CN108280534A - A kind of gas well yield lapse rate prediction technique - Google Patents
A kind of gas well yield lapse rate prediction technique Download PDFInfo
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Abstract
The present invention provides a kind of gas well yield lapse rate prediction techniques, mainly production decline modeling is obtained using multiple linear regression response surface design analysis result, after obtaining annual decline rate and model of influencing factors, significance analysis is carried out to the regression coefficient of the k member quadratic equations of foundation using variance analysis P values, according to significance analysis result, after the not notable item of removal, you can obtain gas well year decreasing model, predict gas well yield lapse rate.This method unstable, analysis result can be affected by human factors the situation more than big and well number for low permeability gas reservoirs gas well liquid loading system, utilize conventional dynamic monitoring information, accurate, fast prediction gas well yield lapse rate.It is object function to enable annual decline rate, and using each influence factor as impact factor, the horizontal result tables of curved surface k factors n, obtain multigroup response surface design analysis experimental program according to response.
Description
Technical field
The invention belongs to gas field development technical fields, and in particular to a kind of gas well yield lapse rate prediction technique, it is especially suitable
Successively decrease evaluation together in hypotonic carbonate gas reservoirs gas well yield, further provides technology branch for in-depth gas field development knowledge of regularity
Support.
Background technology
Production decline is to analyze the important gas reservoir engineering means and content of gas well, gas field development trend and index prediction, is
The basis for optimizing gas well working system, formulating gas field production measure.
Current production rate analysis method of successively decreasing has Arps, Fetkovich, Blasingame etc., these methods are in conventional gas well
It has been widely used in terms of production decline, but has been directed to low permeability gas field gas well, there are certain offices in method applicability, timeliness
It is sex-limited.
Arps decline curves because demand data it is few, using simple, at home and abroad obtain most commonly used application.The party
Method requires flowing bottomhole pressure (FBHP) constant, is to carry out production forecast by establishing empirical equation using yield data after determining Decline type
Method.However, low permeability gas reservoirs gas well liquid loading system is unstable, the stream numerous variation of voltage-frequency, even if part, gas well meets application conditions
It is close that there are gas well related coefficients under different decreasing fashions, it is difficult to intuitive, accurate the problem of judging gas well Decline type.
Fetkovich decline curves also require reservoir parameter in addition to creation data, its essence is establish Arps with it is unstable
The application range of curve is expanded to the flow instabilities stage by the zero dimension function of seepage flow, but it still requires that flowing bottomhole pressure (FBHP) is constant,
And calculating process is relatively cumbersome compared with Arps, and low permeability gas reservoirs application is also limited.
Blasingame decline curves are to consider yield stream pressure and the property of gas PVT on the basis of Fetkovich plates
Matter with pressure variation.This method is in low permeability gas reservoirs using relatively broad, but the data type that this method needs is more, processing procedure
It is increasingly complex, it is easily affected by human factors in evaluation procedure, parameter fitting multi-solution is strong.
In short, currently used production decline method to aspire for stability in production system, sentence know Decline type relative difficulty,
Processing procedure is relative complex, it is difficult to which meeting low permeability gas field production, system is unstable, analysis result is affected by human factors big, gas well
Accurate, fast prediction production decline demand under the susceptible shape of well number.
Invention content
The purpose of the present invention is overcoming the deficiencies of existing technologies, provide a kind of conventional with production etc. using strata pressure, production
The production decline modeling of gas well waterout monitoring materials realizes accurate, fast prediction gas well lapse rate technology.
Technical solution provided by the invention is as follows：
A kind of gas well yield lapse rate prediction technique, includes the following steps：
Step 1) determines k influence factor for influencing gas field gas well lapse rate；
Step 2) obtains the distribution situation of each influence factor according to gas field produce reality；
Step 3) by each influence factor by distributed area carry out be incremented by division, n section of universal formulation, according still further to from it is small to
Big order is arranged in order into the horizontal result tables of response surface design k factors n；
It is object function that step 4), which enables annual decline rate, using each influence factor as impact factor, curved surface k factors n according to response
Horizontal result table obtains multigroup response surface design analysis experimental program；
Step 5) utilizes method for numerical simulation, simulated production each group response surface design to analyze experimental program, obtain different experiments
Lapse rate year by year under scheme；
Step 6) returns to obtain annual decline rate and model of influencing factors using k member quadratic equations；
After step 7) obtains annual decline rate and model of influencing factors, using variance analysis P values to the k member quadratic equations of foundation
Regression coefficient carry out significance analysis, judge that P values less than 0.01 are notable item, remaining is not notable item；
Step 8) is according to significance analysis as a result, after the not notable item of removal, you can gas well year decreasing model is obtained, to gas well
Production decline rate is predicted.
