CN104200059B - A kind of oil-water well behavioural analysis prediction meanss and method - Google Patents
A kind of oil-water well behavioural analysis prediction meanss and method Download PDFInfo
- Publication number
- CN104200059B CN104200059B CN201410355231.4A CN201410355231A CN104200059B CN 104200059 B CN104200059 B CN 104200059B CN 201410355231 A CN201410355231 A CN 201410355231A CN 104200059 B CN104200059 B CN 104200059B
- Authority
- CN
- China
- Prior art keywords
- well
- matrix
- oil
- data
- individual
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The present invention discloses a kind of oil-water well behavioural analysis prediction meanss and method.The technical solution adopted by the present invention includes measurand oil well and the well being arranged in around oil well, it is characterised in that：Crude oil water content sensor and water filling quantity sensor are respectively equipped with the oil well, well, it is respectively used to gather the moisture content of oil well and the water injection rate of well, and the analog quantity of collection is converted into digital quantity through A/D converter, transmitted again by data cable to host computer, the host computer carries out stability identification to gathered data.The present invention provides stability identification algorithm to the data for modeling, understands with step, and algorithm is simple, and effect meets the advantages of requiring.
Description
Technical field
The invention belongs to oil field development prediction field, more particularly to a kind of oil-water well behavioural analysis prediction meanss.
Background technology
The analysis prediction of individual well behavior is an important content in field production management.And use general to oil well at present
Conventional analysis method, such as analysis of manufacturing capability, Production Decline Analysis.These methods are all to isolate oil well and do not consider ambient water
The influence of well.Some the unconventional methods analyzed between well, such as interference test, tracer test technology often all can be caused by
The stopping production of oil well or expense are too high and are seldom used.Study the behavior of well group using numerical simulation mostly now, it is such as polynary
Time Series Analysis Method.
System mode point stable state, unstable state and the transition state fallen between.For oil-water well row
For prediction generally use modeling pattern, the sample of collection is it is assumed to obtain under system stable state, mathematics is then set up
Model is predicted to oil-water well behavior, and for the transition state in potential, initial non-steady state, existing prediction algorithm
It can not distinguish whether the gathered data for modeling is reliable.Therefore for the centre in stabilization process and non-steady state, transition
Stage, or due to the reason such as ageing equipment, system be unstable so that when gathered data deviates former stable state, now utilize
Data comprising unstable information build forecast model, and prediction effect certainly will be caused to deviate systems stabilisation direction, so as to cause erroneous judgement
Generation.And through consulting the algorithm recognized currently without pertinent literature to modeling data, it is therefore necessary to modeling data
Identification analysis, i.e. the initial matrix stability to modeling is carried out to recognize.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of oil-water well behavioural analysis prediction meanss and method.
To solve the above problems, the technical solution adopted by the present invention includes measurand oil well and is arranged in around oil well
Well, it is characterised in that：Crude oil water content sensor and water filling quantity sensor are respectively equipped with the oil well, well, is used respectively
In the moisture content and the water injection rate of well of collection oil well, and the analog quantity of collection is converted into digital quantity through A/D converter, then led to
Cross data cable to transmit to host computer, the host computer carries out stability identification to gathered data.
The method that described device is realized, it is characterised in that the host computer, which carries out stability identification to gathered data, to be included such as
Lower step：
If collectionIndividual data volume, collect enough samples and be divided intoIt is individualMatrix,For
Natural number；
（1）OrderWhenRank matrixFor stable state, its covariance matrix is taken, then characteristic equation be：；
（2）It is considered that remainingIt is individualThe judgement of stability of rank matrix is in normal processes dataOn the basis of scramble
Dynamic change is formed, if disturbance influences little to former process, then it is assumed that be benign disturbance, be otherwise pernicious disturbance, the former can use
In structure forecast model or for more new model, it is expressed as with the language of mathematics：TheIt is individualRank matrix covariance is, corresponding characteristic root and characteristic vector change are turned to：,, thenIt is individualRank square
Battle array covariance characteristic equation be：, above formula neutralizing is obtained；
（3）2 norms are taken to obtain to above formula, asIt is individualThe covariance matrix of rank matrix is aligned
The relative error of normal process data matrix, because covariance matrix is symmetrical matrix, so,
Because matrix belongs to real symmetric matrix and has complete characteristic vector-system in the formula, the maximum of matrix can be asked for using power method iteration
Characteristic value；
（4）Repeat（2）、（3）Until, norm value is less to think normal processes data, otherwise may be considered just
Beginning fault data or fault data, should be abandoned.
The present invention provides stability identification algorithm to the data for modeling, understands with step, and algorithm is simple, and effect is full
The advantages of foot is required.The present invention is applied to the analysis of oil-water well behavior prediction and will further improve prediction accuracy, so that indirectly
Improve field management efficiency.Other apparatus of the present invention have it is simple and easy to apply, it is easy for installation, the advantages of lightweight.
Brief description of the drawings
The present invention is further detailed explanation with reference to the accompanying drawings and detailed description.
Fig. 1 is present invention analysis prediction meanss schematic diagram；
Fig. 2 is profit well group phase change amount change schematic diagram of the present invention.
Embodiment
As shown in figure 1, the oil-water well behavioural analysis prediction meanss of the present invention, including measurand oil well 1 and it is arranged in oil
Crude oil water content sensor 3 and water filling quantity sensor 4 are respectively equipped with well 2 around well, the oil well 1, well 2, respectively
Digital quantity is converted into through A/D converter 5 for gathering the moisture content of oil well and the water injection rate of well, and by the analog quantity of collection,
Transmitted again by data cable to host computer 6, the host computer carries out stability identification to gathered data.
The host computer carries out stability identification to gathered data and comprised the following steps：
If collectionIndividual data volume, collect enough samples and be divided intoIt is individualMatrix,For
Natural number；
（1）OrderWhenRank matrixFor stable state, its covariance matrix is taken（Still use the code name）, then feature
Equation is：；WhereinForRank matrix, is made up of gathered data；For matrixCharacteristic value；ForRank is single
First battle array；It is characterized vector；The equation is solution matrixCharacteristic value obtains formula.
（2）It is considered that remainingIt is individualThe judgement of stability of rank matrix is in normal processes dataOn the basis of scramble
Dynamic change is formed, if disturbance influences little to former process, then it is assumed that be benign disturbance, be otherwise pernicious disturbance, the former can use
In structure forecast model or for more new model, it is expressed as with the language of mathematics：TheIt is individualRank matrix covariance is（For perturbation matrix,, and）, corresponding characteristic root and characteristic vector change（Or
The change of solution）For：,（, and）, thenIt is individualRank matrix covariance
Characteristic equation be：, above formula neutralizing is obtained；
（3）2 norms are taken to obtain to above formula, asIt is individualThe covariance matrix of rank matrix is aligned
The relative error of normal process data matrix, because covariance matrix is symmetrical matrix, so,
Because matrix belongs to real symmetric matrix and has complete characteristic vector-system in the formula, the maximum of matrix can be asked for using power method iteration
Characteristic value；Each alphabetical implication is same（1）Step.Power method is one for solution matrix eigenvalue of maximum and corresponding characteristic vector
Iterative algorithm is planted, is a kind of numerical analysis method, its basic thought is：
Assuming that askingRank matrixCharacteristic value and characteristic vector, first appoint take one it is initialDimensional vector, and（Note：AskInfinite Norm）.Algorithm terminates constant, put；Calculation procedure is as follows（Get a foothold
MarkFor iterations）：
A. calculate;
B. ask;
c.
If d., then approximate characteristic vector is exportedAnd approximate eigenvalue, calculated to terminate
Method.Otherwise put, return to a steps.
（4）Repeat（2）、（3）Until, norm value is less to think normal processes data, otherwise may be considered just
Beginning fault data or fault data, should be abandoned.
Experimental example：
A certain oil field block, the block is 5 areal well patterns, and injector producer distance is 500m.5 well groups are taken to be noted
Adopt correspondence analysis.Oil, the relative position of well are in 5 well groups：Centre is a bite oil well, 4 mouthfuls of well saliva wells of arrangement, west around it
Beijiao and northeast corner are well 1 and well 2, and southwest corner and southeast corner are well 3 and well 4.
If profit well group is regarded as the system of a multiple-input and multiple-output, a bite oil well and surrounding well or a bite well
Production target with surrounding oil well is to constitute a multivariate time series, and 4 mouthfuls of well saliva wells of water ratio in oil well and turnover are gathered respectively
Water injection rate, is monthly gathered once, totally 26 times, history of forming data.Water ratio in oil well and well water injection rate initial data such as table 1 below
It is shown：
System model is used（Autoregression（autoregressive,AR）Model is also known as time series models）Model,
The least square method of recursion of parameter Estimation is set up such as drag, here only progress data stability analysis.
Measurement dataWater ratio in oil well, the water injection rate of well 1, the water injection rate of well 2, the note of well 3 are represented respectively
Water and the water injection rate of well 4, subscriptRepresent current time.This five amounts are by white noiseInfluence is as follows：
1000 samples are gathered under normal circumstances and then white Gaussian noise interference are added in the 1-200 sample, according to above-mentioned
Sample is divided lower 200 5 rank square formations by the method for technical scheme, relative variation is calculated respectively, schematic diagram is as shown in Figure 2, preceding
40 square formations are due to adding Gaussian noise influence, so 2 norms are very big with subsequently being compared fluctuation.Thus it is effective to illustrate this algorithm
's.
The foregoing is only presently preferred embodiments of the present invention, be not intended to limit the invention, it is all the present invention spirit and
Any modifications, equivalent substitutions and improvements made within principle etc., are all contained within protection scope of the present invention.
Claims (1)
1. a kind of oil-water well behavioural analysis prediction meanss, including measurand oil well（1）And it is arranged in the well around oil well
（2）, it is characterised in that：The oil well（1）, well（2）On be respectively equipped with crude oil water content sensor（3）With water filling quantity sensor
（4）, it is respectively used to gather the moisture content of oil well and the water injection rate of well, and by the analog quantity of collection through A/D converter（5）Conversion
Transmitted into digital quantity, then by data cable to host computer（6）, the host computer is to gathered data progress stability identification；
The host computer carries out stability identification to gathered data and comprised the following steps：
If collection Individual data volume , collect enough samples and be divided into It is individual Matrix, For nature
Number；
（1）Order When Rank matrix For stable state, its covariance matrix is taken , then characteristic equation be： ；
（2）Think remaining It is individual The judgement of stability of rank matrix is in normal processes data On the basis of plus shock wave and
Into if disturbance influences little to former process, then it is assumed that be benign disturbance, be otherwise pernicious disturbance, the former is used to build prediction mould
Type or for more new model, is expressed as with the language of mathematics：The It is individual Rank matrix covariance is , accordingly
Characteristic root and characteristic vector, which become, to be turned to： , , then It is individual The characteristic equation of rank matrix covariance
For： , above formula neutralizing is obtained ；
（3）2 norms are taken to obtain to above formula , as It is individual The covariance matrix of rank matrix is to normal
The relative error of process data matrix, because covariance matrix is symmetrical matrix, so ,
Because matrix belongs to real symmetric matrix and has complete characteristic vector-system in the formula, the maximum feature of matrix is asked for using power method iteration
Value；
（4）Repeat（2）、（3）Until , norm value is less to think normal processes data, otherwise it is assumed that being primary fault number
According to or fault data, should abandon.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410355231.4A CN104200059B (en) | 2014-07-24 | 2014-07-24 | A kind of oil-water well behavioural analysis prediction meanss and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410355231.4A CN104200059B (en) | 2014-07-24 | 2014-07-24 | A kind of oil-water well behavioural analysis prediction meanss and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104200059A CN104200059A (en) | 2014-12-10 |
| CN104200059B true CN104200059B (en) | 2017-08-01 |
Family
ID=52085352
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410355231.4A Expired - Fee Related CN104200059B (en) | 2014-07-24 | 2014-07-24 | A kind of oil-water well behavioural analysis prediction meanss and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104200059B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109657613B (en) * | 2018-12-19 | 2023-05-02 | 贵州大学 | Large-scale power grid abnormal load identification method based on power method and parallel computing technology |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1970991A (en) * | 2006-12-06 | 2007-05-30 | 中国石油大学(北京) | Method for metering oil production yield and analyzing and optimizing operating condition of oil well and system thereof |
| CN101145235A (en) * | 2007-06-29 | 2008-03-19 | 中国石化集团胜利石油管理局 | Oil field development decision-making system |
| CN102146788A (en) * | 2011-03-30 | 2011-08-10 | 中国石油化工股份有限公司 | Method for enhancing recovery ratio through three-dimensional streamline regulation of water drive reservoir |
| EP2505971A2 (en) * | 2011-04-01 | 2012-10-03 | Weatherford/Lamb Inc. | Pump controller with multiphase measurement |
| CN103867187A (en) * | 2012-12-07 | 2014-06-18 | 山东天工石油装备有限公司 | Oil well indicator diagram liquid level collecting device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7059180B2 (en) * | 2002-06-10 | 2006-06-13 | Saudi Arabian Oil Company | Water cut rate of change analytic method |
-
2014
- 2014-07-24 CN CN201410355231.4A patent/CN104200059B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1970991A (en) * | 2006-12-06 | 2007-05-30 | 中国石油大学(北京) | Method for metering oil production yield and analyzing and optimizing operating condition of oil well and system thereof |
| CN101145235A (en) * | 2007-06-29 | 2008-03-19 | 中国石化集团胜利石油管理局 | Oil field development decision-making system |
| CN102146788A (en) * | 2011-03-30 | 2011-08-10 | 中国石油化工股份有限公司 | Method for enhancing recovery ratio through three-dimensional streamline regulation of water drive reservoir |
| EP2505971A2 (en) * | 2011-04-01 | 2012-10-03 | Weatherford/Lamb Inc. | Pump controller with multiphase measurement |
| CN103867187A (en) * | 2012-12-07 | 2014-06-18 | 山东天工石油装备有限公司 | Oil well indicator diagram liquid level collecting device |
Non-Patent Citations (3)
| Title |
|---|
| 新型质量流量积算仪的设计;张淼 等;《温州大学学报》;20020325(第1期);第78-81页 * |
| 油井多参数实时监控系统设计;刘晓梅;《中国优秀硕士学位论文全文数据库 信息科技辑》;20101015(第10期);正文第35-39页第4.2.3-4.3.3小节,图4-6 * |
| 用多元时序分析法预测油水井动态;刘会友 等;《石油大学学报(自然科学版)》;20010530;第25卷(第2期);第70-72页第1-2小节 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104200059A (en) | 2014-12-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chow et al. | Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating | |
| Legendre et al. | The consequences of spatial structure for the design and analysis of ecological field surveys | |
| CA3024461A1 (en) | Computer implemented method for generating a field development plan (fdp) for the exploitation of oil and gas reservoirs | |
| CN104269867B (en) | A kind of node power of disturbance transfer distributing equilibrium degree analytical method | |
| Qiu et al. | Seepage Monitoring Models Study of Earth‐Rock Dams Influenced by Rainstorms | |
| Tang et al. | A New Hybrid Forecasting Model Based on Dual Series Decomposition with Long‐Term Short‐Term Memory | |
| CN104200059B (en) | A kind of oil-water well behavioural analysis prediction meanss and method | |
| CN104764414A (en) | FBG heterogeneous strain reconstruction method utilizing swarm optimization | |
| CN102697491B (en) | Identification method and system of characteristic waveform of electrocardiogram | |
| CN111914488B (en) | Data area hydrologic parameter calibration method based on antagonistic neural network | |
| Wang et al. | A Tidal Level Prediction Approach Based on BP Neural Network and Cubic B‐Spline Curve with Knot Insertion Algorithm | |
| CN117131360A (en) | Broadband oscillation monitoring method and system based on waveform data | |
| Huang et al. | Sensor fault detection, localization, and reconstruction for online structural identification | |
| CN109711607B (en) | Water flow connectivity identification method and device based on hysteresis effect | |
| Anifowose et al. | A hybrid of functional networks and support vector machine models for the prediction of petroleum reservoir properties | |
| CN103605880B (en) | Closely spaced mode damping ratio precisely-diagnosing method | |
| Prahadchai et al. | Analysis of maximum precipitation in Thailand using non‐stationary extreme value models | |
| CN116127866A (en) | Marine pipe cable deformation and load forecasting method based on CNN model | |
| CN109490983B (en) | reservoir geomechanical parameter automatic fitting method and system | |
| Wang et al. | Early Warning of Abnormal Bridge Frequencies Based on a Local Correlation Model under Multiple Environmental Conditions | |
| Haghshenas et al. | Modeling and Reliability Evaluation of Magnetically Controlled Reactor based on the Markov Process Technique | |
| CN107038300A (en) | PDC drill bit rock drillability Forecasting Methodology and device under a kind of different temperatures | |
| Taheri et al. | Novel adaptive damping controller for interline power flow controller to improve power system stability | |
| Jafari et al. | Stability analysis of doubly-fed induction generator wind turbine systems using modal analysis | |
| Hou | Traffic flow forecasting in leisure farm areas using artificial neural networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20141210 Assignee: INSTITUTE OF LASER AND OPTOELECTRONICS INTELLIGENT MANUFACTURING, WENZHOU University Assignor: Wenzhou University Contract record no.: X2020330000103 Denomination of invention: A device and method for analysis and prediction of oil and water well behavior Granted publication date: 20170801 License type: Common License Record date: 20201125 |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170801 Termination date: 20210724 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |