CN110644977B - Control method for receiving and sending underground small signals for testing - Google Patents
Control method for receiving and sending underground small signals for testing Download PDFInfo
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- CN110644977B CN110644977B CN201910869471.9A CN201910869471A CN110644977B CN 110644977 B CN110644977 B CN 110644977B CN 201910869471 A CN201910869471 A CN 201910869471A CN 110644977 B CN110644977 B CN 110644977B
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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
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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
- E21B47/04—Measuring depth or liquid level
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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
- E21B47/06—Measuring temperature or pressure
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a method for controlling the receiving and sending of downhole small signals for testing, which comprises the following steps: step one, acquiring underground temperature T, well depth h, underground pressure P and underground humidity F according to a sampling period, calculating an environment influence factor xi according to the underground temperature T, the well depth h, the underground pressure P and the underground humidity F, and when xi is larger than or equal to xi S The transmission frequency of the frequency shift keying device is controlled, where xi S Comparing environmental impact factors; and step two, controlling the sending frequency of the frequency shift keying device according to the underground temperature T, the underground depth h, the underground pressure P, the underground humidity F and the environment influence factor xi. The invention provides a control method for receiving and sending underground small signals for testing, which can adjust the signal sending frequency according to the underground actual environmental factors, realize the problem of underground high-impedance environmental signal transmission and enable the signal transmission to be smoother.
Description
Technical Field
The invention relates to the field of petroleum and natural gas exploration and test, in particular to a control method for receiving and sending underground small signals for testing.
Background
At present, along with the continuous expansion of the offshore oil and gas testing range, the improvement of the timeliness requirement and the increase of high-difficulty wells, the traditional underground tool can not meet the requirements of certain high-difficulty wells. The research on the real-time transmission of the underground small signals has important significance on the exploration and development of petroleum resources, the exploration of the geological structure of an oil layer, the test of the state of an oil-gas well and the maintenance of the sustainable utilization of the resources.
Disclosure of Invention
The invention provides a control method for receiving and sending underground small signals for testing, aiming at solving the technical defects at present, and the control method can adjust the sending frequency of the signals according to the underground actual environmental factors, realize the problem of underground high-impedance environmental signal transmission and enable the signal transmission to be smoother.
The technical scheme provided by the invention is as follows: a control method for receiving and sending small signals under a test well comprises the following steps:
step one, acquiring underground temperature T, well depth h, underground pressure P and underground humidity F according to a sampling period, calculating an environment influence factor xi according to the underground temperature T, the well depth h, the underground pressure P and the underground humidity F, and when xi is larger than or equal to xi S The transmission frequency of the frequency shift keying device is controlled, where ξ S Comparing environmental impact factors;
and step two, controlling the sending frequency of the frequency shift keying device according to the underground temperature T, the underground depth h, the underground pressure P, the underground humidity F and the environment influence factor xi.
Preferably, in the first step, the method for calculating the environmental influence factor ξ is as follows:
wherein, P 0 As theoretical pressure, F 0 Theoretical humidity, T 0 Is the theoretical temperature.
Preferably, the theoretical pressure P is at a well depth h 0 Comprises the following steps:
wherein, κ 1 Is a first correction coefficient, c 1 Is a first empirical coefficient, has a value of 0.98 2 As a second empirical factor, the value is 1.01 and h is the depth of the well.
Preference is given toThat is, the theoretical temperature T at the well depth h 0 Comprises the following steps:
when h is more than or equal to 0 and less than or equal to 20,
when the ratio of h to the total of h is more than 20,
T 0 ＝κ 3 [54.5ln(c 1 h+1)+20(c 2 h-0.98) 0.56 +0.02h 2 +4h-15]；
wherein, κ 2 Is the second correction coefficient, k 3 Is a third correction coefficient, c 1 Is a first empirical coefficient, has a value of 0.98 2 For the second empirical factor, the value is 1.01 and h is the well depth.
Preferably, the theoretical humidity F is at a well depth h 0 Comprises the following steps:
wherein, κ 4 Is a fourth correction coefficient.
