CN105425293A - Seismic attribute clustering method and seismic attribute clustering device - Google Patents
Seismic attribute clustering method and seismic attribute clustering device Download PDFInfo
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Abstract
The invention discloses a seismic attribute clustering method and a seismic attribute clustering device. The method comprises the following steps: determining the band clustering mode, clustering parameters, initial horizon, horizon offset and the size of a sliding time window, wherein the sliding time window refers to the number of sample points contained between two adjacent peaks or two adjacent valleys of a waveform graph of a horizon section to be researched, the number of horizons above the initial horizon is at least equal to half the sliding time window, and the number of horizons below a termination horizon is at least equal to half the sliding time window; using the sliding time window to cluster the seismic attribute data of the horizon section to be researched, and determining the category to which the sample points in each horizon belong; and unifying the clustering results of adjacent horizons according to the spatial similarity, and starting from the horizon below the initial horizon, adjusting the clustering result of each horizon according to the clustering result of the adjacent upper horizon. Multi-attribute clustering analysis is made based on upper and lower band information of the points on the horizon surface, and the clustering results are unified. Reservoir distribution in a horizon section can be showed globally.
Description
Technical field
The present invention relates to technical field of geophysical exploration, particularly relate to a kind of seismic properties clustering method and device.
Background technology
In oil-gas exploration, only have to the geological condition of underground had sufficient understanding and be familiar with after, could judge the hydrocarbon storage situation of survey area.The important means obtaining geological information is exactly the various seismic attributes datas that analysis geological data obtains after mathematic(al) manipulation.Seismic attributes data is prestack or post-stack seismic data normally, about the parameter such as geometric shape, kinematics character, dynamic characteristic of seismic event.By the research to these parameters, the features such as the structure of survey area underground medium, lithology, fluid can be obtained, and then infer the storage information of oil gas.From the seismic attributes data obtained, infer that this process of subterranean geology is commonly referred to seismic attributes analysis through a series of analysis, a kind of wherein the most frequently used method is exactly cluster.
Since the eighties in 20th century, clustering method is progressively introduced in field of seismic exploration, especially in seismic multi-attribute analysis and seismic facies analysis, greatly improve subtle reservoir predictive ability and without the reservoir prediction ability in well control situation.
So-called cluster is exactly the size of difference between the seismic attributes data according to the acquisition of underground medium place, and they are divided into some classifications, and the data differences differing less and different classes of between the data in each class is larger.By carrying out cluster to the seismic properties of collecting, these seismic attributes datas can be divided into several large classification, and then further can analyze the geological condition of survey area.Such as geology Lithofacies dividing is carried out to target area, according to the check analysis of cluster result and result of log interpretation, determine the facies tract corresponding to each classification.Especially, in the process of reservoir prediction, cluster analysis of seismic attributes is very important step, plays a part important.
Current clustering technique concentrates on designated layer plane mostly, reservoir be then integrated distribution in interval, based on the clustering method of layer plane, the reservoir distribution in interval can not be represented well.
Summary of the invention
The invention provides a kind of seismic properties clustering method and device, at least to solve the existing problem that can not represent the reservoir distribution in interval based on the clustering technique on layer plane well.
According to an aspect of the present invention, provide a kind of seismic properties clustering method, comprise: the size determining wave band cluster mode, clustering parameter, initial layers position, layer position side-play amount and time slip-window, wherein, described time slip-window is sample point number contained between sample point number contained between two adjacent peaks in the oscillogram of interval to be studied or two adjacent troughs, more than described initial layers position have the layer figure place of half time slip-window at least, below stop layer position, have the layer figure place of half time slip-window at least; Utilize the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, determine the classification belonging to sample point of each layer of position in described interval to be studied; Unify according to the cluster result of space similarity to adjacent layer position, wherein, from the next layer position of described initial layers position, the cluster result of the cluster result of each layer of position according to the upper layer position be adjacent is adjusted.
In one embodiment, the computing formula of described time slip-window is:
wherein, Δ T represents time slip-window, t
steprepresent sampling step length, t
0on presentation layer position sample point place wave band in apart from the nearest trough of described sample point or the sampling time interval between crest and described sample point.
