fusion ant body.
Optionally, after performing edge detection smoothing filtering processing on the two target-oriented filtering volumes respectively to obtain two smoothing filtering volumes, the method further includes:
and for each smoothing filter, performing coherent energy gradient attribute calculation on the smoothing filter to obtain the smoothing filter carrying the coherent energy gradient attribute.
Optionally, the performing fault tracing on the two target coherence bodies in different directions to obtain ant bodies in two directions, and determining a fusion ant body based on the ant bodies in two directions includes:
carrying out fault tracking on one target coherent body along a first direction to obtain an ant body in the first direction, and carrying out fault tracking on the other target coherent body along a second direction to obtain an ant body in the second direction;
and obtaining an ant body with a first direction and a second direction fused together based on the ant body in the first direction and the ant body in the second direction, wherein the first direction corresponds to the direction of the target sub-direction superposed data body corresponding to the one target coherent body, and the second direction corresponds to the direction of the target sub-direction superposed data body corresponding to the other target coherent body.
In a second aspect, there is provided an apparatus for low order fault identification, the apparatus comprising:
the azimuth-dividing stacking processing module is used for performing azimuth-dividing stacking processing on the original seismic data volume of the target area to obtain two target azimuth-dividing stacking data volumes with vertical azimuths;
the dip angle scanning processing module is used for respectively carrying out multi-window dip angle scanning processing on the two target azimuth-divided superposed data volumes to obtain two direction dip angle volumes;
the guiding filtering processing module is used for respectively carrying out guiding filtering processing on the two direction inclination angle bodies to obtain two target guiding filtering bodies;
the smoothing filter processing module is used for respectively carrying out edge detection smoothing filter processing on the two target guide filter bodies to obtain two smoothing filter bodies;
the coherent body structure processing module is used for respectively carrying out coherent body structure processing on each smooth filter body and the original seismic data body as well as the target guide filter bodies corresponding to the smooth filter bodies, and determining the target coherent body with the most obvious fault enhancement effect in the three sets of coherent bodies;
the fault tracking module is used for respectively carrying out fault tracking on the two target coherent bodies in different directions to obtain ant bodies in two directions, and determining a fusion ant body based on the ant bodies in the two directions;
and the determining module is used for performing flat-section interaction analysis and fracture combination processing on the basis of the ant bodies in the two directions and the fused ant body, and determining the spatial position and the geometric form of the low-order fault of the target region on the fused ant body.
Optionally, the sub-position superposition processing module is configured to:
performing azimuth-based stacking processing on an original seismic data volume of a target area to obtain azimuth-based stacking data volumes of a plurality of azimuths, and extracting a time slice of each azimuth-based stacking data volume;
and determining two target sub-position superposed data volumes with vertical positions in the sub-position superposed data volumes of the plurality of positions based on the time slice of each sub-position superposed data volume.
Optionally, the tilt scanning processing module is configured to:
and respectively carrying out multi-window dip angle scanning processing on the two target azimuth-divided superposed data volumes by using 9 scanning windows to obtain two direction dip angle volumes.
Optionally, the guided filtering processing module is configured to:
and for each direction inclination angle body, carrying out mean value filtering processing, median value filtering processing and main component filtering processing on the direction inclination angle body to obtain a mean value filter body, a median value filter body and a main component filter body, and determining the filter body with the most obvious fault enhancement effect as a target guide filter body in the mean value filter body, the median value filter body and the main component filter body.
Optionally, the guided filtering processing module is configured to:
respectively extracting sections of the mean filter, the median filter and the main component filter at the same structural position;
and determining a filter body with the most obvious fault enhancement effect as a target guide filter body in the mean filter body, the median filter body and the main component filter body based on the section of the mean filter body, the section of the median filter body and the section of the main component filter body.
Optionally, the apparatus further comprises:
the structural curvature body determining module is used for performing structural curvature body processing on each smooth filter body by using a set fractional derivative index to obtain a most positive curvature body and a most negative curvature body of the smooth filter bodies, and determining a structural curvature body with a clearer fault in the most positive curvature body and the most negative curvature body;
the target construction curvature body determining module is used for performing parameter optimization processing on each construction curvature body with clearer faults by using a fractional derivative index different from the set fractional derivative index to obtain one or more new construction curvature bodies, and determining the construction curvature body with the clearest fault in the one or more new construction curvature bodies and the construction curvature body with clearer faults;
and the comparison analysis module is used for performing comparison analysis on the two construction curvature bodies with the clearest faults and the fusion ant body.
Optionally, the apparatus further comprises:
and the attribute calculation module is used for carrying out coherent energy gradient attribute calculation on each smooth filter body to obtain the smooth filter body carrying the coherent energy gradient attribute.
Optionally, the fault tracking module is configured to:
carrying out fault tracking on one target coherent body along a first direction to obtain an ant body in the first direction, and carrying out fault tracking on the other target coherent body along a second direction to obtain an ant body in the second direction;
and obtaining an ant body with a first direction and a second direction fused together based on the ant body in the first direction and the ant body in the second direction, wherein the first direction corresponds to the direction of the target sub-direction superposed data body corresponding to the one target coherent body, and the second direction corresponds to the direction of the target sub-direction superposed data body corresponding to the other target coherent body.
In a third aspect, a terminal is provided, which includes a processor and a memory, where at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the operations performed by the method for low-order fault recognition as described above.
In a fourth aspect, a computer-readable storage medium is provided having at least one instruction stored therein, the at least one instruction being loaded and executed by a processor to perform operations performed by the method of low-order fault identification as described above.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
according to the low-order fault identification method provided by the embodiment of the application, the original seismic data volume of the target area is sequentially subjected to azimuth division processing and multi-window dip angle scanning processing to obtain two direction dip angle volumes, the two direction dip angle volumes are respectively subjected to guiding filtering processing to determine two target guiding filtering volumes with the most obvious fault enhancement effect, and the two target guiding filtering volumes are subjected to edge detection smoothing filtering processing to obtain two smoothing filtering volumes. For each smooth filter body, respectively carrying out coherent body construction processing on an original seismic data body, the smooth filter body and a target guide filter body corresponding to the smooth filter body, determining a target coherent body with an obvious fault enhancement effect, carrying out fault tracking on the coherent body along different directions to obtain ant bodies in two directions, determining a fusion ant body according to the ant bodies in the two directions, carrying out flat-section interactive analysis and fracture combination processing on the fusion ant body, and determining the spatial position and the geometric form of a low-order fault in a target area. By the method and the device, the spatial position and the geometric form of the low-order fault of the target area can be obtained, and the low-order fault with smaller fault distance can be identified.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for low-order fault identification provided by an embodiment of the present application;
FIG. 2 is a comparison graph of time slices of four azimuthally superimposed data volumes provided by an embodiment of the present application;
FIG. 3 is a comparison graph of time slices of a inline tilt and a crossline tilt provided by an embodiment of the present application;
FIG. 4 is a cross-sectional comparison view of a Line2584 of an original seismic data volume, a inline mean filter, an inline median filter, and an inline principal component filter, as provided by embodiments of the present application;
FIG. 5 is a comparison of a time slice of an original seismic data volume, a coherence time slice of the original seismic data volume, a coherence time slice of a inline mean filter, and a coherence time slice of an inline smoothing filter provided by an embodiment of the present application;
fig. 6 is a comparison graph of time slices of a median filter in the direction of the main line, time slices of a ant body in the direction of the main line, and time slices of an ant body fused with a main line crossline provided in an embodiment of the present application;
FIG. 7 is a comparison graph of time slices of a median inline direction filter, a 0.25 line-of-most-positive curvature volume, a 0.75 line-of-most-positive curvature volume, and a 0.75 line-of-most-negative curvature volume provided by an embodiment of the present application;
FIG. 8 is a schematic diagram of an example of well-to-seismic association comparison verification provided in an embodiment of the present application;
fig. 9 is a schematic structural diagram of a low-order fault identification apparatus according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The method for identifying the low-order fault provided by the embodiment of the application can be applied to the technical field of oil and gas field development, and is particularly used for identifying the low-order fault. First, a technician may fire a target area and acquire seismic data reflected from the subsurface formation using receiving equipment. Then, the seismic data can be input into a terminal for storage, the terminal can acquire the seismic data to generate an original seismic data volume of the target area, and the spatial position and the geometric form of the low-order fault of the target area are determined by adopting the method provided by the embodiment of the application. Finally, the technician may identify the low order faults of the target region and guide development of the field of interest for the target region in conjunction with the low order faults of the target region.
Fig. 1 is a flowchart of a method for low-order fault identification according to an embodiment of the present disclosure. Referring to fig. 1, the embodiment includes:
in step 101, the original seismic data volume of the target area is subjected to azimuth-splitting stacking processing to obtain two target azimuth-splitting stacked data volumes with vertical azimuths.
Wherein the seismic data volume is a three-dimensional spatial distribution of subsurface formations. And the sub-azimuth stacking process reflects the relative change relationship of azimuth angles in the seismic horizon. The azimuth-separated stacked data volume refers to seismic data volumes corresponding to different azimuths. The azimuth angle is a horizontal included angle from a north-pointing direction line of a certain point to a target direction line along a clockwise direction.
In implementation, firstly, a worker can shoot in the field, and seismic data reflected by the underground stratum is collected by using receiving equipment to obtain an original seismic data volume of a target area. In order to improve the azimuth precision of analysis points in seismic data, Geoaast (seismic data processing interpretation software) software is utilized to input an original seismic data body, and the original seismic data body is subjected to azimuth-dividing stacking processing according to a plurality of azimuths to obtain an azimuth-dividing stacking data body with a plurality of azimuths. And extracting the time slice of each sub-position superimposed data body, and determining two target sub-position superimposed data bodies with vertical positions in the sub-position superimposed data bodies with a plurality of positions on the basis of the time slice of each sub-position superimposed data body. A time slice refers to a slice taken in a planar manner along a certain time value of an original seismic data volume.