Preferably, in the second step, the controlling the frequency shift keying device by building a BP neural network model includes the following steps:
step 1, according to a sampling period, acquiring underground temperature T, well depth h, underground pressure P and underground humidity F, and determining an environmental influence factor xi;
step 2, normalizing the parameters in sequence, and determining an input layer neuron vector x = { x ] of the three-layer BP neural network 1 ,x 2 ,x 3 ,x 4 ,x 5 In which x 1 Is the downhole temperature coefficient, x 2 Is the well depth coefficient, x 3 Is the downhole pressure coefficient, x 4 Is the downhole coefficient of humidity, x 5 Is an environmental impact factor coefficient;
step 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y = { y = 1 ,y 2 ,…,y m M is the number of hidden nodes;
step 4, obtaining an output layer vector o = { o = 1 ,o 2 }；o 1 Adjusting the coefficient for the first transmission frequency, o 2 Adjusting the coefficient for the first transmit frequency;
step 5, controlling the first transmission frequency and the second transmission frequency to enable
Wherein the content of the first and second substances,
respectively outputting the first three parameters of the layer vector, f, for the ith sampling period max Is the first transmitted maximum frequency, f' max A second transmit maximum frequency; f. of i+1 A first transmission frequency, f 'at the i +1 th sampling period' i+1 The second transmission frequency is the (i + 1) th sampling period.
Preferably, the number m of hidden nodes satisfies:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
Preferably, in step 3, the downhole temperature T, the well depth h, the downhole pressure P, the downhole humidity F, and the environmental influence factor ξ are normalized by the following formula:
wherein x is j For parameters in the input layer vector, X j T, h, P, F, ξ, j =1,2,3,4,5; x jmax And X jmin Respectively a maximum value and a minimum value in the corresponding measured parameters。
Preferably, in the initial state, the first transmission frequency and the second transmission frequency satisfy an empirical value:
f 0 ＝0.72f max
f 0 ′＝0.86f′ max 。
the invention has the following beneficial effects: the invention provides a control method for receiving and sending underground small signals for testing, which is used in an underground high-impedance communication environment, can finish wireless receiving and sending in a high-impedance environment, can adjust signal sending frequency according to underground actual environment factors, realizes the problem of underground high-impedance environment signal transmission, enables the signal transmission to be smoother, and finishes receiving and analysis through a series of small signal extraction technologies.
Drawings
Fig. 1 is a frequency response characteristic diagram of an FIR filter of the present invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
The invention discloses a control method for receiving and transmitting underground small signals for testing, which is based on receiving and transmitting equipment, is arranged underground and comprises a frequency shift keying device, a filter, a forward error correction device, a temperature and humidity sensor, a pressure sensor, a depth sensor and a controller, wherein the controller is connected with and controls the frequency shift keying device, the filter, the forward error correction device, the temperature and humidity sensor, the pressure sensor and the depth sensor to control the frequency shift keying device.
The FSK frequency shift keying technology uses different frequency signals to represent 0,1, the simplest method for generating the FSK signals is to select different frequency templates to operate a DAC aiming at 0.1 through IO of an FPGA to finish the sending of different frequencies, and because the system is characterized in that weak signals are extracted, narrow-band signals are adopted, and good signal-to-noise ratio characteristics are obtained more easily through the channel concentration characteristic of signal energy.
An FIR (Finite Impulse Response) filter, a Finite single-bit Impulse Response filter, also called a non-recursive filter, is the most basic element in a digital signal processing system, and can have strict linear phase-frequency characteristics while ensuring arbitrary amplitude-frequency characteristics, and the unit sampling Response is Finite, so that the filter is a stable system. The FIR filter has a good filtering effect, has a better effect on extracting the narrow-band FSK signal, is a convolution process to facilitate the correlation accumulation of small signals, and is particularly suitable for the initial extraction processing of the signal sampled by the ADC of the system, as shown in FIG. 1.
FEC forward error correction, the redundant part of FEC coding, allows the receiver to detect a limited number of errors that may occur anywhere in the information and can usually correct these errors without retransmission. FEC enables the receiver to correct errors without requiring reverse requested data retransmission, but at the cost of a fixed higher forwarding bandwidth. The system is characterized in that the signal frequency is low, so that the retransmission time is not needed, and correct data can be replied under the condition that any 3 data errors occur every 15 bits by adopting BCH coding in order to avoid the random damage of stratum interference to the signal. And solving a mathematical model through a BCH original source coding formula to complete decoding.
