CN106875471A - Coal measures contains or water barrier Visualization Modeling method - Google Patents

Coal measures contains or water barrier Visualization Modeling method Download PDF

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CN106875471A
CN106875471A CN201710024724.3A CN201710024724A CN106875471A CN 106875471 A CN106875471 A CN 106875471A CN 201710024724 A CN201710024724 A CN 201710024724A CN 106875471 A CN106875471 A CN 106875471A
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data
water barrier
coal measures
water
layer
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CN106875471B (en
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魏久传
李立尧
柳慧敏
史永理
牛会功
张康
谢超
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Shandong University of Science and Technology
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    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects

Abstract

Contain the invention discloses a kind of coal measures or water barrier Visualization Modeling method, including:The preparation of Research foundation data;Corresponding FAULT MODEL is set up according to digitlization structural map and breakpoint data;Water-bearing layer is divided with water barrier, identification is corresponding to be contained or water proof bed boundary, is set up and is contained or water barrier Stratigraphic framework, and corresponding FEM layer model is set up in the method for Kriging regression;Research coal measures sedimentation geologic feature, sedimentary micro facies model is set up with the technology of Sequential Indicator Simulation combination sedimentary micro Distribution Characteristics;Based on phase control techniques with theory, the physical property model in water-bearing layer is set up according to the physical data collected with calculate;According to the final correlation model set up represent coal measures contain or water barrier Spatial Distribution Pattern.Present invention application modeling to containing or water barrier spatial carry out objective description, intuitively reaction contain or water barrier three-dimensional spatial distribution and aquifer water well feature, for coal measures contains or water barrier research provides the foundation of three-dimensional visualization.

Description

Coal measures contains or water barrier Visualization Modeling method
Technical field
The present invention relates to a kind of coal measures contain or water barrier three dimensions spread method, especially a kind of coal measures contain or water proof Layer Visualization Modeling method.
Background technology
The production of coal in China is at the forefront in the world with consumption position, and coal has very important meaning in energy-consuming at home Justice.With the increasing of the mining depth and intensity of mine, the generation of mine water disaster is more and more frequent.Investigate thoroughly the hydrogeology in colliery Condition, intuitively studies water-bearing layer distribution particularly important.
In traditional coal measures contains or water barrier is studied, the main two-dimentional geological map of application, with projective geometry, the technique of painting The principle of geometry and mineral deposits geometry, will contain or water barrier distribution projection is in plane, to containing or water barrier plane configuration with And genesis analysis are described.But, because complicated coal measures contains or water barrier distribution and architectonic influence, by two Dimension, static plane map be difficult to directly perceived, rational expression coal measures contain or water barrier spatial distribution, often occur due to three-dimensional Two-dimentional map and three dimensions geologic content difference caused by spatial distribution understanding deficiency.
In order to prevent mine water disaster, it is necessary to which effective research coal measures more directly perceived contains or water barrier, sets up three-dimensional geological mould Type have become research coal measures directly perceived contain or water barrier spatial effective means.GMS is that domestic conventional coal measures contains at present Or the three-dimensional hydrogeology modeling software of water barrier, it can set up three-dimensional formation entity according to borehole formation.But GMS is in practical application In, relatively it is short of with transitivity modeling aspect for construction modeling.Because construction causes in practical application with the shortcoming of physical property modeling In can not by tomography to containing or the influence of water barrier be indicated, the watery in water-bearing layer can not intuitively be expressed.
The thesis for the doctorate of Chinese Academy of Geological Sciences《North China Plain water-bearing layer anisotropism research-be with Shijiazhuang Luancheng County Example》, Jilin University's journal (geoscience version) 09 month 2011 phase of volume 41 the 5th, disclose horse honor etc.《Entropy weight coupled random The theoretical application in the research of water-bearing layer nonuniform reservoir》, two papers are disclosed with the heterogeneous synthesis in water-bearing layer Index quantification characterizes water-bearing layer synthesis anisotropism, and its calculation process mainly includes:(1) estimated using cloud-Markov model The infiltration coefficient of deposited samples;(2) distributed model of water-bearing layer sedimentary micro is simulated by Markov principles;(3) in this base On plinth, the infiltration coefficient and porosity distributed mode in improved sequential simulation technique construction water-bearing layer are utilized by Facies Control Modeling principle Type.
But, its technology announced also has following a few point defects, first, in open method to sedimentation feature decision Technology is simpler, is only analyzed only in accordance with cumulative relative frequency curve, for geologic feature it is complicated the characteristics of, the result of its analysis is not It is too accurate;Second, Markov principle microfacies modeling belong to stochastic modeling method, and Method of Stochastic has very big not true It is qualitative, only by mathematical computations carry out microfacies modeling can not real reactive deposition microfacies Distribution Pattern.3rd, it is used Technical method using construction modeling technique, it is impossible to embody structural feature to coal measures contain or water barrier control.
The content of the invention
The purpose of the present invention is to overcome above-mentioned existing more single modeling method and construction being modeled and base with physical property The shortcoming of plinth geological research, there is provided a kind of coal measures contains or water barrier Visualization Modeling method.
To achieve the above object, the present invention uses following technical proposals:
A kind of coal measures contains or water barrier Visualization Modeling method, comprises the following steps:
1) collect related Rock character drill hole, mud stone color, drill core, borehole coordinate, aperture absolute altitude, digitlization structural map, Corresponding data are arranged and sorted out by physical property and breakpoint data;
2) utilize step 1) in Rock character drill hole data, corresponding layering is set up at the interface of identification water-bearing layer and water barrier Data, when the thickness ratio of Different Strata is less than 1/10, then ignore relatively thin layer attribute;With reference to periphery borehole data set up every Water layer and the Stratigraphic framework in water-bearing layer;
3) utilize step 1) in breakpoint number according to this and digitlization structural map in tomography cross surface line, the number obtained with interpolation Word structural map is constraint, is set up by breakpoint data and the FAULT MODEL for constructing constraint diagram;
4) in step 3) FAULT MODEL set up after, construction FEM layer model set up before, to the resolution ratio of whole model And identification range laterally and longitudinally is specified, i.e., model carries out gridding;
5) utilize step 2) in individual-layer data, with step 3) and step 4) result is constraints, slotting using Ke Lijin The method of value, obtains initial construction FEM layer model, and the adjustment of construction aspect is carried out according to individual-layer data;
6) utilize step 1) in Rock character drill hole, mud stone color and drill core data, study coal measures sedimentation geology Feature, sedimentary micro facies model is set up with the technology of Sequential Indicator Simulation combination sedimentary micro Distribution Characteristics, using stochastic simulation with The method that deterministic simulation be combined with each other is so that analog result is closer to reality;
7) in step 5) FEM layer model set up on the basis of, according to step 1) in arrange physical data, with reference to corresponding Physical data used by log computation modeling, based on phase control techniques and theory and technology, sets up coal measures and contains or water barrier Physical property model.
The step 3) -7) in, the FAULT MODEL of foundation constructs FEM layer model, sedimentary micro facies model and coal measures contain or Water barrier physical property model is all set up using Petrel softwares.
Petrel is the reservoir geologic modeling software of Schlumberger's exploitation, and perfect construction modeling is the software The characteristics of, according to the breakpoint data in digitized structural map and each layering, using the method for Decided modelling, can set up Three-dimensional visualization FAULT MODEL, these tomographies can effectively control the distribution of formation fluid.
The step 7) in phase control techniques and theory be the further distributed simulation coal measures with sedimentary micro as constraints The physical property characteristic on stratum, sets up correlation model;The theoretical starting point is just to recognize that having differences property between different phases, is research Coal measures contain or water barrier anisotropism basis.
The step 1) in Rock character drill hole data be mainly and be analyzed according to log sheet, or directly observation drilling Rock core, and rock core lithology is divided, distinguish water-bearing layer rock type and water barrier rock type.
The step 3) in have the tomography cross surface line of breakpoint data genaration, the tomography of each substratum in digitlization structural map Cross surface line shows this tomography in this layer of upthrow and downthrow block of position;If there is the situation without tomography cross surface line, according to Breakpoint data control tomography, and the breakpoint of identical tomography is combined in the plane, draw out the tomography cross surface line of different layers position.
The diversity judgement for constructing line according to structural map goes out the property of tomography;Fault properties are judged according to regional stress situation, Reversed fault is normally not present under Regional tension stress condition, and under the conditions of extrusion stress, is with inverse punching or reversed fault then It is main.
The step 4) in the gridding of model refer to the grid that pre-established model is set, the resolution ratio according to grid is big It is small come setting model precision, the gridding of model is the foundation stone of geological model.
The step 5) in, the initial construction FEM layer model obtained by Kriging regression can be due to mechanical operational problem Be not inconsistent with actual geological conditions, some partially due to lack individual-layer data control cause that FEM layer model is overlapped so that generation Tectonic model start a leak;Need to smooth FEM layer model, it is consistent with actual geological condition.
The step 5) in Cokriging estimation be a kind of optimal unbiased estimation method, this method is used for stochastic simulation, Based on known variables, using variogram, treat the unknown-value estimated a little and make Best unbiased estimator, regionalized variable Z (x) Stochastic variable Z* (x) at x is represented with a linear combination:In formula, Z* (x)-wait to estimate a little gram In golden estimate;Z(xi)-wait estimates certain point of surrounding xiThe observation at place, i=1,2,3 ..., n;N is natural number;λi—xiPlace Weight coefficient, represents xiInfluence size of the point to valuation Z* (x).
The step 6) in, coal measures sedimentation feature mainly comprising to the depositional environment of coal measure strata, Sediment Source and Sedimentary micro is studied;The deposition of survey region is judged by the analysis of rock color index, extinct plants and animal, paleao-water depth restoring method Environment, rock color is judged as reducing environment more deeply, illustrates that the depth of water is deeper, occurs being adapted to deep water bioid paleontological fossil more Stone, and color is shallower judges oxygen-enriched environment, illustrates that the depth of water is shallower, occurs diving Skeletal paleontological fossil more;Analysis The composite type and content of heavy mineral indicate the heavy mineral contents such as source area parent rock property, garnet region high to indicate nearly material resource Area, all kinds of heavy mineral content low value regions are remote source area;Calmodulin binding domain CaM sedimentary facies background and other survey log datas, to target The sand thickness in region is divided;In distributed mutually research, well logging lithological profile and sandbody distribution figure are made full use of, recognize phase Mark, is differentiated, to sedimentary micro while marking off the Distribution Characteristics of the plane of sedimentary micro;The plane exhibition of sedimentary micro Cloth should not have jump phase phenomenon according to phase sequence gradation law;On the basis of sedimentary micro planar distribution, by sedimentary micro come about Beam contain or water barrier planar distribution form.