In one embodiment, described wave band cluster mode comprises: the wave band cluster mode based on average, the wave band cluster mode based on attribute split or based on difference and wave band cluster mode.
In one embodiment, if adopt the described wave band cluster mode based on average, the seismic attributes data of described time slip-window to described interval to be studied is utilized to carry out cluster, comprise: according to the wave band at the sample point e place on current layer position, determine the attribute vector of described sample point e according to following formula:
Wherein, the primitive attribute vector of described sample point e is (a1
e, a2
e... ai
e... an
e), ai
ajrepresent i-th property value of a jth point in the some set A in the time slip-window Δ T of described sample point e,
the average property value of i-th attribute of described sample point e when representing that time slip-window is Δ T, i=1,2 ..., n; According to above-mentioned formula, other sample points on described current layer position are calculated, obtain the attribute vector of each sample point on described current layer position; Successively along layer position, each sample point place in described some set A, cluster is carried out to the attribute vector of each sample point.
In one embodiment, if adopt the described wave band cluster mode based on attribute split, utilize the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, comprising:
The all properties of each sample point in the some set A in the time slip-window Δ T of the sample point e on current layer position is carried out split, obtains the wave band attribute vector of described sample point e: (the full attribute of A1, the full attribute of A2,, the full attribute of Aj ...), wherein, j=1,2, Δ T, in described some set A, the full attribute of sample point is (a1, a2, ai ... an); According to above-mentioned steps, other sample points on described current layer position are calculated, obtain the wave band attribute vector of each sample point on described current layer position; Cluster is carried out to the wave band attribute vector of all sample points on described current layer position.
In one embodiment, if adopt described based on difference and wave band cluster mode, utilize the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, comprising: the attribute vector calculating the sample point e on current layer position respectively according to following formula and the distance D of multiple cluster centres prestored:
wherein, ai
ajrepresent i-th property value of a jth point in the some set A in the time slip-window Δ T of sample point e, ai
cjrepresent i-th property value of a jth point in the some set C in the time slip-window Δ T of cluster centre, described cluster centre and classification one_to_one corresponding; Determine that classification that the cluster centre nearest with described sample point e is corresponding is the classification of described sample point e.
In one embodiment, unify to comprise according to the cluster result of space similarity to adjacent layer position: each sample of described 5 initial layers positions is pressed and classifies according to generic, and carry out label by preset order; Steps A 1, according to the space similarity of adjacent layer position, with on layer by layer position adjacent under layer by layer in position, determine with described on the corresponding region of position layer by layer; Steps A 2, according to the label of position layer by layer on described, the label of the sample point under described layer by layer in region, position is adjusted to described on the label of corresponding region in position layer by layer; Repeated execution of steps A1 to A2, until the cluster result adjustment completing all layer positions of described interval to be studied.
In one embodiment, before the seismic attributes data utilizing described time slip-window to described interval to be studied carries out cluster, described method also comprises: carry out following pre-service to the seismic attributes data of described interval to be studied: carry out filtration treatment to the abnormal data in described seismic attributes data; And the seismic attributes data after filtering is normalized.
According to another aspect of the present invention, provide a kind of seismic properties clustering apparatus, comprise: determining unit, with 5 in the size determining wave band cluster mode, clustering parameter, initial layers position, layer position side-play amount and time slip-window, wherein, described time slip-window is sample point number contained between sample point number contained between two adjacent peaks in the oscillogram of interval to be studied or two adjacent troughs, more than described initial layers position have the layer figure place of half time slip-window at least, below stop layer position, have the layer figure place of half time slip-window at least; Cluster cell, for utilizing the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, determines the classification belonging to sample point of each layer of position in described interval to be studied; Adjustment unit, for unifying according to the cluster result of space similarity to adjacent layer position, wherein, from the next layer position of described initial layers position, adjusts the cluster result of each layer of position according to the cluster result of a layer position on it.