The HPSRR has high performance common mode noise ratio, because signals are weak and need more than 10000 times of amplification processing, common mode noise of various power supplies and stratums becomes main interference, and a special suppression method of the common mode signal is particularly important in the design of a preceding stage circuit, including the design of the power supplies, the characteristic design of an operational amplifier circuit, the design of noise shielding, the design of anti-interference of the signals and the like. The design achieves 100dB PSRR through comprehensive consideration of methods in all aspects and reasonable structural design and circuit instrument wiring.
After a series of small-signal extraction processes including, but not limited to, the above, the small-signal extraction sensitivity is finally improved to 10nv.
The invention provides a method for controlling the receiving and sending of downhole small signals for testing, which comprises the following steps:
step one, collecting underground temperature T, well depth h, underground pressure P and underground humidity F according to a sampling period, calculating an environmental influence factor xi according to the underground temperature T, the well depth h, the underground pressure P and the underground humidity F,when xi is more than or equal to xi S The transmission frequency of the frequency shift keying device is controlled, where xi S Comparing environmental impact factors;
and step two, controlling the sending frequency of the frequency shift keying device according to the underground temperature T, the underground depth h, the underground pressure P, the underground humidity F and the environment influence factor xi.
In the first step, the method for calculating the environmental influence factor xi is as follows:
wherein, P 0 As theoretical pressure, F 0 To theoretical humidity, T 0 Is the theoretical temperature.
Theoretical pressure P at well depth h 0 Comprises the following steps:
wherein, κ 1 Is a first correction coefficient, c 1 Is the first empirical coefficient, value is 0.98 2 Is a second empirical coefficient having a value of 1.01, h is well depth in kft; .
Theoretical temperature T at well depth h 0 Comprises the following steps:
when h is more than or equal to 0 and less than or equal to 20,
when the ratio of h to the total of h is more than 20,
T 0 ＝κ 3 [54.5ln(c 1 h+1)+20(c 2 h-0.98) 0.56 +0.02h 2 +4h-15]；
wherein, κ 2 Is the second correction coefficient, k 3 Is a third correction coefficient, c 1 Is the first empirical coefficient, value is 0.98 2 Is a second empirical coefficient, with a value of 1.01, h is the well depth, T 0 As theoretical temperature, unit F。
Theoretical humidity F at well depth h 0 Comprises the following steps:
wherein, κ 4 Is a fourth correction coefficient.
In the third step, the frequency shift keying device is controlled by establishing a BP neural network model, and the method comprises the following steps:
step one S110: and establishing a BP neural network model.
The BP network system structure adopted by the invention is composed of three layers, wherein the first layer is an input layer, n nodes are totally provided, n detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are provided by a data preprocessing module. The second layer is a hidden layer, has m nodes in total, and is determined in a self-adaptive mode by the training process of the network. The third layer is an output layer, p nodes are provided in total, and the output layer is determined by the response actually required by the system.
The mathematical model of the network is:
inputting a vector: x = (x) 1 ,x 2 ,...,x n ) T
Intermediate layer vector: y = (y) 1 ,y 2 ,...,y m ) T
Outputting a vector: o = (o) 1 ,o 2 ,...,o p ) T
In the present invention, the number of input layer nodes is n =5, and the number of output layer nodes is p =2. The number m of hidden layer nodes is estimated by the following formula:
the input signal has 5 parameters expressed as: x is the number of 1 Is downhole temperature coefficient, x 2 Is the well depth coefficient, x 3 Is the downhole pressure coefficient, x 4 Is the downhole coefficient of humidity, x 5 Is the environmental impact factor coefficient;
the data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, the data needs to be normalized to a number between 0-1 before it is input into the artificial neural network.
Specifically, the downhole temperature T measured by using the temperature sensor is normalized to obtain a downhole temperature coefficient:
wherein, T max And T min Maximum and minimum downhole values, respectively.
Similarly, the well depth h measured by the well depth sensor is normalized to obtain a well depth coefficient x 2 ：
Wherein h is max And h min Maximum and minimum well depths, respectively.