For example, after the original seismic data volume is input into the Geoeast software, the sub-azimuth stacking processing can be carried out according to the azimuth angle of 0-45 degrees, the azimuth angle of 45-90 degrees, the azimuth angle of 90-135 degrees and the azimuth angle of 135-180 degrees, and sub-azimuth stacking data volumes of four azimuths are obtained. For the sub-position superimposed data volume of each position, a time slice with a time value of 1280ms of the sub-position superimposed data volume is extracted, as shown in fig. 2, fig. 2 includes time slices with a time value of 1280ms of the sub-position superimposed data volumes of four positions, wherein, the first position is 0-45 degrees of azimuth, the second position is 45-90 degrees of azimuth, the third position is 90-135 degrees of azimuth, and the fourth position is 135-180 degrees of azimuth. According to the sub-azimuth superimposed data bodies corresponding to all azimuth angles in the graph 2, the sub-azimuth superimposed data body with the azimuth angle of 0-45 degrees and the clearer fault enhancement effect of 90-135 degrees can be obtained, the main fracture system in the north-east-south-west trend can be identified by preferably selecting the sub-azimuth superimposed data body with the azimuth angle of 0-45 degrees, and the secondary fracture system perpendicular to the main fracture system can be identified by the sub-azimuth superimposed data body with the azimuth angle of 90-135 degrees. Wherein, the north-south trend refers to the 45 degree north-east direction. The primary fracture system is the fracture that is dominant in the target zone's fracture system, i.e., the large fracture. The secondary fracture system is a small fracture perpendicular to the primary fracture system.
In step 102, multi-window tilt scanning processing is performed on the two target sub-azimuth superimposed data volumes, so as to obtain two directional tilt volumes.
The multi-window dip angle scanning processing is to use a vertical window to carry out dip angle scanning estimation and reflect the relative change relation of the dip angle of the seismic horizon. Dip refers to the angle formed by the maximum dip line of a seismic horizon and its projected line on a horizontal plane.
In the implementation, in order to improve the dip angle precision of analysis points in the seismic data, parameters such as time values, the number of scanning windows, the length of scanning time windows and the like are determined according to a target layer of a target area. In the Geoaast software, multi-window dip angle scanning processing is carried out on two target azimuth-divided superposed data volumes by using 9 scanning windows respectively, and a window with the maximum similarity degree is obtained and used as a dip angle estimation window of an analysis point to obtain two direction dip angle volumes. Wherein the length of the scanning time window is 5 times of the time sampling interval of the input data volume. The number of scanning windows may be 1, 5 or 9.
For example, after the sub-azimuth overlay data volume with the azimuth angle of 0 to 45 ° and the sub-azimuth overlay data volume with the azimuth angle of 90 to 135 ° are obtained according to the above example, the Geoeast software may set the number of scanning windows to 9, perform multi-window tilt angle scanning processing, and output the inline tilt volume and the crossline tilt volume, as shown in fig. 3, where fig. 3 includes time slices of the inline tilt volume and the crossline tilt volume with a time value of 1280 ms. According to the figure 3, the main fracture system can be clearly identified by the main line direction inclination angle body, and the secondary fracture system can be clearly identified by the cross line direction inclination angle body.
In step 103, the two directional inclination angle bodies are respectively subjected to guiding filtering processing to obtain two target guiding filtering bodies.
In implementation, for each direction dip, the direction dip may be subjected to a mean filtering process, a median filtering process, and a principal component filtering process to obtain a mean filter, a median filter, and a principal component filter. And respectively extracting sections of the mean filter, the median filter and the main component filter at the same structural position. And determining the filter body with the most obvious fault enhancement effect as a target-oriented filter body from the mean filter body, the median filter body and the main component filter body based on the section of the mean filter body, the section of the median filter body and the section of the main component filter body. The embodiment of the application carries out guiding filtering processing, improves the signal to noise ratio of the seismic data, makes the continuous and discontinuous characteristics of the same phase axis in the seismic data more obvious, namely the low-order fault is more obvious, and improves the certainty factor of horizon tracking.
For example, after the main measurement line direction inclination angle body and the contact measurement line direction inclination angle body are obtained according to the above example, the main measurement line direction inclination angle body and the contact measurement line direction inclination angle body may be respectively processed by three algorithms of mean filtering, median filtering and main component filtering, so that a mean filter, a median filter and a main component filter corresponding to the main measurement line direction inclination angle body and the contact measurement line direction inclination angle body respectively can be obtained, and 6 filters, which are respectively a main measurement line direction mean filter, a main measurement line direction median filter, a main measurement line direction main component filter, a contact measurement line direction mean filter, a contact measurement line direction median filter and a contact measurement line direction main component filter, are obtained. For example, for the inline direction dip, the original seismic data volume, the inline direction mean filter, the inline direction median filter, and the inline direction main component filter are respectively extracted on the section of the inline Line2604, and as shown in fig. 4, the filter with the most significant fault enhancement effect can be obtained as the inline direction median filter. In general, the filter bodies with the most obvious fault enhancement effect corresponding to the main survey line direction inclination angle body and the cross survey line direction inclination angle body are of the same type. Therefore, the filter body with the most obvious fault enhancement effect corresponding to the main survey line direction inclination angle body is the main survey line direction median filter body, the filter body with the most obvious fault enhancement effect corresponding to the cross survey line direction inclination angle body is the cross survey line direction median filter body, and the main survey line direction median filter body and the cross survey line direction median filter body are used as two target direction filter bodies.
