CN113093274B - Method, device, terminal and storage medium for identifying low-order faults - Google Patents
Method, device, terminal and storage medium for identifying low-order faults Download PDFInfo
- Publication number
- CN113093274B CN113093274B CN202010017122.7A CN202010017122A CN113093274B CN 113093274 B CN113093274 B CN 113093274B CN 202010017122 A CN202010017122 A CN 202010017122A CN 113093274 B CN113093274 B CN 113093274B
- Authority
- CN
- China
- Prior art keywords
- azimuth
- target
- bodies
- filter
- fault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000001427 coherent effect Effects 0.000 claims abstract description 98
- 238000012545 processing Methods 0.000 claims abstract description 92
- 238000010276 construction Methods 0.000 claims abstract description 64
- 230000004927 fusion Effects 0.000 claims abstract description 38
- 238000003708 edge detection Methods 0.000 claims abstract description 17
- 238000012482 interaction analysis Methods 0.000 claims abstract description 10
- 238000001914 filtration Methods 0.000 claims description 87
- 238000009499 grossing Methods 0.000 claims description 47
- 238000011282 treatment Methods 0.000 claims description 39
- 208000010392 Bone Fractures Diseases 0.000 claims description 36
- 206010017076 Fracture Diseases 0.000 claims description 36
- 230000000694 effects Effects 0.000 claims description 31
- 230000008569 process Effects 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000011284 combination treatment Methods 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 4
- 238000011161 development Methods 0.000 abstract description 6
- 238000004458 analytical method Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/364—Seismic filtering
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The application discloses a low-order fault identification method, a device, a terminal and a storage medium, and belongs to the technical field of oil-gas field development. The method comprises the following steps: carrying out azimuth-dividing superposition processing on an original seismic data body, determining a target azimuth-dividing superposition data body, determining a target guide filter body based on guide filter processing of various filter algorithms, respectively carrying out edge detection smooth filter processing to obtain a smooth filter body, carrying out coherent body construction processing on the original seismic data body, the smooth filter body and a corresponding target guide filter body, determining a target coherent body, carrying out 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, carrying out plane-splitting interaction analysis and fracture combination processing on the fusion ant body, and determining the space position and the geometric morphology of a low-order fault. By adopting the method and the device, the space position and the geometric form of the low-order fault can be obtained, and the low-order fault with smaller breaking 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, a device, a terminal and a storage medium.
Background
The low-order fault is a small fault and has the characteristics of small breaking distance, short extension length, few breaking layers and the like. When the residual oil is extracted, the residual oil is highly dispersed and mostly concentrated near the low-order faults, and the low-order faults have great influence on fine water injection 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: a technician observes physical phenomena on the crust of a target region through various ground instruments, performs low-order fault identification by using methods such as seismic section interpretation, inclination angle analysis, variance analysis and the like, and can identify low-order faults with larger breaking distance, such as low-order faults with the breaking distance of more than 10 m.
In carrying out the present application, the inventors have found that the related art has at least the following problems:
The method for identifying the low-order faults in the related technology can only identify the low-order faults with larger breaking distance, and for the low-order faults with smaller breaking distance, such as the low-order faults with the breaking distance of less than 10m, the low-order faults with smaller breaking distance can not be identified because of weak distortion and dislocation of the phase axis.
Disclosure of Invention
The embodiment of the application provides a low-order fault identification method, a device, a terminal and a storage medium, which can solve the problem that a low-order fault with smaller breaking distance cannot be identified. The technical scheme is as follows:
in a first aspect, a method of low-order fault identification is provided, the method comprising:
Carrying out azimuth-dividing superposition processing on the original seismic data body of the target area to obtain two target azimuth-dividing superposition data bodies with vertical azimuth;
Respectively carrying out multi-window inclination angle scanning treatment on the two target azimuth-dividing superposition data bodies to obtain two direction inclination angle bodies;
respectively performing guide filtering treatment on the two direction inclined angle bodies to obtain two target guide filtering bodies;
Respectively carrying out edge detection smoothing filter treatment on the two target guiding filter bodies to obtain two smoothing filter bodies;
For each smooth filter, respectively carrying out coherent body construction processing on the original seismic data body, the smooth filter and the target guide filter corresponding to the smooth filter, and determining a target coherent body with the most obvious fault enhancement effect in the three obtained sets of coherent bodies;
carrying out fault tracking on two target coherent bodies in different directions respectively to obtain two-direction ant bodies, and determining a fusion ant body based on the two-direction ant bodies;
and performing plane-section interaction analysis and fracture combination treatment based on the two-direction ant bodies and the fusion ant body, and determining the spatial position and the geometric form of the low-order fault of the target area on the fusion ant body.
Optionally, the performing azimuth-splitting superposition processing on the original seismic data body of the target area to obtain two target azimuth-splitting superposition data bodies with vertical azimuth includes:
Carrying out azimuth superposition processing on the original seismic data body of the target area to obtain azimuth superposition data bodies of a plurality of azimuths, and extracting a time slice of each azimuth superposition data body;
and determining two target azimuth superimposed data bodies perpendicular to each other in the azimuth superimposed data bodies of the plurality of azimuths based on the time slices of each azimuth superimposed data body.
Optionally, the multi-window inclination angle scanning processing is performed on the two target azimuth-dividing superimposed data bodies to obtain two direction inclination angle bodies, including:
and respectively carrying out multi-window inclination angle scanning treatment on the two target azimuth superposition data bodies by using 9 scanning windows to obtain two direction inclination angle bodies.
Optionally, the performing the guided filtering treatment on the two direction inclination angle bodies respectively to obtain two target guided filtering bodies includes:
and for each direction dip angle body, carrying out mean value filtering treatment, median filtering treatment and principal component filtering treatment on the direction dip angle body to obtain a mean value filter body, a median filter body and a principal component filter body, and determining a filter body with the most obvious fault enhancement effect among the mean value filter body, the median filter body and the principal component filter body as a target guide filter body.
Optionally, the determining, among the mean value filter, the median filter and the principal component filter, a filter with the most obvious fault enhancement effect as a target guiding filter includes:
Respectively extracting sections of the mean filter body, the median filter body and the principal component filter body at the same construction position;
And determining a filter body with the most obvious fault enhancement effect from the mean value filter body, the median filter body and the principal component filter body as a target guide filter body based on the section of the mean value filter body, the section of the median filter body and the section of the principal component filter body.
Optionally, after performing edge detection smoothing filtering processing on the two target guiding filtering bodies respectively to obtain two smoothing filtering bodies, the method further includes:
for each smooth filter body, performing 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 clearer fault in the most positive curvature body and the most negative curvature body;
for each fault clearer construction curvature body, carrying out parameter optimization processing on the fault clearer construction curvature body 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 fault clearer construction curvature body in the one or more new construction curvature bodies and the fault clearer construction curvature body;
comparing and analyzing the structural curvature body with the two most clear faults with the fusion ant body.
Optionally, after performing edge detection smoothing filtering processing on the two target guiding filtering bodies respectively to obtain two smoothing filtering bodies, the method further includes:
And for each smooth filter body, carrying out coherent energy gradient attribute calculation on the smooth filter body to obtain the smooth filter body carrying the coherent energy gradient attribute.
Optionally, the fault tracking of the two target coherent bodies in different directions is performed to obtain two ant bodies in two directions, and the determining of the fusion ant body based on the two 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 fused in the first direction and the second direction based on the ant body in the first direction and the ant body in the second direction, as the fused ant body, wherein the first direction corresponds to the azimuth of the target azimuth superposition data body corresponding to the one target coherent body, and the second direction corresponds to the azimuth of the target azimuth superposition 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 superposition processing module is used for carrying out azimuth-dividing superposition processing on the original seismic data body of the target area to obtain two target azimuth-dividing superposition data bodies with vertical azimuth;
The dip angle scanning processing module is used for respectively carrying out multi-window dip angle scanning processing on the two target azimuth-dividing superposition data bodies to obtain two direction dip angle bodies;
The guiding filtering processing module is used for respectively carrying out guiding filtering processing on the two direction inclined angle bodies to obtain two target guiding filtering bodies;
The smooth filtering processing module is used for respectively carrying out edge detection smooth filtering processing on the two target guiding filter bodies to obtain two smooth filter bodies;
the coherent body construction processing module is used for respectively carrying out coherent body construction processing on the original seismic data body, the smooth filter body and the target guide filter body corresponding to the smooth filter body for each smooth filter body, and determining a target coherent body with the most obvious fault enhancement effect in the three obtained 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 two-direction ant bodies, and determining a fusion ant body based on the two-direction ant bodies;
And the determining module is used for carrying out plane-section interaction analysis and fracture combination processing based on the two-direction ant bodies and the fusion ant body, and determining the space position and the geometric form of the low-order fault of the target area on the fusion ant body.
Optionally, the azimuth-dividing superposition processing module is configured to:
Carrying out azimuth superposition processing on the original seismic data body of the target area to obtain azimuth superposition data bodies of a plurality of azimuths, and extracting a time slice of each azimuth superposition data body;
and determining two target azimuth superimposed data bodies perpendicular to each other in the azimuth superimposed data bodies of the plurality of azimuths based on the time slices of each azimuth superimposed data body.
Optionally, the inclination scanning processing module is configured to:
and respectively carrying out multi-window inclination angle scanning treatment on the two target azimuth superposition data bodies by using 9 scanning windows to obtain two direction inclination angle bodies.
Optionally, the guided filtering processing module is configured to:
and for each direction dip angle body, carrying out mean value filtering treatment, median filtering treatment and principal component filtering treatment on the direction dip angle body to obtain a mean value filter body, a median filter body and a principal component filter body, and determining a filter body with the most obvious fault enhancement effect among the mean value filter body, the median filter body and the principal component filter body as a target guide filter body.
Optionally, the guided filtering processing module is configured to:
Respectively extracting sections of the mean filter body, the median filter body and the principal component filter body at the same construction position;
And determining a filter body with the most obvious fault enhancement effect from the mean value filter body, the median filter body and the principal component filter body as a target guide filter body based on the section of the mean value filter body, the section of the median filter body and the section of the principal component filter body.
Optionally, the apparatus further includes:
The construction curvature body determining module is used for carrying out construction 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 body, and determining a construction curvature body with clearer fault in the most positive curvature body and the most negative curvature body;
The target construction curvature body determining module is used for carrying out parameter optimization processing on the 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 clearer faults in the one or more new construction curvature bodies and the construction curvature body with clearer faults;
And the contrast analysis module is used for carrying out contrast analysis on the two structural curvature bodies with the clearest faults and the fusion ant body.
Optionally, the apparatus further includes:
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 fused in the first direction and the second direction based on the ant body in the first direction and the ant body in the second direction, as the fused ant body, wherein the first direction corresponds to the azimuth of the target azimuth superposition data body corresponding to the one target coherent body, and the second direction corresponds to the azimuth of the target azimuth superposition data body corresponding to the other target coherent body.
In a third aspect, a terminal is provided, the terminal comprising a processor and a memory having stored therein at least one instruction loaded and executed by the processor to perform operations performed by a method of low order fault identification as described above.
In a fourth aspect, a computer readable storage medium having stored therein at least one instruction loaded and executed by a processor to perform operations performed by a method of low order fault identification as described above is provided.
The technical scheme provided by the embodiment of the application has the beneficial effects that:
According to the low-order fault identification method provided by the embodiment of the application, the original seismic data body of the target area is subjected to azimuth dividing treatment and multi-window inclination scanning treatment in sequence to obtain two direction inclination bodies, the two direction inclination bodies are respectively subjected to guide filtering treatment, two target guide filter bodies with the most obvious fault enhancement effect are determined, and the two target guide filter bodies are subjected to edge detection smooth filtering treatment to obtain two smooth filter bodies. For each smooth filter, respectively carrying out coherent body construction treatment on an original seismic data body, the smooth filter and a corresponding target guide filter, determining a target coherent body with 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 plane cross analysis and fracture combination treatment on the fusion ant body, and determining the space position and the geometric form of a low-order fault of a target area. By adopting the method and the device, the space position and the geometric form of the low-order faults of the target area can be obtained, and the low-order faults with smaller breaking distance can be identified.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
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 time slice comparison of four sub-azimuth superimposed data volumes provided by embodiments of the present application;
FIG. 3 is a time slice comparison of a main line direction tilt and a cross-line direction tilt provided by an embodiment of the present application;
FIG. 4 is a Line2584 cross-sectional comparison of an original seismic data volume, a Line-wise mean filter, a Line-wise median filter, and a Line-wise principal component filter provided by an embodiment of the present application;
FIG. 5 is a graph comparing time slices of an original seismic data volume, a coherence time slice of the original seismic data volume, a coherence time slice of a mid-line median filter, and a coherence time slice of a smooth filter in the in-line direction, provided by embodiments of the application;
Fig. 6 is a comparison diagram of a time slice of a median filter in a main line direction, a coherent time slice of a median filter in a main line direction, a time slice of an ant body in a main line direction, and a time slice of an ant body fused with a cross-line of the main line, provided in an embodiment of the present application;
FIG. 7 is a time slice comparison of a median filter in inline direction, a positive inline curvature of 0.25, a positive inline curvature of 0.75, and a negative inline curvature of 0.75 according to an embodiment of the present application;
FIG. 8 is a diagram of an example of well-to-well joint contrast verification provided by an embodiment of the present application;
fig. 9 is a schematic diagram of a device structure for low-order fault identification according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail with reference to the accompanying drawings.
The low-order fault identification method 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 low-order faults. First, a technician may fire a shot at a target area and collect seismic data reflected from the subsurface formation using a receiving device. Then, the seismic data can be input into a terminal for storage, the terminal can acquire the seismic data, an original seismic data body of the target area is generated, 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, technicians can identify low-order faults of the target area and guide development of the oil-gas field of the target area in combination with the low-order faults of the target area.
FIG. 1 is a flow chart of a low-order fault identification method provided by an embodiment of the application. Referring to fig. 1, this embodiment includes:
In step 101, azimuth-dividing superposition processing is performed on an original seismic data volume of a target area, so as to obtain two target azimuth-dividing superposition data volumes with vertical azimuth.
Wherein the seismic data volume is a three-dimensional spatial distribution of the subsurface formation. The azimuth stacking process reflects the relative change relationship of azimuth in the seismic horizon. The azimuth-divided superimposed data volume refers to seismic data volumes corresponding to different azimuth angles. Azimuth refers to the horizontal angle from the north-pointing direction line at a point, clockwise to the target direction line.
In the implementation, firstly, a worker can fire in the field, and the receiving equipment is utilized to collect the seismic data reflected by the underground stratum, so as to obtain an original seismic data body of the target area. In order to improve the azimuth precision of analysis points in the seismic data, geoeast (seismic data processing and interpretation software) software is utilized to input an original seismic data volume, and azimuth-dividing superposition processing is carried out on the original seismic data volume according to a plurality of azimuth angles, so as to obtain azimuth-dividing superposition data volumes of a plurality of azimuth angles. And extracting the time slice of each azimuth superposition data body, and determining two target azimuth superposition data bodies perpendicular to each azimuth in the azimuth superposition data bodies of a plurality of azimuth based on the time slice of each azimuth superposition data body. Where time slicing refers to slicing taken in a planar fashion along a certain time value of the original seismic data volume.
For example, after the original seismic data volume is input in Geoeast software, azimuth superposition processing can be performed according to azimuth angles of 0-45 °, 45 ° -90 °, 90 ° -135 ° and 135 ° -180 ° to obtain four azimuth superimposed data volumes. For the azimuthally superimposed data volume of each azimuth, a time slice with a time value of 1280ms for the azimuthally superimposed data volume is extracted, as shown in FIG. 2, FIG. 2 comprising time slices with time values of 1280ms for the azimuthally superimposed data volumes of four azimuth, wherein azimuth one is azimuth 0-45 °, azimuth two is azimuth 45 ° -90 °, azimuth three is azimuth 90 ° -135 °, and azimuth four is azimuth 135 ° -180 °. According to the azimuth superimposed data body corresponding to each azimuth in fig. 2, the azimuth superimposed data body with the most clear fault enhancement effect of azimuth angle 0-45 degrees can be obtained, the azimuth superimposed data body with the more clear fault enhancement effect of azimuth angle 90-135 degrees can be optimized, the azimuth superimposed data body with the azimuth angle 0-45 degrees can identify the main fracture system of north east-south west trend, and the azimuth superimposed data body with the azimuth angle 90-135 degrees can identify the secondary fracture system perpendicular to the main fracture system. Wherein, the north east-south west trend refers to the north east-45 degree direction. The main fracture system is the dominant fracture in the fracture system of the target area, 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 angle scanning processing is performed on the two target azimuth-dividing superimposed data volumes, respectively, to obtain two direction tilt angle volumes.
The multi-window dip angle scanning process is to utilize a vertical window to perform dip angle scanning estimation and reflect the relative change relation of dip angles of the seismic horizon. The dip refers to the angle formed by the maximum dip line of the seismic horizon and its projection line on the horizontal plane.
In the implementation, in order to improve the inclination angle precision of analysis points in the seismic data, parameters such as a time value, the number of scanning windows, the length of the scanning time windows and the like are determined according to a target layer of a target area. In Geoeast software, multi-window dip angle scanning processing is carried out on two target azimuth superposition data bodies by using 9 scanning windows respectively, and a window with the largest similarity is obtained to be used as a dip angle estimation window of an analysis point, so that two direction dip angle bodies are obtained. Wherein the scanning time window length is 5 times of the time sampling interval of the input data body. The number of scanning windows may be 1, 5 or 9.
For example, after obtaining the azimuth superimposed data body with azimuth angle of 0-45 ° and the azimuth superimposed data body with azimuth angle of 90-135 ° according to the above example, in Geoeast software, the number of scanning windows may be set to 9, and multi-window inclination scanning processing may be performed respectively, and the main line direction inclination body and the crossline direction inclination body may be output, as shown in fig. 3, and fig. 3 includes time slices of the main line direction inclination body and the crossline direction inclination body at time values of 1280 ms. As can be seen from fig. 3, the main fracture system can be clearly identified by the main line direction inclination angle, and the secondary fracture system can be clearly identified by the cross-line direction inclination angle.
In step 103, the two directional tilt angle bodies are respectively subjected to a guide filtering process, so as to obtain two target guide filtering bodies.
