CN110644977A - Control method for receiving and sending underground small signals for testing - Google Patents
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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 xiSThe transmission frequency of the frequency shift keying device is controlled, where xiSComparing environmental impact factors; secondly, sending 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 xiThe frequency is controlled. The invention provides a control method for receiving and sending underground small signals for testing, which 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.
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 transmitting underground small signals for test.
Background
At present, along with the continuous expansion of the offshore oil and gas testing range, the aging requirement is improved, and the requirements of some high-difficulty wells cannot be met by the traditional underground tool due to the increase of the 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 testing 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 transmitting a downhole small signal for testing 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 xiSThe transmission frequency of the frequency shift keying device is controlled, where xiSComparing the 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, P0As theoretical pressure, F0To theoretical humidity, T0Is the theoretical temperature.
Preferably, the theoretical pressure P is at a well depth h0Comprises the following steps:
wherein, κ1Is a first correction coefficient, c1Is a first empirical coefficient, and has a value of 0.98, c2Is the second empirical coefficient, with a value of 1.01, and h is the well depth.
Preferably, the theoretical temperature T is at a well depth h0Comprises 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,
T0＝κ3[54.5 ln(c1h+1)+20(c2h-0.98)0.56+0.02h2+4h-15]；
wherein, κ2Is the second correction coefficient, k3Is a third correction coefficient, c1Is a first empirical coefficient, and has a value of 0.98, c2Is a second empirical coefficient with a value of 1.01 and h is the well depth.
Preferably, the theoretical humidity F is at a well depth h0Comprises the following steps:
wherein, κ4Is 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 network1,x2,x3,x4,x5In which x1Is the downhole temperature coefficient, x2Is the well depth coefficient, x3Is the downhole pressure coefficient, x4Is the downhole coefficient of humidity, x5Is an environmental impact factor coefficient;
and 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y is { y ═ y1,y2,…,ymM is the number of hidden nodes;
and 4, obtaining an output layer vector o ═ o1,o2}；o1For the first transmission frequency adjustment coefficient, o2Adjusting 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 three parameters of the layer vector, f, for the ith sampling periodmaxIs the first transmitted maximum frequency, f'maxA second transmit maximum frequency; f. ofi+1A first transmission frequency, f 'at the i +1 th sampling period'i+1The 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 isjFor parameters in the input layer vector, XjT, h, P, F, ξ, j ═ 1,2,3,4, 5; xj maxAnd Xj minRespectively, a maximum value and a minimum value in the corresponding measured parameter.
Preferably, in the initial state, the first transmission frequency and the second transmission frequency satisfy an empirical value:
f0＝0.72fmax
f0′＝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.
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Fig. 1 is a diagram of the FIR filter frequency response of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description.
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 and 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 component in a digital signal processing system, and can ensure any amplitude-frequency characteristic while having strict linear phase-frequency characteristic, 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 narrow-band FSK signals, is a convolution process beneficial to correlation accumulation of small signals, and is particularly suitable for primary extraction processing of signals sampled by an ADC (analog to digital converter) of the system, as shown in figure 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 gives the receiver the ability 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 of every 15 bits are in error 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 (broadcast channel) original source coding formula to complete decoding.
The HPSRR high-performance common mode noise ratio needs more than 10000 times of amplification processing due to weak signals, 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 front-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 signal 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 10 nv.
The invention provides 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 xiSThe transmission frequency of the frequency shift keying device is controlled, where xiSComparing the 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, P0As theoretical pressure, F0To theoretical humidity, T0Is the theoretical temperature.
Theoretical pressure P at well depth h0Comprises the following steps:
wherein, κ1Is a first correction coefficient, c1Is a first empirical coefficient, and has a value of 0.98, c2Is the second empirical coefficient, with a value of 1.01, and h is the well depth in kft; .
Theoretical temperature T at well depth h0Comprises 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,
T0＝κ3[54.5 ln(c1h+1)+20(c2h-0.98)0.56+0.02h2+4h-15]；
wherein, κ2Is the second correction coefficient, k3Is a third correction coefficient, c1Is a first empirical coefficient, and has a value of 0.98, c2Is a second empirical coefficient having a value of 1.01, h is the well depth, T0Theoretical temperature, unit F.
Theoretical humidity F at well depth h0Comprises the following steps:
wherein, κ4Is 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 provided in total, n detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are given by a data preprocessing module. The second layer is a hidden layer, which has m nodes 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 is determined by the response actually needed by the system.
The mathematical model of the network is:
inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ o (o)1,o2,...,op)T
In the invention, the number of nodes of the input layer is n-5, and the number of nodes of the output layer 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 of1Is the downhole temperature coefficient, x2Is the well depth coefficient, x3Is the downhole pressure coefficient, x4Is the downhole coefficient of humidity, x5Is an environmental impact factor coefficient;
the data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, before data is input into the artificial neural network, the data needs to be normalized to a number between 0 and 1.
Specifically, the downhole temperature T measured by using the temperature sensor is normalized to obtain a downhole temperature coefficient:
wherein, TmaxAnd TminMaximum and minimum downhole values, respectively.
Similarly, the well depth h measured by the well depth sensor is normalized to obtain a well depth coefficient x2：
Wherein h ismaxAnd hminMaximum and minimum well depths, respectively.
Similarly, the vehicle speed coefficient x is obtained by normalizing the vehicle speed V measured by the vehicle speed sensor2：
Wherein, VmaxAnd VminRespectively 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 x3：
Wherein, PmaxAnd PminMaximum and minimum downhole pressures, respectively.
Similarly, the downhole humidity F measured by the humidity pressure sensor is normalized to obtain the downhole humidity coefficient x4：
Wherein, FmaxAnd FminMaximum and minimum downhole moisture values, respectively.
Similarly, the environmental influence factor xi obtained by calculation is normalized to obtain an environmental influence factor coefficient x5：
Wherein ξmaxAnd ximinRespectively, a maximum value and a minimum value of the environmental impact factor.
The 2 parameters of the output signal are respectively expressed as: first transmission frequency adjustment coefficient, o2Adjusting the coefficient for the first transmit frequency;
first transmission frequency adjustment coefficient o1Expressed 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 periodiOutput the first through BP neural networkFirst transmission frequency adjustment coefficient of i sampling periods
Then, the first transmission frequency in the (i + 1) th sampling period is controlled to be fi+1So that it satisfies:
second transmission frequency adjustment coefficient o2Expressed 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 acquired in the ith sampling periodi' outputting a second transmission frequency adjustment coefficient of the ith sampling period through the BP neural network
Then, the second sending frequency in the (i + 1) th sampling period is controlled to be f'i+1So 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 empirical data of a product, and giving a connection weight w between an input node i and a hidden layer node jijConnection weight w between hidden layer node j and output layer node kjkThreshold value theta of hidden layer node jjThreshold value theta of output layer node kk、wij、wjk、θj、θkAre all random numbers between-1 and 1.
Continuously correcting w in the training processijAnd wjkUntil 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.
And solidifying the trained artificial neural network in an FPGA chip to enable a hardware circuit to have the functions of prediction and intelligent decision making, thereby forming intelligent hardware. 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 is 1,2, … ….
S132: sequentially normalizing the 5 parameters to obtain an input layer vector x ═ x { x } of the three-layer BP neural network in the ith sampling period1,x2,x3,x4,x5}。
S133: mapping the input layer vector to the middle layer to obtain the middle layer vector y ═ y in the ith sampling period1,y2,y3,y4}。
S134: mapping the intermediate layer to an output layer to obtain an output layer vector o ═ o { o } in the ith sampling period1,o2}。
S135, controlling the first transmission frequency and the second transmission frequency
Wherein the content of the first and second substances,
respectively the ith miningFirst three parameters of sample period output layer vector, fmaxIs the first transmitted maximum frequency, f'maxA second transmit maximum frequency; f. ofi+1A first transmission frequency, f 'at the i +1 th sampling period'i+1The 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:
f0＝0.72fmax
f0′＝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 to various fields of endeavor with which the invention may be practiced, and further modifications may readily be effected therein by those skilled in the art, without departing from the general concept as defined by the claims and their equivalents, which are not limited to the details given herein and the examples shown and described herein.
Claims (9)
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 xiSThe transmission frequency of the frequency shift keying device is controlled, where xiSComparing 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.
2. The downhole small signal receiving and transmitting control method for test according to claim 1, wherein in the first step, the environmental influence factor ξ is calculated as follows:
wherein, P0As theoretical pressure, F0To theoretical humidity, T0Is the theoretical temperature.
3. The method as claimed in claim 2, wherein the theoretical pressure P is a theoretical pressure at a well depth of h0Comprises the following steps:
wherein, κ1Is a first correction coefficient, c1Is a first empirical coefficient, and has a value of 0.98, c2Is a second empirical coefficient with a value of 1.01 and h is the well depth.
4. The method as claimed in claim 3, wherein the theoretical temperature T is measured at a well depth of h0Comprises 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,
T0＝κ3[54.5ln(c1h+1)+20(c2h-0.98)0.56+0.02h2+4h-15]；
wherein, κ2Is the second correction coefficient, k3Is a third correction coefficient, c1Is a first empirical coefficient, and has a value of 0.98, c2Is a second empirical coefficient with a value of 1.01 and h is the well depth.
5. The method as claimed in claim 4, wherein the theoretical humidity F is set at a well depth of h0Comprises the following steps:
wherein, κ4Is a fourth correction coefficient.
6. The downhole small signal receiving and transmitting control method for the test according to claim 1, wherein in the second step, the frequency shift keying device is controlled by establishing a BP neural network model, comprising 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 network1,x2,x3,x4,x5In which x1Is the downhole temperature coefficient, x2Is the well depth coefficient, x3Is the downhole pressure coefficient, x4Is the downhole coefficient of humidity, x5Is an environmental impact factor coefficient;
and 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y is { y ═ y1,y2,…,ymM is the number of hidden nodes;
and 4, obtaining an output layer vector o ═ o1,o2}；o1For the first transmission frequency adjustment coefficient, o2Adjusting 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 three parameters of the layer vector, f, for the ith sampling periodmaxIs the first transmitted maximum frequency, f'maxA second transmit maximum frequency; f. ofi+1A first transmission frequency, f 'at the i +1 th sampling period'i+1The second transmission frequency is the (i + 1) th sampling period.
7. The downhole small signal receiving and transmitting control method for testing according to claim 6, wherein 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.
8. The method for controlling the reception and transmission of the downhole small signal for the test according to claim 7, wherein in the step 3, the downhole temperature T, the well depth h, the downhole pressure P, the downhole humidity F and the environmental influence factor xi are normalized by determining the environmental influence factor xi as follows:
wherein x isjFor parameters in the input layer vector, XjT, h, P, F, ξ, j ═ 1,2,3,4, 5; xjmaxAnd XjminRespectively, a maximum value and a minimum value in the corresponding measured parameter.
9. The method as claimed in claim 8, wherein the first and second transmission frequencies satisfy an empirical value in an initial state:
f0＝0.72fmax
f0′＝0.86f′max。
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