Similarly, the vehicle speed coefficient x is obtained by normalizing the vehicle speed V measured by the vehicle speed sensor 2 ：
Wherein, V max And V min Respectively the maximum value and the minimum value of the speed of the forklift.
Similarly, the downhole pressure P measured by the pressure sensor is normalized to obtain the downhole pressure coefficient x 3 ：
Wherein, P max And P min Maximum and minimum downhole pressures, respectively.
Similarly, the downhole humidity F measured by the humidity pressure sensor is normalized to obtain the downhole humidity coefficient x 4 ：
Wherein, F max And F min Maximum and minimum downhole moisture values, respectively.
Similarly, the environmental influence factor xi obtained by calculation is normalized to obtain an environmental influence factor coefficient x 5 ：
Wherein xi is max And xi min The maximum and minimum values of the environmental impact factor, respectively.
The 2 parameters of the output signal are respectively expressed as: first transmission frequency adjustment coefficient, o 2 Adjusting the coefficient for the first transmit frequency;
first transmission frequency adjustment coefficient o 1 Expressed as the ratio of the first sending frequency in the next sampling period to the maximum value of the first sending frequency in the current sampling period, i.e. the first sending frequency f collected in the ith sampling period i Outputting a first transmission frequency adjustment coefficient o of the ith sampling period through a BP neural network 1 i Then, the first transmission frequency in the (i + 1) th sampling period is controlled to be f i+1 So that it satisfies:
second transmission frequency adjustment coefficient o 2 Expressed as the ratio of the second transmission frequency in the next sampling period to the maximum value of the second transmission frequency in the current sampling period, i.e. the second transmission frequency f collected in the ith sampling period i ', byThe BP neural network outputs a second sending frequency regulation coefficient of the ith sampling period
Then, the second sending frequency in the (i + 1) th sampling period is controlled to be f' i+1 So that it satisfies:
and step two S120, training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. Obtaining a training sample according to historical experience data of the product, and giving a connection weight w between an input node i and a hidden layer node j ij Connection weight w between hidden layer node j and output layer node k jk Threshold value theta of hidden layer node j j Threshold value θ of output layer node k k 、w ij 、w jk 、θ j 、θ k Are all random numbers between-1 and 1.
Continuously correcting w in the training process ij And w jk Until the system error is less than or equal to the expected error, the training process of the neural network is completed.
As shown in table 1, a set of training samples is given, along with the values of the nodes in the training process.
TABLE 1 training Process node values
Step three S130,
And collecting underground environment parameters and inputting the underground environment parameters into a neural network to obtain a regulation and control coefficient.
The trained artificial neural network is solidified in the FPGA chip, so that the hardware circuit has the functions of prediction and intelligent decision making, and intelligent hardware is formed. After the intelligent hardware is powered on and started,
s131: according to the sampling period, acquiring the underground temperature T, the well depth h, the underground pressure P and the underground humidity F in the ith sampling period, and determining an environmental influence factor xi; wherein i =1,2, \8230;.
S132: sequentially normalizing the 5 parameters to obtain an input layer vector x = { x } of the three-layer BP neural network in the ith sampling period 1 ,x 2 ,x 3 ,x 4 ,x 5 }。
S133: mapping the input layer vector to an intermediate layer to obtain an intermediate layer vector y = { y ] in the ith sampling period 1 ,y 2 ,y 3 ,y 4 }。
S134: mapping the intermediate layer to an output layer to obtain an output layer vector o = { o ] in the ith sampling period 1 ,o 2 }。
S135, controlling the first transmission frequency and the second transmission frequency
Wherein the content of the first and second substances,
respectively outputting the first three parameters of the layer vector, f, for the ith sampling period max Is the first transmitted maximum frequency, f' max A second transmit maximum frequency; f. of i+1 Is the first transmitting frequency, f 'at the i +1 sampling period' i+1 The second transmission frequency is the (i + 1) th sampling period.
