CN106772587A - Seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging - Google Patents
Seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging Download PDFInfo
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Abstract
The invention discloses the seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging, including step：Subdivision is carried out to modeling grid using equal proportion grid cutting algorithm in destination layer, several planes are formed, as the target location that elastic parameter is modeled；Well logging elastic parameter, Seismic Attribute Parameters and deposition phase information are extracted respectively as modeling data, master variable is selected, and select first and second collaboration variable；Using gained master variable, the first collaboration variable and the second collaboration variable as calculating parameter, interpolation calculation is carried out using same position multiphase collocating kriging method, obtain the parameter value of all points to be estimated in plane as multisource data fusion modeling result；Generalizing processing is carried out using anisotropic diffusion, the seismic elastic parameter modeling result after generalizing processing is obtained.The present invention can eliminate calculating abnormity point and border noise, improve the precision of multi information parametric synthesis modeling and incorporated more real geological informations, with preferable applicability.
Description
Technical field
The present invention relates to a kind of seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging, belong to ground
The technical field of parameter model in ball physics.
Background technology
Seismic elastic parameter modeling is a process for multiple information synthesis analysis, under complicated geological environment, keeps ripple
The stability and high accuracy of run-up mould are extremely difficult.Due to whole geological system change be subject to natural environment, layer position buried depth and
The influence of soil and depth of stratum, while architectonic complexity can cause rate pattern that great anisotropism and not is presented
Certainty.Therefore need it is a kind of can comprehensively various geological conditions modeling method come show this anisotropism and reduction not
Certainty.Fine seismic inversion or depth migration result is obtained, the introductory die that wave speed of the earthquake modeling builds is also depended on
Type.Although linear inversion method can utilize the well logging information at well location to build suitable boundary condition, make itself and earthquake information
It is combined, but the accuracy of initial model can largely influence the precision of inverting.Seimic travel time velocity analysis method
It is restrained by the continuous modification to initial model, but it is poor to the adaptability of strong cross directional variations, and in calculating process
Easily produce larger accumulated error.
Marine carbonate rock region complex geologic conditions, anisotropism is strong, and the log data as master variable is again relatively dilute
Less, in order to improve elastic parameter modeling accuracy and reduce uncertain, it is necessary to be added effectively about in limited observation data
Beam condition.The getable constraints of survey region institute is often Seismic Attribute Parameters and deposition phase information, and these information are all
The variation characteristic of master variable can be to a certain extent reacted, therefore need to choose suitable property parameters with constraint modeling process.Base
In collocating kriging elastic parameter modeling method in addition to master variable, while used a secondary variables as constraints,
The effective expression prior information of geological conditions, reduces the uncertainty of modeling.Due to using obtained by cokriging
The estimate Integrative expression parameter value of surrounding sampling point, sampling point weight coefficient not only has with master variable space covariance function
Close, also cooperateed with the constraint control of variable by a kind of, therefore improve the stability of modeling to a certain extent.Obviously, when after
During continuous increase constraint, the cokriging with multiple secondary variables can further improve the accuracy of modeling, but constraint
The extraction of condition still has many difficulties with use.
Row constraint is entered to different master variables using conventional seismic attribute parameter, based on collocating kriging method obtained by build
Often without clearly border and there are a large amount of noises in mould result, it is impossible to the distribution feelings of actual response underground medium elastic parameter
Condition, particularly in the region that media property lateral continuity is poor, it is difficult to differentiate real local dip and produced by calculating
Raw slight error, difficulty is caused to follow-up inverting and related work.
The content of the invention
The technical problems to be solved by the invention are to overcome the deficiencies in the prior art, there is provided one kind is based on being assisted with position multiphase
With the seismic elastic parameter Facies Control Modeling method of Ke Lijin, solve based on the modeling result obtained by collocating kriging method often
Without clearly border and there are a large amount of noises, it is impossible to the problem of the distribution situation of actual response underground medium elastic parameter,
Calculating abnormity point and border noise can be eliminated, the precision of multi information parametric synthesis modeling is improve and has been incorporated more real
Geological information, with preferable applicability.
It is of the invention specific using following technical scheme solution above-mentioned technical problem：
Seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging, comprises the following steps：
Step S1, in destination layer using equal proportion grid cutting algorithm to modeling grid carry out subdivision, in same ratio
Several planes are formed on the position of thickness, as the target location that elastic parameter is modeled；
Step S2, the well logging elastic parameter of known point is extracted in plane respectively and the seismic properties of whole region are distributed in
Parameter and deposition phase information select the low-frequency parameter background value of log parameter as master variable, choosing as modeling data
Seismic Attribute Parameters are selected as the first collaboration variable, and generalizing processing is carried out to deposition phase information using anisotropic diffusion and obtained
To sedimentary facies mathematical model as the second collaboration variable；
Step S3, using step S2 gained master variable, the first collaboration variable and the second collaboration variable as calculating parameter, utilize
Interpolation calculation is carried out with position multiphase collocating kriging method, the parameter value of all points to be estimated in plane is obtained as elastic parameter
Multisource data fusion modeling result；
Step S4, using anisotropic diffusion to step S3 gained multisource data fusion modeling result carry out generalization place
Reason, obtains the seismic elastic parameter modeling result after generalizing processing.
Further, as a preferred technical solution of the present invention：The low frequency of log parameter is selected in the step S2
Parameter background value is specifically included as master variable：
The well logging elastic parameter breathed freely is smoothed using polynomial fitting method, is intended according to different multinomials
Close number of times obtain some groups it is smooth after low-frequency parameter background value；
Selection is more than one group of low-frequency parameter background value of default low-frequency parameter background value excursion as master variable.
Further, as a preferred technical solution of the present invention：In the step S2, belonged to according to master variable and earthquake
Property parameter between coefficient correlation selection Seismic Attribute Parameters as first collaboration variable.
Further, as a preferred technical solution of the present invention：Anisotropic diffusion is utilized in the step S2
Generalizing processing is carried out to deposition phase information, using formula：
Wherein, Z is pending parameter value,It is all directions point of proximity Grad, A, B, C are the weight system of all directions
Number.
Further, as a preferred technical solution of the present invention：Utilized in the step S3 with the collaboration gram of position multiphase
In golden method interpolation calculated, specifically include：
Step S31, using radioactive tritium multiphase kriging method, set up the parameter value modulo of point to be estimated on Calculation Plane
Type；
Step S32, obtained gram according to unbiased optimal conditions in golden equation group, and will gram in golden equation group be converted to matrix；
Covariance and cross covariance between step S33, calculating to be estimated parameter of diverse location, to obtain step S32 institutes
Obtain all variate-values in matrix；
Step S34, the matrix as obtained by solution procedure S32 obtain the weight coefficient of each variable, are substituted into step S31 institutes
The parameter value model for setting up point to be estimated is calculated the parameter value of point to be estimated.
Further, as a preferred technical solution of the present invention：The multi-source of elastic parameter is obtained in the step S3
Data fusion modeling result is：
Wherein, the to be estimated estimates of parameters of position is Z*(u0), the well logging elastic parameter of master variable is Z (ui), u is
Parameter point position, is Y as the Seismic Attribute Parameters of the first collaboration variable1, as the sedimentary facies numeral mould of the second collaboration variable
Type is Y2, weight coefficient is respectively αi、β1And β2。
Further, as a preferred technical solution of the present invention：In the step S4, anisotropic diffusion is used
Generalizing processing is specifically included：
Step S41, the Parameters variation threshold value for setting point to be estimated, will be greater than or treating less than the Parameters variation threshold value for setting
Estimation point parameter value is considered as abnormity point removal；
Step S42, using anisotropic diffusion generalizing processing residue to be estimated parameter value, obtained by iterating
Seismic elastic parameter modeling result after generalizing processing.
