CN108596780B - A kind of Reservoir type division methods and system based on multiple information - Google Patents
A kind of Reservoir type division methods and system based on multiple information Download PDFInfo
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Abstract
The present invention discloses a kind of Reservoir type division methods and system based on multiple information.This method comprises: obtaining the data sensitive to Reservoir type from the log data of multiple sample points；The data sensitive to Reservoir type include neutron data, resistivity, porosity, interval transit time, natural gamma and compensation density；Obtain the actual reservoir type that each sample point that artificial reservoir division obtains is carried out to each sample point；The data sensitive to Reservoir type of each sample point are inputted into deep neural network, the structural parameters for adjusting neural network make neural network export corresponding Reservoir type, to be trained to deep neural network；Trained deep neural network is used to realize the division to Reservoir type according to the data sensitive to Reservoir type of actual measurement.Method and system disclosed by the invention can be improved the precision of reservoir division.
Description
Technical field
The present invention relates to field of petroleum geophysical exploration, draw more particularly to a kind of Reservoir type based on multiple information
Divide method and system.
Background technique
Reservoir type division is one of important content of reservoir description, is the key that carry out oil field development Potentials work
Make.Shale content, permeability etc. are generallyd use in actual production carries out Reservoir type with the closely related parameter of reservoir characteristic
Division, wherein shale content is able to reflect reservoir lithology and physical property characteristic, and permeability can characterize fluid in rock interior stream
Dynamic complexity.But the parameters such as the shale content divided for Reservoir type and permeability can not be directly from the measurement number of well logging
It directly acquires in, but is calculated based on practical logging curve using cross-plot or empirical equation to obtain, these methods
Accuracy depends on the selection of aspect of model parameter, Regional Geological Characteristics and layered characteristic, is differentiated by expert artificial with experience etc.
Factor is affected.Since every kind of log parameter has it to be specifically applicable in stratum, and can be complete without which kind of log parameter
It is suitable for all stratum entirely, limited well logging information is often only utilized when carrying out Reservoir type and dividing, leads to reservoir division
Precision it is not high.
Summary of the invention
The object of the present invention is to provide a kind of Reservoir type division methods and system based on multiple information improve reservoir and draw
The precision divided.
To achieve the above object, the present invention provides following schemes:
A kind of Reservoir type division methods based on multiple information, comprising:
The data sensitive to Reservoir type are obtained from the log data of multiple sample points；It is described sensitive to Reservoir type
Data include neutron data, resistivity, porosity, interval transit time, natural gamma and compensation density；
Obtain the actual reservoir type that each sample point that artificial reservoir division obtains is carried out to each sample point；
The data sensitive to Reservoir type of each sample point are inputted into deep neural network, adjust the structure of neural network
Parameter makes neural network export corresponding Reservoir type, to be trained to the deep neural network；It is trained described
Deep neural network is used to realize the division to Reservoir type according to the data sensitive to Reservoir type of actual measurement.
Optionally, described that the data sensitive to Reservoir type of each sample point are inputted into deep neural network, adjustment mind
Structural parameters through network make neural network export corresponding Reservoir type, so that the deep neural network is trained,
It specifically includes:
Feature is carried out to data sensitive to Reservoir type described in every kind respectively using cross-plot and kernel density estimation method
Statistics, determines the mean value and variance of every kind of data sensitive to Reservoir type, obtains the sample average and sample of every kind of data
This variance；
Each data sensitive to Reservoir type are marked using the sample average and sample variance of every kind of data
Standardization obtains standardized data；
Using the standardized data of each type of each sample point as the input of deep neural network, with linear
Rectify activation primitive of the function as the deep neural network makes by adjusting the structural parameters of the deep neural network
Difference between the Reservoir type data and corresponding actual reservoir type of deep neural network output preset range it
It is interior, to complete the training of deep neural network.
Optionally, include: according to the process that the data sensitive to Reservoir type of actual measurement divide Reservoir type
The data sensitive to Reservoir type for obtaining actual measurement, obtain measured data；
The mean value and variance for calculating every kind of measured data obtain the actual measurement mean value and actual measurement side of every kind of measured data
Difference；
The mean value threshold range and variance threshold values of every kind of data are calculated using the sample average and sample variance of every kind of data
Range；
Judge whether the actual measurement mean value of every kind of measured data is located in corresponding mean value threshold range and every kind of actual measurement number
According to actual measurement variance whether be located within the scope of corresponding variance threshold values, obtain the first judging result；
If first judging result indicates that the actual measurement mean value of any one measured data is located at corresponding mean value threshold value model
Except enclosing and/or actual measurement variance is located at outside corresponding variance threshold values range, then resurveys the data of corresponding types；
If first judging result indicates that the actual measurement mean value of every kind of measured data is located in corresponding mean value threshold range
And actual measurement variance is located within the scope of corresponding variance threshold values, then is standardized to each measured data, obtains standard actual measurement number
According to；
Using the standard measured data of each type as the input of deep neural network, the output of neural network is obtained
Actual measurement Reservoir type.
Invention additionally discloses a kind of Reservoir type dividing system based on multiple information, comprising:
Sample data obtains module, for obtaining the number sensitive to Reservoir type from the log data of multiple sample points
According to；The data sensitive to Reservoir type include neutron data, resistivity, porosity, interval transit time, natural gamma and compensation
Density；
Actual reservoir type acquisition module carries out each sample that artificial reservoir division obtains to each sample point for obtaining
The actual reservoir type of this point；
Neural metwork training module, for the data sensitive to Reservoir type of each sample point to be inputted depth nerve net
Network, the structural parameters for adjusting neural network make neural network export corresponding Reservoir type, thus to the deep neural network
It is trained；The trained deep neural network is used to be realized according to the data sensitive to Reservoir type of actual measurement to reservoir
The division of type.
Optionally, the neural metwork training module, specifically includes:
Sample data mean value and variance computing unit, for utilizing cross-plot and kernel density estimation method respectively to every kind of institute
It states the data sensitive to Reservoir type and carries out characteristic statistics, determine the mean value and side of every kind of data sensitive to Reservoir type
Difference obtains the sample average and sample variance of every kind of data；
Sample data Standardisation Cell, sample average and sample variance for utilizing every kind of data store up each described pair
The data of channel type sensitivity are standardized, and obtain standardized data；
Training unit, for using the standardized data of each type of each sample point as deep neural network
Input, using line rectification function as the activation primitive of the deep neural network, by adjusting the deep neural network
Structural parameters, make the difference between the Reservoir type data and corresponding actual reservoir type of deep neural network output
Within preset range, to complete the training of deep neural network.
Optionally, the Reservoir type dividing system further includes reservoir division module, for according to actual measurement to reservoir class
The data of type sensitivity divide Reservoir type；The reservoir division module, specifically includes:
Measured data acquiring unit obtains measured data for obtaining the data sensitive to Reservoir type of actual measurement；
Measured data mean value and variance computing unit are obtained for calculating the mean value and variance of every kind of measured data
The actual measurement mean value and actual measurement variance of every kind of measured data；
Threshold range computing unit, for calculating the equal of every kind of data using the sample average and sample variance of every kind of data
It is worth threshold range and variance threshold values range；
Judging unit, for judge the actual measurement mean value of every kind of measured data whether be located in corresponding mean value threshold range with
And whether the actual measurement variance of every kind of measured data is located within the scope of corresponding variance threshold values, obtains judging result；
Data adopt unit again, correspond to if indicating that the actual measurement mean value of any one measured data is located at for the judging result
