CN110163442A - A kind of gas well plug-ging prediction technique based on integrated study - Google Patents
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Abstract
The gas well plug-ging prediction technique based on integrated study that the invention discloses a kind of, the present invention specifically includes the following steps: S1, gas well initial data acquisition, the pretreatment of S2, Characteristic Extraction and data, the design of S3, base classifier, S4, ballot polymerization, the judgement of S5, gas well plug-ging state, the present invention relates to gas field development technical fields.The gas well plug-ging prediction technique based on integrated study, there is stronger specific aim and accuracy compared with the gas well plug-ging prediction model of traditional universality, it not only can more accurately judge the hydrops situation of gas well, there is better accuracy and convenience relative to traditional gas well plug-ging model, targetedly gas well plug-ging is obtained to the production status in different gas fields simultaneously and analyzes result, drainage gas production technology is instructed to construct preferably to reach discharge shaft bottom hydrops, restore gas well capacity, further excavate the later development potentiality of gas reservoir, achieve the purpose that the recovery ratio for improving gas reservoir.
Description
Technical field
The present invention relates to gas field development technical field, specially a kind of gas well plug-ging prediction technique based on integrated study.
Background technique
Current gas well plug-ging prediction technique is mainly based upon the critical flow physical theory model of individual particle drop for boundary
Face tension and drag coefficient are constant, and analyze in conjunction with empirical equation the hydrops situation of gas well, but are answered actual
With the calculated result of different models in the process, there are biggish deviations, and the general applicability of calculation method is not strong, to actual
On the one hand the reason of production operation and management bring different degrees of inconvenience, cause error is that liquid shape is had ignored in computation model
Caused by the resistance coefficient of state variation, another aspect is exactly that theoretical model is all using single drop as research object thus by drag force
Coefficient is to be set as fixed value, has ignored the metamorphosis of drop and is imitated by the droplet particles group for influencing each other and generating between drop
Should be so as to cause the calculating deviation of drag coefficient, traditional calculation method is mainly to a certain stage mistake in certain type gas fields
The research method that gets of analysis and conclusion toward data explain the shape of a certain stage gas well plug-ging in another type gas field
Condition, while the analysis of available data is not utilized rationally, gas field is directly influenced to the research of a certain gas field gas well plug-ging
How reasonable development makes more targeted, more reasonable, the more efficient weight for becoming each gas field and facing of gas well plug-ging prediction work
Problem is wanted, therefore we have the characteristics that enhancing recognizer generalization ability using using integrated study, proposed a kind of based on collection
It has solved the above problems at the gas well plug-ging prediction technique of study.
Summary of the invention
(1) the technical issues of solving
In view of the deficiencies of the prior art, the gas well plug-ging prediction technique based on integrated study that the present invention provides a kind of, solution
The calculated result of existing method of having determined different models in actual application process is there are biggish deviation, and calculation method
General applicability is not strong, brings different degrees of inconvenience, while traditional calculation method master to actual production operation and management
If the research method and conclusions that get to the analysis with the passing data of a certain stage in certain type gas fields explain other one
The situation of a certain stage gas well plug-ging in a type gas field, while the analysis of available data is not utilized rationally, thus to certain
The problem of research of one gas field gas well plug-ging directly influences gas field reasonable development.
