CN109633743A - A method of based on waveform separation seismic facies technological prediction coal seam thickness - Google Patents
A method of based on waveform separation seismic facies technological prediction coal seam thickness Download PDFInfo
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- CN109633743A CN109633743A CN201910043205.0A CN201910043205A CN109633743A CN 109633743 A CN109633743 A CN 109633743A CN 201910043205 A CN201910043205 A CN 201910043205A CN 109633743 A CN109633743 A CN 109633743A
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- seismic
- coal seam
- waveform
- seam thickness
- seismic facies
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- 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. analysis, for interpretation, for correction
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- 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. analysis, for interpretation, for correction
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
Abstract
The invention discloses a kind of method based on waveform separation seismic facies technological prediction coal seam thickness, prediction and explanation before not disclosed suitable for coal seam thickness；The present invention utilizes the waveform separation seismic facies technology under borehole restrained, seismic facies recognition training is carried out by extracting seismic trace near well waveform as sample, and establish the mapping relations of coal seam thickness and seismic facies, based on neural network recognization work area seismic facies, and coal seam thickness information is converted by seismic facies information, instruct the prediction work of coal seam thickness.By the above-mentioned means, can be realized the prediction in zonal identical coal seam thickness section and explain work.
Description
Technical field
The present invention relates to a kind of methods based on waveform separation seismic facies technological prediction coal seam thickness, and it is pre- to belong to coal seam thickness
Survey method.
Background technique
This noun of seismic facies comes from oil seismic exploration technology, eighties of last century the seventies latter stage oil seismic exploration neck
The appearance of seismic stratigraphy in domain, this concept of seismic facies are gradually realized and are widely applied.1962, Sloss L L handle
" phase " is defined as the summation of System Domain and its material exhibits when certain rock stratum generates.Nineteen eighty-two, Sheriff point out earthquake
Mutually that seismic signature is formed by by depositional environment (such as marine facies or terrestrial facies), seismic facies it can be appreciated that sedimentary facies in the earthquake
The summation showed on section.The seismic facies understood in seismic sequence is the three-dimensional space as defined by specific seismic reflection parameter
Between seismic reflection unit, it is the seismic response of specific sedimentary facies or geologic body, and the unit is anti-in the earthquake of three-dimensional space
It is different to penetrate feature unit adjacent thereto.
The division of seismic facies is of great significance to oil-gas exploration, and seismic facies parameter is to divide the foundation of seismic facies, also referred to as
For earthquake facies marker.It is manual operations initially dividing seismic facies, it is time-consuming, it is abnormal especially to work as reflecting attribute on seismic profile
When not prominent, this work is even more difficulty.Traditional seismic facies analysis method is relative to growing up in recent years quantitatively
For seismic phase analysis.Traditional seismic facies analysis method is, continuity quality, frequency height strong and weak using seismic reflection amplitude
Low, the reflection attributes such as geometric shape and internal structure work out seismic facies map, carry out the conversion of sedimentary facies between the opposite well of earthquake.It is this
The artificial property of method is larger, has certain uncertainty.
Necessarily contain coal thickness information in seismic data, and amplitude, continuity and frequency bandwidth for being reflected in waveform etc. is a variety of
Among waveform attributes feature, and seismic channel waveform is the concentrated expression of seismic response parameter (amplitude, phase, frequency etc.), only
It is not enough to completely show the information of target zone by single attribute feature.
It can be eliminated using seismic waveform feature progress seismic facies classification and analyze bring using single Seismic Attribute Parameters
Limitation has a good application prospect in three-dimensional work area.The principle of waveform separation seismic facies technology is according between waveform
Difference, then the sample training of neural network is carried out, achieve the purpose that cluster.Waveform separation seismic facies analysis using neural network and
Mode identification technology, to the actual seismic data track in a certain interval by its wave character of trace comparison and seismic properties feature, carefully
Its cross directional variations is portrayed in cause, to obtain seismic anomaly planar distribution, i.e. waveform separation seismic facies map.
Summary of the invention
Goal of the invention: in order to overcome the deficiencies in the prior art, the present invention provides a kind of waveform separation seismic facies skill
The method that art predicts coal seam thickness establishes contacting for coal thickness and seismic facies by waveform, achievees the purpose that predict coal seam thickness.
