CN103790583A - Geological prediction method based on inertia measurement parameters - Google Patents

Geological prediction method based on inertia measurement parameters Download PDF

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CN103790583A
CN103790583A CN201410066819.8A CN201410066819A CN103790583A CN 103790583 A CN103790583 A CN 103790583A CN 201410066819 A CN201410066819 A CN 201410066819A CN 103790583 A CN103790583 A CN 103790583A
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geological
data
mode
geology
prediction method
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CN103790583B (en
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杨金显
张颖
李志鹏
陈超
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Henan University of Technology
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Abstract

In order to detect ore resources and geological disasters or replace drill bits in time according to the influence of different geological conditions on motion of the drill bits, the invention provides a geological prediction method in a multi-dimensional time sequence segmented mode based on inertia measurement parameters. The method includes the steps that acquired signals are segmented, after the data are compensated, a multi-dimensional time sequence is converted into a one-dimensional time sequence through a multi-parameter ARMA model, and newly-acquired data are substituted into P1 after being processed according to a first segment data calculation geological mode, the matching degree of the data and the geological mode P1 is judged through the Euclidean distance, if the data and the geological mode P1 are matched, it is shown that the drill bits are still in the same geological condition, if the data and the geological mode P1 are partially matched, it is shown that the drill bits enter a geological overlapped region; if the data and the geological mode P1 are not matched at all, it is shown that the drill bits enter a new geological condition, a geological mode P2 is built according to the data, and multiple geological modes P3, P4... Pi are repeatedly calculated in this way; the size and the position of a geological region are calculated by combining position information, and the type of the geological region is predicted by combining instance geological data. The method is simple, easy to achieve and high in reliability, and other sensors do not need to be additionally arranged.

Description

Geological Prediction method based on inertia measurement parameter
Technical field
The present invention relates to inertia measurement while drilling field, particularly the Geological Prediction method in drilling well, probing and boring.
Technical background
In drilling well, probing and boring, need to predict geological information, judge whether mineral resources or the disaster geology (as gas) sought, or change in time corresponding drill bit and creep into (different geology needs different drill bits to creep into).
Because drill bit environment is very severe, especially temperature height and impact shock severity.
Present geological survey method mainly contains resistivity method, radioactivity method, (surpassing) sonic method, nuclear magnetic resonance method and (density) imaging method etc., and volume is large, and cost is high and be affected by the external environment large.
The magnetic environment of drill bit is very complicated, changes by uhf electromagnetic wave and acoustic measurement geological stratification, because actual geological stratification interface can be very not clear, may cause larger measure error.
Also has the method for drilling earthquake, predict geological information by vibration signal, general by drillstring vibrations or ground vibration, if creep into very dark, the data that gather have much noise, and vibration signal may comprise the vibration acceleration being produced by dynamic excitation, vibration acceleration and acceleration of gravity signal etc. that differently quality factor produces, there is certain chaos and randomness, signal characteristic is so unobvious, and belong to weak signal, adopt the method for filtering or decomposition to be difficult to the true vibration signal being produced by geologic(al) factor that extracts, because every kind of filtering or decomposition method all have certain service condition, therefore predict that by vibration signal geological information may be inaccurate completely.
Utilize the relevant function method prediction geology of vibration signal, and correlation analysis is a kind of analysis of uncertainty, is a kind of statistical concept.Because vibration signal is that many factors causes, there is randomness, if directly adopt vibration correlation function method, may there is larger discrepancy.In addition, adopting power spectral-density analysis is also to carry out on the basis based on correlation function, if oscillation power spectrum statistics shows as power spectral density---the simple relation curve of frequency, that can predict geology, but is actually very complicated.
Because causing, different geology can drill bit movement parameter change, in order to predict accurately geological information, and volume is little, cost is low and vibration resistance is strong, the inertia measurement parameter providing in conjunction with MEMS inertia measurement-while-drilling system, designs a kind of Geological Prediction method based on vibration, angular velocity, speed, position and instance data.
