CN113719269A - On-line monitoring method for circuit working state of logging-while-drilling instrument - Google Patents
On-line monitoring method for circuit working state of logging-while-drilling instrument Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 26
- 238000005553 drilling Methods 0.000 title claims abstract description 16
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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
- E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
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Abstract
The invention discloses an on-line monitoring method for the working state of a circuit of a logging-while-drilling instrument, which comprises the following steps: the signal acquisition and data processing circuit based on the embedded processor is designed to acquire and calculate EMI signals of the logging-while-drilling instrument, the EMI signals generated by different circuit interfaces are different, and the EMI signals can be used as key characteristics for monitoring the circuit state. The existing instrument and equipment working state detection mode consumes more chips, and occupies a large amount of bus bandwidth during data uploading. The invention is based on power supply EMI signal detection, only one circuit board is arranged on a main line of a vortex power generation source of the logging instrument to collect EMI signals of a plurality of underground digital circuit boards, transient change of the power supply EMI signal of the logging-while-drilling instrument can be effectively detected and identified, the identification of the working state of the circuit board of the logging-while-drilling instrument is realized, and the defects are avoided.
Description
Technical Field
The invention relates to an online monitoring method for a circuit working state of a logging-while-drilling instrument, belonging to the field of power supply quality monitoring.
Background
The petroleum is used as an important energy basis of modern industrial development and is closely related to the development of technology and times, and the logging while drilling technology simultaneously completes logging tasks in the process of drilling. With the increase of the well depth, the working environment of the electric equipment of the logging instrument is diversified and complicated, and the reliable operation of the electronic equipment is more and more difficult to guarantee.
The power supply quality of the power supply of the logging instrument is directly related to the safety of underground operation and the normal work of logging instrument equipment. When the power supply equipment of the logging instrument has an emergency or continuously operates abnormally, the real-time working state of a circuit is influenced, and abnormal positioning analysis cannot be carried out on the situations.
However, in the conventional method for monitoring the working state of the instrument and equipment, the voltage and current sensors are deployed on a plurality of digital circuit boards of the instrument, and then monitoring data are transmitted to a ground system for subsequent processing. The method is easy to realize, but the method consumes more chips and increases the cost, and each circuit board needs to reserve the compact PCB area for placing the voltage and current sensor chips. Meanwhile, the acquired real-time data also occupies a large amount of bus bandwidth. This method is not suitable because of the small bandwidth of the logging instrument data transmission.
The application field of the existing instrument and equipment monitoring and identifying technology mainly comprises: (1) the power supply quality of the aerospace airborne power supply is monitored on line; (2) carrying out online detection and abnormal positioning on a power supply system of the medical equipment; (3) and monitoring the electric energy output of the electric power system in real time. By applying the technology of (1), airborne voltage and current can be acquired and recorded in real time by building an airborne power monitoring system; the application (2) carries out on-line detection and abnormal alarm on the quality of the power supply of the medical equipment through remote control and monitoring; the application (3) can be based on an embedded system, and power quality monitoring hardware is installed to monitor the power output in real time.
In the aspect of logging-while-drilling instruments, technical research on monitoring and management of working states of related circuits does not exist, and safety testing requirements in actual scientific research or production processes are greatly influenced.
Disclosure of Invention
Aiming at the defects and improvement requirements of the prior art, the invention provides an online monitoring method for the working state of a circuit of a logging-while-drilling instrument, which is based on power EMI signal detection and combines a non-invasive single-point analysis method, arranges a learning algorithm on an embedded processor, realizes high-speed data processing and classification, can effectively solve the problem of working state monitoring and identification loss of an underground circuit board, and has certain popularization.
The invention provides an on-line monitoring method for the working state of a circuit of a logging-while-drilling instrument based on power EMI signal characteristic analysis, which comprises the following steps:
s1, an EMI signal acquisition and data processing circuit based on FPGA and STM32 is deployed on an underground vortex power generation source trunk line to acquire EMI signal data and process the EMI signal data in real time;
s2, carrying out denoising and feature enhancement operation on the acquired EMI signal by using a digital signal processing method and a feature extraction algorithm such as a Kalman filtering algorithm and fast Fourier transform;
s3, converting the frequency spectrum component into a dimensionless expression by using direct current removal and normalization processing;
and S4, collecting data by using power EMI signals of different circuit board types to form a training data set and a testing data set, and labeling the data according to whether the circuit board is on line or not. Training a training data set as the input of a lightweight convolutional neural network model to obtain a power supply identification model; the neural network model is compressed and can be loaded into RAM and FLASH inside an STM32 chip.