By seismic properties clustering method of the present invention and device, start with from the band class information up and down of the point layer plane, band class information is utilized to carry out the analysis of many hierarchical cluster attributes, cluster is carried out by the seismic volume data of time slip-window to specific interval, and utilize the cluster result of space fit degree algorithm to adjacent layer position to unify, the reservoir distribution interval can be represented preferably from overall angle.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide a further understanding of the present invention, and form a application's part, schematic description and description of the present invention, for explaining the present invention, does not form limitation of the invention.In the accompanying drawings:
Fig. 1 is the process flow diagram of the seismic properties clustering method of the embodiment of the present invention;
Fig. 2 is the structured flowchart of the seismic properties clustering apparatus of the embodiment of the present invention;
Fig. 3 is the layer model figure of the embodiment of the present invention;
Fig. 4 is the schematic diagram of the wave band cluster mode of the embodiment of the present invention;
Fig. 5 is the layer position cluster centre vertical view of the embodiment of the present invention;
Fig. 6 A is the label distribution schematic diagram of the upper position layer by layer of the embodiment of the present invention;
The label distribution schematic diagram of position layer by layer under Fig. 6 B embodiment of the present invention;
The label unified result schematic diagram of position layer by layer under Fig. 6 C embodiment of the present invention;
Fig. 7 is that the body cluster result of the embodiment of the present invention unifies schematic diagram;
Fig. 8 is the implementing procedure figure of the instantiation of the seismic properties clustering method of the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on embodiments of the invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to protection scope of the present invention.
Current great majority research concentrates on many hierarchical cluster attributes that layer plane is put and analyzes, the change of any physical parameter of sedimentary formation is always reflected in the change of seismic trace waveform shape, and the classification of seismic trace waveform shape represents the real laterally abnormal of seismic signal.Time certain within the scope of window, the waveform character of same zone of interest should be consistent.For the classification of seismic attributes data, not only consider numerical values recited, also will consider the change of waveform.If only consider numerical values recited, do not consider the waveform of seismic signal, the classification results obtained is undesirable.
Embodiments provide a kind of seismic properties clustering method, Fig. 1 is the process flow diagram of the seismic properties clustering method of the embodiment of the present invention.As shown in Figure 1, the method comprises the steps:
Step S101, determine the size of wave band cluster mode, clustering parameter, initial layers position, layer position side-play amount and time slip-window, wherein, time slip-window is sample point number contained between sample point number contained between two adjacent peaks in the oscillogram of interval to be studied or two adjacent troughs, more than initial layers position have the layer figure place of half time slip-window at least, below stop layer position, have the layer figure place of half time slip-window at least.
In concrete enforcement, can according to the size of the waveform character determination time slip-window of concrete interval.Clustering parameter can comprise: classification number.Preferably, the classification number of cluster setting can be 5 or 7.
Step S102, utilizes time slip-window to carry out cluster to the seismic attributes data of interval to be studied, determines the classification belonging to sample point of each layer of position in interval to be studied.
Step S103, unifies according to the cluster result of space similarity to adjacent layer position, wherein, from the next layer position of initial layers position, is adjusted by the cluster result of the cluster result of each layer of position according to the upper layer position be adjacent.
Pass through the above embodiment of the present invention, start with from the band class information up and down of the point layer plane, band class information is utilized to carry out the analysis of many hierarchical cluster attributes, cluster is carried out by the seismic volume data of time slip-window to specific interval, and utilize the cluster result of space fit degree algorithm to adjacent layer position to unify, the reservoir distribution interval can be represented preferably from overall angle.
In the cluster process of reality, the classification number, time window size, the seismic volume attributes data that comprise between designated layer position to be clustered and side-play amount thereof that preset can be inputted at the interface of cluster calculation software, then export as every layer of sample point generic in this interval.Because in interval, adjacent layer position cluster classification may there are differences, so need to unify cluster interval cluster result.
The computing formula of time slip-window can be:
wherein, Δ T represents time slip-window, t
steprepresent sampling step length, t
0on presentation layer position sample point place wave band in apart from the nearest trough of sample point or the sampling time interval between crest and sample point.
Wave band cluster mode can comprise: the wave band cluster mode based on average, the wave band cluster mode based on attribute split or based on difference and wave band cluster mode.
Respectively above-mentioned several wave band cluster mode is described below.
(1) based on the wave band cluster mode of average
Utilize time slip-window to carry out cluster to the seismic attributes data of interval to be studied, comprise the following steps:
According to the wave band at the sample point e place on current layer position, the attribute vector according to following formula determination sample point e:
Wherein, the primitive attribute vector of sample point e is (a1
e, a2
e... ai
e... an
e), ai
ajrepresent i-th property value of a jth point in the some set A in the time slip-window Δ T of sample point e,
the average property value of i-th attribute of sample point e when representing that time slip-window is Δ T, i=1,2 ..., n;
According to above-mentioned formula, other sample points on current layer position are calculated, obtain the attribute vector of each sample point on current layer position;
Layer position, each sample point place in a set A, carries out cluster to the attribute vector of each sample point successively.
Concrete cluster process can adopt clustering method of the prior art, such as, and k-means cluster etc.Repeat no more herein.
(2) based on the wave band cluster mode of attribute split
Utilize time slip-window to carry out cluster to the seismic attributes data of interval to be studied, comprise the following steps:
The all properties of each sample point in the some set A in the time slip-window Δ T of the sample point e on current layer position is carried out split, obtains the wave band attribute vector of sample point e: (the full attribute of A1, the full attribute of A2,, the full attribute of Aj ...), wherein, j=1,2, Δ T, in some set A, the full attribute of sample point is (a1, a2, ai ... an);
According to above-mentioned steps, other sample points on current layer position are calculated, obtain the wave band attribute vector of each sample point on current layer position;
Cluster is carried out to the wave band attribute vector of all sample points on current layer position.Concrete cluster process can adopt clustering method of the prior art, such as, and k-means cluster etc.Repeat no more herein.
(3) based on difference and wave band cluster mode
Utilize time slip-window to carry out cluster to the seismic attributes data of interval to be studied, comprise the following steps:
The attribute vector calculating the sample point e on current layer position respectively according to following formula and the distance D of multiple cluster centres prestored:
wherein, ai
ajrepresent i-th property value of a jth point in the some set A in the time slip-window Δ T of sample point e, ai
cjrepresent i-th property value of a jth point in the some set C in the time slip-window Δ T of cluster centre, cluster centre and classification one_to_one corresponding;
Determine that classification that the cluster centre nearest with sample point e is corresponding is the classification of sample point e.
In one embodiment, unify to be realized by following steps according to the cluster result of space similarity to adjacent layer position: each sample of initial layers position is pressed and classifies according to generic, and carry out label by preset order; Steps A 1, according to the space similarity of adjacent layer position, with on layer by layer position adjacent under layer by layer in position, determine the corresponding region with upper position layer by layer; Steps A 2, according to the label of upper position layer by layer, by under the label of sample point layer by layer in region, position be adjusted to the label of corresponding region in position layer by layer; Repeated execution of steps A1 to A2, until the cluster result adjustment completing all layer positions of interval to be studied.
In one embodiment, utilizing before time slip-window carries out cluster to the seismic attributes data of interval to be studied, said method can also comprise: carry out following pre-service to the seismic attributes data of interval to be studied: carry out filtration treatment to the abnormal data in seismic attributes data; And the seismic attributes data after filtering is normalized.Particularly, Min-max method or Z-Score method can be used to be normalized.
Based on same inventive concept, additionally provide a kind of seismic properties clustering apparatus in the embodiment of the present invention, may be used for the method realized described by above-described embodiment, as described in the following examples.The principle of dealing with problems due to seismic properties clustering apparatus is similar to seismic properties clustering method, and therefore the enforcement of this device see the enforcement of said method, can repeat part and repeat no more.Following used, term " unit " or " module " can realize the software of predetermined function and/or the combination of hardware.Although the system described by following examples preferably realizes with software, hardware, or the realization of the combination of software and hardware also may and conceived.
Fig. 2 is the structured flowchart of the seismic properties clustering apparatus of the embodiment of the present invention, and as shown in Figure 2, this seismic properties clustering apparatus comprises: determining unit 21, cluster cell 22 and adjustment unit 23, be specifically described this structure below.
Determining unit 21, for determining the size of wave band cluster mode, clustering parameter, initial layers position, layer position side-play amount and time slip-window, wherein, time slip-window is sample point number contained between sample point number contained between two adjacent peaks in the oscillogram of interval to be studied or two adjacent troughs, more than initial layers position have the layer figure place of half time slip-window at least, below stop layer position, have the layer figure place of half time slip-window at least;
Cluster cell 22, for utilizing time slip-window to carry out cluster to the seismic attributes data of interval to be studied, determines the classification belonging to sample point of each layer of position in interval to be studied;
Adjustment unit 23, for unifying according to the cluster result of space similarity to adjacent layer position, wherein, from the next layer position of initial layers position, adjusts the cluster result of each layer of position according to the cluster result of a layer position on it.
Pass through said apparatus, start with from the band class information up and down of the point layer plane, band class information is utilized to carry out the analysis of many hierarchical cluster attributes, cluster is carried out by the seismic volume data of time slip-window to specific interval, and utilize the cluster result of space fit degree algorithm to adjacent layer position to unify, the reservoir distribution interval can be represented preferably from overall angle.
The computing formula of time slip-window can be:
wherein, Δ T represents time slip-window, t
steprepresent sampling step length, t
0on presentation layer position sample point place wave band in apart from the nearest trough of sample point or the sampling time interval between crest and sample point.
Wave band cluster mode can comprise: the wave band cluster mode based on average, the wave band cluster mode based on attribute split or based on difference and wave band cluster mode.
Respectively above-mentioned several wave band cluster mode is described below.
(1) based on the wave band cluster mode of average
Cluster cell 22 can comprise:
First determination module, for the wave band according to the sample point e place on current layer position, the attribute vector according to following formula determination sample point e:
Wherein, the primitive attribute vector of sample point e is (a1
e, a2
e... ai
e... an
e), ai
ajrepresent i-th property value of a jth point in the some set A in the time slip-window Δ T of sample point e,
the average property value of i-th attribute of sample point e when representing that time slip-window is Δ T, i=1,2 ..., n;
First computing module, for calculating other sample points on current layer position according to above-mentioned formula, obtains the attribute vector of each sample point on current layer position;
First cluster module, for layer position, each sample point place in a set A successively, carries out cluster to the attribute vector of each sample point.
(2) based on the wave band cluster mode of attribute split
Cluster cell 22 can comprise:
Die section, for all properties of each sample point in the some set A in the time slip-window Δ T of the sample point e on current layer position is carried out split, obtains the wave band attribute vector of sample point e: (the full attribute of A1, the full attribute of A2 ..., the full attribute of Aj,), wherein, j=1,2 ..., Δ T, in some set A, the full attribute of sample point is (a1, a2 ... ai ... an);
Second computing module, for calculating other sample points on current layer position according to above-mentioned steps, obtains the wave band attribute vector of each sample point on current layer position;
Second cluster module, for carrying out cluster to the wave band attribute vector of all sample points on current layer position.
(3) based on difference and wave band cluster mode
Cluster cell 22 can comprise:
3rd computing module, the distance D for the attribute vector calculating the sample point e on current layer position according to following formula respectively and the multiple cluster centres prestored:
wherein, ai
ajrepresent i-th property value of a jth point in the some set A in the time slip-window Δ T of sample point e, ai
cjrepresent i-th property value of a jth point in the some set C in the time slip-window Δ T of cluster centre, cluster centre and classification one_to_one corresponding;
Second determination module, for determining that classification that the cluster centre nearest with sample point e is corresponding is the classification of sample point e.
In one embodiment, adjustment unit 23 comprises: label model, classifies, and carry out label by preset order for being pressed by each sample of initial layers position according to generic; Area determination module, for the space similarity according to adjacent layer position, with on layer by layer position adjacent under layer by layer in position, determine the corresponding region with upper position layer by layer; Adjusting module, for the label according to upper position layer by layer, by under the label of sample point layer by layer in region, position be adjusted to the label of corresponding region in position layer by layer.Recycling area determination module and adjusting module complete respective function, until complete the cluster result adjustment of all layer positions of interval to be studied.
In one embodiment, said apparatus can also comprise: pretreatment unit, for utilizing before time slip-window carries out cluster to the seismic attributes data of interval to be studied, following pre-service is carried out to the seismic attributes data of interval to be studied: filtration treatment is carried out to the abnormal data in seismic attributes data; And the seismic attributes data after filtering is normalized.
Certainly, above-mentioned Module Division just a kind of signal divides, and the present invention is not limited thereto.As long as the Module Division of object of the present invention can be realized, protection scope of the present invention all should be belonged to.
In order to more clearly explain above-mentioned seismic properties clustering apparatus and device, be described below in conjunction with specific embodiment, but it should be noted that this embodiment is only to better the present invention is described, do not form and the present invention is limited improperly.
For K-means clustering algorithm, suppose as follows:
Select n kind seismic properties, attribute-name is respectively a1, a2 ..., ai ..., wherein i=1,2 ..., n.
As shown in Figure 3, suppose that the sampling step length in TimeSlice direction is tstep, choosing layer bit line h1, a h1 increases Δ t downwards and obtains a layer bit line h2.H1 gets an e, body cluster carries out wave band cluster downwards along layer position, here moving window Δ T is introduced, it equals the number of contained sampled point in wave band, and as e point, Δ T is determined by the trough that e is the most contiguous up and down, here be symmetrical based on the upper and lower waveform of e point, if the upper lower wave trough of e is different apart from the time of e, get smaller value, if this smaller value is t0.They are pressed the arrangement of TimeSlice ascending order by a total Δ T sampled point in the wave band of e point, composition set A.The computing formula of Δ T:
Wherein: t0 represents e point, and the most contiguous trough is apart from the sampling time interval of e point up and down, and tstep represents sampling step length.Δ T represents moving window.
Wave band cluster generally has three kinds of processing modes, be respectively the wave band cluster based on average, the wave band cluster based on attribute split, based on difference and wave band cluster, as shown in Figure 4.
(1) based on the wave band cluster of average:
The primitive attribute vector of e: (a1
e, a2
e... ai
e... an
e).Consider the wave band of e point, get the attribute vector of attribute average as e:
Wherein: ai
ajrepresent i-th property value of a jth point in the some set A in the sliding window Δ T of e point,
represent when sliding window is Δ T, the average property value of i-th attribute of e point, i=1,2 ..., n.
Above-mentioned process is carried out to other points on layer position, e point place, carries out cluster along layer position, sampled point place in set A successively.
(2) based on the wave band cluster of attribute split:
The all properties split of the sampled point in the set A corresponding by e point, forms wave band attribute vector;
The primitive attribute vector of e: (a1
e, a2
e... ai
e... an
e).
The wave band attribute vector of e: (the full attribute of A1, the full attribute of A2 ..., the full attribute of Aj ...), wherein j=1,2 ..., Δ T.
(3) based on difference and wave band cluster
Be similar to K-means cluster, first produce at layer plane k the cluster centre pre-set, and distance calculating is different from common K-means cluster.If the cluster centre (see Fig. 5) that C produces when being layer plane h1 initialization cluster centre:
The distance of e to C is calculated as follows:
Wherein: ai
ajrepresent i-th property value of a jth point in the some set A in the sliding window Δ T of e point, ai
cjrepresent i-th property value of a jth point in the some set C in the sliding window Δ T of the cluster centre at e point place, D is distance metric, judges the classification ownership of e according to D.
Cluster labels unification is cluster result this hypothesis similar based on adjacent layer plane, utilizes the corresponding similarity in space to unify the label of adjacent layer plane.Such as, each sample of initial layers position can be pressed and classify according to generic, and press descending label again, be designated as 1,2,, according to upper label order, find out down labels maximum in area of space in position layer by layer respectively, this labels all for lower floor are converted to the label in this region, upper strata, again upper label corresponding in lower floor is converted to by the label that upper label is replaced, executes all layer positions, complete adjustment.
See Fig. 6 A to 6C, Fig. 6 A shows a layer cluster result of plane H, in Fig. 6 B, solid line is layer plane H1 cluster result, dotted line is that a layer cluster result of plane H is (assuming that layer plane H1 and H is adjacent, H1 is positioned at the below of H), after Fig. 6 C shows application cluster labels Unified Algorithm, the renewal result of layer plane H1 cluster labels.
Particularly, first, pre-service is carried out to seismic volume attribute data, in the present embodiment, select Min-max method to carry out pre-service.Then, setting program interface input parameter, determines initial layers position (TopHorizon), layer position side-play amount (AddTimeSlice), clustering algorithm parameter, half time window (HalfTime).The present embodiment medium wave band cluster mode is in the mode based on attribute split.After executing body clustering algorithm, the unification of different layers position cluster result be carried out.From the next layer position of initial layers position, each layer of position adjusts its cluster result according to the cluster result of a layer position on it.Fig. 7 is that the body cluster result of the embodiment of the present invention unifies schematic diagram, and in Fig. 7, classification number is 0 ~ 7.Fig. 8 is the implementing procedure figure of the instantiation of the seismic properties clustering method of the embodiment of the present invention, i.e. input parameter, carries out cluster, and cluster result (label) is unified, stores label after reunification, then carries out visual, facilitate user to check.
In sum, the embodiment of the present invention is started with from the band class information up and down of the point layer plane, utilizes band class information to carry out the analysis of many hierarchical cluster attributes, carries out cluster by the seismic volume data of time slip-window to particular segment, improves Clustering Effect.Utilize the cluster result of space fit degree algorithm to adjacent layer position to unify, the reservoir distribution interval can be represented preferably from overall angle, this be applied in petroleum exploration domain in the past clustering algorithm not available for.Practical application shows that Clustering Effect that the method obtains obviously is better than many hierarchical cluster attributes result a little.
Describe and can be understood in process flow diagram or in this any process otherwise described or method, represent and comprise one or more for realizing the module of the code of the executable instruction of the step of specific logical function or process, fragment or part, and the scope of the preferred embodiment of the present invention comprises other realization, wherein can not according to order that is shown or that discuss, comprise according to involved function by the mode while of basic or by contrary order, carry out n-back test, this should understand by embodiments of the invention person of ordinary skill in the field.
Should be appreciated that each several part of the present invention can realize with hardware, software, firmware or their combination.In the above-described embodiment, multiple step or method can with to store in memory and the software performed by suitable instruction execution system or firmware realize.Such as, if realized with hardware, the same in another embodiment, can realize by any one in following technology well known in the art or their combination: the discrete logic with the logic gates for realizing logic function to data-signal, there is the special IC of suitable combinational logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) etc.
In the description of this instructions, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " example ", " concrete example " or " some examples " etc. means to describe in conjunction with this embodiment or example are contained at least one embodiment of the present invention or example.In this manual, identical embodiment or example are not necessarily referred to the schematic representation of above-mentioned term.And the specific features of description, structure, material or feature can combine in an appropriate manner in any one or more embodiment or example.
Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; the protection domain be not intended to limit the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (9)
1. a seismic properties clustering method, is characterized in that, comprising:
Determine the size of wave band cluster mode, clustering parameter, initial layers position, layer position side-play amount and time slip-window, wherein, described time slip-window is sample point number contained between sample point number contained between two adjacent peaks in the oscillogram of interval to be studied or two adjacent troughs, more than described initial layers position have the layer figure place of half time slip-window at least, below stop layer position, have the layer figure place of half time slip-window at least;
Utilize the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, determine the classification belonging to sample point of each layer of position in described interval to be studied;
Unify according to the cluster result of space similarity to adjacent layer position, wherein, from the next layer position of described initial layers position, the cluster result of the cluster result of each layer of position according to the upper layer position be adjacent is adjusted.
2. method according to claim 1, is characterized in that,
The computing formula of described time slip-window is:
wherein, Δ T represents time slip-window, t
steprepresent sampling step length, t
0on presentation layer position sample point place wave band in apart from the nearest trough of described sample point or the sampling time interval between crest and described sample point.
3. method according to claim 1, is characterized in that, described wave band cluster mode comprises: the wave band cluster mode based on average, the wave band cluster mode based on attribute split or based on difference and wave band cluster mode.
4. method according to claim 3, is characterized in that, if adopt the described wave band cluster mode based on average, utilizes the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, comprising:
According to the wave band at the sample point e place on current layer position, determine the attribute vector of described sample point e according to following formula:
Wherein, the primitive attribute vector of described sample point e is (a1
e, a2
e... ai
e... an
e), ai
ajrepresent i-th property value of a jth point in the some set A in the time slip-window Δ T of described sample point e,
the average property value of i-th attribute of described sample point e when representing that time slip-window is Δ T, i=1,2 ..., n;
According to above-mentioned formula, other sample points on described current layer position are calculated, obtain the attribute vector of each sample point on described current layer position;
Successively along layer position, each sample point place in described some set A, cluster is carried out to the attribute vector of each sample point.
5. method according to claim 3, is characterized in that, if adopt the described wave band cluster mode based on attribute split, utilizes the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, comprising:
The all properties of each sample point in the some set A in the time slip-window Δ T of the sample point e on current layer position is carried out split, obtains the wave band attribute vector of described sample point e: (the full attribute of A1, the full attribute of A2,, the full attribute of Aj ...), wherein, j=1,2, Δ T, in described some set A, the full attribute of sample point is (a1, a2, ai ... an);
According to above-mentioned steps, other sample points on described current layer position are calculated, obtain the wave band attribute vector of each sample point on described current layer position;
Cluster is carried out to the wave band attribute vector of all sample points on described current layer position.
6. method according to claim 3, is characterized in that, if adopt described based on difference and wave band cluster mode, utilize the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, comprising:
The attribute vector calculating the sample point e on current layer position respectively according to following formula and the distance D of multiple cluster centres prestored:
Wherein, ai
ajrepresent i-th property value of a jth point in the some set A in the time slip-window Δ T of sample point e, ai
cjrepresent i-th property value of a jth point in the some set C in the time slip-window Δ T of cluster centre, described cluster centre and classification one_to_one corresponding;
Determine that classification that the cluster centre nearest with described sample point e is corresponding is the classification of described sample point e.
7. method according to any one of claim 1 to 6, is characterized in that, unifies to comprise according to the cluster result of space similarity to adjacent layer position:
Each sample of described initial layers position is pressed and classifies according to generic, and carry out label by preset order;
Steps A 1, according to the space similarity of adjacent layer position, with on layer by layer position adjacent under layer by layer in position, determine with described on the corresponding region of position layer by layer;
Steps A 2, according to the label of position layer by layer on described, the label of the sample point under described layer by layer in region, position is adjusted to described on the label of corresponding region in position layer by layer;
Repeated execution of steps A1 to A2, until the cluster result adjustment completing all layer positions of described interval to be studied.
8. method according to any one of claim 1 to 6, is characterized in that, before the seismic attributes data utilizing described time slip-window to described interval to be studied carries out cluster, described method also comprises:
Following pre-service is carried out to the seismic attributes data of described interval to be studied:
Filtration treatment is carried out to the abnormal data in described seismic attributes data; And the seismic attributes data after filtering is normalized.
9. a seismic properties clustering apparatus, is characterized in that, comprising:
Determining unit, for determining the size of wave band cluster mode, clustering parameter, initial layers position, layer position side-play amount and time slip-window, wherein, described time slip-window is sample point number contained between sample point number contained between two adjacent peaks in the oscillogram of interval to be studied or two adjacent troughs, more than described initial layers position have the layer figure place of half time slip-window at least, below stop layer position, have the layer figure place of half time slip-window at least;
Cluster cell, for utilizing the seismic attributes data of described time slip-window to described interval to be studied to carry out cluster, determines the classification belonging to sample point of each layer of position in described interval to be studied;
Adjustment unit, for unifying according to the cluster result of space similarity to adjacent layer position, wherein, from the next layer position of described initial layers position, adjusts the cluster result of each layer of position according to the cluster result of a layer position on it.
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