Summary of the invention
The object of the invention is to MEMS inertial navigation technology and geological survey technology to combine, design a kind of Geological Prediction method based on inertia measurement parameter, realize and find in time resource, early warning and bit change whether more.
To achieve these goals, design MEMS inertia measurement-while-drilling system, carrying out signal processing and resolving of the angular velocity to gyroscope output and accelerometer output acceleration, obtains acceleration, angular velocity, speed and positional information.Different geology can cause a series of variations such as rate of penetration, acceleration, angular velocity, the Geological Prediction method of the multidimensional time series segmentation pattern of design based on acceleration, angular velocity, speed and position.
Inertia measurement-while-drilling system comprises signal acquisition module, signal processing and prediction module and transport module three parts.
Signal acquisition module comprises triaxial accelerometer, three-axis gyroscope, temperature pick up and amplifier section.Consider that the residing environment of drill bit is severe especially, need improve impact resistance and reduce volume, sensor all adopts MEMS solid state sensor.
Three axis accelerometer is in order to measure three of the drill bit acceleration signals on axially, and three-axis gyroscope is in order to measure three of the drill bit angular velocity on axially, and thermometer is for the temperature-compensating of gyroscope and accelerometer.
Signal is processed with the function of prediction module and is comprised signal processing and Geological Prediction.
Signal processing: the acceleration of drill bit and angular velocity signal carry out temperature-compensating, carries out wavelet filtering processing, then obtains rate signal and position signalling by the integration of acceleration.
Described Geological Prediction---the Geological Prediction method of the multidimensional time series segmentation pattern based on acceleration, angular velocity, speed and position: the acceleration signal, angular velocity signal and the rate signal that gather, every collection length data is one section, every slip
Figure 205833DEST_PATH_IMAGE003
number is got once average, eliminates accidental error.
Consider the relevance between acceleration, angular velocity and speed time series, set up slip autoregression model , carry out model parameter estimation and error-tested, multidimensional time series is transferred to One-dimension Time Series
Figure 80566DEST_PATH_IMAGE005
, wherein
Figure 722899DEST_PATH_IMAGE007
for different data segments.
Geology is divided into different patterns: pattern
Figure 451690DEST_PATH_IMAGE008
, pattern
Figure 888487DEST_PATH_IMAGE009
,
Figure 984619DEST_PATH_IMAGE010
, pattern
Figure 899486DEST_PATH_IMAGE011
, a kind of geology of each model representative.
The data of the 1st section that gather, through average and
Figure 30253DEST_PATH_IMAGE004
after model is processed, as the data of the 1st section
Figure 637952DEST_PATH_IMAGE012
, the later stage gather the the data of section, also pass through average and
Figure 189205DEST_PATH_IMAGE004
after model is processed, form the
Figure 643320DEST_PATH_IMAGE007
different pieces of information section
Figure 953078DEST_PATH_IMAGE013
.
According to the data of the 1st section
Figure 492644DEST_PATH_IMAGE012
, set up mode function , obtain the 1st kind of Geological Mode
Figure 854672DEST_PATH_IMAGE008
.
Because time series in same geology can be consistent substantially, so adopt linear segmented mode function, utilize least-squares estimation mode function parameter.
The data of the 2nd section
Figure 335332DEST_PATH_IMAGE015
bring mode function into
Figure 345882DEST_PATH_IMAGE008
, obtain
Figure 202980DEST_PATH_IMAGE016
.
Calculate
Figure 834950DEST_PATH_IMAGE017
with
Figure 486511DEST_PATH_IMAGE018
between similitude, with Euclidean distance
Figure 669DEST_PATH_IMAGE019
measure.
?
Figure 395878DEST_PATH_IMAGE020
be normalized, because data have certain error, consider error component, an additional error factor
Figure 662780DEST_PATH_IMAGE021
, ,
Figure 17855DEST_PATH_IMAGE023
for maximum Euclidean distance.
Figure 420018DEST_PATH_IMAGE024
illustrate and Geological Mode
Figure 88896DEST_PATH_IMAGE008
coupling, is considered as identical geology; illustrate and Geological Mode
Figure 305431DEST_PATH_IMAGE008
part coupling, is considered as the identical geology of part---overlapping geology;
Figure 776864DEST_PATH_IMAGE026
illustrate and be and Geological Mode do not mate, be considered as different geology.
If
Figure 182623DEST_PATH_IMAGE017
with
Figure 893090DEST_PATH_IMAGE018
between similar, explanation
Figure 433793DEST_PATH_IMAGE015
with Geological Mode
Figure 280526DEST_PATH_IMAGE008
coupling, and then calculate
Figure 350114DEST_PATH_IMAGE027
with Geological Mode
Figure 531565DEST_PATH_IMAGE008
whether mate, until calculate
Figure 875959DEST_PATH_IMAGE028
the data of section
Figure 842778DEST_PATH_IMAGE029
with Geological Mode
Figure 348845DEST_PATH_IMAGE008
part coupling, illustrates that now drill bit enters overlapping geologic province.
Further calculate the
Figure 33905DEST_PATH_IMAGE030
the data of section
Figure 916410DEST_PATH_IMAGE031
with Geological Mode
Figure 737735DEST_PATH_IMAGE008
matching degree, until the
Figure 414704DEST_PATH_IMAGE032
the data of section
Figure 570748DEST_PATH_IMAGE033
geological Mode
Figure 256944DEST_PATH_IMAGE008
do not mate.
According to
Figure 932776DEST_PATH_IMAGE032
the data of section
Figure 46226DEST_PATH_IMAGE033
, set up mode function , obtain the 2nd kind of Geological Mode
Figure 664606DEST_PATH_IMAGE009
.
Continue to go out the 3rd kind of Geological Mode according to the data segment cycle calculations gathering
Figure 460524DEST_PATH_IMAGE035
, the 4th kind of Geological Mode
Figure 744875DEST_PATH_IMAGE036
, and the
Figure 609931DEST_PATH_IMAGE007
plant Geological Mode
Figure 637930DEST_PATH_IMAGE037
.
The Geological Mode calculating
Figure 288354DEST_PATH_IMAGE037
in likely identical, calculate the
Figure 743606DEST_PATH_IMAGE007
plant Geological Mode
Figure 112271DEST_PATH_IMAGE037
with the similitude of former Geological Mode, can, according to the coefficient contrast judgement of mode function, utilize euclidean distance metric similitude, when being less than a threshold value of setting
Figure 678381DEST_PATH_IMAGE038
time, geology is identical.
In conjunction with the position of creeping into, the position of prediction overlapping region, according to
Figure 183312DEST_PATH_IMAGE028
with
Figure 527575DEST_PATH_IMAGE032
the position in moment, calculates geology overlapping region size.
In conjunction with the position of creeping into, the position of prediction geologic province; According to 1 He
Figure 649114DEST_PATH_IMAGE028
the position in moment, the size of calculating the 1st geologic province, calculates other geologic province size and positions by that analogy.
In conjunction with example geologic data, the data that gather in drilling process before, or the different geology of structure are crept into, the data data as an example that gather, select common geological stratification in some probings, as bore layer of sand data, silt layer data, the data of glutinous mud layer, the data of water layer, the data in coal seam, the data of oil reservoir, the data of gas-bearing formation, various lithospheres data, .
Set up example Geological Mode according to instance data
Figure 643932DEST_PATH_IMAGE040
, , ,
Figure 625247DEST_PATH_IMAGE043
,
Figure 635928DEST_PATH_IMAGE044
,
Figure 72726DEST_PATH_IMAGE045
,
Figure 168858DEST_PATH_IMAGE046
, , .
If drill bit creeps into, geology is identical with example geology, and Geological Mode is identical, due to the existence of error, causes coefficient to have some difference.
The Geological Mode calculating according to the data that gather, with the coefficient contrast judgement of example Geological Mode function, utilizes the similitude of two kinds of Geological Modes of euclidean distance metric, because two-mode can not be identical, an error factor is set
Figure 87769DEST_PATH_IMAGE048
, when Euclidean distance is less than
Figure 123727DEST_PATH_IMAGE048
time, can predict that with example geology be identical.
Initial data and deal with data are delivered to signal transmission module.
The invention has the advantages that, (1) the method is simply easy to realize; (2) consider acceleration, angular velocity, speed and location parameter, better than single vibration signal prediction geology that adopts; (3) utilized instance data, predicted the outcome more reliable; (4) need not add other sensors, inertia measurement-while-drilling system provides the parameter of whole needs.
Accompanying drawing explanation
Fig. 1 is inertia measurement-while-drilling system structure chart of the present invention;
Fig. 2 is Geological Prediction method flow diagram of the present invention.
The specific embodiment
Below in conjunction with accompanying drawing explanation the specific embodiment of the present invention.
As shown in Figure 1, inertia measurement-while-drilling system comprises signal acquisition module, signal processing and prediction module and signal transmission module.
Signal acquisition module adopts integration module GY-9150, built-in chip type 16bit AD converter, and 16 bit data outputs (13, magnetic field), Direct Digital IIC interface, gyroscope survey scope is adjustable: ± 250, ± 500, ± 1000 and ± 2000 o/ s, acceleration analysis scope: ± 2, ± 4, ± 8, ± 16g.The maximum certainty of measurement of accelerometer is 0.06mg, and the maximum certainty of measurement of gyroscope is 0.008 o/ s, magnetometer survey precision is 0.02uT, built-in MEMS temperature pick up, very high heat stability (being easy to temperature-compensating) and closely side-play amount tolerance limit, impact antivibration that can anti-10000g (avoiding drill bit to get on the hard object such as rock damages sensor), noise very low (signal of measurement is real signal), price are low, low in energy consumption and volume little (4mm × 4mm × 1.5mm).
Data processing and prediction module adopt based on TMS320C6713 kernel processor chip, realize the signal of inertia measurement-while-drilling system and process and Geological Prediction.
Signal acquisition module and signal processing are sprayed to one deck insulating moulding coating with prediction module, reduce the impact of environment temperature on gyroscope and accelerometer; Use in addition binding agent to reinforce acquisition module and processing and prediction module circuit, improve vibration resistance and reliability.
Transport module adopts the communication of heatproof optical fiber bidirectional.
For signal processing, first system initialization, comprises drill bit movement data (speed, position and azimuth), inertial coodinate system and drill bit moving coordinate system is set; Measure in real time the acceleration of motion of drill bit by accelerometer with by the angular velocity of gyroscope survey drill bit
Figure 93137DEST_PATH_IMAGE050
, adopt the temperature of built-in thermometer output to carry out data temperature-compensating, adopt wavelet transformation to remove normal value deviation; Obtain speed by integrated acceleration
Figure 402896DEST_PATH_IMAGE051
and position
Figure 676883DEST_PATH_IMAGE052
.
Different geology can cause a series of variations such as rate of penetration, acceleration, angular velocity, the measurement parameter that is making full use of inertia measurement-while-drilling system and provide, the Geological Prediction method of the multidimensional time series segmentation pattern of design based on acceleration, angular velocity, speed and position, as Fig. 2.
The acceleration signal, angular velocity signal and the rate signal that gather, every collection
Figure 730289DEST_PATH_IMAGE001
length data is one section, every slip
Figure 38911DEST_PATH_IMAGE053
number is got once average, eliminates accidental error.
Form time series data itself by acceleration, angular velocity and speed and have higher-dimension, complexity, dynamic, strong noise and easily reach large-scale characteristic, amount of calculation is very huge, be difficult to realize, then with
Figure 519571DEST_PATH_IMAGE003
be that a unit does on average, data length is reduced to
Figure 795700DEST_PATH_IMAGE054
.
Consider the relevance between acceleration, angular velocity and speed time series, set up according to slip autoregression model
Figure 387218DEST_PATH_IMAGE055
, carry out model parameter estimation and error-tested, multidimensional time series is transferred to One-dimension Time Series , wherein
Figure 467487DEST_PATH_IMAGE007
for different data segments.
Geology is divided into different patterns: pattern , pattern
Figure 111275DEST_PATH_IMAGE009
,
Figure 394489DEST_PATH_IMAGE010
, pattern
Figure 216951DEST_PATH_IMAGE011
, a kind of geology of each model representative.
The data of the 1st section that gather, through average and
Figure 202094DEST_PATH_IMAGE004
after model is processed, as the data of the 1st section
Figure 400994DEST_PATH_IMAGE012
, the later stage gather the the data of section, also pass through average and
Figure 919DEST_PATH_IMAGE004
after model is processed, form the
Figure 755249DEST_PATH_IMAGE007
different pieces of information section
Figure 226681DEST_PATH_IMAGE013
.
According to the data of the 1st section
Figure 750067DEST_PATH_IMAGE012
, set up mode function
Figure 632441DEST_PATH_IMAGE014
, obtain the 1st kind of Geological Mode
Figure 874066DEST_PATH_IMAGE008
.
Because the time series characteristic in same geology can be consistent substantially, so adopt linear segmented mode function
Figure 883611DEST_PATH_IMAGE057
, utilize least-squares estimation mode function parameter
Figure 261502DEST_PATH_IMAGE058
with
Figure 331090DEST_PATH_IMAGE059
.
The data of the 2nd section bring mode function into
Figure 607667DEST_PATH_IMAGE008
, obtain .
Calculate
Figure 329821DEST_PATH_IMAGE017
with
Figure 280460DEST_PATH_IMAGE018
between similitude, with Euclidean distance
Figure 631807DEST_PATH_IMAGE019
measure.
?
Figure 984291DEST_PATH_IMAGE020
be normalized, because data have certain error, consider error component, an additional error factor
Figure 395681DEST_PATH_IMAGE021
,
Figure 302457DEST_PATH_IMAGE022
, for maximum Euclidean distance.
Figure 913752DEST_PATH_IMAGE024
illustrate and Geological Mode coupling, is considered as identical geology;
Figure 421274DEST_PATH_IMAGE060
illustrate and Geological Mode
Figure 645582DEST_PATH_IMAGE008
part coupling, is considered as the identical geology of part---overlapping geology; illustrate and be and Geological Mode
Figure 460271DEST_PATH_IMAGE008
do not mate, be considered as different geology.
If
Figure 341640DEST_PATH_IMAGE017
with
Figure 369639DEST_PATH_IMAGE018
between similar, explanation
Figure 269330DEST_PATH_IMAGE015
with Geological Mode
Figure 724583DEST_PATH_IMAGE008
coupling, and then calculate
Figure 827668DEST_PATH_IMAGE027
with Geological Mode
Figure 393778DEST_PATH_IMAGE008
whether mate, until calculate
Figure 164288DEST_PATH_IMAGE028
the data of section
Figure 790442DEST_PATH_IMAGE029
with Geological Mode
Figure 380823DEST_PATH_IMAGE008
part coupling, illustrates that now drill bit enters overlapping geologic province.
Further calculate the
Figure 750624DEST_PATH_IMAGE030
the data of section
Figure 624908DEST_PATH_IMAGE031
with Geological Mode matching degree, until to
Figure 499641DEST_PATH_IMAGE032
the data of section
Figure 876395DEST_PATH_IMAGE033
geological Mode
Figure 887077DEST_PATH_IMAGE008
do not mate.
According to
Figure 323874DEST_PATH_IMAGE032
the data of section
Figure 420006DEST_PATH_IMAGE033
, set up mode function
Figure 849719DEST_PATH_IMAGE034
, obtain the 2nd kind of Geological Mode .
Continue to go out the 3rd kind of Geological Mode according to the data segment cycle calculations gathering
Figure 322606DEST_PATH_IMAGE035
, the 4th kind of Geological Mode
Figure 906034DEST_PATH_IMAGE036
, and the plant Geological Mode
Figure 609865DEST_PATH_IMAGE037
.
The Geological Mode calculating
Figure 388465DEST_PATH_IMAGE037
in likely identical, calculate the
Figure 459189DEST_PATH_IMAGE007
plant Geological Mode with the similitude of former Geological Mode, can be according to the coefficient contrast judgement of mode function, the Euclidean distance of computation schema coefficient
Figure 539327DEST_PATH_IMAGE062
, wherein
Figure 19987DEST_PATH_IMAGE063
, utilize
Figure 46848DEST_PATH_IMAGE064
tolerance similitude, when being less than a threshold value of setting
Figure 638367DEST_PATH_IMAGE038
time, geology is identical.
In conjunction with the position of creeping into
Figure 801495DEST_PATH_IMAGE052
, the position of prediction overlapping region, according to
Figure 718635DEST_PATH_IMAGE028
with
Figure 216482DEST_PATH_IMAGE032
the position in moment
Figure 611691DEST_PATH_IMAGE065
with
Figure 894905DEST_PATH_IMAGE066
, calculate geology overlapping region size
Figure 717367DEST_PATH_IMAGE067
.
In conjunction with the position of creeping into
Figure 453242DEST_PATH_IMAGE052
, the position of prediction geologic province; According to 1 He
Figure 652142DEST_PATH_IMAGE028
the position in moment
Figure 789862DEST_PATH_IMAGE068
with
Figure 783226DEST_PATH_IMAGE065
, the size of calculating the 1st geologic province
Figure 255665DEST_PATH_IMAGE069
, calculate by that analogy other geologic province size and positions.
In conjunction with example geologic data, the data that gather in drilling process before, or creep into constructing different geology, the data data as an example that gather, select common geological stratification in some probings, as bore layer of sand data, silt layer data, the data of glutinous mud layer, the data of water layer, the data in coal seam, the data of oil reservoir, the data of gas-bearing formation, various lithospheres data,
Figure 992676DEST_PATH_IMAGE039
.
Set up example Geological Mode according to instance data
Figure 984903DEST_PATH_IMAGE070
(
Figure 414748DEST_PATH_IMAGE040
,
Figure 125215DEST_PATH_IMAGE041
,
Figure 400338DEST_PATH_IMAGE042
,
Figure 512651DEST_PATH_IMAGE043
,
Figure 847817DEST_PATH_IMAGE044
,
Figure 294848DEST_PATH_IMAGE045
, ,
Figure 340481DEST_PATH_IMAGE047
,
Figure 112128DEST_PATH_IMAGE039
).
If drill bit creeps into, geology is identical with example geology, and Geological Mode is identical, due to the existence of error, causes coefficient to have some difference.
The Geological Mode calculating according to the data that gather in drilling process
Figure 531608DEST_PATH_IMAGE037
, with example Geological Mode function
Figure 414114DEST_PATH_IMAGE070
coefficient contrast judgement, utilize the similitude of two kinds of Geological Modes of euclidean distance metric, because two-mode can not be identical, an error factor is set
Figure 235439DEST_PATH_IMAGE048
, when Euclidean distance is less than time, can predict that with example geology be identical.
If similar to example lithosphere, can be directly as this lithosphere, need not understand it is what lithosphere on earth, because they just have similar impact (judging whether more bit change) to drill bit, if Geological Mode does not have similar to example Geological Mode, explanation may be unknown layer, need not be concerned about it, because these are not the resource layers that will look for.
Finally explanation is that above case study on implementation is only unrestricted for technical scheme of the present invention is described, can the present invention be modified or be changed, and not depart from the scope of the technical program, and it all should be encompassed in the middle of claim scope of the present invention.

Claims (8)

1. the Geological Prediction method based on inertia measurement parameter, it is characterized in that different geology can cause a series of variations such as rate of penetration, acceleration, angular velocity, at the measurement parameter that makes full use of inertia measurement-while-drilling system and provide, the Geological Prediction method of the multidimensional time series segmentation pattern of design based on acceleration, angular velocity, speed and position.
2. the Geological Prediction method based on inertia measurement parameter according to claim 1, is characterized in that (1) the acceleration signal, angular velocity signal and the rate signal that gather, every collection
Figure 518877DEST_PATH_IMAGE001
length data is one section, every slip
Figure 700460DEST_PATH_IMAGE003
number is got once average, eliminates accidental error; (2) form time series data itself by acceleration, angular velocity and speed and have higher-dimension, complexity, dynamic, strong noise and easily reach large-scale characteristic, amount of calculation is very huge, be difficult to realize, then with
Figure 624554DEST_PATH_IMAGE003
be that a unit does on average, data length is reduced to
Figure 117852DEST_PATH_IMAGE004
.
3. the Geological Prediction method based on inertia measurement parameter according to claim 1, is characterized in that considering the relevance between acceleration, angular velocity and speed time series, sets up according to slip autoregression model
Figure 683962DEST_PATH_IMAGE005
, carry out model parameter estimation and error-tested, multidimensional time series is transferred to One-dimension Time Series
Figure 188893DEST_PATH_IMAGE006
, wherein for different data segments.
4. the Geological Prediction method based on inertia measurement parameter according to claim 1, is characterized in that geology to be divided into different patterns: pattern
Figure 795641DEST_PATH_IMAGE009
, pattern
Figure 899863DEST_PATH_IMAGE010
, , pattern
Figure 321934DEST_PATH_IMAGE012
, a kind of geology of each model representative.
5. the Geological Prediction method based on inertia measurement parameter according to claim 1, is characterized in that (1) according to the data of the 1st data segment
Figure 524245DEST_PATH_IMAGE013
, set up mode function , obtain the 1st kind of Geological Mode ; (2) because the time series characteristic in same geology can be consistent substantially, so adopt linear segmented mode function
Figure 145217DEST_PATH_IMAGE015
, utilize least-squares estimation mode function parameter
Figure 834824DEST_PATH_IMAGE016
With
Figure 280849DEST_PATH_IMAGE017
; (3) the data of the 2nd data segment
Figure 880457DEST_PATH_IMAGE018
Bring mode function into
Figure 19315DEST_PATH_IMAGE009
, obtain
Figure 196218DEST_PATH_IMAGE019
; (4) calculate
Figure 445934DEST_PATH_IMAGE020
With Between similitude,With Euclidean distance
Figure 803283DEST_PATH_IMAGE022
Measure; ?
Figure 874007DEST_PATH_IMAGE023
Be normalized, because data have certain error, consider error component, an additional error factor
Figure 927414DEST_PATH_IMAGE024
,
Figure 236035DEST_PATH_IMAGE025
,
Figure 716695DEST_PATH_IMAGE026
For maximum Euclidean distance;(5)
Figure 602612DEST_PATH_IMAGE027
Illustrate and Geological Mode
Figure 459709DEST_PATH_IMAGE009
Coupling, is considered as identical geology;
Figure 622837DEST_PATH_IMAGE028
Illustrate and Geological Mode
Figure 274398DEST_PATH_IMAGE009
Part coupling, is considered as the identical geology of part---overlapping geology;
Figure 913190DEST_PATH_IMAGE029
Illustrate and be and Geological Mode Do not mate, be considered as different geology; If (6) With
Figure 7551DEST_PATH_IMAGE021
Between similar, explanation With Geological Mode
Figure 676747DEST_PATH_IMAGE009
Coupling, and then calculate
Figure 345626DEST_PATH_IMAGE030
With Geological Mode Whether mate, until calculate
Figure 686794DEST_PATH_IMAGE031
The data of data segment
Figure 158227DEST_PATH_IMAGE032
With Geological Mode
Figure 681612DEST_PATH_IMAGE009
Part coupling, illustrates that now drill bit enters overlapping geologic province; (7) further calculate
Figure 439352DEST_PATH_IMAGE033
The data of data segment
Figure 680978DEST_PATH_IMAGE034
With Geological Mode
Figure 690522DEST_PATH_IMAGE009
The degree of coupling, until to the
Figure 68414DEST_PATH_IMAGE035
The data of data segment
Figure 262635DEST_PATH_IMAGE036
Geological Mode
Figure 725977DEST_PATH_IMAGE009
Do not mate; (8) according to
Figure 539212DEST_PATH_IMAGE035
The data of data segment
Figure 37190DEST_PATH_IMAGE036
, set up mode function , obtain the 2nd kind of Geological Mode
Figure 352951DEST_PATH_IMAGE010
, continue to go out the 3rd kind of Geological Mode according to the data segment cycle calculations gathering
Figure 704298DEST_PATH_IMAGE038
, the 4th kind of Geological Mode
Figure 56781DEST_PATH_IMAGE039
,And the Plant Geological Mode
Figure 765160DEST_PATH_IMAGE040
.
6. the Geological Prediction method based on inertia measurement parameter according to claim 1, is characterized in that the Geological Mode calculating
Figure 920198DEST_PATH_IMAGE040
in likely identical, calculate the
Figure 127189DEST_PATH_IMAGE008
plant Geological Mode
Figure 834113DEST_PATH_IMAGE040
with the similitude of former Geological Mode, can be according to the coefficient contrast judgement of mode function, the Euclidean distance of computation schema coefficient
Figure 759344DEST_PATH_IMAGE041
, wherein , utilize
Figure 779570DEST_PATH_IMAGE043
tolerance similitude, when being less than a threshold value of setting
Figure 657396DEST_PATH_IMAGE044
time, geology is identical.
7. the Geological Prediction method based on inertia measurement parameter, is characterized in that (1) in conjunction with the position of creeping into
Figure 804344DEST_PATH_IMAGE045
, the position of prediction overlapping region, according to
Figure 301184DEST_PATH_IMAGE031
with
Figure 482767DEST_PATH_IMAGE035
the position in moment
Figure 531494DEST_PATH_IMAGE046
with
Figure 493634DEST_PATH_IMAGE047
, calculate geology overlapping region size
Figure 528586DEST_PATH_IMAGE048
; (2) in conjunction with the position of creeping into , the position of prediction geologic province, according to 1 He the position in moment
Figure 905844DEST_PATH_IMAGE049
with
Figure 744487DEST_PATH_IMAGE046
, the size of calculating the 1st geologic province
Figure 900661DEST_PATH_IMAGE050
; (3) calculate by that analogy other geologic province size and positions.
8. the Geological Prediction method based on inertia measurement parameter according to claim 1, it is characterized in that (1) in conjunction with example geologic data, the data that gather in drilling process before, or creep into constructing different geology, the data that gather data as an example, select common geological stratification in some probings, as bore layer of sand data, silt layer data, the data of glutinous mud layer, the data of water layer, the data in coal seam, the data of oil reservoir, the data of gas-bearing formation, various lithospheres data,
Figure 291191DEST_PATH_IMAGE051
; (2) set up example Geological Mode according to instance data ( ,
Figure 287463DEST_PATH_IMAGE054
,
Figure 848895DEST_PATH_IMAGE055
,
Figure 945027DEST_PATH_IMAGE056
,
Figure 125472DEST_PATH_IMAGE057
,
Figure 256239DEST_PATH_IMAGE058
, ,
Figure 306421DEST_PATH_IMAGE060
,
Figure 290557DEST_PATH_IMAGE051
); (3), if to creep into geology identical with example geology for drill bit, Geological Mode is identical, the Geological Mode calculating according to the data that gather , with example Geological Mode function
Figure 179065DEST_PATH_IMAGE052
coefficient contrast judgement, utilize the similitude of two kinds of Geological Modes of euclidean distance metric, because two-mode can not be identical, an error factor is set
Figure 984210DEST_PATH_IMAGE061
, when Euclidean distance is less than
Figure 506458DEST_PATH_IMAGE061
time, can predict that with example geology be identical; (4) if similar to example lithosphere, can be directly as this lithosphere, need not understand it is what lithosphere on earth, because they just have similar impact (judging whether more bit change) to drill bit, if Geological Mode does not have similar to example Geological Mode, explanation may be unknown layer, need not be concerned about it, because these are not the resource layers that will look for.
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