S5, deploying the lightweight deep learning model by using an embedded processor, for example, deploying a trained lightweight convolutional neural network classification model on an STM32 embedded platform by using STM32Cube.AI;
s6, inputting actual data into a power classification model to classify the state of the circuit boards, wherein the output of the model is whether each circuit board is on line or not; and the classification result is uploaded to a central control system through a 485 bus, the central control system sends the classification result to a ground system by using slurry pulse, and an alarm is given when an offline condition occurs to the underground circuit board.
Furthermore, the EMI signal acquisition and data processing circuit comprises a signal acquisition part and a signal processing part, wherein the signal acquisition part is used for filtering and analog-to-digital conversion processing of the EMI signals, and the signal processing part comprises a time sequence control module and an operation processing module and is used for carrying out data preprocessing and classification on the EMI signals.
Further, taking kalman filtering and fast fourier transform methods as examples: based on EMI signals
The kalman filtering process is represented as: computing Kalman gain
From the observed value y (t)kUpdating an estimate
Calculating an estimated error covariance matrix
Transferring the estimation state to the next moment, and performing filtering iteration; the fast fourier transform process is represented as: for discrete EMI signals x (n), FFT transform X (k) is obtained,
further, the process of removing the dc signal from the denoised EMI signal is represented as:
the normalization process is represented as:
further, the pre-trained convolutional neural network model is subjected to model compression and operation acceleration processing by adopting Tensorflow full integer quantization, and is converted into an optimized code of STM32 MCU.
In summary, the technical effects of the technical solution conceived by the present invention are as follows:
(1) compared with the prior art, the invention does not need to install a voltage sensor and a current sensor on each underground circuit board, thereby saving the chip cost and reducing the PCB area of the underground circuit board. The EMI signal acquisition circuit is deployed on a main line of a vortex power generation source, only a very small amount of underground data transmission bus bandwidth is occupied, and the method has the advantages of non-intrusive mode, single-point measurement and the like.
(2) The inherent characteristics of the EMI signal frequency spectrum characteristics of each circuit board in the well are utilized, and the classification precision of the working state of the circuit board is improved by using a convolutional neural network classification algorithm based on big data driving.
(3) The lightweight model is deployed to STM32, and preprocessing operations such as fast Fourier transform and normalization are realized on an FPGA, so that the whole processing process has strong real-time performance.
Drawings
FIG. 1 is a schematic diagram of a single point monitoring location for power EMI signals in accordance with the present invention;
FIG. 2 is a block diagram of a power EMI signal acquisition and data processing circuit system according to the present invention;
FIG. 3 is a partial frequency spectrum diagram of a power EMI signal according to the present invention;
FIG. 4 is a flow chart of the circuit EMI signal data acquisition according to the present invention;
FIG. 5 is a diagram of a circuit board status recognition result according to the present invention;
FIG. 6 is a diagram of a downhole circuit board state identification test result before and after the model weight reduction process of the present invention;
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Although the scope of practice of the invention is not limited in this respect.
The following are examples.
Example 1:
the schematic diagram of the underground single-point EMI signal monitoring position is shown in FIG. 1, the EMI signal detection point is located on a trunk line behind the power supply switching control circuit, and EMI mixed signals reflected to the trunk line by each circuit board in the well are collected.
The system block diagram of the EMI signal acquisition and data processing circuit based on the FPGA and the STM32 is shown in FIG. 2. The signal acquisition and data processing circuit mainly comprises a signal acquisition part and a signal processing part. In order to prevent EMI signals from being submerged in large direct-current voltage generated by a power supply system and remove useless high-frequency signals, the signal acquisition part is provided with a first-order RC high-pass filter and an eighth-order Butterworth low-pass filter. Weak EMI signals were amplified 100 times to mV level using a homodyne amplifier. An analog-to-digital conversion module in the circuit completes conversion from a 12-bit analog signal to a parallel digital signal output, and an input port of an AD converter is provided with a differential driving circuit for suppressing common-mode interference. Through the design, the original EMI signal with higher signal-to-noise ratio can be acquired.
The EMI signal presents a plurality of wave crests with different frequencies and amplitudes on a frequency domain, and experiments show that the frequency spectrum of the underground circuit board is distributed within 10MHz, the sampling rate is twice of the highest frequency of the signal in consideration of the Nyquist theorem, and the ADC sampling rate is set to be 25 MHz. 4096 points are sampled each time to form a set of time series t in consideration of data amount and subsequent data processing requirementss。
Denoising the EMI time sequence signal by using a Kalman filtering algorithm, and then converting a time domain signal t by using fast Fourier transformsConversion to frequency domain signal xsThe characteristics of the EMI signal may be enhanced. The actually collected local frequency spectrums of the three circuit boards are shown in fig. 3, and the positions and the numbers of the frequency spectrum peaks of the circuit boards show obvious differences. Both the kalman filtering algorithm and the fast fourier transform are done in the FPGA, where an IP core of the fast fourier transform inside the FPGA can be used.