The step 6) in Sequential Indicator Simulation be sequential simulation instruction method, this method is relative to conventional sequence Passing through simulation can preferably process the original sample of various distribution modes, be conducive to the sedimentary micro and water-bearing layer sand body of discreteness Corresponding model is set up based on distributed data;
Sequential Indicator Simulation is comprised the following steps that:
First, it is that initial data is transformed to indicator variable;Sedimentary micro data belong to discreteness distributed data, such number According to threshold value be all discreteness data;Mining area coal measures superstratum is braided channel depositional environment, this parfacies the inside bag It is respectively that mid channel bar deposit, braided channel lag deposit, river course overflow deposition containing three kinds of sedimentary micros, phase data is 1,2,3, institute Some phase datas are in these three values, then threshold value is exactly these three numbers;Corresponding indicator function be exactly Z (u, 1);Z(u,2);Z(u,3);
Secondly, the original phase data of instructionization is carried out stochastic simulation using sequential simulation method;Sequential simulation method Comprise the following steps that:
Mining area grid turns to N number of mesh node, wherein N number of stochastic variable Zi(i=1,2 ..., conditional joint probability n) Model:
FN[Z1,Z2,…,Zn/ (n)]=Prob { Zi≤zi, i=1,2 ..., N/ (n) }
Its conditional cumulative distribution function is understood by above formula:
Z1-Prob{Z1≤z1/(n)};
Z2-Prob{Z2≤z2/(n+1)}
ZN-Prob{ZN≤zN/(n+N-1)};
Wherein, i=1,2 ..., n, n are natural number, and N is positive integer;
According to the conditional probability cumulative distribution function of all kinds of grid variables, then sequential simulation algorithm realizes that step is as follows:
(1) sample is extracted under conditions of the known n initial data in the conditional cumulative distribution function of variable, is obtained First sample is set to z1
(2) by z1Initial data concentration is added to, current initial data is changed into (n+1)=(n) ∪ { Z1=z1, new Under conditions of conditional cumulative distribution function in extract a sample, obtain second sample and be set to z2
(3) repeat step (2), obtain sample z3,…,zN, this group of sample is exactly an analog result;
(4) repeat step (1)-step (3), repeats n times, obtains n such analog result.
Finally, mid channel bar deposit, braided channel lag deposit, river course overflow deposition respectively from the variation letter of different instructionizations Several classes of type, mid channel bar deposit application standard ball-type variogram model:
Wherein, a represents the influence size of variable to become journey.Become journey and represent that its continuity is good less than initial data, randomness It is small;More than initial data, then randomness is big.
Braided channel lag deposit exponential is deteriorated and contains exponential model:
Wherein, C0It is that block gold constant represents Spatial Variability size, a is to become the influence size that journey represents variable, and C is arch Height, represents variable-difference size.Main source direction sets C0Tend to 0, become journey a and be less than initial data;Secondary source direction sets C0Greatly In main source direction, become journey a and be more than initial data;
Deposition overflow from discontinuous form variogram nugget effect model in river course:
C0It is that block gold constant represents Spatial Variability size;C is sagitta, represents variable-difference size, the bigger expression of sagitta Difference is bigger.C+C0Referred to as base station value, characterizes variable overall variability size spatially.
Simulated by the variogram of different sedimentary micros, reach the target that certainty is combined with stochastic model, made Obtain analog result and be more nearly reality.
The step 1) in physical data mainly including water-bearing layer porosity data, the physical data pass through core sample Product test is obtained;According to step 7) described in porosity correction is carried out according to the test of corresponding log data combination core sample, Regression formula is obtained, the physical data with reference to used by corresponding log computation modeling;Step 7) middle modeling physical property used Data are primarily referred to as the porosity value calculated according to regression formula.
The present invention uses the various advanced technologies of Petrel software applications:Such as construct modeling technique, three-dimensional network technology, The technology that Decided modelling be combined with each other with randomness modeling.Especially for the stratum of stronger anisotropism, randomness modeling can To make up the inferior position of Decided modelling well.Using the foundation of Petrel softwares based on phase control techniques and theoretical and random-determination Property Modeling Theory three-dimensional visualization geological model can intuitively react containing or water barrier three-dimensional spatial distribution and water-bearing layer Watery, the foundation of three-dimensional visualization is provided for mine water disaster danger Journal of Sex Research.
The beneficial effects of the invention are as follows:
The present invention has following technique effect relative to the paper in background technology:
Firstth, the present invention for coal measures contain or water barrier sedimentation feature it is more complicated the characteristics of, by Sedimentary Environment Discrimination Technology, provenance analy~sis technology, sedimentary micro spread portray technology and analyze sedimentation feature respectively, more accurately describe coal System contains or water barrier spatial trend.
Secondth, the present invention application technical method that is combined with Decided modelling of stochastic modeling, i.e. Sequential Indicator Simulation and The technology that sedimentary micro Distribution Characteristics are combined so that model more conforms to reality.