In step 104, the two target-oriented filters are respectively subjected to edge detection smoothing filtering processing to obtain two smoothing filters.
Wherein edge detection is the identification of points in the digital image where the brightness change is significant. Smoothing filtering refers to a process of filtering noise.
In implementation, after two target-oriented filters are obtained, edge detection smoothing filtering processing can be performed by using Geoeast software, and two smoothing filters are output.
For example, after two target-oriented filters, namely, the median filter in the main-line direction and the median filter in the crossline direction, are obtained according to the above example, edge detection smoothing filtering processing is performed on the two target-oriented filters, so that two smoothing filters, namely, the median filter in the main-line direction and the median filter in the crossline direction, can be obtained.
Optionally, for each smoothing filter, performing coherent energy gradient attribute calculation on the smoothing filter to obtain the smoothing filter carrying the coherent energy gradient attribute.
The coherent energy gradient attribute refers to amplitude change of the seismic data, and the accuracy of the seismic data can be improved.
In step 105, for each smooth filter, coherent body construction processing is performed on the original seismic data volume, the smooth filter and the target guided filter corresponding to the smooth filter, and a target coherent body with the most obvious fault enhancement effect is determined among the three sets of coherent bodies.
The coherent body structure processing is processing for finding the coherence between each data sample in the seismic data body and the surrounding data. A coherence volume is a volume of seismic data that characterizes the coherence of the seismic data.
In the implementation, in Geoeast software, parameters such as a maximum tilt angle scanning time window and a coherence time window need to be set during coherent body construction processing. And for each smooth filter, determining a maximum dip angle scanning time window according to the time delay of the adjacent channel of the steepest homophase axis by using a 9 multiplied by 9 covariance matrix characteristic algorithm for the original seismic data volume, the smooth filter and the target guide filter corresponding to the smooth filter, and performing coherent body construction processing to obtain three sets of coherent bodies. And respectively extracting the coherent time slices of the three sets of coherence bodies at the same time value, analyzing and comparing, and determining the target coherence body with the most obvious fault enhancement effect in the three sets of coherence bodies. According to the method and the device, through coherent body construction processing, the similarity of the seismic waveforms can be compared through coherent bodies, and the seismic waveforms are discontinuous at points with lower coherence values, namely, faults are discontinuous. The resolution of fault layer identification is improved through a 9 multiplied by 9 covariance matrix characteristic algorithm.
For example, after two smoothing filters, namely, the main line direction smoothing filter and the crossline direction smoothing filter, are obtained according to the above example, for the main line direction smoothing filter, the original seismic data volume, the main line direction smoothing filter and the main line direction median filter are subjected to coherent volume construction processing, respectively, to obtain three coherent volumes. And for the cross survey line direction smooth filter, performing coherent body construction treatment on the original seismic data body, the cross survey line direction smooth filter and the cross survey line direction median filter respectively to obtain three coherent bodies. For the inline direction smoothing filter, as shown in fig. 5, fig. 5 includes a time slice when the time value of the original seismic data volume is 1280ms, a coherence time slice when the time value of the inline direction median filter is 1280ms, and a coherence time slice when the time value of the inline direction smoothing filter is 1280ms, and a coherent body corresponding to the inline direction smoothing filter with the most significant fault enhancement effect can be obtained according to fig. 5, and the coherent body corresponding to the inline direction smoothing filter is used as a target coherent body. And similarly, taking the coherent body corresponding to the smooth filtering body in the direction of the cross survey line as another target coherent body. The coherent time slice refers to a time slice of a coherent body corresponding to the seismic data volume.
In step 106, the two target coherence bodies are respectively subjected to fault tracing in different directions to obtain ant bodies in two directions, and a fused ant body is determined based on the ant bodies in two directions.
The fused ant body is the ant body fused in the first direction and the second direction.
In implementation, in order to track the whole fault, after two target coherent bodies are obtained, a start time value and an end time value are determined according to the stratum thickness of a target area and a time-depth relation obtained by layer position calibration. And carrying out fault tracking on a target coherent body along a first direction by using an ant tracking algorithm to obtain an ant body in the first direction. And carrying out fault tracking on the other target coherent body along a second direction to obtain an ant body in the second direction. And obtaining the ant body with the first direction and the second direction fused according to the ant body in the first direction and the ant body in the second direction, and taking the ant body as the fused ant body.
It should be noted that the first direction is a direction line of the target coherent body identification fault, the azimuth of the corresponding target sub-azimuth superimposed data body is an azimuth range of the identification fault, one target coherent body is consistent with the direction of the target sub-azimuth superimposed data body identification fault corresponding to the target coherent body, and the first direction corresponds to the azimuth of the target sub-azimuth superimposed data body corresponding to the target coherent body, that is, the direction line corresponding to the first direction should be within the azimuth range corresponding to the azimuth. The second direction is a direction line of the other target coherent body identification fault, the position of the corresponding target sub-position superposed data body is an azimuth angle range of the identification fault, the other target coherent body is consistent with the direction of the corresponding target sub-position superposed data body identification fault, the second direction is corresponding to the position of the target sub-position superposed data body corresponding to the other target coherent body, namely the direction line corresponding to the second direction is in the azimuth angle range corresponding to the position.
For example, after obtaining two target coherence bodies, namely a coherence body corresponding to the main line direction smoothing filter body and a coherence body corresponding to the cross line direction smoothing filter body, according to the above example, the coherence body corresponding to the main line direction smoothing filter body is subjected to cross-sectional tracking along the main line direction, and the coherence body corresponding to the cross line direction smoothing filter body is subjected to cross-sectional tracking along the cross line direction, so that an ant body in which the main line direction ant body, the cross line direction ant body, and the main line cross line are fused can be obtained.
In step 107, based on the ant bodies in the two directions and the fused ant body, the flat-section interaction analysis and the fracture combination processing are performed, and the spatial position and the geometric shape of the low-order fault of the target region are determined on the fused ant body.
The flat-section interactive analysis is a comparative analysis by using a plurality of planes and sections at the same structural position in a fault. And the fracture combination processing is to connect the break points of the same fault to form a fault distribution diagram.
In implementation, the same structural position of the target region is subjected to flat-section interactive analysis based on the ant body in the first direction, the ant body in the second direction and the fusion ant body, and the spatial position of the low-order fault of the target region is determined in the fusion ant body. And performing fracture combination processing based on the fracture data on the fused ant body, and determining the geometric form of the low-order fault of the target area in the fused ant body.
For example, after an ant body with main Line direction, an ant body with cross-Line direction and an ant body with main Line and cross-Line are fused is obtained according to the above example, a Line2584 section of the ant body with main Line direction, a Line2584 section of the ant body with cross-Line direction and an ine2584 section of the ant body with main Line and cross-Line are respectively extracted, and a flat cross-section analysis is performed. As shown in fig. 6, the time slice of the median filter in the main-line direction, the time slice of the ant body in the main-line direction and the time slice of the ant body fused with the main-line connecting line in fig. 6 can preliminarily determine the spatial position of the low-order fault. The spatial location of the low order faults may be determined on an ant body where inline crosslines merge to include inline and crossline directions. The ant bodies fused with the main measuring line and the measuring line are subjected to fracture combination, breakpoints belonging to the same fault are connected, and the geometric form of the low-order fault is determined on the ant bodies fused with the main measuring line and the measuring line.
Optionally, for each smooth filter, the structured curvature body is processed by using a set fractional derivative index, so as to obtain a most positive curvature body and a most negative curvature body of the smooth filter, and the structured curvature body with a clearer fault is determined in the most positive curvature body and the most negative curvature body. And for each structural curvature body with clearer faults, performing parameter optimization processing on the structural curvature body with clearer faults by using a fractional derivative index different from the set fractional derivative index to obtain one or more new structural curvature bodies, and determining the structural curvature body with the clearest fault in the one or more new structural curvature bodies and the structural curvature body with clearer faults. And comparing the two construction curvature bodies with the clearest fault with the fused ant body for analysis. The maximum clear structural curvature body of the fault can clearly identify the large fault, the maximum clear structural curvature body of the fault and the ant body fused with the main measuring line contact measuring line can be compared and verified, and the spatial position of the large fault in the maximum clear structural curvature body of the fault and the ant body fused with the main measuring line contact measuring line is the same.
The structural curvature body with the clearest fault is the same as the type of the structural curvature body processing through which the structural curvature body passes. Curvature is the degree of curvature that describes any point on a curve, with greater curvature yielding more. The structural curvature body processing can clearly identify the large fault.
For example, after obtaining two smoothing filters, namely, the inline direction smoothing filter and the crossline direction smoothing filter, according to the above example, the inline direction smoothing filter and the crossline direction smoothing filter are subjected to structural curvature body processing, respectively, and the fractional derivative index is set to 0.75, thereby obtaining an inline most positive curvature body, an inline most negative curvature body, an crossline most positive curvature body, and a crossline most negative curvature body. As shown in fig. 7, fig. 7 processes the inline smoothing filter to obtain an inline most positive curvature volume and an inline most negative curvature volume, and determines a structural curvature volume with a relatively clear cross section as the inline most positive curvature volume. And setting fractional derivative indexes to be 0.75 and 0.25 for the main line most positive curvature body to obtain a structural curvature body with the fractional derivative index of 0.75 and a structural curvature body with the fractional derivative index of 0.25, and determining that the structural curvature body with the clearest fault is the structural curvature body with the fractional derivative index of 0.75, namely the main line most positive curvature body with the fractional derivative index of 0.75 as shown in FIG. 5. The main line maximum curvature body with the fractional derivative index of 0.75 can clearly identify a large fault, and the main line maximum curvature body with the fractional derivative index of 0.75 and an ant body with a main line connecting line fused can be compared and analyzed.
For example, from actual logs, log data of G251, G2-58 and G291-4 wells can be obtained, and as shown in FIG. 8, the Nm-4 formation of the G251 well is broken by 5 meters, and the fracture on the seismic section is unclear. According to the method of the steps 101 to 107, the ant body with the fused main measuring line and the measuring line corresponding to the Nm-4 stratum of the G251 well can be obtained, the breakpoint can be clearly seen on the ant body with the fused main measuring line and the measuring line, and the breakpoint is subjected to fracture combination, as shown in fig. 8, the geometric form of the low-order fault can be determined. Through actual well logging comparison and verification, the low-level sequence fault with the fault distance of 5m can be effectively identified.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
according to the low-order fault identification method provided by the embodiment of the application, the original seismic data volume of the target area is sequentially subjected to azimuth division processing and multi-window dip angle scanning processing to obtain two direction dip angle volumes, the two direction dip angle volumes are respectively subjected to guiding filtering processing to determine two target guiding filtering volumes with the most obvious fault enhancement effect, and the two target guiding filtering volumes are subjected to edge detection smoothing filtering processing to obtain two smoothing filtering volumes. For each smooth filter body, respectively carrying out coherent body construction processing on an original seismic data body, the smooth filter body and a target guide filter body corresponding to the smooth filter body, determining a target coherent body with an obvious fault enhancement effect, carrying out fault tracking on the coherent body along different directions to obtain ant bodies in two directions, determining a fusion ant body according to the ant bodies in the two directions, carrying out flat-section interactive analysis and fracture combination processing on the fusion ant body, and determining the spatial position and the geometric form of a low-order fault in a target area. By adopting the method, the spatial position and the geometric form of the low-order fault in the target area can be quickly, conveniently and effectively obtained, the identification of the fault distance of 5m low-order fault is realized, the identification precision of the low-order fault is effectively improved, so that the development work of the oil and gas field can be more accurately guided, and the method has great economic benefit.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
Based on the same technical concept, an embodiment of the present application further provides an apparatus for determining a target address of a drilling platform, where the apparatus may be a terminal in the foregoing embodiment, as shown in fig. 9, and the apparatus includes:
the azimuth-dividing stacking processing module 901 is used for performing azimuth-dividing stacking processing on the original seismic data volume of the target area to obtain two target azimuth-dividing stacking data volumes with vertical azimuths;
the dip angle scanning processing module 902 is configured to perform multi-window dip angle scanning processing on the two target azimuth-based superimposed data volumes respectively to obtain two directional dip angle volumes;
a guiding filtering processing module 903, configured to perform guiding filtering processing on the two direction inclination angle bodies respectively to obtain two target guiding filtering bodies;
a smoothing filter processing module 904, configured to perform edge detection smoothing filter processing on the two target-oriented filters respectively to obtain two smoothing filters;
the coherent body structure processing module 905 is configured to perform coherent body structure processing on each smooth filter body for the target guided filter body corresponding to the original seismic data body, the smooth filter body, and determine a target coherent body with the most obvious fault enhancement effect among the three sets of coherent bodies;
a fault tracking module 906, configured to perform fault tracking on the two target coherence bodies in different directions respectively to obtain ant bodies in two directions, and determine a fused ant body based on the ant bodies in the two directions;
and a determining module 907, configured to perform a flat-section interaction analysis and a fracture combination process based on the ant bodies in the two directions and the fused ant body, and determine a spatial position and a geometric shape of a low-order fault of the target region on the fused ant body.
Optionally, the sub-position superposition processing module 901 is configured to:
performing azimuth-based stacking processing on an original seismic data volume of a target area to obtain azimuth-based stacking data volumes of a plurality of azimuths, and extracting a time slice of each azimuth-based stacking data volume;
and determining two target sub-position superposed data volumes with vertical positions in the sub-position superposed data volumes of a plurality of positions based on the time slice of each sub-position superposed data volume.
Optionally, the tilt scan processing module 902 is configured to:
and respectively carrying out multi-window dip angle scanning processing on the two target azimuth-divided superposed data volumes by using 9 scanning windows to obtain two direction dip angle volumes.
Optionally, the guided filtering processing module 903 is configured to:
and for each direction inclination body, carrying out mean value filtering processing, median value filtering processing and main component filtering processing on the direction inclination body to obtain a mean value filter body, a median value filter body and a main component filter body, and determining the filter body with the most obvious fault enhancement effect as a target-oriented filter body in the mean value filter body, the median value filter body and the main component filter body.
Optionally, the guided filtering processing module 903 is configured to:
respectively extracting sections of the mean filter, the median filter and the main component filter at the same structural position;
and determining the filter body with the most obvious fault enhancement effect as a target-oriented filter body from the mean filter body, the median filter body and the main component filter body based on the section of the mean filter body, the section of the median filter body and the section of the main component filter body.
Optionally, the apparatus further comprises:
the structural curvature body determining module is used for performing structural curvature body processing on each smooth filter body by using a set fractional derivative index to obtain a most positive curvature body and a most negative curvature body of the smooth filter bodies, and determining a structural curvature body with a clearer fault in the most positive curvature body and the most negative curvature body;
the target construction curvature body determining module is used for performing parameter optimization processing on the construction curvature body with the clearer fault by using a fractional derivative index different from the set fractional derivative index for each construction curvature body with the clearer fault to obtain one or more new construction curvature bodies, and determining the construction curvature body with the clearest fault in the one or more new construction curvature bodies and the construction curvature body with the clearer fault;
and the comparison analysis module is used for performing comparison analysis on the two construction curvature bodies with the clearest faults and the fusion ant body.
Optionally, the apparatus further comprises:
and the attribute calculation module is used for carrying out coherent energy gradient attribute calculation on each smooth filter body to obtain the smooth filter body carrying the coherent energy gradient attribute.
Optionally, the fault tracking module is configured to:
carrying out fault tracking on one target coherent body along a first direction to obtain an ant body in the first direction, and carrying out fault tracking on the other target coherent body along a second direction to obtain an ant body in the second direction;
and obtaining an ant body with the first direction and the second direction fused based on the ant body in the first direction and the ant body in the second direction, wherein the first direction corresponds to the direction of the target sub-direction superposed data body corresponding to one target coherent body, and the second direction corresponds to the direction of the target sub-direction superposed data body corresponding to the other target coherent body.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
the device of low-order fault recognition that this application embodiment provided carries out branch position processing, many windows dip angle scanning processing in proper order through the original seismic data body to the target area, obtains two direction inclination bodies, carries out the direction filtering processing respectively to two direction inclination bodies, confirms two target direction filter bodies that the fault reinforcing effect is most obvious, carries out edge detection smoothing filtering processing to two target direction filter bodies, obtains two smoothing filter bodies. For each smooth filter body, respectively carrying out coherent body construction processing on an original seismic data body, the smooth filter body and a target guide filter body corresponding to the smooth filter body, determining a target coherent body with an obvious fault enhancement effect, carrying out fault tracking on the coherent body along different directions to obtain ant bodies in two directions, determining a fusion ant body according to the ant bodies in the two directions, carrying out flat-section interactive analysis and fracture combination processing on the fusion ant body, and determining the spatial position and the geometric form of a low-order fault in a target area. By the method and the device, the spatial position and the geometric form of the low-order fault of the target area can be obtained, and the low-order fault with smaller fault distance can be identified.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
It should be noted that: the low-level fault recognition apparatus provided in the above embodiment is only illustrated by the division of the above functional modules when performing low-level fault recognition, and in practical applications, the above function allocation may be performed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to perform all or part of the above described functions. In addition, the low-level sequence fault identification device provided by the above embodiment and the low-level sequence fault identification method embodiment belong to the same concept, and the specific implementation process thereof is described in the method embodiment, and is not described herein again.
The embodiment of the application also provides a terminal, which comprises a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to realize the operation executed by the method for identifying the low-order fault.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, is also provided that includes instructions executable by a processor in a terminal to perform the method of low-order fault recognition in the above-described embodiments. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method of low order fault identification, the method comprising:
performing azimuth-based stacking processing on an original seismic data volume of a target area to obtain two target azimuth-based stacking data volumes with vertical azimuths;
respectively carrying out multi-window dip angle scanning processing on the two target azimuth-based superposed data volumes to obtain two direction dip angle volumes;
respectively carrying out guiding filtering processing on the two direction inclination angle bodies to obtain two target guiding filtering bodies;
respectively carrying out edge detection smoothing filtering processing on the two target guide filtering bodies to obtain two smoothing filtering bodies;
for each smooth filter, respectively carrying out coherent body construction processing on the original seismic data body, the smooth filter and a target guide filter body corresponding to the smooth filter, and determining a target coherent body with the most obvious fault enhancement effect in the three sets of coherent bodies;
respectively carrying out fault tracking on the two target coherent bodies in different directions to obtain ant bodies in two directions, and determining a fusion ant body based on the ant bodies in the two directions;
and performing flat-section interaction analysis and fracture combination treatment based on the ant bodies in the two directions and the fused ant body, and determining the spatial position and the geometric form of the low-order fault of the target region on the fused ant body.
