CN103048687A - Novel seismic attribute selecting method - Google Patents
Novel seismic attribute selecting method Download PDFInfo
- Publication number
- CN103048687A CN103048687A CN 201110325877 CN201110325877A CN103048687A CN 103048687 A CN103048687 A CN 103048687A CN 201110325877 CN201110325877 CN 201110325877 CN 201110325877 A CN201110325877 A CN 201110325877A CN 103048687 A CN103048687 A CN 103048687A
- Authority
- CN
- China
- Prior art keywords
- seismic
- attribute
- attributes
- seismic properties
- selection
- 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
Links
Landscapes
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a novel seismic attribute selecting method which comprises the following steps of: (a) selecting one attribute one by one in an unselected attribute combination to form a novel attribute combination Xk+1 and carrying out setting according to the formulas that k=k+1 and Xk=Xk+1; (b) removing r worst attributes one by one from Xk to form a novel attribute combination Xk-1 and carrying out setting according to the formula that k=k-r; and (c) carrying out setting according to the formula that k=d, stopping an algorithm, or carrying out setting according to the formula that Xk=Xk+1 and turning to the first step. According to the invention, seismic attributes can be rapidly selected, the selection steps are simple, the selection result is accurate, and the novel seismic attribute selecting method is convenient to popularize.
Description
Technical field
The present invention relates to a kind of novel system of selection of seismic properties.
Background technology
Along with the development of Discussion of Earthquake Attribute Technology, Seismic Reservoir Prediction has become the effective means that instructs oil-gas exploration and development.Yet because seismic properties is of a great variety, and the relation between the forecasting object is complicated, and different work areas and different reservoir are incomplete same to (the most effective, the most representative) seismic properties of institute's target of prediction sensitivity.Even same work area, same reservoir, forecasting object is different, and corresponding Sensitive Attributes also there are differences.
Because this multi-solution of seismic properties, so that some attribute can have a strong impact on the precision of reservoir prediction, therefore seismic properties is in optimized selection and just seems very necessary.Optimum methods of seismic attributes can significantly improve the precision of Seismic Reservoir Prediction, more effectively carries out reservoir and describes, and further improves the drilling well success ratio, has obvious economic benefit and social benefit.
Owing to seismic properties refers to by prestack or post-stack seismic data, some that derive through mathematic(al) manipulation comprise that outside geometric shape, internal reflection structure, continuity, amplitude, frequency and speed etc. represent the parameter of characteristics of seismic.And the seismic reflection unit in the three dimensions that seismic facies is specific seismic reflection parameter to be limited, it is the seismic response of particular deposition phase or geologic body.Therefore, it is very significant using seismic properties division seismic facies type.At last, explain sedimentary facies and the sedimentary environment of these seismic facies representatives by seismic facies analysis, be converted to the purpose of sedimentary facies to reach seismic facies.
At present, the normal self organizing neural network that adopts carries out cluster analysis to reach the purpose of dividing the seismic facies type to seismic properties, but traditional self organizing neural network just is grouped in the nearest class subset owing to training sample of every input, this training patterns may be too hurry, affect network to the grasp of all training sample features, and then the correctness of impact classification.Also very easily cause simultaneously the vibration of network weight, so that learning time is longer.In addition, the choosing of network parameter such as the gain function of self organizing neural network, bounding function, neighborhood are very stubborn problems, and they change along with the difference of drawing number of categories.In view of these problems of self organizing neural network, the mode that this paper adopts a kind of Fuzzy Self-organizing Neural Network to be combined with seismic properties is carried out SEISMIC PHASE BY PATTERN RECOGNITION.Fuzzy Self-organizing Neural Network is different from traditional self organizing neural network, and it is once to input all training sample points, determines that each sample point is to the subjection degree of every class subset.The adjustment of network weight has considered the characteristic information of all samples, and one takes turns study only adjusts once, has greatly saved learning time.And this method carries out seismic facies analysis, can effectively identify Sedimentary facies and the geological phenomenons such as river course, delta, outfall fan, tomography, lithologic anomalous body, forms a kind of practical, reservoir prediction technique that precision is high.
Seismic properties optimization be exactly optimize the most responsive to Solve problems, the most effectively or the attribute of representative is arranged most, in order to improve the precision of reservoir prediction.Before carrying out the seismic properties optimization process, usually to carry out to all properties that extracts standardization (such as normalization etc.).The optimization of seismic properties starts from " bright spot " technology that 20 century 70s occur, and in this technology, selects reflection wave amplitude and polarity etc., i.e. early stage " expert's optimization ".Along with the development of artificial intelligence technology, most methods are introduced in the optimization method of seismic properties.
At present the optimization method of attribute is more, but it can be divided into two large classes: utilize expertise to be optimized and utilize mathematical method to carry out Automatic Optimal.Expert method can not satisfy the requirement of present reservoir prediction, can only be as a kind of auxiliary means.The outer seismic properties method for optimizing of Present Domestic mainly is mathematical method, mainly contains Karhunen-Loeve transformation, local linear embedding algorithm (LLE), Isometric Maps (ISOMAP), multiple discriminant analysis method (MDA), attribute contribution amount method, searching algorithm, genetic algorithm, Rough Set (RS) etc.
Along with the development of Discussion of Earthquake Attribute Technology, Earthquake Reservoir has also obtained faster development as a branch.Utilize finally multiple seismic properties to carry out the technology of reservoir prediction from early stage single attribute forecast; From early stage expert method artificial intelligence approach finally.From the eighties, " pattern-recognition " is subject to special attention, successively worked out " Fuzzy Pattern Recognition ", " statistical model identification ", " network mode identification " and methods such as " approximation of function ", reservoir prediction technique has obtained fast development after this.Forecasting object develops into predicting reservoir parameter and formation lithology etc. from predicting oil/gas.At present, can be divided into according to Forecasting Methodology: the prediction of approximation of function class and the prediction of pattern-recognition class.The approximation of function class methods mainly are that reservoir parameter etc. is predicted, major parameter comprises Sandstone Percentage, factor of porosity, oil saturation, reservoir thickness, reservoir pressure etc., often adopts BP neural network, radial base neural net, CUSI network etc.The pattern-recognition class methods are mainly used in oil and gas prediction, SEISMIC PHASE BY PATTERN RECOGNITION, and the method for employing is transitioned into self organizing neural network, BP neural network, fractal theory, gray theory etc. from statistical model identification, Fuzzy Pattern Recognition.
The seismic facies analysis technology has also obtained very fast development naturally as the part of reservoir prediction.It is a kind of geological method that utilizes seismic data to carry out geologic interpretation that grows up late 1970s.Development so far, seismic facies analysis by naked eyes judge seismic facies unit various parameters, make the seismic facies map by hand to the seismic facies parameter of self organizing neural network judgement different units, and directly the seismic facies parameter is classified.Initial manual operations, time-consuming taking a lot of work, particularly when reflectance anomaly on the earthquake section was not outstanding, this work was more difficult, had developed into afterwards with statistical model identification and fuzzy clustering and had automatically divided seismic facies.But statistical model identification to attributes extraction and selection require high, can only be applicable to several simple forms, fuzzy clustering method is to set up accurately reasonably aspect the membership function difficulty larger, and when data volume is large operation time long, sometimes can realize hardly.Use afterwards nerual network technique to carry out pattern-recognition and obtained good effect.Some circumstance complications, background knowledge are unclear because artificial neural network can be processed, the indefinite problem of inference rule, and allow sample that larger damaged and distortion is arranged.Just at present about dividing the article of seismic facies, what multiselect was got is the Kohonene self organizing neural network.
One of key of pattern-recognition is not only in seismic properties optimization, and is also significant to improving approximation of function method Seismic Reservoir Prediction precision.In Seismic Reservoir Prediction, usually extract a plurality of attributes, adopt pattern-recognition or approximation of function method to carry out reservoir prediction.But in different regions, the different layers position, be incomplete same to (or effective, most representative) seismic properties of institute's forecasting object sensitivity; Even in areal, same layer position, also be discrepant to the seismic properties of the object sensitivity predicted.Therefore be necessary the optimum methods of seismic attributes in the Study In Reservoir prediction.
At present the optimization method of seismic properties is more, but it can be divided into two large classes: utilize expertise to be optimized and utilize mathematical method to carry out Automatic Optimal.The expert optimizes, and in general the oil field expert knows quite well with the seismic properties of maximum reservoir information certain area, can carry out by rule of thumb seismic properties and select.Sometimes the expert can propose several groups of more excellent attributes or combinations of attributes, but which is organized optimum difficulty and draws a conclusion.This can by calculate misclassification rate (pattern-recongnition method) or predicated error (approximation of function method) and comparing, choose the little person of misclassification rate or predicated error and be optimum seismic properties or seismic properties combination.Compare with expert's optimization method, Mathematics Optimization Method is more complex, and has widely applicability.
The task that seismic properties is selected be from one group of quantity (dimension) for select the seismic properties of D quantity be d (one group of optimum seismic properties of D＞d) is come, and will solve two problems for this reason:
1. the standard of selecting is selected objective function (with J () expression) here, namely will select the seismic properties combination that makes objective function reach optimum and come;
2. to look for a preferably system of selection, in order within the time that allows, find out that optimum group seismic properties.
Summary of the invention
Purpose of the present invention provides a kind of novel system of selection of seismic properties in order to overcome the deficiencies in the prior art and defective, and the novel system of selection of this seismic properties can be selected seismic properties fast, and selects step simple, and selection result is accurate, is convenient to promote.
Purpose of the present invention is achieved through the following technical solutions: a kind of novel system of selection of seismic properties may further comprise the steps:
(a) in not selected combinations of attributes
In be selected into one by one 1 of attribute, form new combinations of attributes X
K+1, put k=k+l, X
k=X
K+1
(b) from X
kIn reject one by one r the poorest attribute, form new combinations of attributes X
K-1, put k=k-r;
(c) put k=d, then stop algorithm, otherwise, X put
k=X
K+1, turn to the first step.
Further, when l＞r, l-r carries out first the first step for from bottom to top, then carries out second step, should put k=0, X when initial
0=φ.
Further, when l＜r, l-r carries out first second step for from top to bottom, then carries out the first step, should put k=D, X when initial
0={ X
1, X
2..., X
D.
In sum, the invention has the beneficial effects as follows: can select fast seismic properties, and select step simple, selection result is accurate, is convenient to promote.
Embodiment
Below in conjunction with embodiment, to the detailed description further of the present invention's do, but embodiments of the present invention are not limited to this.
Embodiment:
The present embodiment relates to a kind of novel system of selection of seismic properties, may further comprise the steps:
(a) in not selected combinations of attributes
In be selected into one by one 1 of attribute, form new combinations of attributes X
K+1, put k=k+l, X
k=X
K+1
(b) from X
kIn reject one by one r the poorest attribute, form new combinations of attributes X
K-1, put k=k-r;
(c) put k=d, then stop algorithm, otherwise, X put
k=X
K+1, turn to the first step.
Further, when l＞r, l-r carries out first the first step for from bottom to top, then carries out second step, should put k=0, X when initial
0=φ.
Further, when l＜r, l-r carries out first second step for from top to bottom, then carries out the first step, should put k=D, X when initial
0={ X
1, X
2..., X
D.
The above only is preferred embodiment of the present invention, is not the present invention is done any pro forma restriction, and any simple modification, the equivalent variations on every foundation technical spirit of the present invention above embodiment done all fall within protection scope of the present invention.
Claims (3)
1. the novel system of selection of a seismic properties is characterized in that, may further comprise the steps:
(a) in not selected combinations of attributes
In be selected into one by one 1 of attribute, form new combinations of attributes X
K+1, put k=k+l, X
k=X
K+1
(b) from X
kIn reject one by one r the poorest attribute, form new combinations of attributes X
K-1, put k=k-r;
(c) put k=d, then stop algorithm, otherwise, X put
k=X
K+1, turn to the first step.
2. the novel system of selection of a kind of seismic properties according to claim 1 is characterized in that, when l＞r, l-r carries out first the first step for from bottom to top, then carries out second step, should put k=0, X when initial
0=φ.
3. the novel system of selection of a kind of seismic properties according to claim 1 is characterized in that, when l＜r, l-r carries out first second step for from top to bottom, then carries out the first step, should put k=D, X when initial
0={ X
1, X
2..., X
D.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110325877 CN103048687A (en) | 2011-10-13 | 2011-10-13 | Novel seismic attribute selecting method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110325877 CN103048687A (en) | 2011-10-13 | 2011-10-13 | Novel seismic attribute selecting method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103048687A true CN103048687A (en) | 2013-04-17 |
Family
ID=48061392
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110325877 Pending CN103048687A (en) | 2011-10-13 | 2011-10-13 | Novel seismic attribute selecting method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103048687A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105277979A (en) * | 2015-10-16 | 2016-01-27 | 中国石油天然气集团公司 | Seismic attribute optimization method and device |
| CN105319582A (en) * | 2014-07-31 | 2016-02-10 | 中国石油天然气股份有限公司 | Seismic attribute parameter selection method and device |
-
2011
- 2011-10-13 CN CN 201110325877 patent/CN103048687A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105319582A (en) * | 2014-07-31 | 2016-02-10 | 中国石油天然气股份有限公司 | Seismic attribute parameter selection method and device |
| CN105277979A (en) * | 2015-10-16 | 2016-01-27 | 中国石油天然气集团公司 | Seismic attribute optimization method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104570083B (en) | Geologic body automatic identifying method based on multi-dimensional earthquake attribute | |
| CN109800863B (en) | Logging phase identification method based on fuzzy theory and neural network | |
| CN110346831B (en) | Intelligent seismic fluid identification method based on random forest algorithm | |
| CN106529667B (en) | Well-log facies recognition and analysis method based on fuzzy deep learning under a kind of big data environment | |
| CN107356958A (en) | A kind of fluvial depositional reservoir substep seismic facies Forecasting Methodology based on geological information constraint | |
| CN112613536B (en) | Near infrared spectrum diesel fuel brand recognition method based on SMOTE and deep learning | |
| Zhu et al. | Rapid identification of high-quality marine shale gas reservoirs based on the oversampling method and random forest algorithm | |
| CN109184677A (en) | Reservoir evaluation methods for heterogeneous alternating layers sand body | |
| CN108846540B (en) | Recovery ratio calibration method and device for tight sandstone gas field | |
| CN105259572A (en) | Seismic facies calculation method based on non-linear automatic classification of multiple attribute parameters of earthquake | |
| CN103376468A (en) | Reservoir parameter quantitative characterization method based on neural network function approximation algorithm | |
| CN108288092A (en) | A method of obtaining tight sand permeability using nuclear magnetic resonance T 2 spectrum form | |
| CN103048688A (en) | Seismic attribute optimal selection method based on three-step method | |
| CN106569272A (en) | Earthquake attribute fusion method based on data property space ascending dimension | |
| CN103049791A (en) | Training method of fuzzy self-organizing neural network | |
| CN106446514A (en) | Fuzzy theory and neural network-based well-log facies recognition method | |
| CN105700016A (en) | Organic bank reservoir earthquake prediction method | |
| CN103049463A (en) | Seismic attribute optimal selection method based on rough set theory | |
| CN103048687A (en) | Novel seismic attribute selecting method | |
| Lu et al. | Identifying flow units by FA-assisted SSOM—An example from the Eocene basin-floor-fan turbidite reservoirs in the Daluhu Oilfield, Dongying Depression, Bohai Bay Basin, China | |
| CN117272841B (en) | Shale gas dessert prediction method based on hybrid neural network | |
| CN103048686A (en) | Method for quickly solving seismic attribute reduction | |
| CN103049632A (en) | Non-supervision dimension reducing method aiming at nonlinear data | |
| CN114575834A (en) | Electromagnetic method high water cut oil reservoir saturation field prediction method based on machine learning | |
| CN117093922A (en) | Improved SVM-based complex fluid identification method for unbalanced sample oil reservoir |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20130417 |