3rd, the present invention emphasizes that combine digitlization structural map, breakpoint data sets up other moulds on the basis of tectonic model Type, fully demonstrated structural feature to coal measures contain or water barrier control.
The present invention petroleum geology is modeled in construction modeling etc. technology, be applied to coal measures water-bearing layer modeling in.Coal measures Water-bearing layer is complicated due to sedimentary structure, and depositional environment is various, and variation of lithological is frequent in causing coal measures superstratum;Due to coal seam with The depositional environment difference of superstratum, causes the anisotropism on stratum very strong;The basic data of coal measure strata is stored up relative to oil Layer will lack.Therefore, coal measures water-bearing layer three-dimensional geological modeling difficulty is significantly greater than petroleum reservoir three-dimensional geological modeling.
It is of the invention compared with existing theoretical and other prior arts, phase control techniques and theory are introduced into coal measures and are contained or water proof The research of layer;Using Petrel softwares, highlight the advantage of its structural fault modeling, and tomography be control water-bearing layer it is important because Element;The spread for applying the means that stochastic modeling is combined with Decided modelling, i.e. Sequential Indicator Simulation combination sedimentary micro is special Levy and establish sedimentary micro facies model, based on phase control techniques with theory, the physical property in water-bearing layer is set up according to corresponding physical characterization data Model, makes model more closing to reality, more conform to coal measures contain or water barrier aeolotropic characteristics.
Modeling means of the present invention are point, line, surface, body modeling means all linked with one another, incremental.With The powerful construction modeling ability basis of Petrel softwares, controls the trend of tomography and inclines according to digitlization structural map and breakpoint data To;The FEM layer model on stratum is set up with Cokriging estimation;Set up heavy with the Distribution Characteristics of Sequential Indicator Simulation combination sedimentary micro Product microfacies model;The spread of reservoir properties and sand body is controlled with sedimentary micro, corresponding model is set up, more accurately description Coal measures contains or water barrier spatial trend, makes model credibility higher.
Brief description of the drawings
Fig. 1 be coal measures of the present invention contain or water barrier Visualization Modeling method flow chart;
Fig. 2 is the H mining areas tomography graphics of foundation in one embodiment of the invention;
Fig. 3 is the H mining areas grid system figure of foundation in one embodiment of the invention;
Fig. 4 is the H mining areas construction FEM layer model of foundation in one embodiment of the invention;
Fig. 5 is the H mining areas sedimentary micro plan of foundation in one embodiment of the invention;
Fig. 6 is the sedimentary micro facies model stochastic simulation result of foundation in one embodiment of the invention;
Fig. 7 is the H mining areas sedimentary micro facies model of foundation in one embodiment of the invention;
Fig. 8 is the H mining areas physical property model of foundation in one embodiment of the invention;
Fig. 9 is the H mining areas rock porosity and interval transit time regression figure of foundation in one embodiment of the invention.
Specific embodiment
The present invention is further described with reference to the accompanying drawings and examples.
Structure, ratio, size depicted in this specification institute accompanying drawings etc., are only used to coordinate interior disclosed in specification Hold, so that those skilled in the art understands and reads, be not limited to enforceable qualifications of the invention, therefore do not have skill Essential meaning in art, the modification of any structure, the change of proportionate relationship or the adjustment of size can be produced the present invention is not influenceed Under raw effect and the purpose to be reached, all should still fall in the range of disclosed technology contents are obtained and can covered. Meanwhile, in this specification it is cited such as " on ", D score, "left", "right", the term of " centre " and " ", be merely convenient to Narration understands, and be not used to limit enforceable scope of the invention, and its relativeness is altered or modified, without substantive change Under technology contents, when being also considered as enforceable category of the invention.
Be not sufficiently reacted out construction and physical property modeling in the modeling of existing water-bearing layer and coal measures is aqueous (every) to improve The problem of the spatial form of layer.
Therefore, the invention provides one kind based on phase control techniques and theory, the coal measures using Petrel softwares contains or water proof Layer modeling method, as shown in figure 1, comprising the following steps:
1) data such as related Rock character drill hole, borehole coordinate, aperture absolute altitude, digitlization structural map, physical property and breakpoint are collected.
Rock character drill hole data are mainly and are analyzed according to log sheet, it is also possible to directly observe drill core.And it is right Rock core lithology carries out classifying rationally, distinguishes water-bearing layer rock type and water barrier rock type.
Table 1 is borehole coordinate and aperture absolute altitude.Borehole coordinate refers to the geodetic coordinates of drilling, X-coordinate and Y-coordinate;Aperture The height above sea level of absolute altitude finger-hole mouthful;Digitlization structural map refers to the coal measure strata contour that interpolation is obtained, and tomography is included in structural map The information such as cross surface line;Table 2 is breakpoint data.Breakpoint data refer to breakpoint depth of the tomography in each substratum.
2) formation properties are divided into water barrier and water-bearing layer by the rock property according to drilling, recognize water-bearing layer and water barrier Interface, set up corresponding individual-layer data, and formation properties are adjusted according to thickness in monolayer and periphery Rock character drill hole.
In water-bearing layer and water barrier divide, the rock property of drilling is the Main Basiss for dividing, such as big set water proof property It is mingled with the aqueous property rock of thin layer in rock, then it is assumed that the aqueous property lithological effects are little.Can unite and be divided into water barrier.
Lithology surface of discontinuity, plane of unconformity, granularity conversion face can as containing or water proof bed boundary identification mark.When aqueous When property rock thickness/water proof property rock thickness is less than 1/10, then it is assumed that the aqueous property rock influences for formation properties Less.
3) figure (2) is using step 1) in breakpoint number according to this and the tomography mould set up of the digitlization structural map that obtains of interpolation Type.
The tomography cross surface line of breakpoint data genaration is had in digitlization structural map, the tomography cross surface line of each substratum shows this Bar tomography is in this layer of upthrow and downthrow block of position.If there is the situation without tomography cross surface line, can be according to breakpoint data Control tomography, the breakpoint of identical tomography is combined in the plane, draws out the tomography cross surface line of different layers position.
The diversity judgement for constructing line according to structural map goes out the property of tomography;Fault properties are judged according to regional stress situation, Reversed fault is normally not present under Regional tension stress condition, and under the conditions of extrusion stress, is with inverse punching or reversed fault then It is main.
4) after FAULT MODEL foundation, construction FEM layer model is set up in the past, it is necessary to the resolution ratio and transverse direction of whole model Identification range with longitudinal direction is specified that is, model carries out gridding.The height of grid resolution determines the essence of generation model Degree.
The gridding of model is the foundation stone of geological model, and the resolution ratio of grid directly affects the precision of model generation. Figure (3) considers the operational capability of geologic feature and the tomography additional computer of distribution in H mining areas, establishes the grid system in H mining areas System.
5) figure (4) is using step 1), 2) in individual-layer data, Rock character drill hole data, with step 3) step (4) result is Constraints, using Kriging regression, obtains initial construction FEM layer model, and the tune of construction aspect is carried out according to individual-layer data It is whole.
The initial construction FEM layer model obtained by Kriging regression can due to mechanical operational problem with actual geology bar Part is not inconsistent, some partially due to lack individual-layer data control cause that FEM layer model is overlapped so that generation tectonic model go out Existing leak.Need to smooth FEM layer model, it is consistent with actual geological condition.
6) step 5) in Cokriging estimation be a kind of optimal unbiased estimation method, this method can be used for random mould Intend, based on known variables, using variogram, treat the unknown-value estimated a little and make Best unbiased estimator.Regionalized variable Z The stochastic variable Z of (x) at x*X () can be represented with a linear combination:Z in formula*(x)-to be estimated point Kriging estimation value;Z(xi)-wait estimates certain point of surrounding xiThe observation at place, i=1,2,3 ..., n;λi—xiThe weighting system at place Number, represents xiPoint is to valuation Z*The influence size of (x).
7) figure (5) is using step 1) in the data, research H mining areas coal measure strata such as lithology, mud stone color, drill core The sedimentation feature of basal water, figure (6) is the H ore deposits set up with the Distribution Characteristics of Sequential Indicator Simulation combination sedimentary micro The sedimentary micro facies model of area's coal measure strata basal water.The method being be combined with each other using stochastic simulation and deterministic simulation is caused Analog result is closer to reality.
Coal measures sedimentation feature is mainly ground comprising the depositional environment to coal measure strata, Sediment Source and sedimentary micro Study carefully.The depositional environment of survey region is judged by methods such as the analysis of rock color index, extinct plants and animal, paleao-water depth reduction.Material resource side To judgement be next step sand body and microfacies spread basis, analyze heavy mineral composite type and content can directly refer to Show source area parent rock property, the change according to its content can also judge source direction, be the spread of the sedimentary micro of next step Lay the foundation.Calmodulin binding domain CaM sedimentary facies background and other survey log datas, the sand thickness to target area are divided.Heavy On the basis of product microfacies distribution pattern, H mining areas well logging lithological profile and sandbody distribution figure are made full use of, facies marker is recognized, to H ore deposits Area's sedimentary micro is differentiated, while marking off the Distribution Characteristics of the plane of sedimentary micro.The planar distribution of sedimentary micro is pressed Photograph sequence gradation law, should not there is jump phase phenomenon.H mining areas coal measures is constrained by the sedimentary micro in H mining areas to contain or water barrier Planar distribution form.
The braided channel depositional environment of H mining areas basal water is divided into three kinds of sedimentary micros by figure (5):Mid channel bar deposit, Braided channel lag deposit, river course are overflowed deposition.The phase data of three types is expressed as 1,2,3.The deposition for scheming (6) is micro- Different types of phase data is carried out phase model the result after Sequential Indicator Simulation, the sedimentary micro mould in the water-bearing layer in figure (7) Type is with step 7) in the result of sedimentary micro planar distribution enter row constraint, reach and combine deposition ground using Sequential Indicator Simulation Method The effect of matter research.
8) step 7) in Sequential Indicator Simulation be sequential simulation instruction method, this method is sequential relative to conventional Simulation can preferably process the original sample of various distribution modes, be conducive to the sedimentary micro and water-bearing layer sand body point of discreteness Corresponding model is set up based on cloth data.
