CN110222981A - A kind of reservoir classification evaluation method based on the secondary selection of parameter - Google Patents
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Abstract
The invention discloses a kind of reservoir classification evaluation methods based on the secondary selection of parameter, and the correlation analysis of introducing and classification, reservoir evaluation parameter including reservoir evaluation parameter, primary selection, the secondary selection of reservoir evaluation parameter and the K- mean cluster of reservoir evaluation parameter determine reservoir classification and evaluation standard.The beneficial effects of the present invention are: it is preferable to determine reservoir evaluation parameters by secondary, evaluating reservoir is completed by K- mean cluster, so that the selection of parameter is more accurate and reliable, Reservoir Classification result is more credible, the secondary selection of reservoir parameter quantitatively preferably can go out reservoir evaluation parameter, and standard is accurately unified, reservoir evaluation methods of the invention combine macroscopic view and micro-parameter, make finally obtained evaluation criterion more comprehensively, accurately.
Description
Technical field
The present invention relates to a kind of reservoir classification evaluation method, specially a kind of Reservoir Classification based on the secondary selection of parameter is commented
Valence method belongs to reservoir classification and evaluation technical field.
Background technique
Reservoir classification and evaluation is an important element task in oil-gas exploration and development, with every analytical test technology
Progress brings the parameter of numerous characterization reservoirs, and how effectively, accurately Selecting All Parameters as final evaluation parameter and are completed to store up
Layer evaluation is always the problem of scholars pay close attention to.At this stage, there are many method for carrying out evaluation of classification according to selected parameter, but
When Selecting All Parameters, be mostly rule of thumb, can not accurate quantitative analysis, standard disunity.
At this stage, reservoir evaluation methods are primarily present the problem of the following aspects:
(1) lack unified preferred method when choosing evaluation parameter.Traditional evaluating reservoir is most in preferred parameter
It is that evaluation parameter is directly determined according to expertise, the standard disunity that can thus make evaluation parameter choose.
(2) in the specific standards range for determining all kinds of reservoir evaluation parameters, equally lack quantitative method, therefore can make
Different evaluation criterions is obtained with identical data and identical parameter at different researchers.
(3) relatively fewer due to analyzing means of testing in the past, the usually only macroscopic view storage of specified reservoir evaluation standard
Layer parameter, evaluation parameter be not comprehensive.Now with the raising of analysis means of testing, a plurality of types of microcosmic reservoir ginsengs can be obtained
Number, but still no and macroscopical reservoir parameter combines at present, comprehensive reservoir evaluation standard is obtained, also without establishing side accordingly
Method.
Summary of the invention
The object of the invention is that providing to solve the above-mentioned problems and having studied the reservoir based on the secondary selection of parameter
Evaluation of classification method completes evaluating reservoir by K- mean cluster, so that parameter by secondary it is preferable to determine reservoir evaluation parameter
Selection it is more accurate and reliable, a kind of more believable reservoir classification and evaluation side based on the secondary selection of parameter of Reservoir Classification result
Method.
The present invention is through the following technical solutions to achieve the above objectives: a kind of Reservoir Classification based on the secondary selection of parameter is commented
Valence method, comprising the following steps:
The introducing and classification of step 1, reservoir evaluation parameter
The analysis test data for collecting the reflection reservoir properties such as the experiment of pressure mercury and corresponding conventional physical property measurement, will count
According to processing calculating is carried out, some key parameters for needing characterization reservoir obtained by calculation are obtained, by the above parameter according to it
Characterization function is classified；
The correlation analysis of step 2, reservoir evaluation parameter
All evaluation parameters are done into itself and main physical parameter by the methods of principal component analysis or grey correlation analysis
Permeability or porosity do correlation analysis；
The primary selection of step 3, reservoir evaluation parameter
On the basis of reservoir parameter classification and correlation analysis, being averaged for all kinds of parameter related coefficient absolute values is calculated
Value determines every selected quantity as final evaluation parameter of a kind of parameter in conjunction with expertise in this, as weight coefficient,
Complete the primary selection of reservoir evaluation parameter；
The secondary selection of step 4, reservoir evaluation parameter
According to the primary selection of correlation analysis and reservoir evaluation parameter as a result, in every a kind of reservoir evaluation parameter preferably
The parameter of best corresponding number with permeability or porosity correlation, it is complete as the final preferred parameter of reservoir classification and evaluation
At the secondary selection of reservoir evaluation parameter；
Step 5, K- mean cluster determine reservoir classification and evaluation standard
The reservoir evaluation parameter preferably gone out is arranged, the average value of all samples is calculated, as K- mean cluster
Initial cluster center, as needed give number of clusters, enter data into software, obtain reservoir using K- means clustering algorithm
Evaluation of classification standard, and with the evaluation of classification of this standard completion reservoir.
Preferably, in the step 1, the analysis test data of collection includes average pore throat radius, median radius, largest hole
Pressure mercury experiments experiment data, including porosity, permeability including larynx radius, efficiency of mercury withdrawal, middle duty pressure, replacement pressure etc. etc.
Conventional physical property measurement data inside, and reservoir qualitative index is introduced by calculating using above data, under mainstream throat radius
Limit, the parameters such as hardly possible flowing throat radius.
Wherein, reservoir qualitative index has preferable correlation with micro throat structural parameters, can be real to pressure mercury is not done
The sample spot tested characterizes micro throat structure, mainstream throat radius lower limit and difficult flowing throat radius using reservoir qualitative index
The validity of venturi can be characterized.Above-mentioned parameter can be divided into three classes according to the characterization function of parameter, characterization reservoir macroscopic view is special
The parameter of sign includes permeability, porosity, reservoir qualitative index, characterize Pore throat size parameter include average pore throat radius, in
It is worth radius, maximum pore throat radius, the parameter for characterizing percolation ability includes efficiency of mercury withdrawal, middle duty pressure, replacement pressure, mainstream venturi
Radius lower limit, difficult flowing throat radius.
Preferably, in the step 3, the parameter of calculating includes three classes, is respectively as follows: characterization reservoir gross feature parameter, table
Levy Pore throat size parameter and characterization percolation ability parameter.
Preferably, in the step 4, reservoir qualitative index is chosen in the parameter of characterization reservoir gross feature, is being characterized
Duty pressure and difficult flowing throat radius, choose intermediate value half in the parameter of characterization percolation ability in choosing in the parameter of Pore throat size
Diameter adds permeability, and totally 5 parameters are as the parameter for establishing reservoir classification and evaluation standard.
The beneficial effects of the present invention are: should be designed rationally based on the reservoir classification evaluation method of the secondary selection of parameter, pass through
Secondary it is preferable to determine reservoir evaluation parameters, evaluating reservoir are completed by K- mean cluster, so that the selection of parameter more accurately may be used
It leans on, Reservoir Classification result is more credible, and the secondary selection of reservoir parameter quantitatively can preferably go out reservoir evaluation parameter, and standard
Accurate unified, reservoir evaluation methods of the invention combine macroscopic view and micro-parameter, make finally obtained evaluation criterion more
Comprehensively, accurately.
Detailed description of the invention
Fig. 1 is flow diagram of the present invention.
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.
Referring to Fig. 1, a kind of reservoir classification evaluation method based on the secondary selection of parameter, comprising the following steps:
The introducing and classification of step 1, reservoir evaluation parameter, collecting includes average pore throat radius, median radius, maximum pore throat
Pressure mercury experiments experiment data, including porosity, permeability including radius, efficiency of mercury withdrawal, middle duty pressure, replacement pressure etc. etc. exist
Interior conventional physical property measurement data, and reservoir qualitative index is introduced by calculating using above data, mainstream throat radius lower limit,
The parameters such as hardly possible flowing throat radius.Wherein, reservoir qualitative index has preferable correlation with micro throat structural parameters, can be right
The sample spot for not doing pressure mercury experiment characterizes micro throat structure, mainstream throat radius lower limit and difficulty using reservoir qualitative index
Flowing throat radius can characterize the validity of venturi.Above-mentioned parameter can be divided into three classes according to the characterization function of parameter, table
The parameter of sign reservoir gross feature includes permeability, porosity, reservoir qualitative index, and it includes average for characterizing the parameter of Pore throat size
Pore throat radius, median radius, maximum pore throat radius, the parameter for characterizing percolation ability includes efficiency of mercury withdrawal, middle duty pressure, row's drive pressure
Power, mainstream throat radius lower limit, difficult flowing throat radius；
The correlation analysis of step 2, reservoir evaluation parameter does all parameters in step A by Principal Component Analysis
The correlation analysis of each parameter and permeability；
Each parameter is as follows: with permeability related coefficient
The primary selection of step 3, reservoir evaluation parameter, calculating three classes parameter are averaged with permeability related coefficient absolute value
Value is it is found that the average value of the related coefficient absolute value of characterization reservoir gross feature parameter is 0.60, characterization Pore throat size parameter
The average value of related coefficient absolute value is 0.64, and the average value for characterizing the related coefficient absolute value of percolation ability parameter is 0.59,
The average value for characterizing the related coefficient absolute value of Pore throat size parameter is maximum.In conjunction with expertise, it may be determined that characterization reservoir is macro
The parameter for seeing feature chooses 1, in addition permeability totally 2, the parameter for characterizing Pore throat size chooses 2, characterizes percolation ability
Parameter chooses 1；
The secondary selection of step 4, reservoir evaluation parameter is tied according to the primary selection of correlation analysis and reservoir evaluation parameter
Fruit chooses reservoir qualitative index in the parameter of characterization reservoir gross feature, chooses intermediate value in the parameter of characterization Pore throat size
Pressure and difficult flowing throat radius, choose median radius in the parameter of characterization percolation ability, add permeability, totally 5 ginsengs
Number is as the parameter for establishing reservoir classification and evaluation standard；
Step 5, K- mean cluster determine reservoir classification and evaluation standard, and each sample data are arranged, various kinds is calculated
The average value of 5 parameters of product sets number of clusters as 3, enters data into software as the initial cluster center of K- mean cluster
In, cluster result is obtained, and suitably correct by expertise, obtains final reservoir classification and evaluation standard.
Reservoir classification and evaluation standard is as follows:
According to the above standard, each sample point parameter is compareed, completes the evaluation of classification of reservoir, as a result are as follows:
Working principle: when using the reservoir classification evaluation method based on the secondary selection of parameter, pass through pressure firstly, collecting
Mercury, conventional Physical Property Analysis etc. test the reservoir parameter directly obtained by experiment and other need to be calculated parameter, and presses it
Characterization function is classified；Secondly, parameters and most important physical parameter are done correlation analysis；Third calculates all kinds of
The correlation average value of parameter determines the quantity for choosing all kinds of parameters as final evaluation parameter in conjunction with expertise, completes ginseng
Several primary selections；4th, according to all kinds of number of parameters, the several parameters best with infiltration rate dependence are chosen, as final
Reservoir evaluation parameter completes the secondary selection of parameter；Finally, obtaining reservoir classification and evaluation standard by K- mean cluster.This hair
Bright beneficial effect is: the present invention has investigated one kind using data, application parameter second selecting and the cluster such as physical property and pressure mercury
Reservoir classification evaluation method, compared to traditional reservoir evaluation methods, this method can more accurately choose evaluation parameter, as a result
It is more credible.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (4)
1. a kind of reservoir classification evaluation method based on the secondary selection of parameter, it is characterised in that: the following steps are included:
The introducing and classification of step 1, reservoir evaluation parameter
The analysis test data for collecting the experiment of pressure mercury and the reflection reservoir properties such as corresponding conventional physical property measurement, by data into
Row processing calculates, and some key parameters for needing characterization reservoir obtained by calculation is obtained, by the above parameter according to its characterization
Function is classified；
The correlation analysis of step 2, reservoir evaluation parameter
All evaluation parameters are done it by the methods of principal component analysis or grey correlation analysis to permeate with main physical parameter
Rate or porosity do correlation analysis；
The primary selection of step 3, reservoir evaluation parameter
On the basis of reservoir parameter classification and correlation analysis, the average value of all kinds of parameter related coefficient absolute values is calculated, with
This determines every selected quantity as final evaluation parameter of a kind of parameter, completes storage as weight coefficient in conjunction with expertise
The primary selection of layer evaluation parameter；
The secondary selection of step 4, reservoir evaluation parameter
According to the primary selection of correlation analysis and reservoir evaluation parameter as a result, in every a kind of reservoir evaluation parameter preferably with infiltration
The parameter of saturating rate or the best corresponding number of porosity correlation completes storage as the final preferred parameter of reservoir classification and evaluation
The secondary selection of layer evaluation parameter；
Step 5, K- mean cluster determine reservoir classification and evaluation standard
The reservoir evaluation parameter preferably gone out is arranged, the average value of all samples is calculated, as the first of K- mean cluster
Beginning cluster centre gives number of clusters as needed, enters data into software, obtain Reservoir Classification using K- means clustering algorithm
Evaluation criterion, and with the evaluation of classification of this standard completion reservoir.
2. a kind of reservoir classification evaluation method based on the secondary selection of parameter according to claim 1, it is characterised in that: institute
State in step 1, the analysis test data of collection include average pore throat radius, median radius, maximum pore throat radius, efficiency of mercury withdrawal,
Pressure mercury experiments experiment data including middle duty pressure, replacement pressure etc., the conventional physical property including porosity, permeability etc. are surveyed
Data are tried, and introduce reservoir qualitative index by calculating using above data, mainstream throat radius lower limit, hardly possible flowing throat radius
Etc. parameters.
Wherein, reservoir qualitative index has preferable correlation with micro throat structural parameters, can press mercury experiment to not doing
Sample spot characterizes micro throat structure using reservoir qualitative index, and mainstream throat radius lower limit and difficult flowing throat radius can be with
Characterize the validity of venturi.Above-mentioned parameter can be divided into three classes according to the characterization function of parameter, characterization reservoir gross feature
Parameter includes permeability, porosity, reservoir qualitative index, and the parameter for characterizing Pore throat size includes average pore throat radius, intermediate value half
Diameter, maximum pore throat radius, the parameter for characterizing percolation ability includes efficiency of mercury withdrawal, middle duty pressure, replacement pressure, mainstream throat radius
Lower limit, difficult flowing throat radius.
3. a kind of reservoir classification evaluation method based on the secondary selection of parameter according to claim 1, it is characterised in that: institute
It states in step 3, the parameter of calculating includes three classes, is respectively as follows: characterization reservoir gross feature parameter, characterization Pore throat size parameter and table
Levy percolation ability parameter.
4. a kind of reservoir classification evaluation method based on the secondary selection of parameter according to claim 1, it is characterised in that: institute
It states in step 4, reservoir qualitative index is chosen in the parameter of characterization reservoir gross feature, selected in the parameter of characterization Pore throat size
Middle duty pressure and difficult flowing throat radius are taken, chooses median radius in the parameter of characterization percolation ability, adds permeability, altogether
5 parameters are as the parameter for establishing reservoir classification and evaluation standard.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113239955A (en) * | 2021-04-07 | 2021-08-10 | 长江大学 | Carbonate reservoir rock classification method |
| CN115749760A (en) * | 2022-11-28 | 2023-03-07 | 中海石油(中国)有限公司海南分公司 | Reservoir fluid property evaluation method based on measurement and recording combination |
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| CN103900942A (en) * | 2014-01-10 | 2014-07-02 | 中国石油大学(华东) | Method for continuously characterizing microscopic pore throat parameters of reservoir based on high-pressure mercury injection analysis |
| CN109492860A (en) * | 2018-09-26 | 2019-03-19 | 中国石油天然气股份有限公司 | A kind of tight sandstone reservoir multi-parameter quantitative evaluation method based on gas well deliverability |
| CN109711429A (en) * | 2018-11-22 | 2019-05-03 | 中国石油天然气股份有限公司 | A kind of evaluating reservoir classification method and device |
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| US20130110524A1 (en) * | 2011-10-26 | 2013-05-02 | Nansen G. Saleri | Management of petroleum reservoir assets using reserves ranking analytics |
| CN103900942A (en) * | 2014-01-10 | 2014-07-02 | 中国石油大学(华东) | Method for continuously characterizing microscopic pore throat parameters of reservoir based on high-pressure mercury injection analysis |
| CN109492860A (en) * | 2018-09-26 | 2019-03-19 | 中国石油天然气股份有限公司 | A kind of tight sandstone reservoir multi-parameter quantitative evaluation method based on gas well deliverability |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113239955A (en) * | 2021-04-07 | 2021-08-10 | 长江大学 | Carbonate reservoir rock classification method |
| CN115749760A (en) * | 2022-11-28 | 2023-03-07 | 中海石油(中国)有限公司海南分公司 | Reservoir fluid property evaluation method based on measurement and recording combination |
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