Mean value threshold range except and/or actual measurement variance be located at outside corresponding variance threshold values range, then resurvey corresponding types
Data；
Measured data Standardisation Cell, if for the judging result indicate every kind of measured data actual measurement mean value be located at pair
In the mean value threshold range answered and actual measurement variance is located within the scope of corresponding variance threshold values, then carries out standard to each measured data
Change, obtains standard measured data；
Reservoir division unit, for using the standard measured data of each type as the input of deep neural network,
Obtain the actual measurement Reservoir type of the output of neural network.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: disclosed by the invention to be based on
The Reservoir type division methods and system of multiple information, by combining neutron data, resistivity, porosity, interval transit time, nature
A variety of data such as gamma and compensation density establish answering between a variety of data and Reservoir type using the study of deep neural network
Miscellaneous non-linear relation realizes that the automation and intelligentification of Reservoir type divides.The present invention is divided by automation and intelligentification, is avoided
The interference of human factor improves the precision of Reservoir type division, while utilizing a variety of data, realizes melting for multiple information
It closes, further improves reservoir division precision.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is that the present invention is based on the method flow diagrams of the Reservoir type division methods embodiment of multiple information；
Fig. 2 is effective distribution figure of different log parameters in different type reservoir；
Fig. 3 is the network structure of deep neural network of the present invention；
Fig. 4 is the function curve schematic diagram that objective function gradually becomes flat in deep neural network training process；
Fig. 5 is the reservoir division result figure obtained using the Reservoir type division methods of the invention based on multiple information；
Fig. 6 is that the present invention is based on the system construction drawings of the Reservoir type dividing system embodiment of multiple information.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The purpose of the present invention is making full use of the polynary well logging informations such as sound electric discharge to carry out the direct division of Reservoir type, use
Well logging multiple information in actual production data establishes Reservoir type sample database, and polynary well logging is constructed using deep learning network
Complex nonlinear relationship between information and Reservoir type, the comprehensive utilization of the polynary well logging information such as development sound electric discharge, so as to
The direct division of Reservoir type in enough real logging datas, avoid calculating indirectly after the parameters such as shale content, permeability again into
Row Reservoir type existing uncertainty when dividing, the influence for avoiding human factor from dividing Reservoir type promote Reservoir type
The automation and intelligent level of prediction.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Fig. 1 is that the present invention is based on the method flow diagrams of the Reservoir type division methods embodiment of multiple information.
Referring to Fig. 1, it is somebody's turn to do the Reservoir type division methods based on multiple information, comprising:
Step 101: the data sensitive to Reservoir type are obtained from the log data of multiple sample points；It is described to reservoir class
The data of type sensitivity include neutron data, resistivity, porosity, interval transit time, natural gamma and compensation density.
By to a variety of different types of well loggings such as sound wave, radioactivity, the resistivity studied in the existing well-log information in work area
Information is screened, and the logs such as natural gamma, resistivity, natural potential, the neutron sensitive to lithology is sorted out, to infiltration
The various related logging curves such as neutron, density, the sound wave of rate sensitivity.The present invention in view of dividing Reservoir type in actual production
When it is main according to shale content and Permeability Parameters, choose neutron data, resistivity, porosity, interval transit time, natural gamma,
The multiple information source that 6 kinds of log parameters such as compensation density are divided as Reservoir type.It can also further increase on this basis
Other parameter, but on condition that increased parameter has Effective Numerical on each Reservoir type, then rejecting hole collapse is tight
The data located again and the abnormal numerical value due to caused by logger or measurement error, for building Reservoir type numeralization sample database
Basic data basis is provided.
Step 102: obtaining the actual reservoir class that each sample point that artificial reservoir division obtains is carried out to each sample point
Type.
To 6 kinds of neutron data of selection, resistivity, porosity, interval transit time, natural gamma, compensation density log parameters
It carries out building library, carries out environmental correction for the feature of each well data, and unit is all unified to SI units, it is ensured that no
The data that same time, different instruments, different operation personnel's measurement obtain can be efficiently used.On this basis according to existing
Shale content, Permeability Parameters and formation testing result at different depth data sample carry out Reservoir type artificial division,
By the data of each sample point be respectively divided into non-reservoir, I class reservoir (reservoir of oil and gas of relative ideal, potentiality to be exploited are big) and
The three types such as II class reservoir (possible reservoir of oil and gas, have certain potentiality to be exploited), and use respectively digital 0,1,2 as
Label, it is established that the Reservoir type numeralization sample database of polynary well logging information at each sample point.The non-reservoir contains for shale
Biggish reservoir is measured, substantially without potentiality to be exploited；The I class reservoir is the reservoir of oil and gas of relative ideal, and potentiality to be exploited is big；It is described
II class reservoir is possible reservoir of oil and gas, has certain potentiality to be exploited.
Step 103: the data sensitive to Reservoir type of each sample point being inputted into deep neural network, adjust nerve net
The structural parameters of network make neural network export corresponding Reservoir type, to be trained to the deep neural network；Training
The good deep neural network is used to realize the division to Reservoir type according to the data sensitive to Reservoir type of actual measurement.
The step 103 specifically includes:
Feature is carried out to data sensitive to Reservoir type described in every kind respectively using cross-plot and kernel density estimation method
Statistics, determines the mean value and variance of every kind of data sensitive to Reservoir type, obtains the sample average and sample of every kind of data
This variance；
Each data sensitive to Reservoir type are marked using the sample average and sample variance of every kind of data
Standardization obtains standardized data；
Using the standardized data of each type of each sample point as the input of deep neural network, with linear
Rectify activation primitive of the function as the deep neural network makes by adjusting the structural parameters of the deep neural network
Difference between the Reservoir type data and corresponding actual reservoir type of deep neural network output preset range it
It is interior, to complete the training of deep neural network.
The step 103 specifically can be used following specific embodiment and realize:
After setting up Reservoir type sample database, it is necessary to carry out characteristic statistics and standard to the multiple parameters in sample database
Change, it is intended to ensure that the distribution of each parameter in sample database is uniformly reasonable, while be the pre- of subsequent real data Reservoir type
It surveys and parameter foundation is provided.The present invention carries out the work of sample database characteristic statistics using cross-plot and Density Estimator figure method joint
(cross-plot is exactly that every two parameter does intersection analysis respectively all with the distribution characteristics of statistical data, and cuclear density method is in probability theory
In be used to estimate the density function of parameter, thus obtain it is following required for statistical nature), obtain each parameter in sample database
Mean μiAnd variances sigmai(i be data class number, share neutron data, resistivity, porosity, interval transit time, natural gamma,
6 kinds of log parameters of compensation density, therefore i=1,2 ... ..., 6), specify effective point of different log parameters in different type reservoir
Cloth range, Fig. 2 are effective distribution figure of different log parameters in different type reservoir.The distribution is used to carry out in fact
Judge whether measured data can be used to judge Reservoir type when survey.Then according to the Density Estimator peak of each parameter
Value and bandwidth determine every kind of log parameter for discrimination possessed by Reservoir type classification.Especially work as parameter distribution range
Unevenly, it then needs to continue additional sample library information when Reservoir type sample number deficiency, i.e. sample number is unsatisfactory for training condition also
Need to continue to collect more more fully data, the rational relation between building Reservoir type and multiple information lays the foundation.
In view of the influence in actual production data due to factors such as construction team, instrumentation and testings, need to polynary ginseng
Number information is standardized, and optimizes the data structure of Reservoir type sample database.The present invention is using following formula to multiple information library
Data are standardized, it is ensured that the data fit mean value after standardizing is 0, the normal distribution that variance is 1, it may be assumed that
Wherein μiFor log data mean value corresponding in sample database, σiFor corresponding sample data variance, i is data class
It numbers (i=1,2 ... ..., 6), j represents different sample points (j=1,2 ... ..., n (n is sample point sum)), xijRepresent mark
Parameter before standardization, ZijRepresent the parameter after standardization.