Sequential Indicator Simulation is comprised the following steps that:
First, it is that initial data is transformed to indicator variable.Sedimentary micro data belong to discreteness distributed data, such number According to threshold value can be all discreteness data.H mining areas coal measures superstratum is braided channel depositional environment, in this parfacies Bread contains three kinds of sedimentary micros, is respectively that mid channel bar deposit, braided channel lag deposit, river course overflow deposition, phase data is 1,2, 3, all of phase data is in these three values, then threshold value is exactly these three numbers.Corresponding indicator function is exactly Z (u,1);Z(u,2);Z(u,3).
The original phase data of instructionization is finally carried out stochastic simulation using sequential simulation method.The tool of sequential simulation method Body step is as follows:
H mining areas grid turns to 362987 mesh nodes, wherein 362987 stochastic variable Zi(i=1,2 ..., 362987) conditional joint probabilistic model:
F362987[Z1,Z2,…,Z362987/ (362987)]=Prob { Zi≤zi,i-1,2,…,362987/(362987)}
Its conditional cumulative distribution function is understood by above formula:
Z1-Prob{Z1≤z1/(362987)};
Z2-Prob{Z2≤z2/(362987+1)}
Z362987-Prob{Z362987≤z362987/(362987+362987-1)}
According to the conditional probability cumulative distribution function of all kinds of grid variables, then sequential simulation algorithm realizes that step is as follows:
(1) sample is extracted under conditions of known 362987 initial data in the conditional cumulative distribution function of variable This, obtains first sample and is set to z1
(2) by z1Initial data concentration is added to, current initial data is changed into (362987+1)=(362987) ∪ { Z1 =z1, a sample is extracted in conditional cumulative distribution function in a new condition, obtain second sample and be set to z2
(3) repeat step (2), obtain sample z3,…,z362987, this group of sample is exactly an analog result;
(4) repeat step (1)-step (3), repeats 362987 times, obtains 362987 such analog results.
Finally, mid channel bar deposit, braided channel lag deposit, river course overflow deposition respectively from different variogram classes Type, mid channel bar deposit application standard ball-type variogram model:
Wherein, become journey a and take 2, less than initial data, embody continuity good.
Braided channel lag deposit exponential is deteriorated and contains exponential model:
Wherein, main source direction sets C0Tend to 0, become journey a and be less than 3;Secondary source direction sets C0More than main source direction, Become journey a and be more than 3;
Deposition overflow from discontinuous form variogram nugget effect model in river course:
C0Block gold constant takes 0.21;C sagitta takes 0.94;Obtain C+C0Base station value is 1.15.
Simulated by the variogram of different sedimentary micros, reach the target that certainty is combined with stochastic model, made Obtain analog result and be more nearly reality.
9) figure (8) is in step 5) FEM layer model set up on the basis of, according to step 1) in arrange drilling physical data, Based on phase control techniques with theory, with reference to Kriging regression method, set up coal measures contain or water barrier physical property model.
Water-bearing layer lithology is mainly middle kern stone, also comprising most packsand and a small amount of siltstone, due to ground The difference of layer lithology causes the non-homogeneous degree of physical property stronger.People are contributed to recognize water-bearing layer using the method for stochastic simulation Complexity, the preferably discreteness of reflection reservoir property, anisotropism is characterized greater advantage.
Physical data mainly includes the porosity and permeability data in water-bearing layer.Physical data can be surveyed by core sample Examination is obtained, and porosity correction is carried out further according to log data combination laboratory experiment, and corresponding physical property number is obtained according to log data According to.The spread border of sand body is the border of physical property modeling in plane.
In order to preferably study the physical property in coal measures water-bearing layer, the present invention collects the physical property experiment data of drill core, schemes (9) According to physical data and the method for acoustic travel time logging curve comparison, dependency relation is set up, obtain regression formula:
Y=1.3222x-1.2577, wherein coefficient correlation (R2) it is 0.7601, coefficient correlation is stronger.Surveyed with interval transit time Based on well data, the physical property distribution characteristics of all drilling controls in research area is set up.According to physical property planar characteristics of distribution, with plane Upper sedimentary micro spread enters row constraint, finally gives the physical property model in water-bearing layer.
It is of the invention compared with existing theory and technology, by phase control techniques and theoretical introduce that coal measures contains or water barrier grinds Study carefully;Using Petrel softwares, the advantage of its structural fault modeling is highlighted, and tomography is the key factor for controlling water-bearing layer;Should FEM layer model is established with the means that Decided modelling is combined with stochastic modeling, based on phase control techniques with theory, root The physical property model in water-bearing layer is set up according to corresponding physical characterization data, makes model more closing to reality, more conformed to coal measures and contain or water proof The aeolotropic characteristics of layer.
Although above-mentioned be described with reference to accompanying drawing to specific embodiment of the invention, not to present invention protection model The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not Need the various modifications made by paying creative work or deformation still within protection scope of the present invention.

Claims (10)

1. a kind of coal measures contains or water barrier Visualization Modeling method, it is characterized in that, comprise the following steps:
1) related Rock character drill hole, mud stone color, drill core, borehole coordinate, aperture absolute altitude, digitlization structural map, physical property are collected With breakpoint data, corresponding data are arranged and sorted out;
2) utilize step 1) in Rock character drill hole data, the interface of identification water-bearing layer and water barrier sets up corresponding individual-layer data, When Different Strata thickness ratio is less than 1/10, then ignore relatively relatively thin layer attribute;Water barrier is set up with reference to periphery borehole data With the Stratigraphic framework in water-bearing layer;
3) utilize step 1) in breakpoint number according to this and digitlization structural map in tomography cross surface line, the digitlization obtained with interpolation Structural map is constraint, is set up by breakpoint data and the FAULT MODEL for constructing constraint diagram;
4) in step 3) FAULT MODEL set up after, construction FEM layer model set up before, to the resolution ratio and horizontal stroke of whole model Specified that is, model carries out gridding to the identification range with longitudinal direction;
5) utilize step 2) in individual-layer data, with step 3) and step 4) result is constraints, using Kriging regression Method, obtains initial construction FEM layer model, and the adjustment of construction aspect is carried out according to individual-layer data;
6) utilize step 1) in Rock character drill hole, mud stone color and drill core data, study coal measures sedimentation geologic feature, Sedimentary micro facies model is set up with the Distribution Characteristics of Sequential Indicator Simulation combination sedimentary micro, using stochastic simulation and deterministic simulation The method be combineding with each other is so that analog result is closer to reality;
Comprise the following steps that:
First, it is that initial data is transformed to indicator variable;Sedimentary micro data belong to discreteness distributed data.On the coal measures of mining area Stratum is covered for braided channel depositional environment, and this parfacies the inside includes three kinds of sedimentary micros, is respectively mid channel bar deposit, braided channel Lag deposit, river course are overflowed deposition, and phase data is 1,2,3, and all of phase data is in these three values, then threshold Value is exactly these three numbers;Corresponding indicator function is exactly Z (u, 1);Z(u,2);Z(u,3);
Secondly, the original phase data of instructionization is carried out stochastic simulation using sequential simulation method;
Finally, mid channel bar deposit, braided channel lag deposit, river course overflow deposition respectively with the variogram class of different instructionizations Type, mid channel bar deposit application standard ball-type variogram model;Braided channel lag deposit exponential is deteriorated and contains exponential model;River course Deposition of overflowing selects discontinuous form variogram nugget effect model;
Simulated by the variogram of different sedimentary micro instructionizations, reach the target that certainty is combined with stochastic model, So that analog result is more nearly reality;
7) in step 5) FEM layer model set up on the basis of, according to step 1) in arrange physical data, with reference to corresponding well logging Physical data used by curve computation modeling, by phase control techniques with theory based on, set up coal measures contain or water barrier physical property mould Type.
2. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 3)- 7) in, the FAULT MODEL of foundation, construction FEM layer model, sedimentary micro facies model and coal measures contain or water barrier physical property model be all should Set up with Petrel softwares.
3. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 7) in Phase control techniques and theory be the physical property characteristic of further distributed simulation coal measure strata with sedimentary micro as constraints, set up Correlation model.
4. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 1) in Rock character drill hole data are mainly and are analyzed according to log sheet, or directly observe drill core, and rock core lithology is entered Row is divided, and distinguishes water-bearing layer rock type and water barrier rock type.
5. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 3) in The tomography cross surface line of breakpoint data genaration is had in digitlization structural map, the tomography cross surface line of each substratum shows that this tomography exists This layer of upthrow and downthrow block of position;If there is the situation without tomography cross surface line, tomography is controlled according to breakpoint data, by phase Combined in the plane with the breakpoint of tomography, draw out the tomography cross surface line of different layers position;
The diversity judgement for constructing line according to structural map goes out the property of tomography;Fault properties are judged according to regional stress situation, in area Reversed fault is occurred without under the conditions of the tensile stress of domain, and under the conditions of extrusion stress, then based on reversed fault.
6. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 4) in Model gridding be geological model foundation stone, the resolution ratio of grid directly affects the precision of model generation.
7. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 5) in Cokriging estimation is a kind of optimal unbiased estimation method, and this method is used for stochastic simulation, based on known variables, using change Difference function, treats the unknown-value estimated a little and makes Best unbiased estimator, and stochastic variable Z* (x) of regionalized variable Z (x) at x is used One linear combination is represented:In formula, Z* (x)-wait to estimate Kriging estimation value a little;Z(xi)-wait is estimated Certain point x around pointiThe observation at place, i=1,2,3 ..., n;N is natural number;λi—xiThe weight coefficient at place, represents xiPoint is to estimating The influence size of value Z* (x).
8. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 5) In, the initial construction FEM layer model obtained by Kriging regression can be due to mechanical operational problem with actual geological conditions not Symbol, some partially due to lack individual-layer data control cause that FEM layer model is overlapped so that generation tectonic model occur leakage Hole;Need to smooth FEM layer model, it is consistent with actual geological condition.
9. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 6) In, sequential simulation method is comprised the following steps that:
Mining area grid turns to N number of mesh node, wherein N number of stochastic variable Zi(i=1,2 ..., conditional joint probabilistic model n):
FN[Z1,Z2,…,Zn/ (n)]=Prob { Zi≤zi, i=1,2 ..., N/ (n) }
Its conditional cumulative distribution function is understood by above formula:
Z1-Prob{Z1≤z1/(n)};
Z2-Prob{Z2≤z2/(n+1)}
ZN-Prob{ZN≤zN/(n+N-1)};
Wherein, i=1,2 ..., n, n are natural number, and N is positive integer;
Conditional probability cumulative distribution function according to grid variable, then sequential simulation algorithm realize that step is as follows:
(1) sample is extracted under conditions of the known n initial data in the conditional cumulative distribution function of variable, first is obtained Individual sample is set to z1
(2) by z1Initial data concentration is added to, current initial data is changed into (n+1)=(n) ∪ { Z1=z1, in new bar A sample is extracted in conditional cumulative distribution function under part, second sample is obtained and is set to z2
(3) repeat step (2), obtain sample z3,…,zN, this group of sample is exactly an analog result;
(4) repeat step (1)-step (3), repeats n times, obtains n such analog result.
