CN111335876A - Self-adaptive tracking prediction control method for petroleum drilling well track - Google Patents

Self-adaptive tracking prediction control method for petroleum drilling well track Download PDF

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CN111335876A
CN111335876A CN202010134229.XA CN202010134229A CN111335876A CN 111335876 A CN111335876 A CN 111335876A CN 202010134229 A CN202010134229 A CN 202010134229A CN 111335876 A CN111335876 A CN 111335876A
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value
model
sequence
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weighting coefficient
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鲁港
钟晓明
缑柏弘
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Beijing Sleton Control Technologies Co ltd
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/02—Determining slope or direction
    • E21B47/022—Determining slope or direction of the borehole, e.g. using geomagnetism

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Abstract

A petroleum drilling well track self-adaptive tracking prediction control method comprises the steps of establishing a cubic smooth exponential model, establishing a controlled object model according to a time series relation, solving the model to obtain a predicted value of well track parameters, and continuously and iteratively adjusting the model by adopting a self-adaptive correction method, so that the prediction model can change in real time to correspond to changes in real drilling engineering. The invention solves the problems of uncertain parameters and uncertain model forms for establishing an accurate mathematical model; the rules of the well track parameters are emphatically analyzed, and the predicted data is more accurate and reliable; the self-adaptive correction method can adjust the model parameters on line in real time, so that the prediction model is more accurate.

Description

Self-adaptive tracking prediction control method for petroleum drilling well track
Technical Field
The invention relates to the field of directional drilling, in particular to a self-adaptive tracking and predicting control method for a petroleum drilling well track.
Background
In the actual drilling process on site, in order to ensure target centering and avoid well bore quality accidents, the well deviation and the direction of a drill bit are required to be rapidly predicted and monitored in real time. Therefore, a fast prediction method meeting the above requirements is also needed to be researched as an auxiliary prediction means for controlling the well track. The analysis method for the prediction technology at home and abroad in recent years comprises the following steps: the mechanical analysis method of drill string mechanics and drill string, stratum interaction mode, using the resultant force direction of drill bit as actual drilling direction, using the axis of drill bit as actual drilling direction, using limit curvature as drilling curvature to determine drilling direction, using rock-drill bit interaction model to determine drilling direction, etc. because of the many factors influencing well track, there are structure of lower drilling tool combination, drill bit type, drilling industry parameter, geometry of drilled well body and stratum characteristic, etc., therefore, it is very difficult to achieve accurate prediction only by theoretical model. Because the theoretical models all relate to a plurality of parameters, preparation data at the early stage of prediction is huge, some parameters cannot be accurately and timely obtained in the drilling process, and can be obtained only by drilling experiences in adjacent areas, so that the popularization and the application of the theoretical models in the field are influenced.
Disclosure of Invention
The invention aims to provide a self-adaptive tracking, predicting and controlling method for a petroleum drilling well track, thereby solving the problems in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an adaptive tracking and predicting control method for a petroleum drilling well track comprises the following steps:
s1, establishing a smooth index prediction model
The prediction model is as follows:
Y＝at+bt·L+ctL2
in the formula, Y is a prediction target, t is a sequence which progresses by unit well depth, L is the well depth of a measuring point, atAs a primary smoothing factor, btIs a quadratic smoothing coefficient, ctIs a cubic smoothing coefficient;
wherein the smoothing coefficient at、bt、ctThe determination method comprises the following steps:
Figure BDA0002396765050000021
Figure BDA0002396765050000022
Figure BDA0002396765050000023
wherein α is an autonomously selected weighting factor,
Figure BDA0002396765050000024
respectively a primary smooth value, a secondary smooth value and a tertiary smooth value of a t sequence sampling point;
s2, solving the prediction model
First, an initial value is determined
Figure BDA0002396765050000025
And calculating statistical data x of sampling points at the time ttThe smoothing formula of (1):
Figure BDA0002396765050000026
Figure BDA0002396765050000027
Figure BDA0002396765050000028
in the formula: x is the number oftAs statistical data for the t-sequence of sample points, t 1,2, 3.,
Figure BDA0002396765050000029
is the once smoothed value of the t-1 sequence sampling points,
Figure BDA00023967650500000210
is the second smoothed value of the t-1 sequence sampling points,
Figure BDA00023967650500000211
the three smooth values of the sampling points of the t-1 sequence are obtained;
s3, adopting error feedback and signal tracking self-adapting method to correct α
The weighting coefficient α is changed according to the tracking signal, the tracking signal is changed according to the prediction error, the error feedback at the moment can adjust the value of the weighting coefficient α in real time, the weighting coefficient α can continuously correct the selection error of the initial value, so that a correct prediction model is established, α is sett、QtThe weighting coefficients and the tracking signals of the t sequence respectively, and the correction method of the weighting coefficient α is as follows:
αt＝|Qt|
Qt＝Et/At
Et＝γet＋(1-γ)Et-1
At＝γ|et|＋(1-γ)At-1
Figure BDA00023967650500000212
in the formula, Et、AtRespectively obtaining a smoothing error and an absolute error of the statistical data of the t sequence sampling points; y istIs the measured value of the t sequence sampling point;
Figure BDA0002396765050000031
is a predicted value of a t-1 sequence to a t sequence sampling point; e.g. of the typetIs a prediction error; gamma is an error coefficient;
s4, applying the weighting coefficient α corrected in step S3 to the prediction model in step S1, and iteratively adjusting the weighting coefficient α continuously, so as to adapt the prediction model to the change in the actual drilling process in time.
Preferably, the predicted target comprises a well angle and an azimuth angle; the statistical data includes well angle data and azimuth data.
Preferably, the prediction parameters of the prediction model are the inclination angle and the azimuth angle of the borehole trajectory.
Preferably, the weighting coefficient α is obtained by taking the weighting coefficient α as small as the development trend of the drilling process is stable, taking the weighting coefficient α as large as systematic change of the drilling process, and taking the weighting coefficient α as large as the assumed initial value when the original well depth measurement data is insufficient.
The invention has the beneficial effects that: according to the self-adaptive tracking and predicting control method for the petroleum drilling well track, the smooth index model is adopted, a controlled object model can be established according to a time series relation, and the problems that the parameters for establishing an accurate mathematical model are uncertain and the model form is uncertain are solved; the model provided by the invention has the advantages that the analysis of various influence factors is abandoned, the rule of the well track parameters is emphatically analyzed, and the predicted data is more accurate and reliable; the self-adaptive correction method can adjust the model parameters on line in real time, so that the prediction model is more accurate.
Drawings
FIG. 1 is a flow chart of wellbore trajectory prediction based on an adaptive tracking method;
FIG. 2 is a well deviation and azimuth prediction table.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
An adaptive tracking and predicting control method for a petroleum drilling well track comprises the following steps:
s1, establishing a prediction model, and obtaining a parameter prediction value of any given well depth according to the parameters of known measuring points;
s2, comparing the predicted value with the corresponding measured value, correcting the weighting coefficient of the prediction model, and finally establishing an accurate prediction model;
and S3, repeating the steps S1 and S2, and performing iterative prediction on the borehole trajectory parameters of the next well depth.