Technical solution: to achieve the above object, the technical solution adopted by the present invention are as follows:
A method of based on waveform separation seismic facies technological prediction coal seam thickness, include the following steps:
(1) according to the borehole data of the whole district, coal seam thickness is divided into N number of continuous coal thickness section；
(2) to n-th of coal thickness section, the most apparent R of selected characteristicn(drilling of selection is more, divides more smart for a drilling
Seismic channel beside carefully) extracts model trace waveform, using model trace waveform as training sample as model trace；
(3) all training samples of neural network learning are utilized, the seismic facies distributed intelligence of the whole district is obtained；It specifically includes as follows
Step:
(31) all model traces of the whole district that step (2) are extracted are divided into more than two groupings, define coal seam thickness to be predicted
Position is target track, calculates the Manhattan distance between all target tracks (non-model trace) and all model traces by road, that is, counts
Calculate the summation of the sampling difference value between all target track waveforms and each model trace waveform sampling point:
Wherein: A indicates model trace, AiIndicate i-th of wavelet of model trace A；B indicates target track B, BiIndicate target track B's
I-th of wavelet；Target track and model trace are seismic channel, and each seismic channel has G wavelet, and M indicates model trace A and target track B
Between Manhattan distance；
(32) target track is divided into Manhattan where the smallest model trace by the value for comparing Manhattan distance
Group in, each target track and each model trace can only divide in a group, and according to this principle, all target tracks are divided
Into different model trace groups；
(33) target track in same group is clustered, forms seismic waveform flat distribution map, i.e. seismic facies map；
(4) seismic facies map of the whole district is construed to corresponding coal thickness section, achievees the purpose that predict coal seam thickness.
Preferably, the step (4), which has, includes the following steps:
(41) response relation between the attribute and coal seam thickness of seismic channel waveform is analyzed；The seismic channel of different coal thickness reflections
Waveform is inevitable different, and this phenomenon can be characterized often through certain waveform attributes features, such as the tuning effect of echo amplitude
It answers, echo amplitude is correlation within λ/4 0≤H < in coal seam thickness H, and λ is seismic wave wavelength；But single category
Property feature is not enough to completely show target zone information；
(42) relationship between the shape and seismic facies of seismic channel waveform is analyzed；Seismic channel waveform is the base of seismic data
Plinth is the general characteristic of earthquake information, can sufficiently be excavated using seismic channel waveform separation seismic facies technology and to be contained in waveform
Institute's difinite quality and quantitative information, by waveform separation seismic facies point analysis, waveform of different shapes will necessarily classify in different
Seismic facies, but seismic facies technology cannot direct forecasting coal thickness information；
(43) using seismic channel waveform as medium, the mapping relations between coal seam thickness and seismic facies map are established；Drilling is taken off
The seismic trace near well waveform shown carries out neural network learning as training sample and weaves seismic facies map, judges identical seismic facies area
Domain is identical coal seam thickness hierarchical region.
The utility model has the advantages that the method for waveform separation seismic facies technological prediction coal seam thickness provided by the invention, with the prior art
It compares, has the advantage that 1, compared with single attribute forecasting coal thickness information, the present invention has comprehensively considered each attribute in waveform
Feature improves the precision of prediction；2, the present invention discloses information to drill as constraint, and traditional seismic facies qualitative analysis is carefully drawn
For quantitative analysis, seismic facies information indirect is creatively construed to coal thickness information.
Detailed description of the invention
Fig. 1 is implementation flow chart of the invention；
Fig. 2 is three-dimensional wedge-shaped coal geology illustraton of model；
Fig. 3 is the part seismic channel waveform extracted from three-dimensional wedge-shaped coal geology model；
Fig. 4 is three-dimensional wedge-shaped coal geology model coal thickness plan view；
Fig. 5 is the coal thickness plan view predicted based on the present invention.
Specific embodiment
The present invention will be further explained with reference to the accompanying drawing.
A method of based on waveform separation seismic facies technological prediction coal seam thickness, include the following steps:
(1) according to the borehole data of the whole district, coal seam thickness is divided into N number of continuous coal thickness section；
(2) to n-th of coal thickness section, the most apparent R of selected characteristicn(drilling of selection is more, divides more smart for a drilling
Carefully), the seismic trace near well waveform of these drillings is extracted as model trace training sample；
(3) all model trace training samples of neural network learning are utilized, the seismic facies distributed intelligence of the whole district is obtained；Specific packet
Include following steps:
(31) all model traces of the whole district that step (2) are extracted are divided into more than two groupings, define coal seam thickness to be predicted
Position is target track, calculates the Manhattan distance between all target tracks and all model traces by road, that is, calculates all targets
The summation of sampling difference value between road waveform and each model trace waveform sampling point:
Wherein: A indicates model trace, AiIndicate i-th of wavelet of model trace A；B indicates target track B, BiIndicate target track B's
I-th of wavelet；Target track and model trace are seismic channel, and each seismic channel has G wavelet, and M indicates model trace A and target track B
Between Manhattan distance；The Manhattan distance of identical two model trace training samples is 0；
(32) target track is divided into Manhattan where the smallest model trace by the value for comparing Manhattan distance
Group in, each target track and each model trace can only divide in a group, and according to this principle, all target tracks are divided
Into different model trace groups；Classify according to similarity to all target tracks；
(33) target track in same group is clustered, forms the seismic waveform plane distribution with obvious geological Significance
Figure, i.e. seismic facies map；
(4) seismic facies map of the whole district is construed to corresponding coal thickness section, achievees the purpose that predict coal seam thickness.