The present invention uses above-mentioned technical proposal, can produce following technique effect：
The present invention is carried out by the extraction to multiple variable parameter values and based on collocated co-Kriging method to multi-parameter
Elastic parameter integration modeling, the method for obtaining reasonably optimizing parameter in modeling process, realize using sedimentary facies numeral mould
Type improves the modeling accuracy of geophysics field elastic parameter as the Facies Control Modeling of collaboration variable, is expanded using anisotropy
Arching pushing removal remains the local message that geologic body is included while calculating noise.Finally realize the phased of multi-parameter fusion
Modeling, improves the accuracy and integrality of the elastic parameter modeling based on conventional data.Calculating abnormity point can effectively be eliminated
With border noise, improve multi information parametric synthesis modeling precision and incorporated more real geological informations, be have compared with
The elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging of good applicability.
Brief description of the drawings
Fig. 1 is the elastic parameter Facies Control Modeling method flow diagram based on same position multiphase collocating kriging of the invention.
Fig. 2 is equal proportion mesh generation schematic diagram of the invention.
Well logging initial parameter that Fig. 3 is used by example of the present invention and it is smooth after low-frequency parameter background value schematic diagram.
The sedimentary facies mathematical model schematic diagram that Fig. 4 is used by example of the present invention.
The elastic parameter Facies Control Modeling method pair based on same position multiphase collocating kriging that Fig. 5 is used by example of the present invention
Well logging transverse wave speed parameter is modeled resulting result schematic diagram.
Specific embodiment
Embodiments of the present invention are described with reference to Figure of description.
As shown in figure 1, the elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging proposed by the present invention, main
Modeling mesh generation, multi-source information is included to extract, expand based on the interpolation calculation of gold in many restrictions or checking relations in five elements in same position and based on anisotropy
The generalizing processing four of arching pushing is most of, specific as follows：
Step S1, in destination layer using equal proportion grid cutting algorithm to modeling grid carry out subdivision, as shown in Fig. 2
Multiple planes are formed on the position of same ratio thickness, as the target location that elastic parameter is modeled；If target location does not have
Data point then uses the closest point of vertical direction as the value of target location.Wherein, in plane exist several known points and
To be estimated, the known point can directly obtain well logging elastic parameter.
Step S2, multi-source information extract, respectively extract plane on all known points well logging elastic parameter and be distributed in
The Seismic Attribute Parameters and deposition phase information of whole region select the low-frequency parameter background value of log parameter as modeling data
Used as master variable, selection Seismic Attribute Parameters are used as the first collaboration variable, and utilize anisotropic diffusion to sedimentary facies
Information carries out generalizing processing and obtains sedimentary facies mathematical model as the second collaboration variable；Analyze data body correlation and to being extracted
Data carry out preliminary treatment, realize multisource data fusion modeling procedure, improve geophysics elastic parameter modeling accuracy.The process
Comprise the following steps：
Step S21, all well logging elastic parameter low frequency background values for extracting known point, attempt different fitting number of times and tie
Close modeling demand to compare with well logging initial data, acquisition disclosure satisfy that the data frequency of modeling accuracy requirement, such as Fig. 3 institutes
Show.
It is specially：
Step S211, all well logging elastic parameter datas of known point are carried out with smooth place using polynomial fitting method
Reason, the low-frequency parameter background value after multigroup smoothing is obtained according to different fitting of a polynomial number of times.
Step S212, drafting low-frequency parameter background value curve map, and default low-frequency parameter background values are set to 40m
Range, due to the actual initial data less than well logging collection of frequency needed for modeling result, therefore changes according to elastic parameter
Feature is selected to one group low frequency background value of the reflection more than 40m ranges as master variable, makes in Depth Domain data volume
Low frequency background value can reflect big section rock stratum and reflect certain variation tendency in coating region.