In the initial state, the first transmission frequency and the second transmission frequency satisfy an empirical value:
f 0 ＝0.72f max
f 0 ′＝0.86f′ max 。
while embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (7)
1. A control method for receiving and sending downhole small signals for testing is characterized by comprising the following steps:
step one, acquiring underground temperature T, well depth h, underground pressure P and underground humidity F according to a sampling period, calculating an environment influence factor xi according to the underground temperature T, the well depth h, the underground pressure P and the underground humidity F, and when xi is larger than or equal to xi S The transmission frequency of the frequency shift keying device is controlled, where ξ S Comparing environmental impact factors;
the method for calculating the environmental influence factor xi is as follows:
wherein, P 0 As theoretical pressure, F 0 To theoretical humidity, T 0 Is the theoretical temperature;
step two, controlling the sending frequency of the frequency shift keying device according to the underground temperature T, the underground depth h, the underground pressure P, the underground humidity F and the environment influence factor xi:
the method for controlling the frequency shift keying device by establishing the BP neural network model comprises the following steps:
step 1, according to a sampling period, acquiring underground temperature T, well depth h, underground pressure P and underground humidity F, and determining an environmental influence factor xi;
step 2, normalizing the parameters in sequence, and determining an input layer neuron vector x = { x ] of the three-layer BP neural network 1 ,x 2 ,x 3 ,x 4 ,x 5 In which x 1 Is the downhole temperature coefficient, x 2 For the depth of the wellCoefficient, x 3 Is the downhole pressure coefficient, x 4 Is the downhole coefficient of humidity, x 5 Is an environmental impact factor coefficient;
step 3, mapping the input layer neuron vector to a hidden layer, wherein the hidden layer vector y = { y = { (y) } 1 ,y 2 ,Λ,y m M is the number of hidden nodes;
step 4, obtaining an output layer vector o = { o = 1 ,o 2 }；o 1 For the first transmission frequency adjustment coefficient, o 2 Adjusting the coefficient for the first transmit frequency;
step 5, controlling the first transmission frequency and the second transmission frequency to ensure that
Wherein the content of the first and second substances,
respectively outputting the first two parameters of the layer vector, f, for the ith sampling period max Is the first transmitted maximum frequency, f' max A maximum frequency for the second transmission; f. of i+1 First transmission frequency at i +1 th sampling period, f i ′ +1 The second transmission frequency is the (i + 1) th sampling period.
2. The method for controlling the small downhole signal reception and transmission for testing according to claim 1, wherein the theoretical pressure P is a theoretical pressure at a well depth of h 0 Comprises the following steps:
wherein, κ 1 Is a first correction coefficient, c 1 Is firstEmpirical coefficient, value 0.98, c 2 For the second empirical factor, the value is 1.01 and h is the well depth.
3. The method as claimed in claim 2, wherein the theoretical temperature T is measured at a well depth of h 0 Comprises the following steps:
when h is more than or equal to 0 and less than or equal to 20,
when the ratio of h to the total of h is more than 20,
T 0 ＝κ 3 [54.5ln(c 1 h+1)+20(c 2 h-0.98) 0.56 +0.02h 2 +4h-15]；
wherein, κ 2 Is the second correction coefficient, k 3 Is a third correction coefficient, c 1 Is the first empirical coefficient, value is 0.98 2 For the second empirical factor, the value is 1.01 and h is the well depth.
4. The method as claimed in claim 3, wherein the theoretical humidity F is set at a well depth of h 0 Comprises the following steps:
wherein, κ 4 Is a fourth correction coefficient.
5. The method for controlling the reception and transmission of the downhole small signal for testing according to claim 4, wherein the number m of hidden nodes satisfies the following condition:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
6. The method for controlling the downhole small signal receiving and sending for the test according to claim 5, wherein in the step 2, the downhole temperature T, the well depth h, the downhole pressure P, the downhole humidity F and the environmental influence factor ξ are normalized by the following formula:
wherein x is j For parameters in the input layer vector, X j T, h, P, F, ξ, j =1,2,3,4,5; x jmax And X jmin Respectively, a maximum value and a minimum value in the corresponding measured parameter.
7. The method as claimed in claim 6, wherein in the initial state, the first transmitting frequency and the second transmitting frequency satisfy the empirical value:
f 0 ＝0.72f max
f 0 ′＝0.86f m ′ ax 。
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