The prediction model adopts a cubic exponential smoothing prediction model, and the expression of the prediction model is as follows:
Y＝at+bt·L+ctL2
in the formula, Y is a prediction target, and the prediction target comprises a well deviation angle and an azimuth angle; t is the sequence progressing with unit well depth, L is the well depth of the measuring point, atAs a primary smoothing factor, btIs a quadratic smoothing coefficient, ctIs a cubic smoothing coefficient;
wherein the smoothing coefficient at、bt、ctThe determination method comprises the following steps:
Figure BDA0002396765050000041
Figure BDA0002396765050000042
Figure BDA0002396765050000043
wherein α is an autonomously selected weighting factor,
Figure BDA0002396765050000044
respectively a primary smooth value, a secondary smooth value and a tertiary smooth value of a sampling point in the t sequence;
the weighting coefficient α is a key parameter, the size of the weighting coefficient controls the significance of the time sequence in the prediction calculation, and reflects the magnitude of error correction, the larger the α, the larger the correction magnitude, the larger the degree of bias on current data and recent information, therefore, the selection of the α value with the minimum prediction error is the key for ensuring the success of prediction.
S2, solving the prediction model
First, an initial value is determined
Figure BDA0002396765050000045
And calculating statistical data x at the time ttThe smoothing formula of (1):
Figure BDA0002396765050000046
Figure BDA0002396765050000047
Figure BDA0002396765050000048
in the formula: x is the number oftIs statistical data of t sequence sampling points, the statistical data xtIncluding the elevation angle data and azimuth angle data, t 1,2,3, …;
Figure BDA0002396765050000049
is the first smoothed value of the t-1 sequence,
Figure BDA00023967650500000410
is the second smoothed value of the t-1 sequence,
Figure BDA0002396765050000051
is the third smoothed value of the t-1 sequence;
the effect of the error caused by improper initial value selection can be quickly adjusted by continuously correcting the weighting coefficients α in the prediction process, thereby relaxing the strict limitation on initial value selection, which is generally optional
Figure BDA0002396765050000052
Or
Figure BDA0002396765050000053
The weighting coefficient α has the rule that if the drilling process is stable and the prediction error is caused by some random factors, the α value should be properly reduced to reduce the correction range and keep the prediction model with long-term information, and if the drilling process has systematic change, such as changing drilling tool, stratum mutation, etc., the α value should be properly increased to greatly correct the original model and make it quickly follow the change of the prediction target, such asIf the original well bore measurement data is insufficient, such as during the initial drilling stage, and the initial value is assumed manually for prediction, the α value should be properly increased to make the model more dependent on the recent information and correct the error of the early information to quickly approach the actual drilling process, however, the α value should not be too large to cause the over-correction condition.
S3, adopting error feedback and signal tracking self-adapting method to correct α
The weighting coefficient α is changed according to the tracking signal, the tracking signal is changed according to the prediction error, the error feedback at the moment can adjust the value of the weighting coefficient α in real time, the weighting coefficient α can continuously correct the selection error of the initial value, so that a correct prediction model is established, α is sett、QtThe weighting coefficients and the tracking signals of the t-sequence sampling points respectively, and the correction method of the weighting coefficient α is as follows:
αt＝|Qt|
Qt＝Et/At
Et＝γet+(1-γ)Et-1
At＝γ|et|+(1-γ)At-1
Figure BDA0002396765050000054
in the formula, Et、AtRespectively a smoothing error and an absolute error in the case of a t sequence; y istIs a t-sequence measured value, including the angle of inclination and the azimuth;
Figure BDA0002396765050000055
is a predicted value of the t sequence in the t-1 sequence, including a well deviation angle and an azimuth angle; e.g. of the typetIs a prediction error; gamma is an error coefficient;
the error feedback adjusts the weighting factor α value each time, thereby achieving iterative predictive adjustment of the predictive modeltEntirely due to random errors and therefore obey a normal distribution with a mean value of 0And E (E)t) 0, i.e. Et→0，Qt→ 0, no deviation in prediction, good tracking of actual drilling process by prediction model, αtAutomatically taking a small value. When the prediction is biased, QtGreater value, αtThe automatic gain can make the prediction model adapt to the change of the real drilling process quickly.
S4, applying the weighting coefficient α corrected in step S3 to the prediction model in step S1, and iteratively adjusting the weighting coefficient α continuously, so as to adapt the prediction model to the change in the actual drilling process in time.
Examples
When the prediction model is used for tracking and predicting the borehole trajectory, the flow of the implementation process is shown in figure 1, firstly, the parameters of the borehole trajectory measuring point are input as initial values, the prediction parameters comprise a well inclination angle β and an azimuth angle phi at the moment, the well depth L of the measuring point and the unit well depth T which is advanced during prediction are input, and then the well depth L of the prediction point is inputL+TRespectively obtaining the well depth L by predicting through the prediction modelL+TAt a well angle β and an azimuth angle phi, and a drilling depth LL+TAt this time, the angle β and the azimuth angle φ are measured and the actual angle β is derived therefromL+TAnd azimuth angle phiL+TIn the embodiment, according to the algorithm, the relative error of the inclination angle is less than 1% and the relative error of the azimuth angle is less than 0.5%, and the prediction result in the embodiment is shown in fig. 2.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
according to the self-adaptive tracking and predicting control method for the petroleum drilling well track, the smooth index model is adopted, a controlled object model can be established according to a time series relation, and the problems that the parameters for establishing an accurate mathematical model are uncertain and the model form is uncertain are solved; the model provided by the invention has the advantages that the analysis of various influence factors is abandoned, the rule of the well track parameters is emphatically analyzed, and the predicted data is more accurate and reliable; the self-adaptive correction method can adjust the model parameters on line in real time, so that the prediction model is more accurate.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.