10. coal measures as claimed in claim 1 contains or water barrier Visualization Modeling method, it is characterized in that, the step 6) In,
Standard ball-type variogram model is:
Z ( u ) 0 u = 0 3 2 &CenterDot; u a - 1 2 &CenterDot; u 3 a 3 0 < u &le; a 1 u > a
Wherein, a represents the influence size of variable to become journey, becomes journey and represents that its continuity is good less than initial data, and randomness is small; More than initial data, then randomness is big;
Index is deteriorated:
Z ( u ) = C 0 + C ( 1 - e - u a )
Wherein, C0It is that block gold constant represents Spatial Variability size, a is to become the influence size that journey represents variable, and C is sagitta, is represented Variable-difference size;Main source direction sets C0Tend to 0, become journey a and be less than initial data;Secondary source direction sets C0More than principal goods Source direction, becomes journey a and is more than initial data;
Discontinuous form variogram nugget effect model is:
Z ( u ) = 0 u = 0 C 0 + C u > 0
C0It is that block gold constant represents Spatial Variability size;C is sagitta, represents variable-difference size, and sagitta is bigger to represent difference more Greatly;C+C0Referred to as base station value, characterizes variable overall variability size spatially.
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108196312A (en) * 2018-01-16 2018-06-22 中国地质科学院岩溶地质研究所 The recognition methods of water-bearing layer and water barrier in a kind of hydrogeology
CN108563832A (en) * 2018-03-19 2018-09-21 核工业北京化工冶金研究院 A kind of lithofacies physical property analogy method for ground-dipping uranium extraction flow field
CN108693572A (en) * 2018-03-12 2018-10-23 太原理工大学 A kind of ground stress field inversion method based on three-dimensional modeling
CN109783944A (en) * 2019-01-21 2019-05-21 中国石油大学(北京) Three-dimensional Flow model of element method for building up, device and equipment
CN110531413A (en) * 2019-08-21 2019-12-03 中国矿业大学 A kind of advanced Visualization Modeling method of craven fault
CN110702632A (en) * 2019-09-29 2020-01-17 核工业北京地质研究院 Three-dimensional modeling method for hyperspectral information of deep rock and ore
CN110910499A (en) * 2019-11-22 2020-03-24 中山大学 Method and device for constructing geological environment carrier fault model based on Revit software
CN112505754A (en) * 2020-10-29 2021-03-16 中国石油集团工程咨询有限责任公司 Method for collaborative partitioning sedimentary microfacies by well-seismic based on high-precision sequence grid model

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103514630A (en) * 2013-10-16 2014-01-15 北京石油化工学院 Fault structure three-dimensional modeling method
CN104331533A (en) * 2014-09-18 2015-02-04 山东科技大学 Poor-sealing drill hole mining response value simulation method
CN105427376A (en) * 2015-10-28 2016-03-23 中国矿业大学(北京) Three-dimensional dynamic visualization method of water inrush of coal seam roof

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103514630A (en) * 2013-10-16 2014-01-15 北京石油化工学院 Fault structure three-dimensional modeling method
CN104331533A (en) * 2014-09-18 2015-02-04 山东科技大学 Poor-sealing drill hole mining response value simulation method
CN105427376A (en) * 2015-10-28 2016-03-23 中国矿业大学(北京) Three-dimensional dynamic visualization method of water inrush of coal seam roof

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
USMAN M, KLIAZOVICH D, GRANELLI F, ET AL: "Numerical modeling of contaminant transport resulting from dissolution of a coal-tar pool in an experimental aquifer", 《HYDROGEOLOGY JOURNAL》 *
尹会永, 魏久传, 郭建斌,等: "高承压含水层上煤层安全开采技术探讨", 《中国矿业》 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108196312A (en) * 2018-01-16 2018-06-22 中国地质科学院岩溶地质研究所 The recognition methods of water-bearing layer and water barrier in a kind of hydrogeology
CN108196312B (en) * 2018-01-16 2019-11-19 中国地质科学院岩溶地质研究所 It is a kind of hydrogeology in water-bearing layer and water barrier recognition methods
CN108693572A (en) * 2018-03-12 2018-10-23 太原理工大学 A kind of ground stress field inversion method based on three-dimensional modeling
CN108693572B (en) * 2018-03-12 2020-07-03 太原理工大学 Geostress field inversion method based on three-dimensional modeling
CN108563832A (en) * 2018-03-19 2018-09-21 核工业北京化工冶金研究院 A kind of lithofacies physical property analogy method for ground-dipping uranium extraction flow field
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CN110531413A (en) * 2019-08-21 2019-12-03 中国矿业大学 A kind of advanced Visualization Modeling method of craven fault
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