CN107201894A - The method that feature based parameter recognizes well track pattern - Google Patents
The method that feature based parameter recognizes well track pattern Download PDFInfo
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- CN107201894A CN107201894A CN201610157414.4A CN201610157414A CN107201894A CN 107201894 A CN107201894 A CN 107201894A CN 201610157414 A CN201610157414 A CN 201610157414A CN 107201894 A CN107201894 A CN 107201894A
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- Prior art keywords
- well track
- model
- characteristic parameter
- well
- pattern
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP 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
Abstract
The invention discloses a kind of method that feature based parameter recognizes well track pattern, it the described method comprises the following steps：Characterize well track pattern with well track model, different well track models takes on a different character parameter；The acquiring method of various well track aspect of model parameters is set up, and calculates according to deviational survey data the characteristic parameter of well track model；The evaluation index of structure well track pattern-recognition is evaluated the characteristic parameter best suits actual well track pattern to filter out.Compared with prior art, the invention provides a kind of selection foundation of well track pattern, the precision and reliability of well track monitoring and control are improved；Existing drilling technology and instrument instrument need not be changed by implementing the method for the present invention, and method is succinct, be easy to application.
Description
Technical field
The present invention relates to oil gas drilling field, the side that feature based parameter recognizes well track pattern is in particulard relate to
Method.
Background technology
When carrying out oil gas drilling, the well track form that different steerable drilling modes are got out is also different.At present,
Main steerable drilling mode has：3 kinds of slide-and-guide, rotary steering, compound direction etc., and well track mould
Type then has more than 10 kinds.Well track is monitored and controlled to be accurate it may first have to recognize well according to actual conditions
Trajectory model.
But, in the prior art, the also recognition methods without well track pattern can only be led according to different
Relatively reasonable well track pattern is selected to drilling mode.Up to the present, although having obtained some on leading
To the result of study of relation between drilling mode and well track pattern, but in theory can't Strict Proof
Which kind of well track pattern is different steerable drilling modes specifically meet.This results in the section of well track model selection
The property learned existing defects, have a strong impact on the precision and reliability of well track monitoring and control.
Therefore, for the precision and reliability that improve well track monitoring and control, urgent need to resolve well track mould
The recognition methods problem of formula.
The content of the invention
In order to improve well track monitoring and the precision and reliability of control, the invention provides a kind of feature based
The method that parameter recognizes well track pattern, the described method comprises the following steps：
Well track pattern is characterized with a variety of well track models, different well track models has different
Characteristic parameter；
The acquiring method of the characteristic parameter of every kind of well track model is set up, and is calculated according to deviational survey data
The characteristic parameter of every kind of well track model；
Build the evaluation index of well track pattern-recognition and the characteristic parameter is evaluated to filter out most
Meet actual well track pattern.
According to one embodiment of the invention, the well track model comprising space circular arc model, cylindrical spiral model,
Natural curve model and permanent instrument surface model, every kind of well track model have 2 well tracks respectively
Characteristic parameter, wherein：
The well track characteristic parameter of the space circular arc model is hole curvature and initial tool face angle；
The well track characteristic parameter of the cylindrical spiral model is curvature of the well track on vertical cross section
With the curvature in horizontal projection；
The well track characteristic parameter of the natural curve model is rate of deviation and rate of azimuth change；
The well track characteristic parameter of the permanent instrument surface model is hole curvature and tool face azimuth.
According to one embodiment of the invention, for every kind of well track model, the well track is set up respectively
Characteristic parameter computational methods；Using the well track deviational survey data, for each of the well track
Section is surveyed, the characteristic parameter of every kind of well track model is calculated respectively.
According to one embodiment of the invention, the identification interval of selected well track pattern includes several surveys
Section, in the identification is interval, for every kind of well track model, can obtain the well track special
Levy the average value and standard deviation of parameter；Consider the well track characteristic parameter different dimensions and they to well
The Different Effects degree of eye trajectory model identification, based on the standard deviation of the well track characteristic parameter, builds well
The evaluation index of eye trajectory model identification.
According to one embodiment of the invention, in identification is interval, multiple different well track models obtain it is multiple not
Same evaluation index numerical value, therefrom filters out evaluation index most the superior, the well track model corresponding to it is
Best suit the well track pattern of actual conditions.
Compared with prior art, the present invention compensate for well track model selection without according to can be according to defect, according to reality
Bore track characteristic recognize and well track model is provided, so as to improve well track monitoring with control precision and
Reliability；Existing Measurement While Drilling Data need to only be utilized by implementing the method for the present invention, it is not necessary to change existing brill
Well technique and instrument instrument, method is succinct, be easy to application.
The further feature or advantage of the present invention will be illustrated in the following description.Also, the part of the present invention is special
Levy or advantage will be become apparent by specification, or be appreciated that by implementing the present invention.The present invention
Purpose and certain advantages can by specifically noted step in specification, claims and accompanying drawing come
Realize or obtain.
Brief description of the drawings
Accompanying drawing is used for providing a further understanding of the present invention, and constitutes a part for specification, with the present invention
Embodiment be provided commonly for explain the present invention, be not construed as limiting the invention.In the accompanying drawings：
Fig. 1 is according to one embodiment of the invention method execution flow chart.
Embodiment
Describe embodiments of the present invention in detail below with reference to drawings and Examples, whereby implementation of the invention
Personnel can fully understand how application technology means solve technical problem to the present invention, and reach technique effect
Simultaneously the present invention is embodied according to above-mentioned implementation process in implementation process.If it should be noted that do not constitute conflict,
Each feature in each embodiment and each embodiment in the present invention can be combined with each other, the technology formed
Scheme is within protection scope of the present invention.
In the prior art, the recognition methods also without well track pattern, can only be according to different steerable drillings
Mode selects relatively reasonable well track pattern.Up to the present, although having obtained some on steerable drilling
The result of study of relation between mode and well track pattern, but in theory can't Strict Proof difference lead
Which kind of well track pattern specifically met to drilling mode.This science for resulting in well track model selection is deposited
In defect.
In order to correctly recognize well track pattern, the present invention proposes a kind of feature based parameter to recognize well rail
The method of mark pattern.Well track pattern can be characterized with well track model, and different well track models
Take on a different character parameter.For example, the characteristic parameter of space circular arc model is hole curvature and initial tool face
Angle, and the characteristic parameter of natural curve model is rate of deviation and rate of azimuth change.For on well track
For a series of measuring points, if the characteristic parameter of certain well track model is held essentially constant, just have reason to recognize
There is this specific changing rule and feature for well track, so can be described with the well track model and
Monitor well track.
Based on above-mentioned principle, in the present invention, the deviational survey data of well track are obtained first, then characterizes and counts
The characteristic parameter of well track is calculated, is finally based on the standard deviation of well track characteristic parameter which kind of well rail evaluated
Mark model best suits actual conditions, so as to recognize and determine well track pattern.
Next the specific implementation process of one embodiment of the invention is described in detail based on accompanying drawing.The flow chart of accompanying drawing
In can be performed in the computer system comprising such as one group computer executable instructions the step of show.Although
The logical order of each step is shown in flow charts, but in some cases, can be with different from herein
Order performs shown or described step.
As shown in Figure 1：
Step S1：Characterize well track pattern
Well track pattern can be characterized with well track model, and different well track models have it is different
Characteristic parameter, these characteristic parameters determine the spatial shape of well track.In the prior art, conventional well
Eye locus model has space arc model, cylindrical spiral model, natural curve model and permanent instrument surface model.Often
Plant well track model has 2 well track characteristic parameters respectively：
The well track characteristic parameter of space circular arc model is hole curvature and initial tool face angle (step S11)；
The well track characteristic parameter of cylindrical spiral model is curvature and water of the well track on vertical cross section
Curvature (step S12) on flat perspective view；
The well track characteristic parameter of natural curve model is rate of deviation and rate of azimuth change (step S13)；
The well track characteristic parameter of permanent instrument surface model is hole curvature and tool face azimuth (step S14).
Step S2：Calculate the characteristic parameter of well track model
For every kind of well track model, the computational methods of well track characteristic parameter are set up respectively；Utilize well
Track deviational survey data, calculate each characteristic parameter for surveying every kind of well track model in section respectively.
Specifically, for two adjacent measuring points on drilling trajectory, if deviational survey data are respectively (Li-1,αi-1,φ1-1)
(Li,αi,φ1), then the well section between the two measuring points is i-th of survey section [Li-1,Li], it can characterize as follows simultaneously
Calculate the characteristic parameter of well track：
1. space circular arc model
The characteristic parameter of space circular arc model is hole curvature and initial tool face angle, and its calculation formula is
Wherein
cosεi-1,i=cos αi-1cosαi+sinαi-1sinαicos(φi-φi-1) (2)
In formula, i is survey segment mark；L is well depth, unit m；α is hole angle, unit (°)；φ is azimuth,
Unit (°)；ε is angle of bend, unit (°)；κ is hole curvature, unit (°)/m；ω0For initial tool face angle,
Unit (°).
2. cylindrical spiral model
The characteristic parameter of cylindrical spiral model is curvature and horizontal projection of the well track on vertical cross section
On curvature, its calculation formula is
In formula, i is survey segment mark；κvFor the curvature on vertical cross section, unit (°)/m；κhFor floor projection
Curvature on figure, unit (°)/m.
3. natural curve model
The characteristic parameter of natural curve model is rate of deviation and rate of azimuth change, and its calculation formula is
In formula, i is survey segment mark；καFor rate of deviation, unit (°)/m；κφFor rate of azimuth change, unit
(°)/m。
4. permanent instrument surface model
The characteristic parameter of permanent instrument surface model is hole curvature and tool face azimuth, and its calculation formula is
In formula, i is survey segment mark；κ is hole curvature；ω is tool face azimuth, unit (°).
In actual drilling process, the well depth of drilling trajectory, well can be measured using instruments such as measurement while drillings (MWD)
The data such as oblique angle and azimuth, so as to obtain a series of deviational survey data (step S21) of measuring points.Utilize these
Deviational survey data and above-mentioned formula, can calculate each characteristic parameter for surveying every kind of well track model in section.
Step S3：Recognize well track pattern
First, step S31 is performed, the identification for choosing well track pattern is interval.The well track of full well can be with
Only with a kind of well track pattern, multiple well sections can also be divided into and different well track moulds are respectively adopted
Formula.Obviously, the former identification interval is that from well head to shaft bottom, and the well track of the latter is by multiple different modes
Well section combine, wherein to correspond to an identification interval for each well section.
Then, step S32 is performed, the standard deviation of characteristic parameter under various well track patterns is asked for.Different wells
The characteristic parameter of eye locus model is different, but every kind of well track model has 2 characteristic parameters.For narration side
Just, this 2 characteristic parameters are unified is represented with u and v.Then, for i-th of survey section, various well rails
The characteristic parameter of mark model is respectively
1. space circular arc model
2. cylindrical spiral model
3. natural curve model
4. permanent instrument surface model
So, in the identification is interval, if having n survey section, the arithmetic average of well track characteristic parameter
It is worth and is
In formulaAndRespectively characteristic parameter u and v arithmetic mean of instantaneous value.
Characterized, therefore in the present embodiment, used due to the confidence level available standards difference of arithmetic mean of instantaneous value
The standard deviation of well track characteristic parameter weighs the confidence level of its average value.That is the mark of well track characteristic parameter
Quasi- difference is
σ in formulauAnd σvRespectively characteristic parameter u and v standard deviation.
Further, step S33 is performed, the evaluation index of well track pattern-recognition is built.In step S32
In, there are dimension inconsistence problems in the standard deviation of well track characteristic parameter.It is empty for characteristic parameter v
Between the dimension of arc model and permanent instrument surface model be angle, and the amount of cylindrical spiral model and natural curve model
Guiding principle is curvature.Because the characteristic parameter of above-mentioned well track model only has curvature and two kinds of tool face azimuth parameter, and
Most of characteristic parameters are form for curvature., can be by the standard deviation of tool face azimuth for the ease of being compared and evaluating
Change the dimension calculated as curvature.I.e.：
So far, in the identification is interval, the standard deviation of the characteristic parameter of various well track models can be obtained,
And it is identical dimension that its standard deviation, which has changed calculation,.
As it was previously stated, the uniformity and confidence level of well track characteristic parameter can be evaluated with its standard deviation, mark
Quasi- difference is smaller, and its consistency and confidence level are better.However, the characteristic parameter of every kind of well track model is all phase
It is mutually independent, and their influence degrees to well track pattern-recognition also differ, so occurring wherein
One characteristic parameter standard deviation is smaller and situation that the standard deviation of another characteristic parameter is larger.To solve this
Problem, the present invention considers the weight of each characteristic parameter, constructs the evaluation index of well track pattern-recognition.I.e.：
σ=wuσu+(1-wu)σv (13)
In formula, σ is the evaluation index of well track pattern, unit (°)/m；wuParameter u weight is characterized,
Dimensionless.
Finally, step S34 is performed, filters out and best suits actual well track pattern.Contrast various well rails
The evaluation index of mark model, its evaluation index σ recklings are the well track model for best suiting actual conditions.
Because every kind of well track model both corresponds to specific well track pattern, so can screen and identify accordingly
Best suit the well track pattern of actual conditions.
Compared with prior art, the present invention compensate for well track model selection without according to can be according to defect, according to reality
Bore track characteristic recognize and well track model is provided, so as to improve well track monitoring with control precision and
Reliability；Existing Measurement While Drilling Data need to only be utilized by implementing the method for the present invention, it is not necessary to change existing brill
Well technique and instrument instrument, method is succinct, be easy to application.
The present invention is further described with reference to embodiment.During certain horizontal well construction, using rotary steering
Drilling technology is crept into, the deviational survey obtained using measurement while drilling (Measure While Drilling, MWD) instrument
Data are shown in Table 1.Technique according to the invention scheme, can calculate various well track models characteristic parameter,
The parameters such as average value, standard deviation, are shown in Table 2.For every kind of well track model, if the power of 2 characteristic parameters
Weight is respectively 50%, then can obtain evaluation result as shown in table 2.As a result show：In this embodiment, due to
The evaluation index σ of natural curve model is minimum, so well track best suits natural curve model.
| Measuring point sequence number | Well depth (m) | Hole angle (°) | Azimuth (°) |
| 0 | 2100.00 | 40.00 | 70.00 |
| 1 | 2109.00 | 42.60 | 73.00 |
| 2 | 2120.00 | 45.70 | 76.50 |
| 3 | 2128.00 | 48.00 | 79.00 |
| 4 | 2137.00 | 50.50 | 82.00 |
| 5 | 2149.00 | 54.00 | 86.00 |
Table 1
Table 2
While it is disclosed that embodiment as above, but described content is only to facilitate understand the present invention
And the embodiment used, it is not limited to the present invention.Method of the present invention can also have other a variety of realities
Apply example.Without departing from the spirit of the present invention, those skilled in the art, which work as, to make according to the present invention
Go out various corresponding changes or deformation, but these corresponding changes or deformation should all belong to the claim of the present invention
Protection domain.
Claims (5)
1. a kind of method that feature based parameter recognizes well track pattern, it is characterised in that methods described bag
Include following steps：
Well track pattern is characterized with a variety of well track models, different well track models has different
Characteristic parameter；
The acquiring method of the characteristic parameter of every kind of well track model is set up, and is calculated according to deviational survey data
The characteristic parameter of every kind of well track model；
The evaluation index of structure well track pattern-recognition is evaluated the characteristic parameter most to be accorded with filtering out
Close actual well track pattern.
2. according to the method described in claim 1, it is characterised in that the well track model includes space
Arc model, cylindrical spiral model, natural curve model and permanent instrument surface model, every kind of well track mould
Type has 2 well track characteristic parameters respectively, wherein：
The well track characteristic parameter of the space circular arc model is hole curvature and initial tool face angle；
The well track characteristic parameter of the cylindrical spiral model is curvature of the well track on vertical cross section
With the curvature in horizontal projection；
The well track characteristic parameter of the natural curve model is rate of deviation and rate of azimuth change；
The well track characteristic parameter of the permanent instrument surface model is hole curvature and tool face azimuth.
3. method according to claim 1 or 2, it is characterised in that for every kind of well track
Model, sets up the computational methods of the characteristic parameter of the well track respectively；Utilize the well track deviational survey number
According to, for the well track each survey section, the characteristic parameter of every kind of well track model is calculated respectively.
4. according to method according to any one of claims 1 to 3, it is characterised in that selected well rail
The identification interval of mark pattern includes several and surveys section, in the identification is interval, for every kind of well rail
Mark model, can obtain the average value and standard deviation of the well track characteristic parameter；Consider the well track
The different dimensions of characteristic parameter and their Different Effects degree to well track pattern-recognition, based on the well
The standard deviation of eye track characteristic parameter, builds the evaluation index of well track pattern-recognition.
5. according to method according to any one of claims 1 to 4, it is characterised in that in identification is interval,
Multiple different evaluation index numerical value are obtained by multiple different well track models, evaluation index is therefrom filtered out
Most the superior, the well track model corresponding to the evaluation index most the superior is best suit actual conditions described
Well track pattern.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112145156A (en) * | 2020-07-16 | 2020-12-29 | 中国石油大学(华东) | Self-adaptive inclination measurement calculation method for well track |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112145156A (en) * | 2020-07-16 | 2020-12-29 | 中国石油大学(华东) | Self-adaptive inclination measurement calculation method for well track |
| CN112145156B (en) * | 2020-07-16 | 2021-05-07 | 中国石油大学(华东) | Self-adaptive inclination measurement calculation method for well track |
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