Further explanation is made to the present invention below with reference to example.
Step 1: 100 Inline lines are arranged in the three-dimensional wedge-shaped coal geology total 1000m of model shown in Fig. 2；Through analyzing
Learn that coal seam is located at 400m, thickness change is 0~5m, density 1.41g/cm3, velocity of longitudinal wave 1820m/s.It is determined based on coal seam
When window, when window need comprising roof and bottom plate information.
Step 2: coal seam thickness 0m linear increment to 5m, be divided into 0-0.7,0.7-1.4,1.4-2.0,2.0-2.6,
24 samples are extracted as model trace, mark in the eight continuous coal thickness sections 2.6-3.2,3.2-3.8,3.8-4.4,4.4-5.0 altogether
Remember respective coal thickness information and extracts its waveform.
Step 3: by the Manhattan distance value in road computation model road and whole district's target track, with minimal distance principle to wave
Shape is divided into eight classes, obtains the plan view with eight class seismic facies, as shown in Figure 3.
Step 4: being eight coal thickness sections, last earthquake by these eighth types of seismic facies interpretations in conjunction with the classification standard of step 2
Phasor is converted into coal thickness information distributing plan, as shown in Figure 4, Figure 5.
The above is only a preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (3)
1. a kind of method based on waveform separation seismic facies technological prediction coal seam thickness, characterized by the following steps:
(1) according to the borehole data of the whole district, coal seam thickness is divided into N number of continuous coal thickness section；
(2) to n-th of coal thickness section, the most apparent R of selected characteristicnSeismic channel beside a drilling extracts model as model trace
Road waveform, using model trace waveform as training sample；
(3) all training samples of neural network learning are utilized, the seismic facies distributed intelligence of the whole district, the i.e. seismic facies of the whole district are obtained
Figure；
(4) seismic facies map of the whole district is construed to corresponding coal thickness section, achievees the purpose that predict coal seam thickness.
2. the method according to claim 1 based on waveform separation seismic facies technological prediction coal seam thickness, it is characterised in that:
The step (3), which has, to be included the following steps:
(31) all model traces of the whole district that step (2) are extracted are divided into more than two groupings, define coal seam thickness position to be predicted
For target track, the Manhattan distance between all target tracks and all model traces is calculated by road, that is, calculates all target track waves
The summation of sampling difference value between shape and each model trace waveform sampling point:
Wherein: A indicates model trace, AiIndicate i-th of wavelet of model trace A；B indicates target track B, BiIndicate the i-th of target track B
A wavelet；Target track and model trace are seismic channel, and each seismic channel has G wavelet, and M is indicated between model trace A and target track B
Manhattan distance；
(32) target track is divided into Manhattan apart from the group where the smallest model trace by the value for comparing Manhattan distance
Interior, each target track and each model trace can only divide in a group, and according to this principle, all target tracks are divided into not
In same model trace group；
(33) target track in same group is clustered, forms seismic waveform flat distribution map, i.e. seismic facies map.
3. the method according to claim 1 based on waveform separation seismic facies technological prediction coal seam thickness, it is characterised in that:
The step (4), which has, to be included the following steps:
(41) due to different coal thickness have different seismic channel wave characters, according to drilling disclose coal seam thickness information,
The response relation between seismic trace near well waveform and coal seam thickness is established, and seismic waveform by all wells is grouped；
(42) based on seismic channel waveform grouping braiding seismic facies map, therefore every kind of seismic facies represents one group of seismic channel waveform；
(43) using seismic channel waveform as medium, the mapping relations between coal seam thickness and seismic facies are established, seismic facies information is turned
Turn to coal seam thickness information.
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| CN112379442A (en) * | 2020-11-02 | 2021-02-19 | 中国石油天然气集团有限公司 | Seismic waveform classification method and device |
| CN112684497A (en) * | 2019-10-17 | 2021-04-20 | 中国石油天然气集团有限公司 | Seismic waveform clustering method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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Application publication date: 20190416 |