After standardization is completed, the structure carried out to neural network is needed to be defined.In projected depth neural network
When need for Reservoir type sample database feature carry out Precise spraying, specify the number of plies of deep neural network, every layer of nerve
The network architecture parameters such as first number and activation primitive.Fig. 3 is the network structure of deep neural network of the present invention.Referring to
Fig. 3, the present invention constructs the deep neural network model comprising 5 hidden layers, wherein the neuron number of each hidden layer
Mesh is respectively 20,40,80,40,20.Line rectification function (Relu function) is used in network design is used as activation primitive,
To which overfitting problem be effectively relieved and accelerate network convergence process, and Softmax layers are added to before output layer.The network
Structure matrix form can indicate are as follows:
g(W(1)a(1)+b(1))=a(2)
g(W(2)a(2)+b(2))=a(3)
g(W(n-1)a(n-1)+b(n-1))=y
Wherein, g (x) indicates the operational form of activation primitive, and the activation primitive that the present invention uses is ReLu function, i.e. g (x)
=max (0, x), n indicate the number of plies (n=6, i.e. the hidden layer number of plies add the sum of output layer in the present invention) of deep neural network, b(i)Indicate bias term, wherein a(i)(i=1 ..., n-1) be middle layer feature vector (as i=1, a(1)Sample used by representing
Polynary well logging information in this library), W(i)Indicate the weight matrix for being mapped to i+1 layer in network structure from i-th layer, y representative sample
The Reservoir type marked in this library.
After network structure definition is completed, depth is input to using all data in multiple information Reservoir type sample database
It is trained in degree neural network, setting models parameter (the weighted value W of network random first when training(i)With bias term b(i)), model parameter numerical value is adjusted by iterating, after ensuring the multiple information in sample database by exporting after the network
Reservoir type y' and sample database in Reservoir type y between minimum, the specific objective function expression that reaches objective function error
Form is as follows:
The present invention uses regularization method to prevent over-fitting, i.e., in each repetitive exercise in the training process
A part of neuron is set to be not involved in training at random, so that the relation of interdependence between neuron is reduced, to improve neural network
Generalization ability；The model parameter in trained iterative process, i.e. basis are updated using the optimization algorithm declined based on gradient
Objective function calculates the gradient of each parameter in network model, is updated using the gradient value to network parameter, and set
The maximum number of iterations of model parameter be 5000 times, when training reach maximum number of iterations or objective function in an iterative process by
Grade when becoming flat can deconditioning, Fig. 4 is the letter that objective function gradually becomes flat in deep neural network training process
Number curve schematic diagram, it can be seen that the slope of objective function is gradually lower and represents objective function and gradually become flat.When reaching
It is the network model parameter W that can be reserved for training and obtain to this condition(i)And b(i)(i=1 ..., n-1).
After training is completed, so that it may carry out the division of Reservoir type using training result.
Include: according to the process that the data sensitive to Reservoir type of actual measurement divide Reservoir type
The data sensitive to Reservoir type for obtaining actual measurement, obtain measured data；
The mean value and variance for calculating every kind of measured data obtain the actual measurement mean value and actual measurement side of every kind of measured data
Difference；
The mean value threshold range and variance threshold values of every kind of data are calculated using the sample average and sample variance of every kind of data
Range；
Judge whether the actual measurement mean value of every kind of measured data is located in corresponding mean value threshold range and every kind of actual measurement number
According to actual measurement variance whether be located within the scope of corresponding variance threshold values, obtain the first judging result；
If first judging result indicates that the actual measurement mean value of any one measured data is located at corresponding mean value threshold value model
Except enclosing and/or actual measurement variance is located at outside corresponding variance threshold values range, then resurveys the data of corresponding types；
If first judging result indicates that the actual measurement mean value of every kind of measured data is located in corresponding mean value threshold range
And actual measurement variance is located within the scope of corresponding variance threshold values, then is standardized to each measured data, obtains standard actual measurement number
According to；
Using the standard measured data of each type as the input of deep neural network, the output of neural network is obtained
Actual measurement Reservoir type.
It can specifically use and the division that mode carries out Reservoir type is implemented as follows:
Filter out the multiple information of needs from numerous log datas first when carrying out real data Reservoir type and dividing
(i.e. 6 kinds of neutron data, resistivity, porosity, interval transit time, natural gamma, compensation density data) and data outliers are rejected,
Calculate the average value mu ' and variances sigma ' of each multiple information in real data, and by average value mu ' and variances sigma ' and present invention foundation
Sample database in multiple information scope of statistics be compared, in the comparison use following standard:
μ × (1- α) < μ ' < μ × (1+ α)
σ × (1-2 α) < σ ' < σ × (1+2 α)
Wherein, α is the confidence level parameter of prediction data, and the present invention sets α=0.1.When real data feature is unsatisfactory for
When formula, shows that the distribution characteristics of the information in the multivariate data and sample database of Reservoir type to be divided has big difference, be unsatisfactory for storing up
Whether reasonable the primary condition that channel type divides, need to reexamine the data, if unreasonable need to resurvey data.Work as input
When the statistical nature of real data meets above formula, then this method can be directly used to carry out Reservoir type and divide in the data.So
Afterwards, the multiple information treated in prediction data is standardized, and the network model parameter W and b that are obtained using training are to reality
The polynary well logging information of input is carried out Reservoir type and is divided, to obtain the Reservoir type division result that the data correspond to and defeated
Out at figure.
Fig. 5 is the reservoir division result figure obtained using the Reservoir type division methods of the invention based on multiple information.
Referring to Fig. 5, the precision that can be improved reservoir division is divided using the Reservoir type of the invention based on multiple information.
Fig. 6 is that the present invention is based on the system construction drawings of the Reservoir type dividing system embodiment of multiple information.
Referring to Fig. 6, it is somebody's turn to do the Reservoir type dividing system based on multiple information, comprising:
Sample data obtains module 601, sensitive to Reservoir type for obtaining from the log data of multiple sample points
Data；The data sensitive to Reservoir type include neutron data, resistivity, porosity, interval transit time, natural gamma and benefit
Repay density；
Actual reservoir type acquisition module 602, for obtain to each sample point carry out artificial reservoir division obtain it is each
The actual reservoir type of a sample point；
Neural metwork training module 603, for the data input depth sensitive to Reservoir type of each sample point is refreshing
Through network, the structural parameters for adjusting neural network make neural network export corresponding Reservoir type, thus to the depth nerve
Network is trained；The trained deep neural network is used for the data realization pair sensitive to Reservoir type according to actual measurement
The division of Reservoir type.
The neural metwork training module 603, specifically includes:
Sample data mean value and variance computing unit, for utilizing cross-plot and kernel density estimation method respectively to every kind of institute
It states the data sensitive to Reservoir type and carries out characteristic statistics, determine the mean value and side of every kind of data sensitive to Reservoir type
Difference obtains the sample average and sample variance of every kind of data；
Sample data Standardisation Cell, sample average and sample variance for utilizing every kind of data store up each described pair
The data of channel type sensitivity are standardized, and obtain standardized data；
Training unit, for using the standardized data of each type of each sample point as deep neural network
Input, using line rectification function as the activation primitive of the deep neural network, by adjusting the deep neural network
Structural parameters, make the difference between the Reservoir type data and corresponding actual reservoir type of deep neural network output
Within preset range, to complete the training of deep neural network.
Reservoir division module 604, for being divided according to the data sensitive to Reservoir type of actual measurement to Reservoir type；
The reservoir division module 604, specifically includes:
Measured data acquiring unit obtains measured data for obtaining the data sensitive to Reservoir type of actual measurement；
Measured data mean value and variance computing unit are obtained for calculating the mean value and variance of every kind of measured data
The actual measurement mean value and actual measurement variance of every kind of measured data；
Threshold range computing unit, for calculating the equal of every kind of data using the sample average and sample variance of every kind of data
It is worth threshold range and variance threshold values range；
Judging unit, for judge the actual measurement mean value of every kind of measured data whether be located in corresponding mean value threshold range with
And whether the actual measurement variance of every kind of measured data is located within the scope of corresponding variance threshold values, obtains judging result；
Data adopt unit again, correspond to if indicating that the actual measurement mean value of any one measured data is located at for the judging result
Mean value threshold range except and/or actual measurement variance be located at outside corresponding variance threshold values range, then resurvey corresponding types
Data；
Measured data Standardisation Cell, if for the judging result indicate every kind of measured data actual measurement mean value be located at pair
In the mean value threshold range answered and actual measurement variance is located within the scope of corresponding variance threshold values, then carries out standard to each measured data
Change, obtains standard measured data；
Reservoir division unit, for using the standard measured data of each type as the input of deep neural network,
Obtain the actual measurement Reservoir type of the output of neural network.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: disclosed by the invention to be based on
The Reservoir type division methods and system of multiple information, by combining neutron data, resistivity, porosity, interval transit time, nature
A variety of data such as gamma and compensation density establish answering between a variety of data and Reservoir type using the study of deep neural network
Miscellaneous non-linear relation realizes that the automation and intelligentification of Reservoir type divides.The present invention is divided by automation and intelligentification, is avoided
The interference of human factor improves the precision of Reservoir type division, while utilizing a variety of data, realizes melting for multiple information
It closes, further improves reservoir division precision.
For the system disclosed in the embodiment, since it is corresponded to the methods disclosed in the examples, so the ratio of description
Relatively simple, reference may be made to the description of the method.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (2)
1. a kind of Reservoir type division methods based on multiple information characterized by comprising
The data sensitive to Reservoir type are obtained from the log data of multiple sample points；The data sensitive to Reservoir type
Including neutron data, resistivity, porosity, interval transit time, natural gamma and compensation density；
Obtain the actual reservoir type that each sample point that artificial reservoir division obtains is carried out to each sample point；
The data sensitive to Reservoir type of each sample point are inputted into deep neural network, adjust the structural parameters of neural network
Neural network is set to export corresponding Reservoir type, to be trained to the deep neural network；The trained depth
Neural network is used to realize the division to Reservoir type according to the data sensitive to Reservoir type of actual measurement；
It is described that the data sensitive to Reservoir type of each sample point are inputted into deep neural network, adjust the structure of neural network
Parameter specifically includes the corresponding Reservoir type of neural network output to be trained to the deep neural network:
Characteristic statistics are carried out to data sensitive to Reservoir type described in every kind respectively using cross-plot and kernel density estimation method,
The mean value and variance for determining every kind of data sensitive to Reservoir type, obtain the sample average and sample side of every kind of data
Difference；
Each data sensitive to Reservoir type are standardized using the sample average and sample variance of every kind of data,
Obtain standardized data；
Using the standardized data of each type of each sample point as the input of deep neural network, with line rectification
Activation primitive of the function as the deep neural network makes described by adjusting the structural parameters of the deep neural network
Deep neural network output Reservoir type data and corresponding actual reservoir type between difference within preset range, from
And complete the training of deep neural network；
Include: according to the process that the data sensitive to Reservoir type of actual measurement divide Reservoir type
The data sensitive to Reservoir type for obtaining actual measurement, obtain measured data；
The mean value and variance for calculating every kind of measured data obtain the actual measurement mean value and actual measurement variance of every kind of measured data；
The mean value threshold range and variance threshold values range of every kind of data are calculated using the sample average and sample variance of every kind of data；
Judge whether the actual measurement mean value of every kind of measured data is located in corresponding mean value threshold range and every kind of measured data
Whether actual measurement variance is located within the scope of corresponding variance threshold values, obtains the first judging result；
If first judging result indicate the actual measurement mean value of any one measured data be located at corresponding mean value threshold range it
Outer and/or actual measurement variance is located at outside corresponding variance threshold values range, then resurveys the data of corresponding types；
If it is in corresponding mean value threshold range and real that first judging result indicates that the actual measurement mean value of every kind of measured data is located at
It surveys variance to be located within the scope of corresponding variance threshold values, then each measured data is standardized, obtains standard measured data；
Using the standard measured data of each type as the input of deep neural network, the reality of the output of neural network is obtained
Survey Reservoir type.
2. a kind of Reservoir type dividing system based on multiple information characterized by comprising
Sample data obtains module, for obtaining the data sensitive to Reservoir type from the log data of multiple sample points；Institute
Stating the data sensitive to Reservoir type includes neutron data, resistivity, porosity, interval transit time, natural gamma and compensation density；
Actual reservoir type acquisition module carries out each sample point that artificial reservoir division obtains to each sample point for obtaining
Actual reservoir type；
Neural metwork training module, for the data sensitive to Reservoir type of each sample point to be inputted deep neural network,
The structural parameters of adjustment neural network make neural network export corresponding Reservoir type, to carry out to the deep neural network
Training；The trained deep neural network is used to be realized according to the data sensitive to Reservoir type of actual measurement to Reservoir type
Division；
The neural metwork training module, specifically includes:
Sample data mean value and variance computing unit, for utilizing cross-plot and kernel density estimation method respectively to right described in every kind
The data of Reservoir type sensitivity carry out characteristic statistics, determine the mean value and variance of every kind of data sensitive to Reservoir type,
Obtain the sample average and sample variance of every kind of data；
Sample data Standardisation Cell, the sample average and sample variance for utilizing every kind of data are to each described to reservoir class
The data of type sensitivity are standardized, and obtain standardized data；
Training unit, for using the standardized data of each type of each sample point as the defeated of deep neural network
Enter, using line rectification function as the activation primitive of the deep neural network, by adjusting the knot of the deep neural network
Structure parameter makes the difference between the Reservoir type data and corresponding actual reservoir type of the deep neural network output pre-
Within the scope of if, to complete the training of deep neural network；
The Reservoir type dividing system further includes reservoir division module, for the data pair sensitive to Reservoir type according to actual measurement
Reservoir type is divided；The reservoir division module, specifically includes:
Measured data acquiring unit obtains measured data for obtaining the data sensitive to Reservoir type of actual measurement；
Measured data mean value and variance computing unit obtain every kind for calculating the mean value and variance of every kind of measured data
The actual measurement mean value and actual measurement variance of measured data；
Threshold range computing unit calculates the mean value threshold of every kind of data for the sample average and sample variance using every kind of data
It is worth range and variance threshold values range；
Judging unit, for judging whether the actual measurement mean value of every kind of measured data is located in corresponding mean value threshold range and every
Whether the actual measurement variance of kind measured data is located within the scope of corresponding variance threshold values, obtains judging result；
Data adopt unit again, if indicating the actual measurement mean value of any one measured data positioned at corresponding equal for the judging result
Except value threshold range and/or actual measurement variance is located at outside corresponding variance threshold values range, then resurveys the data of corresponding types；
Measured data Standardisation Cell, if indicating the actual measurement mean value of every kind of measured data positioned at corresponding for the judging result
In mean value threshold range and actual measurement variance is located within the scope of corresponding variance threshold values, then is standardized to each measured data,
Obtain standard measured data；
Reservoir division unit, for obtaining using the standard measured data of each type as the input of deep neural network
The actual measurement Reservoir type of the output of neural network.
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| CN111694856B (en) * | 2020-06-11 | 2021-03-12 | 中国石油大学(北京) | Intelligent prediction method and device for reservoir sensitivity |
| CN112253087A (en) * | 2020-10-20 | 2021-01-22 | 河南理工大学 | Biological disturbance reservoir physical property calculation method based on multi-source logging data |
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