Claims (4)

1. An adaptive tracking and predicting control method for a petroleum drilling well track is characterized by comprising the following steps:
s1, establishing a smooth index prediction model
The prediction model is as follows:
Y＝at+bt·L+ctL2
in the formula, Y is a prediction target, t is a sequence which progresses by unit well depth, L is the well depth of a measuring point, atAs a primary smoothing factor, btIs a quadratic smoothing coefficient, ctIs a cubic smoothing coefficient;
wherein the smoothing coefficient at、bt、ctThe determination method comprises the following steps:
Figure FDA0002396765040000011
Figure FDA0002396765040000012
Figure FDA0002396765040000013
wherein α is an autonomously selected weighting factor,
Figure FDA0002396765040000014
respectively a primary smooth value, a secondary smooth value and a tertiary smooth value of a t sequence sampling point;
s2, solving the prediction model
First, an initial value is determined
Figure FDA0002396765040000015
And calculating statistical data x of sampling points at the time ttThe smoothing formula of (1):
Figure FDA0002396765040000016
Figure FDA0002396765040000017
Figure FDA0002396765040000018
in the formula: x is the number oftThe statistical data for the t-sequence of sample points, t 1,2,3, …,
Figure FDA0002396765040000019
is the once smoothed value of the t-1 sequence sampling points,
Figure FDA00023967650400000110
is the second smoothed value of the t-1 sequence sampling points,
Figure FDA00023967650400000111
the three smooth values of the sampling points of the t-1 sequence are obtained;
s3, adopting error feedback and signal tracking self-adapting method to correct α
The weighting coefficient α changes according to the tracking signal, which changes according to the prediction error, the error feedback at this time can adjust the value of the weighting coefficient α in real time, and the weighting coefficient α can continuously correct the selection error of the initial value, thereby establishing correct predictionMeasuring model, setting αt、QtThe weighting coefficients and the tracking signals of the t sequence respectively, and the correction method of the weighting coefficient α is as follows:
αt＝|Qt|
Qt＝Et/At
Et＝γet+(1-γ)Et-1
At＝γ|et|+(1-γ)At-1
Figure FDA0002396765040000021
in the formula, Et、AtRespectively obtaining a smoothing error and an absolute error of the statistical data of the t sequence sampling points; y istIs the measured value of the t sequence sampling point;
Figure FDA0002396765040000022
is a predicted value of a t-1 sequence to a t sequence sampling point; e.g. of the typetIs a prediction error; gamma is an error coefficient;
s4, applying the weighting coefficient α corrected in step S3 to the prediction model in step S1, and iteratively adjusting the weighting coefficient α continuously, so as to adapt the prediction model to the change in the actual drilling process in time.
2. The method of claim 1, wherein the predicted target comprises a skew angle and an azimuth angle; the statistical data includes well angle data and azimuth data.
3. The method of claim 1, wherein the predicted parameters of the predictive model are the inclination and azimuth of the borehole trajectory.
4. The method for controlling the self-adaptive tracking and predicting of the petroleum drilling well track according to claim 1, wherein the value of the weighting coefficient α is determined according to the following steps that the value of the weighting coefficient α is small when the development trend of the drilling process is stable, the value of the weighting coefficient α is large when the drilling process is changed systematically, and the value of the weighting coefficient α is large when the original well depth measurement data is insufficient and needs to be considered as an assumed initial value.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112463555A (en) * 2020-10-28 2021-03-09 苏州浪潮智能科技有限公司 Prediction method, device, equipment and storage medium for available time of storage pool capacity
RU2772264C1 (en) * 2021-11-10 2022-05-18 Открытое Акционерное Общество Научно-Производственное Объединение «Буровая техника» Method for designing and controlling the profile parameters of a directional well

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103883311A (en) * 2013-03-22 2014-06-25 中国石油化工股份有限公司 Composite oriented drilling target-entering situation prediction method
CN103967479A (en) * 2013-02-01 2014-08-06 中国石油化工股份有限公司 Predicting method for target-entering situation of rotary steerable drilling
CN104481400A (en) * 2014-12-10 2015-04-01 中国石油化工股份有限公司 Three-dimensional (3D) horizontal well borehole track controlling method
CN105422088A (en) * 2015-11-11 2016-03-23 中国煤炭科工集团太原研究院有限公司 Coal mine roadway geological parameter on-line monitoring system
CN106652443A (en) * 2016-10-21 2017-05-10 长春理工大学 Longitudinal and transverse similar highway short-time traffic prediction method
CN107829683A (en) * 2017-04-27 2018-03-23 成都全富科技有限公司 A kind of horizontal collecting well group well track of shale gas and boring method
CN108734360A (en) * 2018-06-19 2018-11-02 哈尔滨工业大学 One kind being based on modified ELM prediction models multidimensional telemetry intelligent interpretation method
CN109272142A (en) * 2018-08-14 2019-01-25 苏州朗动网络科技有限公司 Data predication method, device, computer equipment and storage medium based on event
CN110120154A (en) * 2019-05-28 2019-08-13 武汉理工大学 The traffic prediction technique of detector data is utilized under large-scale road network
CN110765648A (en) * 2019-11-08 2020-02-07 长江大学 Visual control device and control method for petroleum drilling trajectory

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103967479A (en) * 2013-02-01 2014-08-06 中国石油化工股份有限公司 Predicting method for target-entering situation of rotary steerable drilling
CN103883311A (en) * 2013-03-22 2014-06-25 中国石油化工股份有限公司 Composite oriented drilling target-entering situation prediction method
CN104481400A (en) * 2014-12-10 2015-04-01 中国石油化工股份有限公司 Three-dimensional (3D) horizontal well borehole track controlling method
CN105422088A (en) * 2015-11-11 2016-03-23 中国煤炭科工集团太原研究院有限公司 Coal mine roadway geological parameter on-line monitoring system
CN106652443A (en) * 2016-10-21 2017-05-10 长春理工大学 Longitudinal and transverse similar highway short-time traffic prediction method
CN107829683A (en) * 2017-04-27 2018-03-23 成都全富科技有限公司 A kind of horizontal collecting well group well track of shale gas and boring method
CN108734360A (en) * 2018-06-19 2018-11-02 哈尔滨工业大学 One kind being based on modified ELM prediction models multidimensional telemetry intelligent interpretation method
CN109272142A (en) * 2018-08-14 2019-01-25 苏州朗动网络科技有限公司 Data predication method, device, computer equipment and storage medium based on event
CN110120154A (en) * 2019-05-28 2019-08-13 武汉理工大学 The traffic prediction technique of detector data is utilized under large-scale road network
CN110765648A (en) * 2019-11-08 2020-02-07 长江大学 Visual control device and control method for petroleum drilling trajectory

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
侯永辉: "中长期电力负荷预测的研究与应用", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 *
张伟文: "钻井周期预测新方法_以塔里木油田为例", 《钻井工程》 *
罗辰辉等: "基于动态系数的三次指数平滑算法负载预测", 《计算机测量与控制》 *
高德利等: "《井眼轨迹控制》", 31 December 1994 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112463555A (en) * 2020-10-28 2021-03-09 苏州浪潮智能科技有限公司 Prediction method, device, equipment and storage medium for available time of storage pool capacity
CN112463555B (en) * 2020-10-28 2022-12-27 苏州浪潮智能科技有限公司 Method, device, equipment and storage medium for predicting available time of storage pool capacity
RU2772264C1 (en) * 2021-11-10 2022-05-18 Открытое Акционерное Общество Научно-Производственное Объединение «Буровая техника» Method for designing and controlling the profile parameters of a directional well

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