In the implementation, for each direction dip angle body, the mean value filtering process, the median value filtering process and the principal component filtering process may be performed on the direction dip angle body, so as to obtain a mean value filtering body, a median value filtering body and a principal component filtering body. And respectively extracting the sections of the mean value filter body, the median filter body and the principal component filter body at the same construction position. And determining a filter body with the most obvious fault enhancement effect from the mean value filter body, the median filter body and the principal component filter body as a target guiding filter body based on the section of the mean value filter body, the section of the median filter body and the section of the principal component filter body. According to the embodiment of the application, the guiding filtering processing is performed, so that the signal-to-noise ratio of the seismic data is improved, the continuous and discontinuous characteristics of the same phase axis in the seismic data are more obvious, namely, the low-order faults are more obvious, and the certainty of horizon tracking is improved.
For example, after the main line direction inclination angle body and the crossline direction inclination angle body are obtained according to the above examples, the main line direction inclination angle body and the crossline direction inclination angle body may be processed by three algorithms including mean filtering, median filtering and main component filtering, and mean filtering, median filtering and main component filtering corresponding to the main line direction inclination angle body and the crossline direction inclination angle body may be obtained, so that 6 filtering bodies are obtained, which are respectively a main line direction mean filtering body, a main line direction median filtering body, a main line direction main component filtering body, a crossline direction mean filtering body, a crossline direction median filtering body and a crossline direction main component filtering body. For example, for the inclination angle body in the direction of the main Line, the original seismic data body, the mean filter body in the direction of the main Line, the median filter body in the direction of the main Line and the section of the main component filter body in the direction of the main Line in the Line2604 are respectively extracted, and as shown in fig. 4, the filter body with the most obvious fault enhancement effect can be obtained as the median filter body in the direction of the main Line. In general, the filter body with the most obvious fault enhancement effect corresponding to the main line direction inclination angle body and the cross line direction inclination angle body is of the same type. Therefore, the filter body with the most obvious fault enhancement effect corresponding to the inclination angle body in the main line direction is the median filter body in the main line direction, the filter body with the most obvious fault enhancement effect corresponding to the inclination angle body in the cross line direction is the median filter body in the cross line direction, and the median filter body in the main line direction and the median filter body in the cross line direction are used as two target guide filter bodies.
In step 104, edge detection smoothing filter processing is performed on the two target guide filter bodies, respectively, to obtain two smoothing filter bodies.
Wherein edge detection is identifying points in the digital image where the brightness change is significant. Smoothing filtering refers to the process of filtering noise.
In implementation, after obtaining two target-oriented filters, edge detection smoothing filtering processing may be performed using Geoeast software, outputting two smoothing filters.
For example, after obtaining two target guiding filter bodies, namely, a median filter body in the direction of the main line and a median filter body in the direction of the cross-line according to the above example, performing edge detection smoothing filter processing on the two target guiding filter bodies respectively, so as to obtain two smoothing filter bodies, namely, a smoothing filter body in the direction of the main line and a smoothing filter body in the direction of the cross-line respectively.
Optionally, for each smoothing filter, performing coherent energy gradient attribute calculation on the smoothing filter to obtain a smoothing filter carrying coherent energy gradient attribute.
The coherent energy gradient attribute refers to amplitude variation of the seismic data, and can improve the accuracy of the seismic data.
In step 105, for each smoothing filter, coherent structure processing is performed on the original seismic data volume, the smoothing filter, and the target guide filter corresponding to the smoothing filter, and among the three obtained sets of coherent volumes, the target coherent volume with the most obvious fault enhancement effect is determined.
The coherent volume structure processing is processing for obtaining coherence with surrounding data for each data sample in the seismic data volume. A coherence volume is a volume of seismic data that characterizes the coherence of the seismic data.
In the implementation, parameters such as a maximum tilt scanning time window and a coherence time window need to be set in Geoeast software during coherent structure processing. For each smooth filter, the original seismic data body, the target guide filter corresponding to the smooth filter and the smooth filter are subjected to coherent body construction processing by adopting a 9 multiplied by 9 covariance matrix characteristic algorithm, adopting a self-adaptive coherent time window, and determining a maximum dip angle scanning time window according to the adjacent channel time delay quantity of the steepest phase axis to obtain three sets of coherent bodies. And respectively extracting coherent time slices of three sets of coherent bodies at the same time value, analyzing and comparing, and determining a target coherent body with the most obvious fault enhancement effect in the three sets of coherent bodies. Through coherent body construction processing, the embodiment of the application can compare the similarity of the seismic waveforms through the coherent body, and the seismic waveforms are discontinuous at the point with lower coherence value, namely fault discontinuity. The resolution of broken layer identification is improved through a 9×9 covariance matrix feature algorithm.
For example, after two smoothing filter bodies, i.e., a main line direction smoothing filter body and a crossline direction smoothing filter body, are obtained according to the above example, for the main line direction smoothing filter body, coherent body construction processing is performed on the original seismic data body, the main line direction smoothing filter body, and the main line direction median filter body, respectively, to obtain three coherent bodies. And respectively carrying out coherent body construction processing on the original seismic data body, the cross-line direction smoothing filter body and the cross-line direction median filter body to obtain three coherent bodies. As shown in fig. 5, for the inline direction smoothing filter, fig. 5 includes a time slice when the original seismic data volume time value is 1280ms, a coherence time slice when the inline direction median filter time value is 1280ms, and a coherence time slice when the inline direction smoothing filter time value is 1280ms, and according to fig. 5, the coherence corresponding to the inline direction smoothing filter with the most obvious fault enhancement effect can be obtained, and the coherence corresponding to the inline direction smoothing filter is used as a target coherence. Similarly, the coherent body corresponding to the crossline smoothing filter body is used as another target coherent body. The coherence time slice refers to a time slice of a coherence volume corresponding to the seismic data volume.
In step 106, fault tracking is performed on the two target coherent bodies in different directions to obtain two-direction ant bodies, and based on the two-direction ant bodies, a fusion ant body is determined.
The fusion ant body refers to an 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 the target area and the time-depth relationship obtained by horizon calibration. And carrying out fault tracking on one target coherent body along the first direction by utilizing 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 the second direction to obtain an ant body in the second direction. According to the first direction and the second direction, the first direction and the second direction are fused to obtain the fusion ant, and the fusion ant is used as the fusion ant.
The first direction is a direction line of the object coherence body identification fault, the direction of the corresponding object azimuth superposition data body is an azimuth angle range of the identification fault, the direction of the object coherence body identification fault is consistent with the direction of the corresponding object azimuth superposition data body identification fault, and the first direction corresponds to the direction of the object azimuth superposition data body corresponding to the object coherence body, namely, the direction line corresponding to the first direction is in the azimuth angle range corresponding to the direction. The second direction is the direction line of the fault identified by the other target coherent body, the azimuth of the corresponding target azimuth superimposed data body is the azimuth range of the fault identified by the other target coherent body, the direction of the fault identified by the other target coherent body and the corresponding target azimuth superimposed data body is consistent, and the second direction corresponds to the azimuth of the target azimuth superimposed data body corresponding to the other target coherent body, namely the direction line corresponding to the second direction is in the azimuth range corresponding to the azimuth.
For example, after obtaining two target coherent bodies, namely, a coherent body corresponding to the smooth filter body in the main line direction and a coherent body corresponding to the smooth filter body in the cross-line direction according to the above example, performing fault tracking on the coherent body corresponding to the smooth filter body in the main line direction along the main line direction, and performing fault tracking on the coherent body corresponding to the smooth filter body in the cross-line direction along the cross-line direction, so as to obtain an ant body in the main line direction, an ant body in the cross-line direction and an ant body fused with the cross-line.
In step 107, a planar cross-section interaction analysis and fracture combination process is performed based on the two-directional ant body and the fusion ant body, and the spatial position and geometry of the low-order fault of the target region are determined on the fusion ant body.
The plane-section interaction analysis is to use a plurality of planes and sections to conduct comparison analysis at the same construction position in the fault. The fracture combination treatment is to connect the break points of the same fault to form a fault distribution diagram.
In implementation, the planar-cross-section interaction analysis is performed on the same structural position of the target area based on the first-direction ant body, the second-direction ant body and the fusion ant body, and the spatial position of the low-order fault of the target area is determined in the fusion ant body. Based on breakpoint data on the fusion ant body, performing fracture combination treatment, and determining the geometric form of the low-order fault of the target area in the fusion ant body.
For example, after the main Line direction ant body, the cross-Line direction ant body, and the main Line cross-Line fused ant body are obtained according to the above example, the Line2584 cross-section of the main Line direction ant body, the Line2584 cross-section of the cross-Line direction ant body, and the ine2584 cross-section of the main Line cross-Line fused ant body are extracted, respectively, and a horizontal cross-section interaction analysis is performed. As shown in fig. 6, fig. 6 includes a time slice of the median filter in the main line direction, a coherent time slice of the median filter in the main line direction, a time slice of the ant body in the main line direction, and a time slice of the ant body in which the main line crossline and the main line are fused, and the spatial position of the low-order fault can be primarily determined. In order to include the main line direction and the crossline direction, the spatial position of the low-order fault can be determined on the ant body where the main line and the crossline are fused. And (3) carrying out fracture combination on the ant body fused by the inter-measuring lines of the main measuring lines, connecting break points belonging to the same fault, and determining the geometric form of the low-order fault on the ant body fused by the inter-measuring lines of the main measuring lines.
Optionally, for each smooth filter body, performing 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 clearer faults in the most positive curvature body and the most negative curvature body. And for each structural curvature body with clearer faults, carrying out parameter optimization processing on the structural curvature body with clearer faults by using a fractional derivative index different from a set fractional derivative index to obtain one or more new structural curvature bodies, and determining the structural curvature body with clearer faults in the one or more new structural curvature bodies and the structural curvature body with clearer faults. And comparing and analyzing the structural curvature body with the two clearest faults and the fusion ant body. The structural curvature body with the clearest fault can be clearly identified to be large, and the structural curvature body with the clearest fault and the ant body fused with the crossline of the main line can be compared and verified, so that the spatial positions of the large fault in the structural curvature body with the clearest fault and the ant body fused with the crossline of the main line are found to be the same.
Wherein the construction curvature body with the clearest fault is the same as the type of construction curvature body treatment through which the construction curvature body passes. Curvature is the degree of curvature that describes any point on a curve, the greater the curvature, the more curved. The construction curvature volume processing can clearly identify large faults.
For example, after two smoothing filter bodies, i.e., a main line direction smoothing filter body and a crossline direction smoothing filter body, are obtained according to the above example, curvature body processing is performed on the main line direction smoothing filter body and the crossline direction smoothing filter body, and a fractional derivative index is set to 0.75, so as to obtain a main line most positive curvature body, a main line most negative curvature body, a crossline most positive curvature body and a crossline most negative curvature body. As shown in fig. 7, the smoothing filter body in the direction of the main line is processed to obtain the most positive curvature body of the main line and the most negative curvature body of the main line, and the construction curvature body with clearer faults is determined to be the most positive curvature body of the main line. For the inline most positive curvature, the fractional derivative index is set to 0.75 and 0.25, and a structural curvature with the fractional derivative index of 0.75 and a structural curvature with the fractional derivative index of 0.25 are obtained, and as shown in fig. 5, the structural curvature with the clearest fault is determined to be the structural curvature with the fractional derivative index of 0.75, namely, the inline most positive curvature with the fractional derivative index of 0.75. The positive curvature body of the main line with the fractional derivative index of 0.75 can clearly identify a large fault, and the positive curvature body of the main line with the fractional derivative index of 0.75 can be compared and analyzed with the ant body fused with the crossline of the main line.
For example, from actual well logging, log data for G251, G2-58 and G291-4 wells may be obtained, and as shown in FIG. 8, a 5 meter break in the Nm-4 formation for G251 may be found, with the break in the seismic profile being unclear. According to the method of steps 101 to 107, the ant body with the integrated main line and the cross line corresponding to the Nm-4 stratum of the G251 well can be obtained, the break point can be clearly seen on the ant body with the integrated main line and the cross line, and the break point is subjected to breaking combination, so that the geometric form of the low-order fault can be determined as shown in fig. 8. Through actual logging comparison verification, the embodiment of the application can effectively identify the low-order faults with the breaking distance of 5 m.
The technical scheme provided by the embodiment of the application has the beneficial effects that:
According to the low-order fault identification method provided by the embodiment of the application, the original seismic data body of the target area is subjected to azimuth dividing treatment and multi-window inclination scanning treatment in sequence to obtain two direction inclination bodies, the two direction inclination bodies are respectively subjected to guide filtering treatment, two target guide filter bodies with the most obvious fault enhancement effect are determined, and the two target guide filter bodies are subjected to edge detection smooth filtering treatment to obtain two smooth filter bodies. For each smooth filter, respectively carrying out coherent body construction treatment on an original seismic data body, the smooth filter and a corresponding target guide filter, determining a target coherent body with 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 plane cross analysis and fracture combination treatment on the fusion ant body, and determining the space position and the geometric form of a low-order fault of a target area. By adopting the method, the space position and the geometric form of the low-order faults of the target area can be obtained rapidly, conveniently and effectively, the identification of the low-order faults with the breaking distance of 5m can be realized, and the identification precision of the low-order faults can be effectively improved, so that the development work of the oil-gas field can be guided more accurately, and the method has great economic benefit.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein.
Based on the same technical concept, the embodiment of the present application further provides a device for determining a target address of a drilling platform, where the device may be a terminal in the foregoing embodiment, as shown in fig. 9, and the device includes:
the azimuth-dividing and stacking processing module 901 is used for carrying out azimuth-dividing and stacking processing on the original seismic data body of the target area to obtain two target azimuth-dividing and stacking data bodies with vertical azimuth;
the dip angle scanning processing module 902 is configured to perform multi-window dip angle scanning processing on the two target azimuth-dividing superimposed data volumes, so as to obtain two directional dip angle volumes;
the guiding filtering processing module 903 is configured to perform guiding filtering processing on the two direction tilt angle bodies respectively, so as to obtain two target guiding filtering bodies;
The smoothing filter processing module 904 is configured to perform edge detection smoothing filter processing on the two target guiding filter bodies respectively, so as to obtain two smoothing filter bodies;
The coherent body construction processing module 905 is configured to perform coherent body construction processing on the original seismic data body, the smooth filter body, and the target guide filter body corresponding to the smooth filter body, respectively, and determine a target coherent body with the most obvious fault enhancement effect in the obtained three sets of coherent bodies;
The fault tracking module 906 is configured to perform fault tracking on two target coherent bodies in different directions respectively to obtain two ant bodies in two directions, and determine a fusion ant body based on the two ant bodies in two directions;
And the determining module 907 is used for performing plane cross-section interaction analysis and fracture combination processing based on the two-direction ant bodies and the fusion ant body, and determining the spatial position and the geometric form of the low-order fault of the target area on the fusion ant body.
Optionally, the azimuth-dividing superposition processing module 901 is configured to:
Carrying out azimuth superposition processing on the original seismic data body of the target area to obtain azimuth superposition data bodies of a plurality of azimuths, and extracting a time slice of each azimuth superposition data body;
Based on the time slice of each azimuth superimposed data volume, two azimuth-perpendicular target azimuth superimposed data volumes are determined in the azimuth superimposed data volumes of the plurality of azimuths.
Optionally, the tilt scanning processing module 902 is configured to:
and respectively carrying out multi-window inclination angle scanning treatment on the two target azimuth superposition data bodies by using 9 scanning windows to obtain two direction inclination angle bodies.
Optionally, the guiding filtering processing module 903 is configured to:
And for each direction dip angle body, carrying out mean value filtering treatment, median filtering treatment and principal component filtering treatment on the direction dip angle body to obtain a mean value filtering body, a median filtering body and a principal component filtering body, and determining a filtering body with the most obvious fault enhancement effect from the mean value filtering body, the median filtering body and the principal component filtering body as a target guiding filtering body.
Optionally, the guiding filtering processing module 903 is configured to:
respectively extracting sections of the mean filter, the median filter and the principal component filter at the same construction position;
And determining a filter body with the most obvious fault enhancement effect from the mean value filter body, the median filter body and the principal component filter body as a target guiding filter body based on the section of the mean value filter body, the section of the median filter body and the section of the principal component filter body.
Optionally, the apparatus further comprises:
the construction curvature body determining module is used for processing the construction curvature body by using a set fractional derivative index for each smooth filter body to obtain a most positive curvature body and a most negative curvature body of the smooth filter body, and determining the construction curvature body with clearer faults in the most positive curvature body and the most negative curvature body;
the target construction curvature body determining module is used for carrying out parameter optimization processing on the construction curvature body with clearer faults by using a fractional derivative index different from a set fractional derivative index to obtain one or more new construction curvature bodies, and determining the construction curvature body with clearer faults in the one or more new construction curvature bodies and the construction curvature body with clearer faults;
and the contrast analysis module is used for carrying out contrast analysis on the two structural 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 the smooth filter body for 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 fused in the first direction and the second direction based on the ant body in the first direction and the ant body in the second direction, and taking the ant body fused in the first direction and the second direction as a fused ant body, wherein the first direction corresponds to the azimuth of the target azimuth superposition data body corresponding to one target coherent body, and the second direction corresponds to the azimuth of the target azimuth superposition data body corresponding to the other target coherent body.
The technical scheme provided by the embodiment of the application has the beneficial effects that:
according to the low-order fault identification device provided by the embodiment of the application, the original seismic data body of the target area is subjected to azimuth dividing treatment and multi-window inclination scanning treatment in sequence to obtain two direction inclination bodies, the two direction inclination bodies are respectively subjected to guide filtering treatment, two target guide filter bodies with the most obvious fault enhancement effect are determined, and the two target guide filter bodies are subjected to edge detection smooth filtering treatment to obtain two smooth filter bodies. For each smooth filter, respectively carrying out coherent body construction treatment on an original seismic data body, the smooth filter and a corresponding target guide filter, determining a target coherent body with 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 plane cross analysis and fracture combination treatment on the fusion ant body, and determining the space position and the geometric form of a low-order fault of a target area. By adopting the method and the device, the space position and the geometric form of the low-order faults of the target area can be obtained, and the low-order faults with smaller breaking distance can be identified.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
It should be noted that: in the low-order fault recognition device provided in the above embodiment, only the division of the above functional modules is used for illustration during low-order fault recognition, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the apparatus for low-order fault identification provided in the above embodiment and the method embodiment for low-order fault identification belong to the same concept, and detailed implementation processes of the apparatus and the method embodiment are detailed in the method embodiment and are not described herein again.
The embodiment of the application also provides a terminal, which comprises a processor and a memory, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to realize the operation executed by the low-order fault identification method.
In an exemplary embodiment, a computer readable storage medium, such as a memory comprising instructions executable by a processor in a terminal to perform the method of low order fault identification of the above embodiments is also provided. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
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 for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
Claims (9)
1. A method of low-order fault identification, the method comprising:
Carrying out azimuth-dividing superposition processing on an original seismic data body of a target area to obtain two target azimuth-dividing superposition data bodies with vertical azimuth, wherein the two target azimuth-dividing superposition data bodies with vertical azimuth are respectively: the system comprises a main fracture system and a secondary fracture system, wherein the main fracture system is provided with a main fracture system, and the secondary fracture system is provided with a sub-fracture system;
Respectively carrying out multi-window inclination angle scanning treatment on the two target azimuth-dividing superposition data bodies to obtain two direction inclination angle bodies;
respectively performing guide filtering treatment on the two direction inclined angle bodies to obtain two target guide filtering bodies;
Respectively carrying out edge detection smoothing filter treatment on the two target guiding filter bodies to obtain two smoothing filter bodies;
For each smooth filter, respectively adopting a 9 multiplied by 9 covariance matrix characteristic algorithm for the original seismic data body, the smooth filter and the target guide filter corresponding to the smooth filter, adopting a self-adaptive coherence time window, determining a maximum dip angle scanning time window according to the adjacent channel time delay amount of the steepest phase axis, carrying out coherent body construction processing based on the maximum dip angle scanning time window, and determining a target coherent body with the most obvious fault enhancement effect in the three obtained sets of coherent bodies;
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; based on the ant body in the first direction and the ant body in the second direction, obtaining an ant body fused in the first direction and the second direction as a fused ant body, wherein the first direction is a direction line of the object coherence body identification fault, the direction line corresponding to the first direction is in an azimuth angle range corresponding to the azimuth of the object azimuth superposition data body corresponding to the object coherence body, the second direction is a direction line of the object coherence body identification fault, and the direction line corresponding to the second direction is in an azimuth angle range corresponding to the azimuth of the object azimuth superposition data body corresponding to the other object coherence body;
And performing plane-section interaction analysis and fracture combination treatment based on the first-direction ant body, the second-direction ant body and the fusion ant body, and determining the spatial position and the geometric form of the low-order fault of the target area on the fusion ant body.
2. The method of claim 1, wherein the performing a azimuth stack process on the original seismic data volume of the target area to obtain two azimuth-perpendicular target azimuth stack data volumes comprises:
Carrying out azimuth superposition processing on the original seismic data body of the target area to obtain azimuth superposition data bodies of a plurality of azimuths, and extracting a time slice of each azimuth superposition data body;
and determining two target azimuth superimposed data bodies perpendicular to each other in the azimuth superimposed data bodies of the plurality of azimuths based on the time slices of each azimuth superimposed data body.
3. The method according to claim 1, wherein the performing multi-window tilt scanning on the two target azimuth stacked data volumes to obtain two direction tilt volumes includes:
and respectively carrying out multi-window inclination angle scanning treatment on the two target azimuth superposition data bodies by using 9 scanning windows to obtain two direction inclination angle bodies.
4. The method according to claim 1, wherein the performing the guided filtering on the two directional tilt bodies to obtain two target guided filter bodies includes:
and for each direction dip angle body, carrying out mean value filtering treatment, median filtering treatment and principal component filtering treatment on the direction dip angle body to obtain a mean value filter body, a median filter body and a principal component filter body, and determining a filter body with the most obvious fault enhancement effect among the mean value filter body, the median filter body and the principal component filter body as a target guide filter body.
5. The method according to claim 4, wherein the determining, as the target-oriented filter, a filter having the most significant tomographic enhancement effect among the mean filter, the median filter, and the principal component filter, comprises:
Respectively extracting sections of the mean filter body, the median filter body and the principal component filter body at the same construction position;
And determining a filter body with the most obvious fault enhancement effect from the mean value filter body, the median filter body and the principal component filter body as a target guide filter body based on the section of the mean value filter body, the section of the median filter body and the section of the principal component filter body.
6. The method according to claim 1, wherein after performing edge detection smoothing filtering processing on the two target-oriented filtering bodies respectively to obtain two smoothing filtering bodies, the method further comprises:
for each smooth filter body, performing 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 clearer fault in the most positive curvature body and the most negative curvature body;
for each fault clearer construction curvature body, carrying out parameter optimization processing on the fault clearer construction curvature body 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 fault clearer construction curvature body in the one or more new construction curvature bodies and the fault clearer construction curvature body;
comparing and analyzing the structural curvature body with the two most clear faults with the fusion ant body.
7. The method according to claim 1, wherein after performing edge detection smoothing filtering processing on the two target-oriented filtering bodies respectively to obtain two smoothing filtering bodies, the method further comprises:
And for each smooth filter body, carrying out coherent energy gradient attribute calculation on the smooth filter body to obtain the smooth filter body carrying the coherent energy gradient attribute.
8. An apparatus for low-order fault identification, the apparatus comprising:
The azimuth-dividing and stacking processing module is used for carrying out azimuth-dividing and stacking processing on an original seismic data body of a target area to obtain two target azimuth-dividing and stacking data bodies with vertical azimuth, wherein the two target azimuth-dividing and stacking data bodies with vertical azimuth are respectively: the system comprises a main fracture system and a secondary fracture system, wherein the main fracture system is provided with a main fracture system, and the secondary fracture system is provided with a sub-fracture system;
The dip angle scanning processing module is used for respectively carrying out multi-window dip angle scanning processing on the two target azimuth-dividing superposition data bodies to obtain two direction dip angle bodies;
The guiding filtering processing module is used for respectively carrying out guiding filtering processing on the two direction inclined angle bodies to obtain two target guiding filtering bodies;
The smooth filtering processing module is used for respectively carrying out edge detection smooth filtering processing on the two target guiding filter bodies to obtain two smooth filter bodies;
The coherent body construction processing module is used for carrying out coherent body construction processing on each smooth filter body, adopting a self-adaptive coherent time window for the original seismic data body, the smooth filter body and a target guide filter body corresponding to the smooth filter body respectively according to a 9 multiplied by 9 covariance matrix characteristic algorithm, determining a maximum dip angle scanning time window according to the adjacent channel time delay quantity of the steepest phase axis, carrying out coherent body construction processing on the basis of the maximum dip angle scanning time window, and determining a target coherent body with the most obvious fault enhancement effect in the three obtained sets of coherent bodies;
The fault tracking module is used for 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; based on the ant body in the first direction and the ant body in the second direction, obtaining an ant body fused in the first direction and the second direction as a fused ant body, wherein the first direction is a direction line of the object coherence body identification fault, the direction line corresponding to the first direction is in an azimuth angle range corresponding to the azimuth of the object azimuth superposition data body corresponding to the object coherence body, the second direction is a direction line of the object coherence body identification fault, and the direction line corresponding to the second direction is in an azimuth angle range corresponding to the azimuth of the object azimuth superposition data body corresponding to the other object coherence body;
And the determining module is used for carrying out plane cross section interaction analysis and fracture combination processing based on the first-direction ant body, the second-direction ant body and the fusion ant body, and determining the space position and the geometric form of the low-order fault of the target area on the fusion ant body.
9. A terminal comprising a processor and a memory having stored therein at least one instruction that is loaded and executed by the processor to perform the operations performed by the low order fault identification method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010017122.7A CN113093274B (en) | 2020-01-08 | 2020-01-08 | Method, device, terminal and storage medium for identifying low-order faults |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010017122.7A CN113093274B (en) | 2020-01-08 | 2020-01-08 | Method, device, terminal and storage medium for identifying low-order faults |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113093274A CN113093274A (en) | 2021-07-09 |
| CN113093274B true CN113093274B (en) | 2024-05-28 |
Family
ID=76663368
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010017122.7A Active CN113093274B (en) | 2020-01-08 | 2020-01-08 | Method, device, terminal and storage medium for identifying low-order faults |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113093274B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114002739B (en) * | 2021-11-11 | 2024-01-26 | 中海石油(中国)有限公司 | Edge detection method, device and medium based on geometric non-parallel statistical attribute |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104459801A (en) * | 2014-12-10 | 2015-03-25 | 中国石油天然气集团公司 | Coherence enhancement processing method used for recognizing fault |
| CN105334534A (en) * | 2015-10-21 | 2016-02-17 | 中国石油大学(华东) | Low order fault interpretation method based on construction mode guidance |
| CN107765301A (en) * | 2017-10-13 | 2018-03-06 | 中国煤炭地质总局地球物理勘探研究院 | The method for quickly identifying and device of coal seam craven fault |
| CN109143348A (en) * | 2017-06-28 | 2019-01-04 | 中国石油化工股份有限公司 | 3D seismic data tomography enhanced processing method |
-
2020
- 2020-01-08 CN CN202010017122.7A patent/CN113093274B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104459801A (en) * | 2014-12-10 | 2015-03-25 | 中国石油天然气集团公司 | Coherence enhancement processing method used for recognizing fault |
| CN105334534A (en) * | 2015-10-21 | 2016-02-17 | 中国石油大学(华东) | Low order fault interpretation method based on construction mode guidance |
| CN109143348A (en) * | 2017-06-28 | 2019-01-04 | 中国石油化工股份有限公司 | 3D seismic data tomography enhanced processing method |
| CN107765301A (en) * | 2017-10-13 | 2018-03-06 | 中国煤炭地质总局地球物理勘探研究院 | The method for quickly identifying and device of coal seam craven fault |
Non-Patent Citations (5)
| Title |
|---|
| 地层倾角信息在断层精细刻画中的应用――以渤海湾盆地A油田为例;田涛 等;《地球物理学进展》;第32卷(第5期);第2236-2240页 * |
| 地震地质一体化解释技术在复杂潜山勘探中的应用――以黄骅坳陷乌马营潜山带为例;王文庆 等;《石油地球物理勘探》;53(第S1期);第242-248页 * |
| 地震多属性断裂识别技术在中拐凸起石炭系中的应用;仲伟军 等;《石油地球物理勘探》(第S2期);第135-139页 * |
| 多属性分析技术在碳酸盐岩断溶体预测中的应用;徐红霞 等;《石油地球物理勘探》;第52卷;第158-163页 * |
| 蚂蚁追踪技术在三维地震精细解释中的应用――以淮北祁南煤矿82采区为例;庄益明 等;《煤田地质与勘探》;46(第02期);第173-176页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113093274A (en) | 2021-07-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109425896B (en) | Dolomite oil and gas reservoir distribution prediction method and device | |
| Du et al. | Earthquake relocation using cross-correlation time delay estimates verified with the bispectrum method | |
| CN109001801B (en) | Fault variable-scale identification method based on multiple iteration ant colony algorithm | |
| CN111399048B (en) | Method for calculating correlation attribute and data weighted reconstruction of broken solution | |
| US11536866B2 (en) | Diffracted wave imaging method, device and electronic apparatus | |
| CN105974407A (en) | Ground penetrating radar underground horizon detection method | |
| CN109425891B (en) | Fracture imaging quality detection method based on geological model | |
| CN113093274B (en) | Method, device, terminal and storage medium for identifying low-order faults | |
| Savard et al. | Detection and location of low‐frequency earthquakes using cross‐station correlation | |
| CN114994758A (en) | Wave impedance extraction and structure characterization method and system for carbonate fracture control reservoir | |
| CN109100803B (en) | Method and device for determining micro-fracture | |
| CN114578424A (en) | Method and device for fusing azimuth-divided attributes of wide-azimuth seismic data | |
| CN110954958A (en) | Crack and fault prediction method and system | |
| Donias et al. | New fault attribute based on robust directional scheme | |
| CN112684498A (en) | Reservoir fracture prediction method and system based on wide-azimuth seismic data | |
| CN113640876A (en) | Method for finely identifying trapping column by using chaotic body attribute | |
| CN111474580B (en) | Azimuth angle gather extraction method and system based on offset vector piece | |
| CN111458751B (en) | Comprehensive time depth calibration method and device | |
| Zhang et al. | Source effects on regional seismic discriminant measurements | |
| CN114139229A (en) | Fault enhancement method, fault development interpretation method, storage medium, and electronic apparatus | |
| CN109490952B (en) | Seismic coherence analysis method and system | |
| CN111290020A (en) | Fracture detection method and device based on structural filtering processing and frequency division attribute fusion | |
| Shumway et al. | Mixed signal processing for regional and teleseismic arrays | |
| Yang et al. | Fractal Slope‐Based Seismic Wave Detection Method | |
| CN105204070B (en) | A kind of inclined detection method and device of big gun |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |