CN103901467A - Method for tracking positions of three-dimensional seismic data - Google Patents

Method for tracking positions of three-dimensional seismic data Download PDF

Info

Publication number
CN103901467A
CN103901467A CN201410100235.8A CN201410100235A CN103901467A CN 103901467 A CN103901467 A CN 103901467A CN 201410100235 A CN201410100235 A CN 201410100235A CN 103901467 A CN103901467 A CN 103901467A
Authority
CN
China
Prior art keywords
extreme point
cluster
carried out
seismic data
extreme
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.)
Pending
Application number
CN201410100235.8A
Other languages
Chinese (zh)
Inventor
陈小二
王颀
邹文
张洞君
范昆
王静
赵尧
巫盛洪
巫骏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Geophysical Prospecting Co of CNPC Chuanqing Drilling Engineering Co Ltd
Original Assignee
Geophysical Prospecting Co of CNPC Chuanqing Drilling Engineering Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Geophysical Prospecting Co of CNPC Chuanqing Drilling Engineering Co Ltd filed Critical Geophysical Prospecting Co of CNPC Chuanqing Drilling Engineering Co Ltd
Priority to CN201410100235.8A priority Critical patent/CN103901467A/en
Publication of CN103901467A publication Critical patent/CN103901467A/en
Pending legal-status Critical Current

Links

Images

Landscapes

  • Image Generation (AREA)

Abstract

The invention discloses a method for tracking positions of three-dimensional seismic data. The method comprises the steps that (A) extreme points are searched for in the three-dimensional seismic data; (B) the found extreme points are clustered to obtain cluster groups corresponding to all the extreme points and position fragments where the extreme points are located; (C) the position fragments which are adjacent in space and have the extreme points in the same cluster group are merged. By means of the method, automatic tracking of the positions can be achieved, manual interference is not needed in the overall process, and complete and continuous positions can be obtained.

Description

The method of 3D seismic data tracing of horizons
Technical field
The present invention relates to seismic prospecting data and explain field.More particularly, relate to the method for 3D seismic data tracing of horizons.
Background technology
Layer position explains that (tracing of horizons) is the important step that geologic information is explained.Explain and can detect underground structure by layer position, provide support for excavating subsurface reservoir.Be mainly to adopt artificial or semi-automatic trace horizon at present, current method is main or rely on artificial participation, and the problem of bringing is the artificial restriction that will inevitably be subject to subjective experience that participates in explaining; In the face of increasing geological data, the efficiency of manual interpretation is very low, can only, for minority zone of interest position provides explanation, become the bottleneck in seismic interpretation at every turn; And the precision of manual interpretation is not high, being difficult to provides basic data for follow-up accurate seismic data interpretation work.Along with continuous research and the improvement of tracing of horizons method, depend on computer implemented tracing of horizons algorithm and constantly propose, efficiency and the effect of tracking all improve to some extent.
But there are some problems in existing method, such as, under complicated geological environment, follow the trail of weak effect, the layer position obtaining is imperfect, needs artificial connection and fills, and can not realize full-automatic.There is at present a kind of conventional, simple and fast tracing of horizons method, implicit Markov (Markov) theoretical model of the method utilization carries out the tracking of echo time delay and extracts reflection horizon, the variation of the echo time delay of certain point of underground each medium layer is usually only relevant with more front time delay, irrelevant with more front Delay Variation, so just can utilize the known prior imformation of each point interface time delay above, by the relation of the implicit Markov chain of time delay between each point, detect down some echo time delays, thereby complete the tracking to echo time delay.The underground medium layer that but this method is only applicable to is simple in structure, smooth, echoed signal is strong, follows the trail of result when very weak for complicated a little interfacial structure (as cavity, subside etc.) or echoed signal very poor.
Summary of the invention
The object of the present invention is to provide a kind of method of 3D seismic data tracing of horizons.
An aspect of of the present present invention provides a kind of method of 3D seismic data tracing of horizons, comprises A) in 3D seismic data, search extreme point; B) extreme point finding is carried out to cluster, obtain the layer bit slice section at cluster corresponding to each extreme point group and each extreme point place; C) a spatially adjacent layer bit slice section for the extreme point that includes same cluster is merged.
Preferably, at step B) in, according to the clustering algorithm based on density, the extreme point finding is divided into different bunches, one bunch is equivalent to a layer bit slice section.
Preferably, at step B) in, according to the waveform similarity of extreme point, extreme point is carried out to cluster, obtain the cluster group that each extreme point is corresponding.
Preferably, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step comprise, draw the wave shape eigen coefficient of each extreme point according to Chebyshev's way of fitting.
Preferably, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step also comprise and comprising, set up mixed Gauss model according to wave shape eigen coefficient and carry out cluster.
Preferably, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step also comprise and comprising, obtain the estimated value of the parameter of mixed Gauss model by maximum likelihood method, the estimated value of described parameter is carried out cluster for substitution mixed Gauss model to extreme point.
According to the present invention, can realize the automatic tracing to layer position, overall process, without manual intervention, can obtain complete, continuous layer position.
By part in ensuing description set forth the present invention other aspect and/or advantage, some will be clearly by descriptions, or can pass through enforcement of the present invention and learning.
Accompanying drawing explanation
By the detailed description of carrying out below in conjunction with accompanying drawing, above and other objects of the present invention, feature and advantage will become apparent, wherein:
Fig. 1 illustrates a kind of according to an embodiment of the invention process flow diagram of method of 3D seismic data tracing of horizons;
Fig. 2 illustrates and includes according to an embodiment of the invention the extreme point of same cluster group and the exemplary plot of two adjacent layer bit slice sections spatially.
Fig. 3 illustrates the sectional view of the result of the full tracing of horizons in certain work area according to an embodiment of the invention.
Embodiment
Below, describe in detail with reference to the accompanying drawings according to exemplary embodiment of the present invention.
Fig. 1 illustrates a kind of according to an embodiment of the invention process flow diagram of method of 3D seismic data tracing of horizons.
As shown in Figure 1, in step 101, in 3D seismic data, search extreme point.
Described extreme point can comprise maximum point and maximum point, and maximum point is the crest in seismic waveshape, and minimum point is the trough in seismic waveshape.Because the lookup method is here prior art, do not describe in detail at this.
In step 102, the extreme point finding is carried out to cluster, obtain the layer bit slice section at cluster corresponding to each extreme point group and each extreme point place.
Preferably, clustering method can comprise two kinds, and the first is: by the clustering algorithm based on density, the extreme point finding is divided into different bunches, one bunch is equivalent to a layer bit slice section; The second is: according to the waveform similarity of extreme point, extreme point is carried out to cluster, obtain the cluster group that each extreme point is corresponding.
The density connectedness of the clustering algorithm utilization layer position based on density can be found the layer position of arbitrary shape fast.Its basic thought is: for the each extreme point in a layer position, the object comprising in its given radius neighborhood Eps can not be less than a certain given minimal amount Minpts.Eps and Minpts are preset value.The maximum set that a complete layer position is defined as to the connected extreme point of density, as long as the density (number of extreme point) of neighborhood exceedes Minpts, expansion is just continued in layer position.
According to the clustering algorithm based on density, the extreme point finding is divided into the method step of different bunches as follows: choose arbitrarily an extreme point p; Be not less than minPts if the extreme value comprising in the neighborhood of extreme point p is counted, find out the extreme point comprising in its field as one bunch, and this extreme point of mark P is for accessed; Process and allly in this bunch be not marked as accessed extreme point with the identical method of last step, thereby to bunch expanding; Repeat above-mentioned three steps, until all extreme points are all processed.
Utilize said method the extreme point finding in step 101 can be divided into different bunches, one bunch is equivalent to a layer bit slice section.Because the layer position that the factors such as tomography cause is discontinuous, same layer position may be divided into one or even multiple fragment.
The step of according to the waveform similarity of extreme point, extreme point being carried out to cluster in step 102, in order to simplify Wave data, reduces calculated amount, and this step can comprise, draws the wave shape eigen coefficient of each extreme point according to Chebyshev's way of fitting.By fitting coefficient b j(j=0,1,2 ... N) vector [b of composition 0, b 1..., b n] as the wave shape eigen coefficient of extreme point, N is matching exponent number.
Can carry out cluster to the wave shape eigen coefficient of extreme point obtained above according to the various algorithms that can carry out cluster to data sample point.Because the waveform character of the extreme point of same layer position meets Gaussian distribution, therefore, preferably, step 102 can also comprise step, sets up mixed Gauss model carry out cluster according to wave shape eigen coefficient.The wave shape eigen coefficient of supposing all extreme points is proper vector b={b k, b kprobability density function can be expressed as:
f ( b | c , u , Σ ) = ∏ k ∈ ϵ f ( b k | c k , u c k , Σ c k ) = ∏ k ∈ ϵ ( 2 π ) - n A / 2 | Σ c k | - 1 / 2 exp { - ( b k - u c k ) ′ Σ - 1 c k ( b k - u c k ) / 2 }
Wherein
Figure BDA0000478529600000047
proper vector b kmarginal distribution, n arelevant to Chebyshev's matching exponent number, be proper vector b kdimension.C k∈ 1,2 ..., n cthe class mark of presentation class, c kthe set of composition is c, is always divided into n cclass, n cbeing the input parameter of limited gauss hybrid models algorithm, is the valuation about single Gaussian distribution number in model,
Figure BDA0000478529600000048
represent the average of Gaussian distribution,
Figure BDA0000478529600000049
represent corresponding variance, the numbering that k is extreme point, ε represents the sum of extreme point.
Preferably, adopt the maximum Likelihood of greatest hope (EM) Algorithm for Solving above formula, obtain with minor function:
u c ^ = 1 | ϵ | Σ k ∈ ϵ b k
Σ c = 1 | ϵ | ^ Σ k ∈ ϵ ( b k - u c ^ ) 2
c k = arg ^ max c k f ( b k | c k , u c , Σ c )
Figure BDA0000478529600000045
with
Figure BDA0000478529600000046
be respectively u, the estimated value of ∑ and c, they are mutually related each other, obtain corresponding estimated value by the continuous iteration of EM algorithm.The estimated value substitution mixed Gauss model of above-mentioned each parameter is carried out to cluster to extreme point, can obtain the cluster group that each extreme point is corresponding.
In step 103, a spatially adjacent layer bit slice section for the extreme point that includes same cluster group is merged.
In the layer bit slice section obtaining according to the clustering algorithm based on density, may exist the layer position of causing due to factors such as tomographies discontinuous, same layer position is divided into one or even multiple fragment, by a spatially adjacent layer bit slice section for the extreme point that includes same cluster group is merged, the fragment of same layer position is merged obtain large, complete layer like this.
Fig. 2 includes the extreme point of same cluster group and the exemplary plot of two adjacent layer bit slice sections spatially, in two layer bit slice sections, has two clustering cluster d based on Density Clustering xand d y, they are continuous layer bit slice sections in space, in Fig. 2, stain is the extreme point that same cluster is rolled into a ball that belongs to obtaining according to extreme point waveform character cluster.
Fig. 3 illustrates the sectional view of the result of the full tracing of horizons in certain work area according to an embodiment of the invention.According to the present invention, can realize the automatic tracing to layer position, overall process is without manual intervention.As shown in Figure 3, the layer position in sectional view is complete, continuously.
Although specifically shown with reference to its exemplary embodiment and described the present invention, but it should be appreciated by those skilled in the art, in the case of not departing from the spirit and scope of the present invention that claim limits, can carry out the various changes in form and details to it.

Claims (6)

1. a method for 3D seismic data tracing of horizons, comprising:
A) in 3D seismic data, search extreme point;
B) extreme point finding is carried out to cluster, obtain the layer bit slice section at cluster corresponding to each extreme point group and each extreme point place;
C) by include the extreme point of same cluster group and spatially an adjacent layer bit slice section merge.
2. method according to claim 1, wherein, at step B) in, according to the clustering algorithm based on density, the extreme point finding is divided into different bunches, one bunch is equivalent to a layer bit slice section.
3. method according to claim 1, wherein, at step B) in, according to the waveform similarity of extreme point, extreme point is carried out to cluster, obtain the cluster group that each extreme point is corresponding.
4. method according to claim 3, wherein, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step comprise, draw the wave shape eigen coefficient of each extreme point according to Chebyshev's way of fitting.
5. method according to claim 4, wherein, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step also comprise and comprising, set up mixed Gauss model according to wave shape eigen coefficient and carry out cluster.
6. method according to claim 5, wherein, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step also comprise and comprising, obtain the estimated value of the parameter of mixed Gauss model by maximum likelihood method, the estimated value of described parameter is carried out cluster for substitution mixed Gauss model to extreme point.
CN201410100235.8A 2014-03-18 2014-03-18 Method for tracking positions of three-dimensional seismic data Pending CN103901467A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201410100235.8A CN103901467A (en) 2014-03-18 2014-03-18 Method for tracking positions of three-dimensional seismic data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201410100235.8A CN103901467A (en) 2014-03-18 2014-03-18 Method for tracking positions of three-dimensional seismic data

Publications (1)

Publication Number Publication Date
CN103901467A true CN103901467A (en) 2014-07-02

Family

ID=50992915

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201410100235.8A Pending CN103901467A (en) 2014-03-18 2014-03-18 Method for tracking positions of three-dimensional seismic data

Country Status (1)

Country Link
CN (1) CN103901467A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104199096A (en) * 2014-09-12 2014-12-10 吉林大学 Extraction method and device of horizons of seismic data cube
CN104199092A (en) * 2014-08-31 2014-12-10 电子科技大学 Multi-level framework based three-dimensional full-horizon automatic tracking method
CN105182410A (en) * 2015-09-02 2015-12-23 电子科技大学 Seismic data layered characteristic reinforcement drafting method
CN105182411A (en) * 2015-09-10 2015-12-23 中国石油天然气集团公司 Method and device used for determining pre-stack seismic horizon
CN104181596B (en) * 2014-08-27 2017-01-11 中国石油集团东方地球物理勘探有限责任公司 Geologic horizon automatic tracking method and device
CN111796324A (en) * 2019-04-09 2020-10-20 中国石油天然气股份有限公司 Seismic all-horizon tracking method and device

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4953142A (en) * 1989-01-06 1990-08-28 Marathon Oil Company Model-based depth processing of seismic data
CN1797039A (en) * 2004-12-29 2006-07-05 中国石油天然气集团公司 Method for automatic tracking 3D geological horizon
CN101506686A (en) * 2006-06-21 2009-08-12 特拉斯帕克地球科学公司 Interpretation of geologic depositional systems
CN102066980A (en) * 2008-05-22 2011-05-18 埃克森美孚上游研究公司 Seismic horizon skeletonization
CN102819688A (en) * 2012-08-29 2012-12-12 电子科技大学 Two-dimensional seismic data full-layer tracking method based on semi-supervised classification
CN103592681A (en) * 2013-09-16 2014-02-19 电子科技大学 Signal classification based seismic image horizon tracking method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4953142A (en) * 1989-01-06 1990-08-28 Marathon Oil Company Model-based depth processing of seismic data
CN1797039A (en) * 2004-12-29 2006-07-05 中国石油天然气集团公司 Method for automatic tracking 3D geological horizon
CN101506686A (en) * 2006-06-21 2009-08-12 特拉斯帕克地球科学公司 Interpretation of geologic depositional systems
CN102066980A (en) * 2008-05-22 2011-05-18 埃克森美孚上游研究公司 Seismic horizon skeletonization
CN102819688A (en) * 2012-08-29 2012-12-12 电子科技大学 Two-dimensional seismic data full-layer tracking method based on semi-supervised classification
CN103592681A (en) * 2013-09-16 2014-02-19 电子科技大学 Signal classification based seismic image horizon tracking method

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104181596B (en) * 2014-08-27 2017-01-11 中国石油集团东方地球物理勘探有限责任公司 Geologic horizon automatic tracking method and device
CN104199092A (en) * 2014-08-31 2014-12-10 电子科技大学 Multi-level framework based three-dimensional full-horizon automatic tracking method
CN104199096A (en) * 2014-09-12 2014-12-10 吉林大学 Extraction method and device of horizons of seismic data cube
CN104199096B (en) * 2014-09-12 2016-08-24 吉林大学 A kind of seismic data cube layer plane extracting method and device
CN105182410A (en) * 2015-09-02 2015-12-23 电子科技大学 Seismic data layered characteristic reinforcement drafting method
CN105182411A (en) * 2015-09-10 2015-12-23 中国石油天然气集团公司 Method and device used for determining pre-stack seismic horizon
CN111796324A (en) * 2019-04-09 2020-10-20 中国石油天然气股份有限公司 Seismic all-horizon tracking method and device
US11531130B2 (en) 2019-04-09 2022-12-20 Petrochina Company Limited Seismic full horizon tracking method, computer device and computer-readable storage medium
CN111796324B (en) * 2019-04-09 2023-02-10 中国石油天然气股份有限公司 Seismic all-horizon tracking method and device

Similar Documents

Publication Publication Date Title
CN102981182B (en) 2D seismic data all-horizon automatic tracking method based on unsupervised classification
CN109709603B (en) Seismic horizon identification and tracking method and system
CN103901467A (en) Method for tracking positions of three-dimensional seismic data
Pauget et al. A global approach in seismic interpretation based on cost function minimization
US9121971B2 (en) Hybrid method of combining multipoint statistic and object-based methods for creating reservoir property models
US8666149B2 (en) Method for editing a multi-point facies simulation
CN104573705A (en) Clustering method for building laser scan point cloud data
CN104297785A (en) Lithofacies constrained reservoir physical property parameter inversion method and device
CN104280771A (en) Three-dimensional seismic data waveform semi-supervised clustering method based on EM algorithm
CN103592681A (en) Signal classification based seismic image horizon tracking method
CN104181596A (en) Geologic horizon automatic tracking method and device
CN105093290B (en) A kind of automatic formation trace method based on waveform morphology
CN106772587A (en) Seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging
CN104573333A (en) Method for optimizing of model selection based on clustering analysis
CN103969683A (en) Method for picking position faces in batched mode based on constraint in three-dimensional seismic interpretation
Tahmasebi Structural adjustment for accurate conditioning in large-scale subsurface systems
Figueiredo et al. A seismic facies analysis approach to map 3D seismic horizons
CN105116447A (en) A geological river direction discrimination method based on curvature-abnormal stripes
CN108508481B (en) A kind of method, apparatus and system of longitudinal wave converted wave seismic data time match
CN104570113A (en) Method for self-adaptively removing strong reflection of earthquake
CN113419278B (en) Well-seismic joint multi-target simultaneous inversion method based on state space model and support vector regression
CN104714250A (en) Practical internal substratum automatic interpretation method
CN116958470B (en) Geological modeling method and device integrating Markov chain and multipoint statistics
CN110443801A (en) A kind of salt dome recognition methods based on improvement AlexNet
CN112578446B (en) Method and system for depicting formation reflection disorder degree

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20140702

RJ01 Rejection of invention patent application after publication