Further include the verification to step 8) year decreasing model, when respectively to test scatterplot value and model predication value as transverse and longitudinal
The straight line that coordinate fitting obtains is Y=R2X, R2Model is reliable when more than 0.999.
The annual decline rate is with model of influencing factorsWherein,
Annual decline rate y in response Surface Analysis response output, x1、x2、……xkFor the influence factor of regression model, β0、βi、βij
For regression coefficient, ε is error term, and k is the number of influence factor.
The influence factor is permeability, strata pressure, well control reserves and production with production.
The k >=2, n 4-6.
Lapse rate for predicting low permeability gas reservoirs gas well.
The beneficial effects of the invention are as follows：
This gas well dynamic reserve evaluation method provided by the invention, can be directed to low permeability gas reservoirs gas well liquid loading system it is unstable,
Analysis result is affected by human factors the situation more than big and well number, utilizes conventional dynamic monitoring information, accurate, fast prediction gas
Well production lapse rate.
It is described in further details below in conjunction with attached drawing.
Description of the drawings
Fig. 1 is the first annual decline rate experiment scatterplot and model predication value comparison diagram；
Fig. 2 is that low permeability gas field X wells calculate annual decline rate curve using decreasing model；
Fig. 3 is that low permeability gas field X wells actual production predicts correlation curve with decreasing model.
Specific implementation mode
Embodiment 1：
A kind of gas well yield lapse rate prediction technique is present embodiments provided, is included the following steps：
Step 1) determines k influence factor for influencing gas field gas well lapse rate；
Step 2) obtains the distribution situation of each influence factor according to gas field produce reality；
Step 3) by each influence factor by distributed area carry out be incremented by division, n section of universal formulation, according still further to from it is small to
Big order is arranged in order into the horizontal result tables of response surface design k factors n；
It is object function that step 4), which enables annual decline rate, using each influence factor as impact factor, curved surface k factors n according to response
Horizontal result table obtains multigroup response surface design analysis experimental program；
Step 5) utilizes method for numerical simulation, simulated production each group response surface design to analyze experimental program, obtain different experiments
Lapse rate year by year under scheme；
Step 6) returns to obtain annual decline rate and model of influencing factors using k member quadratic equations；
After step 7) obtains annual decline rate and model of influencing factors, using variance analysis P values to the k member quadratic equations of foundation
Regression coefficient carry out significance analysis, judge that P values less than 0.01 are notable item, remaining is not notable item；
Step 8) is according to significance analysis as a result, after the not notable item of removal, you can gas well year decreasing model is obtained, to gas well
Production decline rate is predicted.
The principle of the invention：Production decline modeling mainly is obtained using multiple linear regression response surface design analysis result, it is excellent
Point is that only relying on conventional dynamic monitoring data can be carried out intuitively analyzing, and wide adaptation range is not limited by working condition.
Embodiment 2：
On the basis of embodiment 1, a kind of gas well yield lapse rate prediction technique is present embodiments provided, to gas field of pacifying the border region
Production decline rate is predicted, is included the following steps：
Step 1) determines the major influence factors for influencing gas field gas well lapse rate：Permeability, strata pressure, well control reserves and
Production is with production；
Step 2) obtains permeability, strata pressure, well control reserves and production with production parameter value according to gas field produce reality
Distribution situation；
Step 3) is respectively equidistantly divided permeability, strata pressure, well control reserves and production with production by distributed area,
Five sections of universal formulation are arranged in order into four factor of response surface design, five horizontal result table according still further to order from small to large；Knot
Fruit corresponds to table 1；
1 response surface design of table, 4 factor, 5 horizontal result table
It is object function that step 4), which enables annual decline rate, with permeability, strata pressure, well control reserves, matches and produces as impact factor,
Four factor of curved surface, five horizontal result table according to response obtains multigroup response surface design analysis more than 30 group of experimental program；
Step 5) utilizes method for numerical simulation, simulated production each group response surface design to analyze experimental program, obtain different experiments
Lapse rate year by year under scheme；As a result table 2 is corresponded to；
2 response surface design of table analyzes experimental design and numerical simulation calculation result table
Step 6) by divide in table 2 annual decline rate in response Surface Analysis response export, utilize quaternary quadratic equation return
Return to obtain annual decline rate and model of influencing factors, form is：
Wherein, annual decline rate y in response Surface Analysis response output, x1、x2、……xkFor the influence of regression model
Factor, β0、βi、βijFor regression coefficient, ε is error term, and k is the number of influence factor.
Step 7) obtains year by year after lapse rate basic model, using variance analysis P values to the multiple linear equation of foundation
Regression coefficient carries out significance analysis, and the results are shown in Table 3, judges that P values less than 0.01 are notable item, remaining is not notable
；
3 response surface design analyzing influence factor reciprocal effect result table of table
P values analysis result shows that notable single factor test, quadratic term and permeability and well control reserves reciprocal effect are aobvious in table 3
It writes；
Step 8) is according to significance analysis as a result, after the not notable item of removal, you can obtains low permeability gas field gas well passing year by year
Subtract model：
RiFor the i-th annual decline rate, wherein：
R1=-0.090551+0.094042 × A+0.024932 × B-0.20879 × C+0.042396 × D-8.15528
×10-3A×
C-4.96369×10-3A2-5.18527×10-4B2+0.036151C2-3.63577×10-3D2 (2)
R2=-0.050507+0.089606 × A+0.021573 × B-0.20557 × C+0.036962 × D-7.56924
×10-3A×
C-4.78208×10-3A2-4.50191×10-4B2+0.036003C2-3.21836×10-3D2 (3)
R3=-0.09066+0.05406 × A+0.0177 × B-0.21102 × C-7.65047 × 10-3A×C-4.44929
×10-3A2-
3.69869×10-4B2+0.038081C2 (4)
R4=0.11869+0.0851045 × A+0.015206 × B-0.21399 × C-7.60067 × 10-3A×C-
4.0441×10-3
A2-3.19384×10-4B2+0.038998C2 (5)
Remaining time can also obtain successively；
Step 9) is with the first annual decline rate R of foundation1It is reliable using numerical analysis method verification model for prediction model
Property.Fig. 1 shows that the first annual decline rate model experiment scatterplot value and model predication value are preferably distributed in (R around straight line Y=X2＞
0.999), show that the model is reliable, may be employed.
Embodiment 3：
On the basis of embodiment 2, the present embodiment compares production decline modeling predicted value by taking the gas field areas M X wells of pacifying the border region as an example
With the well practical condition.The X well production times are longer, reservoir permeability 0.12mD, strata pressure 17.47MPa, dynamic reserve
1.2×108m3, production is with production 4.2 × 10 before testing4m3/d。
Using formula (2)-formula (5), the well annual decline rate can be calculated year by year, the results are shown in Figure 2；Successively decreased using Fig. 2 middle ages
Rate prediction X well productions successively decrease situation and with actual production compare, the results are shown in Figure 3, shows that the gas well yield decreasing model is pre-
It is almost the same with actual production to survey situation, can be used to the variation tendency for predicting to successively decrease with yield.
Successively decreased using this method gas field forecast production that has been applied to pacify the border region, 702 mouthfuls of gas well is evaluated, wherein 413 mouthfuls are production
The unstable gas well of system successively decreases 772 mouthfuls in conjunction with the methods of Arps, Blasingame prediction gas well yield, improves low permeability gas reservoirs
The efficiency and accuracy that gas well yield is successively decreased.Meanwhile being successively decreased situation according to gas well yield, 153 well of optimization gas well working system,
Extend 0.8 year gas field stable production period；Sentence situations such as knowing wellbore effusion, guidance draining with production and actual production capacity difference according to theory
92,000,000 side of tolerance (increasing production 0.3 ten thousand sides/day by individual well) is increased production in 93 mouthfuls of gas production measure year.
In conclusion the present invention solves, low permeability gas reservoirs gas well liquid loading system is unstable, analysis result is by human factor shadow
Problem big, more than well number is rung, evaluable well number, range and the accurate precision of production decline are substantially expanded.The application attestation party
Method is applicable in, is easy, can save a large amount of manpowers, has larger practical value.
The present embodiment is calculated without narration decreasing model in detail or the known or common skill of the method for numerical simulation category industry
Art means, do not describe one by one here.
The foregoing examples are only illustrative of the present invention, does not constitute the limitation to protection scope of the present invention, all
Be with the present invention it is same or analogous design all belong to the scope of protection of the present invention within.
Claims (6)
1. a kind of gas well yield lapse rate prediction technique, which is characterized in that include the following steps：
Step 1) determines k influence factor for influencing gas field gas well lapse rate；
Step 2) obtains the distribution situation of each influence factor according to gas field produce reality；
Step 3) is carried out each influence factor by distributed area to be incremented by division, universal formulation n section, according still further to from small to large
Order is arranged in order into the horizontal result tables of response surface design k factors n；
It is object function that step 4), which enables annual decline rate, and using each influence factor as impact factor, curved surface k factors n is horizontal according to response
As a result table obtains multigroup response surface design analysis experimental program；
Step 5) utilizes method for numerical simulation, simulated production each group response surface design to analyze experimental program, obtain different experiments scheme
Under lapse rate year by year；
Step 6) returns to obtain annual decline rate and model of influencing factors using k member quadratic equations；
After step 7) obtains annual decline rate and model of influencing factors, time using variance analysis P values to the k member quadratic equations of foundation
Return coefficient to carry out significance analysis, judge that P values less than 0.01 are notable item, remaining is not notable item；
Step 8) is according to significance analysis as a result, after the not notable item of removal, you can gas well year decreasing model is obtained, to gas well yield
Lapse rate is predicted.
2. a kind of gas well yield lapse rate prediction technique according to claim 1, it is characterised in that：Further include to step 8)
The verification of year decreasing model, when respectively to test straight line that scatterplot value and model predication value are fitted as transverse and longitudinal coordinate for Y
=R2X, R2Model is reliable when more than 0.999.
3. a kind of gas well yield lapse rate prediction technique according to claim 1, it is characterised in that：The annual decline rate with
Model of influencing factors isWherein, annual decline rate y curved surfaces in response
The response of analysis exports, x1、x2、……xkFor the influence factor of regression model, β0、βi、βijFor regression coefficient, ε is error term, k
For the number of influence factor.
4. a kind of gas well yield lapse rate prediction technique according to claim 1, it is characterised in that：The influence factor is
Permeability, strata pressure, well control reserves and production are with production.
5. a kind of gas well yield lapse rate prediction technique according to claim 1, it is characterised in that：The k >=2, n 4-
6。
6. a kind of gas well yield lapse rate prediction technique according to claim 1, it is characterised in that：For predicting hypotonic gas
Hide the lapse rate of gas well.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113236207A (en) * | 2021-07-13 | 2021-08-10 | 西南石油大学 | Fixed yield decreasing prediction method for water producing gas well in strong heterogeneity reservoir |
| CN113323656A (en) * | 2021-06-16 | 2021-08-31 | 中海石油(中国)有限公司 | Development index prediction method for closed condensate gas reservoir and computer-readable storage medium |
| CN114021821A (en) * | 2021-11-08 | 2022-02-08 | 四川省科源工程技术测试中心 | Gas reservoir recovery rate prediction method based on multiple regression |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102609780A (en) * | 2011-01-24 | 2012-07-25 | 河南理工大学 | Novel method for predicting gas emission quantity of mine |
| CN106199725A (en) * | 2016-08-16 | 2016-12-07 | 中国石油化工股份有限公司 | A kind of coal petrography thickness prediction method and device based on positive amplitude summation attribute |
-
2017
- 2017-12-25 CN CN201711418087.4A patent/CN108280534A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102609780A (en) * | 2011-01-24 | 2012-07-25 | 河南理工大学 | Novel method for predicting gas emission quantity of mine |
| CN106199725A (en) * | 2016-08-16 | 2016-12-07 | 中国石油化工股份有限公司 | A kind of coal petrography thickness prediction method and device based on positive amplitude summation attribute |
Non-Patent Citations (1)
| Title |
|---|
| 陈余: "低渗气藏气井产量递减分析及预测方法研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113323656A (en) * | 2021-06-16 | 2021-08-31 | 中海石油(中国)有限公司 | Development index prediction method for closed condensate gas reservoir and computer-readable storage medium |
| CN113236207A (en) * | 2021-07-13 | 2021-08-10 | 西南石油大学 | Fixed yield decreasing prediction method for water producing gas well in strong heterogeneity reservoir |
| CN113236207B (en) * | 2021-07-13 | 2021-09-10 | 西南石油大学 | Fixed yield decreasing prediction method for water producing gas well in strong heterogeneity reservoir |
| CN114021821A (en) * | 2021-11-08 | 2022-02-08 | 四川省科源工程技术测试中心 | Gas reservoir recovery rate prediction method based on multiple regression |
| CN114021821B (en) * | 2021-11-08 | 2023-07-14 | 四川省科源工程技术测试中心有限责任公司 | Gas reservoir recovery ratio prediction method based on multiple regression |
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