CN110374579A - With brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm - Google Patents
With brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm Download PDFInfo
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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
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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
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
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Abstract
The invention discloses one kind with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm.It include: under input high and low frequency logging mode with brill orientation electromagnetic wave resistivity logging data；Multi-resolution decomposition is carried out by ensemble empirical mode decomposition method to high and low frequency Electric Log Data；Prediction model is established to the time series under the different frequency range after ensemble empirical mode decomposition method is decomposed using least square method supporting vector machine；It the power such as carries out to the predicted value of prediction model under different frequency range to sum to obtain final prediction result.Prediction model of the present invention has stronger adaptivity, and required manual intervention is less.
Description
Technical field
The invention belongs to drilling well, well logging, well logging, directional well engineering fields, and in particular to a kind of to survey with brill orientation electromagnetic wave
Well depth shallow resistivity forecast Control Algorithm.
Background technique
It is to carry out stratum oil-containing, gassiness, aqueous or oil-water common-layer qualitative evaluation using conductive characteristic Formation Resistivity Measurement
Important evidence, while be also quantitative assessment reservoir hydrocarbon saturation one of important parameter.In high angle hole or horizontal well
In drilling process, to be maintained at well track in desired reservoir, need to adjust drill bit at any time at strata interface, urgently
A kind of energy is needed to carry out well logging and the well geosteering system of real-time geosteering and complex hydrocarbon layer drilling.It is traditional with brill
Resistivity logging tool is due to winding transmitting and receiving antenna using axial, thus do not have position sensing capability, it can not be independent
Determine stratum azimuth tendency and target position.Have the characteristics that real-time, efficient with orientation electromagnetic wave logging is bored, can be effectively reduced
Borehole fluid invasion is influenced caused by measuring formation resistivity, has important meaning to accurate evaluation reservoir oil-containing, gas bearing condition
Justice.With orientation electromagnetic wave logging instrument is bored by transmitting and receiving antenna inclination or laterally winding, pass through change antenna tilt and peace
Holding position achievees the purpose that adjust electromagnetic field emissions direction, survey so that receiving antenna be made to obtain the electromagnetic wave containing azimuth information
Signal is measured, then the electromagnetic wave signal is decoupled and be calculated azimuthal resistivity measurement response.Well logging information not only wraps
Relationship between Amplitude Ration and phase difference containing electromagnetic wave signal measured by formation resistivity and receiving antenna also includes orientation
The relevant information of angle and magnetic field strength and electromagnetic wave measurement signal orientation amplitude attenuation and phase drift.But due to brill orientation
Electromagnetic wave logging instrument investigative range is limited, can not carry out resistivity and ground to the unknown search coverage in stratum effectively in real time
Matter situation is assessed in advance, can not effectively control drill bit drilling direction, it is difficult to realize the problems such as drilling well is oriented in real time.
Summary of the invention
1, the purpose of the present invention
In order to solve the problems in the existing technology the present invention, provides a kind of electric with the orientation electromagnetic wave logging depth is bored
Resistance rate forecast Control Algorithm, this method can be to the unknown stratum in certain direction bored in the layer of orientation electromagnetic wave logging instrument location
Deep and shallow resistivity predicted in real time and Drilling Control.
2, the technical solution adopted in the present invention
The invention discloses one kind with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm, comprising:
Step 1, input high and low frequency logging mode under with bore orientation electromagnetic wave resistivity logging data (or resistivity survey
Well data time series), logging frequency is megahertz or Gigahertz under high frequency logging mode, frequency of logging well under low frequency logging mode
Rate is hertz or kHz；
The selection of high frequency logging mode and low frequency logging mode is depended on to measured direction electricity in formation at target locations a certain range
The average value of magnetic wave resistivity；The average value of measured direction electromagnetic resistivity is calculated according to formula (1) in a certain range:
In formula (1), R1Indicate that the average value of orientation electromagnetic resistivity under high frequency logging mode, n indicate measuring point sum, i
Indicate measuring point number, s indicates the deep resistivity measured under high frequency logging mode；R2Indicate orientation electromagnetism under low frequency logging mode
The average value of wave resistivity, m indicate that measuring point sum, j indicate measuring point number, and q indicates the shallow electricity measured under low frequency logging mode
Resistance rate；R indicates R1And R2Average value；The threshold value of default high frequency logging mode and low frequency logging mode；Select high frequency logging mode
N measuring point is respectively measured formation at target locations respectively with low frequency logging mode；When the value of R is greater than and is equal to threshold value, high frequency is selected to survey
Well mode；When the value of R is less than threshold value, low frequency logging mode is selected；
Under step 2, high frequency logging mode DATA REASONING mode using depthkeeping measure, depthkeeping fathom can for centimetre,
Decimetre, rice, hundred meters；Under low frequency logging mode DATA REASONING mode use Timing measurement, Timing measurement chronomere can for the second,
Divide, hour；According to the difference of the geological conditions on detected stratum, two kinds of measurement patterns be can be interchanged；
Under depthkeeping measurement pattern, measurement data needs to compensate；Compensation way is according to shown in formula (2):
In formula (2), i indicates that first practical measuring point, i+1 indicate that the practical measuring point of the latter, n indicate i+1 measuring point
Compensation number (usual n=i+1), piIndicate the actual measured value of i-th of measuring point, pi+1Indicate the actual measurement of i+1 measuring point
Value, pnIndicate the offset of i+1 measuring point；
Under Timing measurement mode, measurement data needs to compensate；Compensation way is according to shown in formula (3):
In formula (3), j indicates that first measuring point, j+1 indicate that the latter measuring point, m indicate the compensation number of+1 measuring point of jth
(usual m=j+1), tjIndicate the actual measured value of j-th of measuring point, tj+1Indicate the actual measured value of+1 measuring point of jth, tmIt indicates
The offset of+1 measuring point of jth；
Step 3, to high and low frequency Electric Log Data, i.e. deep, shallow resistivity log data passes through empirical mode decomposition side
Method carries out multi-resolution decomposition, and decomposable process is adaptive, quantity of the decomposition scale depending on input sample；
Step 4, using least square method supporting vector machine under the different frequency range after ensemble empirical mode decomposition method is decomposed
Time series, that is, intrinsic mode component and trend term establish prediction model；
Step 5 carries out etc. power to the predicted value of the time series predicting model under different frequency range and sums finally to be predicted
As a result；
When step 6, prediction model input sample are deep resistivity, output result is deep resistivity predicted value；Prediction model
When input sample is shallow resistivity, output result is shallow resistivity predicted value；The output result of prediction model provides drilling geology
Navigation information.
Further, the step 3: the decomposition number of intrinsic mode component and trend term be it is adaptive, depend on it is defeated
Enter the size of the sample size of model.
Further, the step 3: the prediction scale of prediction model depends on the size of the quantity of input sample.
Further, the step 3: the prediction scale employing mode of prediction model includes time or distance, be the second,
Point, hour or centimetre, decimeter, meter, hundred meters.
Further, the precision of prediction of prediction model and the kernel function of least square method supporting vector machine are related, kernel function packet
Include Polynomial kernel function, Radial basis kernel function, Sigmoid kernel function.
Further, the optimized parameter of kernel function is obtained using grid data service or cross-validation method.
Further, when prediction model input sample is with orientation electromagnetic wave logging depth resistivity is bored, output result is deep
Resistivity predicted value；When prediction model input sample is with orientation electromagnetic wave logging shallow resistivity is bored, output result is shallow resistance
Rate predicted value.
3, beneficial effect of the present invention
(1) present invention utilizes the auto-adaptive time sequence decomposition strategy of ensemble empirical mode decomposition method, to brill orientation electromagnetism
Wave Electric Log Data (or Electric Log Data time series) is handled.It reduces and is surveyed with brill orientation electromagnetic resistivity
The nonlinear characteristic of well data.
(2) present invention establishes based on least square method supporting vector machine the intrinsic mode component and trend term of different frequency range
Prediction model；The obtained prediction result of prediction model that the present invention utilizes least square method supporting vector machine to establish is surveyed to brill
Drilling well during well carries out geosteering.Due to the depth of input, the difference of shallow resistivity original data volume, prediction model it is pre-
Measurement ruler degree and precision of prediction can be different.Prediction model has stronger adaptivity, and required manual intervention is less.
Detailed description of the invention
Fig. 1 is that the present invention illustrates with the process for boring orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm embodiment
Figure.
Fig. 2 is empirical mode decomposition multi-resolution decomposition schematic diagram.
Fig. 3 is the prediction process that least square method supporting vector machine establishes prediction model.
Specific embodiment
Below with reference to the attached drawing in present example, the technical solution in present example is clearly and completely retouched
It states, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on the present invention
Embodiment, those skilled in the art's every other embodiment obtained under the premise of not doing creative work belongs to
Protection scope of the present invention.
Present example is described in further detail below in conjunction with attached drawing.
Embodiment
The present invention provides one kind with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm.This method will be with brill
Deep and shallow resistivity log data measured by orientation electromagnetic wave logging instrument establishes machine learning as primary data sample collection
Model is trained the deep and shallow resistivity log data measured by machine learning model, thus to the unknown spy of instrument
The depth formation resistivity surveyed in range carries out the PREDICTIVE CONTROL of different scale, provides geosteering information to real-time drilling.
Examples of the present invention will be described by way of reference to the accompanying drawings, in which: Fig. 1 is that the present invention is surveyed with orientation electromagnetic wave is bored
The flow diagram of well depth shallow resistivity forecast Control Algorithm embodiment.Fig. 2 is empirical mode decomposition multi-resolution decomposition schematic diagram.
Fig. 3 is the prediction process that least square method supporting vector machine establishes prediction model.
Based on empirical mode decomposition method to boring, orientation electromagnetic wave logging is deep, shallow resistivity data (or resistivity logging number
According to time series) it is decomposed.
With orientation electromagnetic wave logging deep and shallow resistivity prediction technique is bored, include the following steps:
The first step, choose under high and low frequency logging mode with boring azimuthal resistivity log data (or Electric Log Data
Time series)；DATA REASONING mode is measured using depthkeeping under high frequency logging mode, and logging frequency is megahertz under high frequency logging mode
Hereby or Gigahertz, such as every 1 meter of drilling when measure a data, depthkeeping fathom can for centimetre, decimeter, meter, hundred meters.
DATA REASONING mode uses Timing measurement under low frequency logging mode, under low frequency logging mode logging frequency be hertz or
KHz；Such as one data of measurement in every 1 minute, Timing measurement chronomere can be the second, divide, hour；
The selection of high frequency logging mode and low frequency logging mode is depended on to measured direction electricity in formation at target locations a certain range
The average value of magnetic wave resistivity.The average value of measured direction electromagnetic resistivity is calculated according to formula (1) in a certain range.
In formula (1), R1Indicate that the average value of orientation electromagnetic resistivity under high frequency logging mode, n indicate measuring point sum, i
Indicate measuring point number, s indicates the deep resistivity measured under high frequency logging mode.R2Indicate orientation electromagnetism under low frequency logging mode
The average value of wave resistivity, m indicate that measuring point sum, j indicate measuring point number, and q indicates the shallow electricity measured under low frequency logging mode
Resistance rate.R indicates R1And R2Average value.Selecting the threshold value of high frequency logging mode and low frequency logging mode is 100 ohm/meters.It selects
High frequency logging mode and low frequency logging mode respectively measure formation at target locations 10 measuring points respectively, and 10 measuring point measured values are substituted into formula
(1) in.When the value of R is greater than and is equal to 100 ohm/meter, high frequency logging mode is selected.When the value of R is less than 100 ohm/meter,
Select low frequency logging mode.
Under depthkeeping measurement pattern, measurement data needs to compensate.Compensation way is according to shown in formula (2).
In formula (2), i indicates that first practical measuring point, i+1 indicate that the practical measuring point of the latter, n indicate i+1 measuring point
Compensation number (usual n=i+1), piIndicate the actual measured value of i-th of measuring point, pi+1Indicate the actual measurement of i+1 measuring point
Value, pnIndicate the offset of i+1 measuring point.
Under Timing measurement mode, measurement data needs to compensate.Compensation way is according to shown in formula (3).
In formula (3), j indicates that first measuring point, j+1 indicate that the latter measuring point, m indicate the compensation number of+1 measuring point of jth
(usual m=j+1), tjIndicate the actual measured value of j-th of measuring point, tj+1Indicate the actual measured value of+1 measuring point of jth, tmIt indicates
The offset of+1 measuring point of jth.
Using empirical mode decomposition method to azimuthal resistivity log data progress multi-resolution decomposition is bored, it is decomposed into different frequencies
The intrinsic mode component and trend term of section, when empirical mode decomposition carries out multi-resolution decomposition, decomposable process is adaptive, decomposition
Scale depends on the quantity of input sample.
Second step, using least square method supporting vector machine to the different frequency range after ensemble empirical mode decomposition method is decomposed
Intrinsic mode component and trend term establish prediction model.The intrinsic mode component of different frequency range and trend term foundation are based on respectively
The prediction model of least square method supporting vector machine, in prediction model comprising training sample, test sample, match value, measured value and
Predicted value, the prediction scale and precision of prediction of prediction model are related with the size of input sample, prediction scale can be it is short-term, in
It is phase, long-term.The precision of prediction of prediction model and the kernel function of least square method supporting vector machine are related, and kernel function mainly has multinomial
Kernel function, Radial basis kernel function, Sigmoid kernel function.The optimal of kernel function is obtained using grid data service or cross-validation method
Parameter.
Third step the power such as carries out to the predicted value for the prediction model that intrinsic mode component under different frequency range and trend are established
Summation obtains final prediction result, true value and predicted value is compared, to verify the accurate of prediction model prediction result
Property.The prediction scale of prediction model is different, and the precision of prediction result is different.The accuracy of prediction result is with input with brill orientation
Electric Log Data amount is related with the selection of kernel function.The prediction scale of prediction model can be time or distance.Prediction
The unit of time can be second, minute, hour, and the unit of Prediction distance can be millimeter, centimetre, decimeter, meter, inch, foot.
The prediction scale of prediction model can be short-term, mid-term, long-term.
4th step, when prediction model input sample is with orientation electromagnetic wave logging depth resistivity is bored, output result is deep electricity
Resistance rate predicted value.When prediction model input sample is with orientation electromagnetic wave logging shallow resistivity is bored, output result is shallow resistivity
Predicted value；Conducive to prediction model output as a result, can be provided for drilling well with bore geosteering information.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Within the technical scope of the present disclosure, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the protection model of claims
Subject to enclosing.
Claims (7)
1. a kind of with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm, characterized by comprising:
Under step 1, input high and low frequency logging mode with boring orientation electromagnetic wave resistivity logging data (or resistivity logging number
According to time series), logging frequency is megahertz or Gigahertz under high frequency logging mode, and logging frequency is under low frequency logging mode
Hertz or kHz；
The selection of high frequency logging mode and low frequency logging mode is depended on to measured direction electromagnetic wave in formation at target locations a certain range
The average value of resistivity；The average value of measured direction electromagnetic resistivity is calculated according to formula (1) in a certain range:
In formula (1), R1Indicate that the average value of orientation electromagnetic resistivity under high frequency logging mode, n indicate that measuring point sum, i indicate to survey
Point number, s indicate the deep resistivity measured under high frequency logging mode；R2Indicate orientation electromagnetic wave resistance under low frequency logging mode
The average value of rate, m indicate that measuring point sum, j indicate measuring point number, and q indicates the shallow resistivity measured under low frequency logging mode；R
Indicate R1And R2Average value；The threshold value of default high frequency logging mode and low frequency logging mode；Select high frequency logging mode and low frequency
Logging mode respectively measures formation at target locations n measuring point respectively；When the value of R is greater than and is equal to threshold value, high frequency logging mode is selected；
When the value of R is less than threshold value, low frequency logging mode is selected；
Under step 2, high frequency logging mode DATA REASONING mode using depthkeeping measure, depthkeeping fathom can for centimetre, decimeter,
Rice, hundred meters；DATA REASONING mode uses Timing measurement under low frequency logging mode, and Timing measurement chronomere can be the second, divide, be small
When；According to the difference of the geological conditions on detected stratum, two kinds of measurement patterns be can be interchanged；
Under depthkeeping measurement pattern, measurement data needs to compensate；Compensation way is according to shown in formula (2):
In formula (2), i indicates that first practical measuring point, i+1 indicate that the practical measuring point of the latter, n indicate the compensation of i+1 measuring point
It numbers (usual n=i+1), piIndicate the actual measured value of i-th of measuring point, pi+1Indicate the actual measured value of i+1 measuring point, pn
Indicate the offset of i+1 measuring point；
Under Timing measurement mode, measurement data needs to compensate；Compensation way is according to shown in formula (3):
In formula (3), j indicates that first measuring point, j+1 indicate that the latter measuring point, m indicate the compensation number of+1 measuring point of jth (usually
M=j+1), tjIndicate the actual measured value of j-th of measuring point, tj+1Indicate the actual measured value of+1 measuring point of jth, tmIndicate jth+1
The offset of a measuring point；
Step 3, be deep, shallow resistivity log data to high and low frequency Electric Log Data by ensemble empirical mode decomposition method into
Row multi-resolution decomposition, decomposable process are adaptive, quantity of the decomposition scale depending on input sample；
Step 4, using least square method supporting vector machine under the different frequency range after ensemble empirical mode decomposition method is decomposed when
Between sequence, that is, intrinsic mode component and trend term establish prediction model；
Step 5 carries out etc. power to the predicted value of the time series predicting model under different frequency range and sums to obtain final prediction result；
When step 6, prediction model input sample are deep resistivity, output result is deep resistivity predicted value；Prediction model input
When sample is shallow resistivity, output result is shallow resistivity predicted value；The output result of prediction model provides geosteering while drilling
Information.
2. according to claim 1 with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm, it is characterised in that
The step 3: the decomposition number of intrinsic mode component and trend term be it is adaptive, sample size depending on input model
Size.
3. according to claim 1 with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm, it is characterised in that
The step 3: the prediction scale of prediction model depends on the size of the quantity of input sample.
4. according to claim 1 with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm, it is characterised in that
The step 3: the prediction scale employing mode of prediction model includes time or distance, be the second, point, hour or centimetre, decimeter,
Rice, hundred meters.
5. according to claim 4 with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm, it is characterised in that
The precision of prediction of prediction model and the kernel function of least square method supporting vector machine are related, and kernel function includes Polynomial kernel function, diameter
To base kernel function, Sigmoid kernel function.
6. according to claim 5 with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm, it is characterised in that
The optimized parameter of kernel function is obtained using grid data service or cross-validation method.
7. according to claim 1 with brill orientation electromagnetic wave logging deep and shallow resistivity forecast Control Algorithm, it is characterised in that:
When prediction model input sample is with orientation electromagnetic wave logging depth resistivity is bored, output result is deep resistivity predicted value；Prediction
When mode input sample is with orientation electromagnetic wave logging shallow resistivity is bored, output result is shallow resistivity predicted value.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113882853A (en) * | 2020-07-03 | 2022-01-04 | 中国石油化工股份有限公司 | Method for transmitting near-bit logging while drilling data |
| CN117211758A (en) * | 2023-11-07 | 2023-12-12 | 克拉玛依市远山石油科技有限公司 | Intelligent drilling control system and method for shallow hole coring |
| CN113882853B (en) * | 2020-07-03 | 2024-06-04 | 中国石油化工股份有限公司 | Method for transmitting near-bit logging while drilling data |
-
2019
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113882853A (en) * | 2020-07-03 | 2022-01-04 | 中国石油化工股份有限公司 | Method for transmitting near-bit logging while drilling data |
| CN113882853B (en) * | 2020-07-03 | 2024-06-04 | 中国石油化工股份有限公司 | Method for transmitting near-bit logging while drilling data |
| CN117211758A (en) * | 2023-11-07 | 2023-12-12 | 克拉玛依市远山石油科技有限公司 | Intelligent drilling control system and method for shallow hole coring |
| CN117211758B (en) * | 2023-11-07 | 2024-04-02 | 克拉玛依市远山石油科技有限公司 | Intelligent drilling control system and method for shallow hole coring |
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Application publication date: 20191025 |