Since EMI signals are amplified during the acquisition process, the input bias of the amplifier is inevitably mixedVoltage, which occupies a large component in the frequency spectrum. For spectrum data xsPerforming a de-DC operation, denoted as
In the convolutional neural network, data needs to be subjected to normalization processing, so that the data distribution of an input layer is approximately the same, the problem of numerical value explosion caused by initialization, unstable training and the like can be avoided, and the convergence rate of the model is increased. The normalization process is represented as:
wherein, x [ n ]]scaleFor the normalized data vector, x [ n ]]minIs the minimum value of the sample data vector, x [ n ]]maxIs the maximum value of the sample data vector.
After three high-speed digital-analog mixed circuit boards of the test circuit 1, the test circuit 2 and the test circuit 3 are hung on the transformation circuit shown in fig. 1, by switching on and off the circuit boards, EMI signals mixed by different circuits can be obtained. The circuit arrangement is shown in fig. 5, and the circuit states are classified into 8 types in total. The circuit EMI signal data acquisition flow chart is shown in FIG. 4. 400 groups of data are collected for each type of circuit state, 3200 groups of power supply EMI signal sample data are collected in total, and each group of data contains 4096 frequency point measurement values. 70% of the total number of power EMI samples were used as the training set and the remaining 30% as the test set. And identifying and verifying the test set by using the power state monitoring and identifying model, and judging the working state type of the circuit board.
The method is characterized in that a prestrained convolutional neural network model is subjected to model compression and operation acceleration processing by adopting Tensorflow full-integer quantization, an STM32Cube-AI library is converted into an optimized code of an STM32 MCU, only about dozens of kB exist, and then the optimized code can be deployed on STM32, so that online monitoring can be carried out, as shown in FIG. 6.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. An on-line monitoring method for the working state of a circuit of a logging-while-drilling instrument is characterized by comprising the following steps:
s1, an EMI signal acquisition and data processing circuit based on FPGA and STM32 is deployed on an underground vortex power generation source trunk line to acquire EMI signal data and process the EMI signal data in real time;
s2, carrying out denoising and feature enhancement operation on the acquired EMI signal by using a digital signal processing method and a feature extraction algorithm such as a Kalman filtering algorithm and fast Fourier transform;
s3, converting the frequency spectrum component into a dimensionless expression by using direct current removal and normalization processing;
and S4, collecting data by using power EMI signals of different circuit board types to form a training data set and a testing data set, and labeling the data according to whether the circuit board is on line or not. Training a training data set as the input of a lightweight convolutional neural network model to obtain a power supply identification model; the neural network model is compressed and can be loaded into RAM and FLASH inside an STM32 chip.
S5, deploying the lightweight deep learning model by using an embedded processor, for example, deploying a trained lightweight convolutional neural network classification model on an STM32 embedded platform by using STM32Cube.AI;
s6, inputting actual data into a power classification model to identify the state of the circuit board, wherein the output of the model is whether each circuit board is on-line or not; and the classification result is uploaded to a central control system through a 485 bus, the central control system sends the classification result to a ground system by using slurry pulse, and an alarm is given when an offline condition occurs to the underground circuit board.
2. The method for on-line monitoring of the operational status of the LWD instrument as recited in claim 1, wherein said signal acquisition and data processing circuit of step S1 includes two parts, namely signal acquisition and signal processing, the signal acquisition part is used for filtering and analog-to-digital conversion processing of the EMI signal, and the signal processing part includes a timing control module and an operation processing module, and is used for performing data preprocessing and classification on the EMI signal.
3. The method for online monitoring of the circuit operating state of the logging-while-drilling instrument according to claim 1, wherein the step S2 specifically includes, for example, kalman filtering and fast fourier transform methods:
based on EMI signals
Computing Kalman gain
From the observed value y (t)kUpdating an estimate
Calculating an estimated error covariance matrix
The estimated state is transferred to the next time, and for the discrete EMI signal x (n), FFT is converted into
4. The method for online monitoring of the circuit operating state of the logging-while-drilling instrument as recited in claim 1, wherein the step S3 specifically comprises:
for the denoised EMI signal, the denoised DC signal is expressed as
Normalized to
5. The method for on-line monitoring of the circuit working state of the logging-while-drilling instrument as recited in claim 1, wherein the step S5 is specifically to perform model compression and operation acceleration processing on a pre-trained convolutional neural network model by using tenserflow full-integer quantization, and convert the model into an optimized code of STM32 MCU.
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| CN117054229A (en) * | 2023-10-12 | 2023-11-14 | 中海油田服务股份有限公司 | Fixing device and method for testing reliability of circuit board of logging while drilling instrument |
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