2. The method of claim 1, wherein the performing azimuth-split stacking processing on the original seismic data volumes of the target area to obtain two azimuth-split target stacked data volumes with orthogonal azimuths comprises:
performing azimuth-based stacking processing on an original seismic data volume of a target area to obtain azimuth-based stacking data volumes of a plurality of azimuths, and extracting a time slice of each azimuth-based stacking data volume;
and determining two target sub-position superposed data volumes with vertical positions in the sub-position superposed data volumes of the plurality of positions based on the time slice of each sub-position superposed data volume.
3. The method according to claim 1, wherein the performing multi-window tilt scanning processing on the two target azimuthally superimposed data volumes respectively to obtain two directional tilt volumes comprises:
and respectively carrying out multi-window dip angle scanning processing on the two target azimuth-divided superposed data volumes by using 9 scanning windows to obtain two direction dip angle volumes.
4. The method according to claim 1, wherein the performing the directional filtering process on the two directional dip bodies to obtain two target directional filters respectively comprises:
and for each direction inclination angle body, carrying out mean value filtering processing, median value filtering processing and main component filtering processing on the direction inclination angle body to obtain a mean value filter body, a median value filter body and a main component filter body, and determining the filter body with the most obvious fault enhancement effect as a target guide filter body in the mean value filter body, the median value filter body and the main component filter body.
5. The method of claim 4, wherein the determining, as the target-oriented filter, the filter with the most significant fault enhancement effect among the mean filter, the median filter, and the principal component filter comprises:
respectively extracting sections of the mean filter, the median filter and the main component filter at the same structural position;
and determining a filter body with the most obvious fault enhancement effect as a target guide filter body in the mean filter body, the median filter body and the main component filter body based on the section of the mean filter body, the section of the median filter body and the section of the main component filter body.
6. The method according to claim 1, wherein after performing the edge detection smoothing filtering process on the two target-oriented filter volumes respectively to obtain two smoothing filter volumes, the method further comprises:
for each smooth filter body, carrying out construction curvature body processing by using a set fractional derivative index to obtain a most positive curvature body and a most negative curvature body of the smooth filter body, and determining a construction curvature body with a clearer fault in the most positive curvature body and the most negative curvature body;
for each structure curvature body with clearer faults, performing parameter optimization processing on the structure curvature body with clearer faults by using a fractional derivative index different from the set fractional derivative index to obtain one or more new structure curvature bodies, and determining the structure curvature body with the clearest faults in the one or more new structure curvature bodies and the structure curvature body with clearer faults;
and carrying out comparative analysis on the two construction curvature bodies with the clearest faults and the fusion ant body.
7. The method according to claim 1, wherein after performing the edge detection smoothing filtering process on the two target-oriented filter volumes respectively to obtain two smoothing filter volumes, the method further comprises:
and for each smoothing filter, performing coherent energy gradient attribute calculation on the smoothing filter to obtain the smoothing filter carrying the coherent energy gradient attribute.
8. The method as claimed in claim 1, wherein the step of performing fault tracing on the two target coherence bodies in different directions to obtain ant bodies in two directions, and determining a fused ant body based on the ant bodies in two directions comprises:
carrying out fault tracking on one target coherent body along a first direction to obtain an ant body in the first direction, and carrying out fault tracking on the other target coherent body along a second direction to obtain an ant body in the second direction;
and obtaining an ant body with a first direction and a second direction fused together based on the ant body in the first direction and the ant body in the second direction, wherein the first direction corresponds to the direction of the target sub-direction superposed data body corresponding to the one target coherent body, and the second direction corresponds to the direction of the target sub-direction superposed data body corresponding to the other target coherent body.
9. An apparatus for low order fault identification, the apparatus comprising:
the azimuth-dividing stacking processing module is used for performing azimuth-dividing stacking processing on the original seismic data volume of the target area to obtain two target azimuth-dividing stacking data volumes with vertical azimuths;
the dip angle scanning processing module is used for respectively carrying out multi-window dip angle scanning processing on the two target azimuth-divided superposed data volumes to obtain two direction dip angle volumes;
the guiding filtering processing module is used for respectively carrying out guiding filtering processing on the two direction inclination angle bodies to obtain two target guiding filtering bodies;
the smoothing filter processing module is used for respectively carrying out edge detection smoothing filter processing on the two target guide filter bodies to obtain two smoothing filter bodies;
the coherent body structure processing module is used for respectively carrying out coherent body structure processing on each smooth filter body and the original seismic data body as well as the target guide filter bodies corresponding to the smooth filter bodies, and determining the target coherent body with the most obvious fault enhancement effect in the three sets of coherent bodies;
the fault tracking module is used for respectively carrying out fault tracking on the two target coherent bodies in different directions to obtain ant bodies in two directions, and determining a fusion ant body based on the ant bodies in the two directions;
and the determining module is used for performing flat-section interaction analysis and fracture combination processing on the basis of the ant bodies in the two directions and the fused ant body, and determining the spatial position and the geometric form of the low-order fault of the target region on the fused ant body.
10. A terminal, comprising a processor and a memory, wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to perform operations performed by the method of low order fault recognition according to any one of claims 1 to 8.
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