Step S22, extraction Seismic Attribute Parameters, being obtained by statistical method can reflect the ginseng that master variable changes in side
Several classes of type, calculates the coefficient correlation of whole region different earthquake attribute volume and well logging low frequency background value, according to result of calculation
Select blanket seismic properties as the first collaboration variable, realize the optimization of modeling parameters.It is comprised the following steps：
Curve map at step S221, each seismic properties difference well location of drafting, counts the change of seismic properties at different well locations
Change trend, macroscopically understands the variation characteristic of the Seismic Attribute Parameters in different sedimentary facies belts, by seismic properties feature at well location
With deposition phase information comparative analysis.
By the phase between the master variable obtained by step S21 and each seismic properties at the whole known well locations of step S222, statistics
Relation number, with reference to step S221 seismic properties and the degree of agreement for depositing phase information, the selection larger seismic properties of coefficient correlation
Parameter is used as the first collaboration variable.
Step S23, will deposition phase information sedimentary facies mathematical model is converted to by way of classification designator, make sedimentary facies
For control condition is added to during Modeling Calculation, real Facies Control Modeling is realized.Detailed process is as follows：
Step S231, the sedimentary facies description in geology investigation result, represent that sedimentary facies changes using numeral, and numeral is big
It is small with away from land distance dependent, specifically：1st, 2 reef flat facies in platform are represented；3 represent tableland tidal-flat facies；4th, 5 PLATFORM MARGIN OR SLOPE is represented
Phase, represents and instead of conventional word description, so obtained digitlization deposition phase information can be with straight using quantitative numeral
Connect constraint modeling process；So that the sedimentary facies numeral of each point is represented and deposition phase information can be added into modeling in plane
In calculating process.
Step S232, phase information is deposited to resulting digitlization using anisotropic diffusion carry out generalizing processing, obtain
To sedimentary facies mathematical model as the second collaboration variable；
The computing formula of the anisotropic diffusion is as follows：
Wherein, Z is pending parameter value,It is all directions point of proximity Grad, A, B, C are the weight system of all directions
Number, i, j, k are the relative position of handled point, and m is the iterations of point.Generalizing processing can be obtained by solution formula (1)
Sedimentary facies model afterwards.
Wherein, by constantly changing pending position, generalization place is carried out to whole digitlization sedimentary facies information data body
Reason, finally gives the sedimentary facies mathematical model in handled plane, as shown in Figure 4.
Step S3, using step S2 gained master variable, the first collaboration variable and the second collaboration variable as calculating parameter, utilize
Interpolation calculation is carried out with position multiphase collocating kriging method, the parameter value of all points to be estimated in plane is obtained as elastic parameter
Multisource data fusion modeling result.
Wherein, the acquisition process of the parameter value of all points to be estimated is as follows in plane：
Step S31, with the master variable described in step S2, first collaboration variable and second collaboration variable as calculating parameter,
Interpolation calculation is carried out according to radioactive tritium multiphase kriging method, the parameter value model of point to be estimated on Calculation Plane is set up：
Wherein, the to be estimated estimates of parameters of position is Z*(u0), well logging master variable parameter is Z (ui), u is parameter point
Position, the Seismic Attribute Parameters as the first collaboration variable of extraction are Y1, as the sedimentary facies numeral mould of the second collaboration variable
Type is Y2, weight coefficient is respectively αi、β1And β2。
Step S32, according to unbiased optimal conditions, make golden variance in estimation gram minimum by building Lagrange's equation, can
With golden equation group in obtaining gram：
Wherein C represent parameter between covariance or cross covariance, in order to it is clearer expression parameter between relation, by equation
Group is converted to matrix form：
Wherein M represents covariance matrix, and Λ represents master variable weight coefficient vector, and ε represents error parameter, the expression formula of M
For：
Step S33, calculate to be estimated of diverse location covariance between log parameter, seismic properties and sedimentary facies and
Cross covariance, can obtain all variate-values in matrix M；
Step S34, the weight coefficient that each variable can be obtained by solution formula (4), being substituted into formula (2) can count
Calculation obtains to be estimated parameter value.
Above-mentioned steps S31 to S34 can calculate to be estimated parameter value in plane, in order to obtain in whole planes
The parameter value of all points to be estimated, then change to be estimated position, the parameter value being calculated in step S34 is considered as known
Point, repeat step S3 calculates next to be estimated parameter value, terminates after the completion of a little whole calculating of institute in plane.
Step S4, generalizing processing is carried out to step 3 gained multisource data fusion modeling result using anisotropic diffusion,
Obtain the seismic elastic parameter modeling result after generalizing processing.Specifically include：
Step S41, the Parameters variation threshold value for setting point to be estimated, will be greater than or less than setup parameter change threshold wait estimate
The parameter value of enumeration is considered as abnormity point removal, i.e. statistical parameter excursion by negative value and in the range of maximum number level
Parameter value is considered as abnormity point, then removes the abnormity point in step S3 multisource data fusion modeling results.
Step S42, using anisotropic diffusion generalizing processing to remove abnormity point multivariate data integration modeling result
Treatment, i.e., to remaining to be estimated parameter value generalizing processing, after formula (1) iterates and can obtain generalizing processing
Seismic elastic parameter modeling result, as shown in figure 5, acquired results remain complete Parameters variation border and localized variation, and
And the noise that interference models definition is largely eliminated, can as can be seen from the figure be obtained using the method for the present invention
Obtain more reasonably elastic parameter modeling result.
Therefore, The inventive method achieves the Facies Control Modeling by the use of sedimentary facies mathematical model as collaboration variable, improve
The modeling accuracy of geophysics field elastic parameter, remains ground while being removed and calculated noise using anisotropic diffusion
The local message that plastid is included, can eliminate calculating abnormity point and border noise, improve multi information parametric synthesis modeling
Precision has simultaneously incorporated more real geological informations, with preferable applicability.
Embodiments of the present invention are explained in detail above in conjunction with accompanying drawing, but the present invention is not limited to above-mentioned implementation
Mode, in the ken that those of ordinary skill in the art possess, can also be on the premise of present inventive concept not be departed from
Make a variety of changes.
Claims (7)
1. based on the seismic elastic parameter Facies Control Modeling method with position multiphase collocating kriging, it is characterised in that including following step
Suddenly：
Step S1, in destination layer using equal proportion grid cutting algorithm to modeling grid carry out subdivision, in same ratio thickness
Position on form several planes, as elastic parameter model target location；
Step S2, respectively extract plane on known point well logging elastic parameter and be distributed in whole region Seismic Attribute Parameters and
Deposition phase information selects the low-frequency parameter background value of log parameter as master variable as modeling data, selects earthquake
Property parameters are used as the first collaboration variable, and deposition phase information is carried out generalizing processing and deposited using anisotropic diffusion
Phase mathematical model is used as the second collaboration variable；
Step S3, using step S2 gained master variable, first collaboration variable and second collaboration variable as calculating parameter, using same position
Multiphase collocating kriging method carries out interpolation calculation, obtains the parameter value of all points to be estimated in plane as many of elastic parameter
Source data integration modeling result；
Step S4, using anisotropic diffusion to step S3 gained multisource data fusion modeling result carry out generalizing processing, obtain
Seismic elastic parameter modeling result after to generalizing processing.
2. the seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging according to claim 1, its
It is characterised by, selects the low-frequency parameter background value of log parameter as master variable in the step S2, specifically includes：
The well logging elastic parameter extracted is smoothed using polynomial fitting method, according to different fitting of a polynomials
Number obtain some groups it is smooth after low-frequency parameter background value；
Selection is more than one group of low-frequency parameter background value of default low-frequency parameter background value excursion as master variable.
3. the seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging according to claim 1, its
It is characterised by, in the step S2, is made according to the coefficient correlation selection Seismic Attribute Parameters between master variable and Seismic Attribute Parameters
It is the first collaboration variable.