(2) technical solution
In order to achieve the above object, the present invention is achieved by the following technical programs: a kind of gas well based on integrated study
Hydrops prediction technique, specifically includes the following steps:
The acquisition of S1, gas well initial data: gas well plug-ging simulation test data or connection gas field Production database are obtained, is mentioned
The creation data of individual well is taken, and data set is saved as to the format of csv file；
The pretreatment of S2, Characteristic Extraction and data: the data bi-directional scaling for first acquiring step S1 is allowed to fall into
One small specific sections removes the unit limitation of data, and is translated into nondimensional pure values, in order to not commensurate
Or the index of magnitude is able to carry out and compares and weight, while carrying out to missing values, invalid value and the repetition values that initial data is concentrated
Cleaning, correct or understand error value, removal data unit and scope limitation, to be translated into dimensionless, variance and
It is worth consistent pure values, for the feature of not commensurate or magnitude, is able to carry out and compares and weighted calculation, carried out with calculation formula
Data prediction, in pretreated regulation, the mean value of all data points is 0 for each attribute or each column, and variance is
1, random division is carried out to the collection containing label data handled well, is divided into training set and test set, and data set input vector is turned
It is changed to matrix；
The design of S3, base classifier: after the pretreatment for completing step S2 data, optimal gas well plug-ging is pre- in order to obtain
Model is surveyed, preferable three classifier algorithms of performance is selected, data is input in classifier algorithm in a manner of matrix, are passed through
Three machine learning algorithm models provide three sorter models for subsequent integrated study, and each base classifier algorithm passes through super
The way of search of parameter automatic search module obtains optimal classification device, after obtaining each base sorter model, needs to each
Base sorter model is verified；
S4, ballot polymerization: the verifying collection data matrix after step S2 pretreatment is input to step S3 trained three
In kind base sorter model, then by output as a result, including classification and weight as new input being input to ballot polymerization
In classifier, it is polymerize by way of ballot, the sorter model after polymerization obtains final result；
S5, gas well plug-ging state judge: entering data into after corresponding computer program output parameter as gas well
Predicted state.
Preferably, the creation data of monocrystalline includes gas production, oil jacket pressure, casing programme, payzone depth in the step S1
Degree, gas production, Liquid output and tubing size.
Preferably, the feature vector selected in the step S2 has: pay zone depth, pressure, gas production, Liquid output and oil pipe
Size.
Preferably, the calculation formula of data prediction is in the step S2WithY in formulaiIt represents " dimensionless, variance and the consistent pure values of mean value ", xiIt represents " individual data ",It represents " average value ", n represents " data number ", behalf " standard deviation ".
Preferably, training set accounts for 70% in the step S2, and test set accounts for 30%, and the dimension of matrix is (- 1,5), dimension
- 1 represents training set or test set data volume sum in degree.
Preferably, three classifier algorithms include that random forests algorithm, limit tree algorithm and guidance are poly- in the step S3
Hop algorithm.
Preferably, cross validation or leaving-one method are used in the step S3, to be verified to each base sorter model,
To determine the judging result of sorter model.
Preferably, the ballot polymerization in the step S4 is using the ballot mode for having weight, i.e., by three kinds of model predictions
It as a result is the average value of a certain class weight as standard, as soft ballot.
(3) beneficial effect
The gas well plug-ging prediction technique based on integrated study that the present invention provides a kind of.Have compared with prior art following
The utility model has the advantages that
(1), it is somebody's turn to do the gas well plug-ging prediction technique based on integrated study, with traditional critical flow based on individual particle drop
Physical theory model compares, and has abandoned traditional single Physical Modeling, is extracted by the algorithm of integrated study main
Creation data parameter as feature vector, optimized parameter is obtained in the way of grid search and forms base classifier, then
Submodel is polymerize by the way of soft ballot, finally the classifier mould after integrated study ballot classifier polymerization
Type exports final gas well plug-ging judging result, which can carry out the gas just for the gas field development situation of acquisition data
The gas well plug-ging in field is predicted, with stronger specific aim and accurately compared with the gas well plug-ging prediction model of traditional universality
Property, it not only can more accurately judge the hydrops situation of gas well, while can obtain to the production status in different gas fields
Targetedly gas well plug-ging analysis as a result, preferably instruct drainage gas production technology to construct with reach discharge shaft bottom hydrops, it is extensive
Multiple gas well capacity, further excavates the later development potentiality of gas reservoir, achievees the purpose that the recovery ratio for improving gas reservoir.
(2), it is somebody's turn to do the gas well plug-ging prediction technique based on integrated study, with traditional critical flow based on individual particle drop
Physical theory model compares, and gets rid of the limitation of traditional gas well plug-ging physical model, is not simply to research object liquid
Drop or liquid mould are analyzed, but use main creation data parameter as feature vector, are collected to gas well plug-ging situation
At study, there is better accuracy and convenience relative to traditional gas well plug-ging model.
(3), should gas well plug-ging prediction technique based on integrated study, by can be into for different gas field development situations
The different gas well plug-gings of row predict training set, have stronger specific aim compared with the gas well plug-ging prediction model of traditional universality
And adaptability.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention figure；
Fig. 2 is the gas well plug-ging prediction result contrast table figure of present invention comparison case.
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 embodiment of the present invention provides a kind of technical solution referring to FIG. 1-2: a kind of gas well plug-ging based on integrated study is pre-
Survey method, specifically includes the following steps:
The acquisition of S1, gas well initial data: gas well plug-ging simulation test data or connection gas field Production database are obtained, is mentioned
The creation data of individual well is taken, and data set is saved as to the format of csv file；
The pretreatment of S2, Characteristic Extraction and data: since the precision and dimension of the test data of different gas wells may not
Unanimously, before being classified, data need to be pre-processed.The data bi-directional scaling that step S1 is acquired first, is allowed to fall
Enter a small specific sections, remove the unit limitation of data, and be translated into nondimensional pure values, in order to different lists
The index of position or magnitude is able to carry out and compares and weight, at the same missing values, invalid value and the repetition values that initial data is concentrated into
Row cleaning, corrects or understands error value, during gas liquid two-phase flow, gas phase and liquid phase are always carried out in different fluidised forms
Transformation, the key point of transformation are tolerance, liquid measure and pressure, and the purpose of gas well plug-ging prediction is that prediction is by the energy of gas
No drop to be drained into well head from shaft bottom, the empirical equation and actual conditions of bottom pressure source physical model have certain difference,
Well head pressure can obtain directly and accurately from apparatus, while the calculating parameter for influencing gas flow rate and flow is also
There are pressure influence, such as Reynolds number, drag coefficient.Therefore the data cases of real case of the invention are directed to, in removal data
Unit and scope limitation, dimensionless, variance and the consistent pure values of mean value are translated into, for not commensurate or magnitude
Feature, be able to carry out and compare and weighted calculation, carry out data prediction with calculation formula, in pretreated regulation, for
The mean value of all data points is 0 for each attribute or each column, variance 1, is carried out to the collection containing label data handled well random
It divides, is divided into training set and test set, and data set input vector is converted into matrix；
The design of S3, base classifier: after the pretreatment for completing step S2 data, optimal gas well plug-ging is pre- in order to obtain
Model is surveyed, preferable three classifier algorithms of performance is selected, data is input in classifier algorithm in a manner of matrix, are passed through
Three machine learning algorithm models provide three sorter models for subsequent integrated study, and each base classifier algorithm passes through super
The way of search of parameter automatic search module obtains optimal classification device, after obtaining each base sorter model, needs to each
Base sorter model is verified；
S4, ballot polymerization: the verifying collection data matrix after step S2 pretreatment is input to step S3 trained three
In kind base sorter model, then by output as a result, including classification and weight as new input being input to ballot polymerization
In classifier, it is polymerize by way of ballot, the sorter model after polymerization obtains final result, for gas well
Hydrops forecasting problem, each machine learning model performance is all very excellent, but passes through point after integrated study ballot polymerization
The accuracy rate of class device model model more single than other will be high.So Ensemble Learning Algorithms are asked for solving prediction gas well plug-ging
It inscribes and is more suitable for opposite other machines learning algorithm；
S5, gas well plug-ging state judge: entering data into after corresponding computer program output parameter as gas well
Predicted state, for simpler expression gas well state, setting " -1 " represent the non-hydrops of gas well, " 0 " represents the critical hydrops of gas well,
" 1 " represents gas well hydrops.
In the present invention, in step S1 the creation data of monocrystalline include gas production, oil jacket pressure, casing programme, pay zone depth,
Gas production, Liquid output and tubing size.
In the present invention, the feature vector selected in step S2 has: pay zone depth, pressure, gas production, Liquid output and oil pipe ruler
It is very little.
In the present invention, the calculation formula of data prediction is in step S2WithY in formulaiIt represents " dimensionless, variance and the consistent pure values of mean value ", xiIt represents " individual data ",It represents " average value ", n represents " data number ", behalf " standard deviation ".
In the present invention, training set accounts for 70% in step S2, and test set accounts for 30%, and the dimension of matrix is (- 1,5), dimension
In -1 represent training set or test set data volume sum.
In the present invention, three classifier algorithms include random forests algorithm, limit tree algorithm and guidance polymerization in step S3
Algorithm.
In the present invention, cross validation or leaving-one method are used in step S3, to be verified to each base sorter model, with
Determine the judging result of sorter model.
In the present invention, the ballot polymerization in step S4 is using the ballot mode for having weight, i.e., by three kinds of model prediction knots
Fruit is the average value of a certain class weight as standard, as soft ballot.
Compare case
For technical solution of the present invention and the advantage of preferably withdrawing deposit, examination is simulated with the famous special sodium gas well plug-ging of the industry
For testing, displaying is compared, as shown in table Fig. 2.
Such as table Fig. 2 it is found that prediction result of the invention is substantially better than traditional gas well plug-ging prediction technique, with actual observation
Hydrops situation degree of correspondence it is higher, so, this method not only simplifies the mechanism study of traditional complexity, and gas well plug-ging is pre-
The result of survey is higher, provides effective theory support and rationally guidance for the Efficient Development in gas field and whole decision and deployment,
Compared with traditional critical flow physical theory model based on individual particle drop, traditional single physical model has been abandoned
Method extracts main creation data parameter as feature vector, in the way of grid search by the algorithm of integrated study
It obtains optimized parameter and forms base classifier, then submodel is polymerize by the way of soft ballot, finally by integrated
Sorter model after study ballot classifier polymerization exports final gas well plug-ging judging result, and the judging result is just for adopting
The gas field development situation of collection data can carry out the gas well plug-ging prediction in the gas field, predict with the gas well plug-ging of traditional universality
Model, which is compared, has stronger specific aim and accuracy, not only can more accurately judge the hydrops situation of gas well, together
When the production status in different gas fields can be obtained targetedly gas well plug-ging analysis as a result, preferably instructing water drainage-gas recovery technology
Technology construction restores gas well capacity, further excavates the later development potentiality of gas reservoir, reach and mention to reach discharge shaft bottom hydrops
The purpose of the recovery ratio of high gas reservoir, while compared with traditional critical flow physical theory model based on individual particle drop,
The limitation for getting rid of traditional gas well plug-ging physical model, be not research object drop or liquid mould are analyzed simply, and
It is using main creation data parameter as feature vector, integrated study is carried out to gas well plug-ging situation, relative to traditional
Gas well plug-ging model has better accuracy and convenience, by that can carry out different gas for different gas field development situations
Well hydrops predicts training set, has stronger specific aim and adaptation compared with the gas well plug-ging prediction model of traditional universality
Property.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (8)
1. a kind of gas well plug-ging prediction technique based on integrated study, it is characterised in that: specifically includes the following steps:
The acquisition of S1, gas well initial data: gas well plug-ging simulation test data or connection gas field Production database are obtained, is extracted single
The creation data of well, and data set is saved as to the format of csv file；
The pretreatment of S2, Characteristic Extraction and data: the data bi-directional scaling for first acquiring step S1 is allowed to fall into one
Small specific sections remove the unit limitation of data, and are translated into nondimensional pure values, in order to not commensurate or amount
The index of grade, which is able to carry out, to be compared and weights, while missing values, invalid value and the repetition values concentrated to initial data are cleaned,
It corrects or understands error value, in the unit and scope limitation of removal data, it is consistent with mean value to be translated into dimensionless, variance
Pure values the feature of not commensurate or magnitude is able to carry out and is compared and weighted calculation, it is pre- to carry out data with calculation formula
Processing, in pretreated regulation, the mean value of all data points is 0 for each attribute or each column, variance 1, to place
The collection containing label data managed carries out random division, is divided into training set and test set, and data set input vector is converted to square
Battle array；
The design of S3, base classifier: after the pretreatment for completing step S2 data, optimal gas well plug-ging predicts mould in order to obtain
Type selects preferable three classifier algorithms of performance, data is input in classifier algorithm in a manner of matrix, pass through three
Machine learning algorithm model provides three sorter models for subsequent integrated study, and each base classifier algorithm passes through hyper parameter
The way of search of automatic search module obtains optimal classification device, after obtaining each base sorter model, needs to each base point
Class device model is verified；
S4, ballot polymerization: the verifying collection data matrix after step S2 pretreatment is input to the trained three kinds of bases of step S3
In sorter model, then by output as a result, including classification and weight as new input is input to ballot polymerization classification
In device, it is polymerize by way of ballot, the sorter model after polymerization obtains final result；
S5, gas well plug-ging state judge: entering data into after corresponding computer program output parameter as the prediction of gas well
State.
2. a kind of gas well plug-ging prediction technique based on integrated study according to claim 1, it is characterised in that: the step
The creation data of monocrystalline includes gas production, oil jacket pressure, casing programme, pay zone depth, gas production, Liquid output and oil pipe in rapid S1
Size.
3. a kind of gas well plug-ging prediction technique based on integrated study according to claim 1, it is characterised in that: the step
The feature vector selected in rapid S2 has: pay zone depth, pressure, gas production, Liquid output and tubing size.
4. a kind of gas well plug-ging prediction technique based on integrated study according to claim 1, it is characterised in that: the step
The calculation formula of data prediction is in rapid S2WithY in formulaiGeneration
Table " dimensionless, variance and the consistent pure values of mean value ", xiIt represents " individual data ",It represents " average value ", n represents " data number
Mesh ", behalf " standard deviation ".
5. a kind of gas well plug-ging prediction technique based on integrated study according to claim 1, it is characterised in that: the step
Training set accounts for 70% in rapid S2, and test set accounts for 30%, and the dimension of matrix is (- 1,5), and -1 represents training set or test in dimension
Collect data volume sum.
6. a kind of gas well plug-ging prediction technique based on integrated study according to claim 1, it is characterised in that: the step
Three classifier algorithms include random forests algorithm, limit tree algorithm and guidance aggregating algorithm in rapid S3.
7. a kind of gas well plug-ging prediction technique based on integrated study according to claim 1, it is characterised in that: the step
Cross validation or leaving-one method are used in rapid S3, to verify to each base sorter model, to determine sentencing for sorter model
Disconnected result.
8. a kind of gas well plug-ging prediction technique based on integrated study according to claim 1, it is characterised in that: the step
Ballot polymerization in rapid S4 is using the ballot mode for having weight, i.e., is the flat of a certain class weight by three kinds of model prediction results
Mean value is as standard, as soft ballot.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111598724A (en) * | 2020-05-19 | 2020-08-28 | 四川革什扎水电开发有限责任公司 | Time-interval integration method for day-ahead prediction of warehousing flow of small and medium-sized reservoirs |
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| CN111598724B (en) * | 2020-05-19 | 2022-07-22 | 四川革什扎水电开发有限责任公司 | Time-interval integration method for day-ahead prediction of warehousing flow of small and medium reservoirs |
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