4. the seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging according to claim 1, its
It is characterised by, generalizing processing is carried out to deposition phase information using anisotropic diffusion in the step S2, using formula：
Wherein, Z be pending parameter value, ▽ Z be all directions point of proximity Grad, A, B, C for all directions weight coefficient, i,
J, k are the relative position of handled point, and m is the iterations of point.
5. the seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging according to claim 1, its
It is characterised by, is calculated using same position multiphase collocating kriging method interpolation in the step S3, specifically includes：
Step S31, using radioactive tritium multiphase kriging method, set up the parameter value model of point to be estimated on Calculation Plane；
Step S32, obtained gram according to unbiased optimal conditions in golden equation group, and will gram in golden equation group be converted to matrix；
Covariance and cross covariance between step S33, each parameter of calculating diverse location point to be estimated, to obtain step S32 institutes
Obtain all variate-values in matrix；
Step S34, the matrix as obtained by solution procedure S32 obtain the weight coefficient of each variable, are substituted into step S31 and are set up
The parameter value model of point to be estimated is calculated the parameter value of point to be estimated.
6. the seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging according to claim 1, its
It is characterised by, the multisource data fusion modeling result that elastic parameter is obtained in the step S3 is：
Wherein, the to be estimated estimates of parameters of position is Z*(u0), the well logging elastic parameter of master variable is Z (ui), u is parameter
Point position, is Y as the Seismic Attribute Parameters of the first collaboration variable1, as second collaboration variable sedimentary facies mathematical model be
Y2, weight coefficient is respectively αi、β1And β2。
7. the seismic elastic parameter Facies Control Modeling method based on same position multiphase collocating kriging according to claim 1, its
It is characterised by, in the step S4, is specifically included using anisotropic diffusion generalizing processing：
Step S41, the Parameters variation threshold value for setting point to be estimated, will be greater than or less than the point to be estimated of setup parameter change threshold
Parameter value is considered as abnormity point removal；
Step S42, the parameter value using the remaining point to be estimated of anisotropic diffusion generalizing processing, obtain general by iterating
Seismic elastic parameter modeling result after change treatment.
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| CN109655894A (en) * | 2017-10-12 | 2019-04-19 | 中国石油化工股份有限公司 | Carbonate rock ancient stream channel seismic inversion low frequency model construction method and system |
| CN109696704A (en) * | 2019-01-28 | 2019-04-30 | 中国海洋石油集团有限公司 | A kind of seismic aeolotropy δ modeling method based on p-wave impedance constraint |
| CN110941014A (en) * | 2018-09-25 | 2020-03-31 | 中国石油化工股份有限公司 | Interpolation method of seismic local velocity data based on collaborative kriging method |
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| CN113176613A (en) * | 2021-04-21 | 2021-07-27 | 中国石油大学(华东) | Multi-information fusion low-frequency model building method based on three-level body control |
| CN116522688A (en) * | 2023-06-29 | 2023-08-01 | 北京城建勘测设计研究院有限责任公司 | Well control multi-information fusion engineering geological modeling method and device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101634718A (en) * | 2009-05-05 | 2010-01-27 | 中国石油集团西北地质研究所 | Method for predicting reservoir layer inverted by multi-well logging parameter under restriction condition of relative wave impedance |
| EP2270548A1 (en) * | 2009-06-26 | 2011-01-05 | IFP Energies nouvelles | Method for modifying the proportions of facies when calibrating the history of a geological model |
| CN102147479A (en) * | 2011-01-11 | 2011-08-10 | 中国海洋石油总公司 | Modelling method of reservoir space physical property parameters |
| CN103543478A (en) * | 2012-07-11 | 2014-01-29 | 中国石油化工股份有限公司 | Geologic morphological interpolation KM (Kriging and Multiple-point geostatistics) method |
| CN104749624A (en) * | 2015-03-03 | 2015-07-01 | 中国石油大学(北京) | Method for synchronously realizing seismic lithofacies identification and quantitative assessment of uncertainty of seismic lithofacies identification |
| CN105353412A (en) * | 2015-12-14 | 2016-02-24 | 中国石油大学(华东) | Calculating method and system of well-to-seismic integration average speed field |
| CN105701319A (en) * | 2016-03-08 | 2016-06-22 | 中国石油大学(华东) | Sedimentary microfacies modeling method under horizontal well rule development well net |
| CN107607996A (en) * | 2017-08-23 | 2018-01-19 | 电子科技大学 | Based on phased sequential co-simulation Geological Modeling |
-
2017
- 2017-02-23 CN CN201710098888.0A patent/CN106772587A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101634718A (en) * | 2009-05-05 | 2010-01-27 | 中国石油集团西北地质研究所 | Method for predicting reservoir layer inverted by multi-well logging parameter under restriction condition of relative wave impedance |
| EP2270548A1 (en) * | 2009-06-26 | 2011-01-05 | IFP Energies nouvelles | Method for modifying the proportions of facies when calibrating the history of a geological model |
| CN102147479A (en) * | 2011-01-11 | 2011-08-10 | 中国海洋石油总公司 | Modelling method of reservoir space physical property parameters |
| CN103543478A (en) * | 2012-07-11 | 2014-01-29 | 中国石油化工股份有限公司 | Geologic morphological interpolation KM (Kriging and Multiple-point geostatistics) method |
| CN104749624A (en) * | 2015-03-03 | 2015-07-01 | 中国石油大学(北京) | Method for synchronously realizing seismic lithofacies identification and quantitative assessment of uncertainty of seismic lithofacies identification |
| CN105353412A (en) * | 2015-12-14 | 2016-02-24 | 中国石油大学(华东) | Calculating method and system of well-to-seismic integration average speed field |
| CN105701319A (en) * | 2016-03-08 | 2016-06-22 | 中国石油大学(华东) | Sedimentary microfacies modeling method under horizontal well rule development well net |
| CN107607996A (en) * | 2017-08-23 | 2018-01-19 | 电子科技大学 | Based on phased sequential co-simulation Geological Modeling |
Non-Patent Citations (5)
| Title |
|---|
| HONGBING ZHANG 等: ""Adaptive reconstruction method of impedance model with absolute and relative constraints"", 《JOURNAL OF APPLIED GEOPHYSICS》 * |
| 刘文玲 等: ""同位协同克里金方法在储层横向预测中的应用"", 《勘探地球物理进展》 * |
| 吴胜和 编著: "《储层表征与建模》", 31 March 2010, 石油工业出版社 * |
| 张宏兵 等: ""基于构造约束的波阻抗边界刻画"", 《地球物理学进展》 * |
| 郭莉 等: ""同位协同随机建模在储层预测中的应用"", 《大庆石油地质与开发》 * |
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| CN107817521A (en) * | 2017-10-27 | 2018-03-20 | 河海大学 | It is a kind of based on improved into golden lithology distributions probability recognition methods in decigram |
| CN107817521B (en) * | 2017-10-27 | 2019-07-12 | 河海大学 | It is a kind of based on improved at lithology distributions probability recognition methods golden in decigram |
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| CN109696704A (en) * | 2019-01-28 | 2019-04-30 | 中国海洋石油集团有限公司 | A kind of seismic aeolotropy δ modeling method based on p-wave impedance constraint |
| CN113176613A (en) * | 2021-04-21 | 2021-07-27 | 中国石油大学(华东) | Multi-information fusion low-frequency model building method based on three-level body control |
| CN113176613B (en) * | 2021-04-21 | 2022-11-01 | 中国石油大学(华东) | Multi-information fusion low-frequency model building method based on three-level body control |
| CN116522688A (en) * | 2023-06-29 | 2023-08-01 | 北京城建勘测设计研究院有限责任公司 | Well control multi-information fusion engineering geological modeling method and device |
| CN116522688B (en) * | 2023-06-29 | 2023-09-15 | 北京城建勘测设计研究院有限责任公司 | Well control multi-information fusion engineering geological modeling method and device |
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