In-well ultra-long cable adaptive communication system and method based on LMS (Least Mean Square)

Abstract

The invention provides an LMS-based in-well ultra-long cable adaptive communication system, and the system comprises an FIR filtering module which is used for receiving a signal from an in-well cable; the OFDM receiver and the UART communication module are respectively connected with the FIR filtering module; the upper computer communication module is respectively connected with the OFDM receiver and the UART communication module; the training sequence transmitting module is connected with the upper computer communication module; the DA control module is connected with the training sequence transmitting module; the cross-correlation calculation module is connected with the upper computer communication module; and the AD control module and the impedance matching control module are respectively connected with the cross-correlation calculation module. According to the method, the problem of unsmooth communication caused by cable distribution parameter change in current logging engineering is solved.

Description

Technical Field

[0001] This invention relates to the field of adaptive communication technology for ultra-long cables in wells, and in particular to an adaptive communication system and method for ultra-long cables in wells based on LMS. Background Technology

[0002] Adaptive communication plays a crucial role in downhole communication. With the increasing demand for deep well communication, especially in ultra-long cable communication, adaptive communication technology has become essential. This field requires communication systems capable of operating under harsh conditions such as high temperature and pressure, high noise interference, and constantly changing cable distribution parameters.

[0003] Adaptive communication is a key technology that automatically adjusts filter parameters in a communication system to adapt to constantly changing channel conditions, and also automatically adjusts the terminal impedance to achieve impedance matching, thereby improving communication quality and resistance to interference. In well-drilled communication, ultra-long cables can introduce signal distortion, noise, and transmission loss, problems that need to be effectively addressed. The Least Mean Square (LMS) algorithm is a commonly used adaptive filtering technique that, based on the statistical properties of signals, can automatically adjust filter parameters to reduce signal distortion and improve communication reliability.

[0004] Therefore, the development of an adaptive communication system for ultra-long cables in wells based on LMS has significant theoretical and practical implications. It can address the unique challenges of well communication and improve communication quality and system performance. This filter technology will bring innovation and progress to the field of well communication to meet the ever-increasing communication demands.

[0005] Long-range detection is a crucial performance indicator for well logging projects. However, considering that downhole radar systems need to operate in high-temperature, high-pressure environments at depths of 1000–6000 m, with the wellbore filled with mud (while most commercially available downhole radars developed abroad are suitable for shallow formation exploration within 1000 m, without mud or high-temperature, high-pressure environments), the extremely long communication distance and the changes in distributed parameters and resistance caused by the stretching of the logging cable will severely affect the communication rate. As the required logging depth continues to increase, problems arising from ultra-deep well exploration need to be addressed. Cable-related issues require priority consideration. First, the same logging system needs to use logging cables of different lengths and distributed parameters in multiple logging missions, a situation that needs to be addressed in practical engineering experiments, requiring the system to have impedance detection capabilities. Simultaneously, well logging radar systems with integrated transmission and acquisition functions are often heavy, and the extremely long cables are prone to cable failures under gravity, necessitating a detection device to monitor the cable status in real time.

[0006] Well logging projects often utilize mainstream seven-core armored cables. Each core consists of seven twisted copper wires, covered with an insulation layer, and then covered with two layers of steel armor of different thicknesses wound in opposite directions. The cable in the well can be approximated as a narrow-band, low-frequency communication channel, with an effective frequency range of approximately 1–271 kHz. The communication channel of the cable in the well is typically subject to various adverse effects, including but not limited to high-temperature and high-pressure environments, variations in cable distributed parameters, and resistance changes caused by cable tension. These factors combine to lead to channel non-ideals, manifested as high transmission loss, signal distortion, and noise introduction.

[0007] In downhole communication, ensuring reliable data transmission is crucial, as the proper functioning of downhole equipment depends on effective communication. Traditional communication filters struggle to handle the complexity and variability of downhole cable channels, thus necessitating a highly adaptive filter technology capable of providing reliable communication performance under constantly changing channel conditions.

[0008] In summary, the unique characteristics of underground cable communication channels, including high temperature and high pressure environments, variations in distributed parameters, and resistance fluctuations, introduce significant channel non-ideals. Traditional ground terminal communication equipment is ill-equipped to meet these challenges. Summary of the Invention

[0009] This invention provides an adaptive communication system and method for ultra-long cables in wells based on LMS (Logging Management System), which solves the problem of communication disruption caused by changes in cable distribution parameters in current well logging projects. It is expected to significantly improve the performance of well communication, including reducing transmission loss, signal distortion, and noise suppression, thereby ensuring the reliability of communication between downhole equipment and the surface in extreme environments. It has broad application prospects in the field of well communication and will bring substantial improvements to communication efficiency and data transmission reliability in energy exploration and other engineering projects.

[0010] The first aspect of the embodiments of this specification discloses an adaptive communication system for ultra-long cables in wells based on LMS, comprising:

[0011] FIR filter module, used to receive signals from the cable in the well;

[0012] The OFDM receiver and the UART communication module are respectively connected to the FIR filtering module;

[0013] The host computer communication module is connected to the OFDM receiver and the UART communication module respectively;

[0014] The training sequence transmission module is connected to the host computer communication module;

[0015] The DA control module is connected to the training sequence transmission module;

[0016] The cross-correlation calculation module is connected to the host computer communication module;

[0017] The AD control module and the impedance matching control module are respectively connected to the cross-correlation calculation module.

[0018] In some embodiments of this specification, the FIR filtering module includes:

[0019] An FIR filter is used to receive signals from the cable in the well via RAM;

[0020] The communication module is connected to the FIR filter;

[0021] The error calculation module is connected to the FIR filter;

[0022] A DDS frequency synthesizer is connected to the error calculation module;

[0023] The coefficient update module is connected to the communication module, the error calculation module, and the FIR filter, respectively.

[0024] In some embodiments of this specification, the cross-correlation calculation module includes a correlation operator, and the reflection coefficient formula of the correlation operator is:

[0025]

[0026] Among them, R ss R is the peak value of the autocorrelation of the reference signal. 12 The peak value of the cross-correlation between the reflected signal and the reference signal is max(R). 12 ) represents the maximum value of the cross-correlation peak under different loads, min(R) 12 ) represents the minimum peak value of cross-correlation under different loads, and Γ represents the reflection coefficient;

[0027] R is obtained using the reflection coefficient formula. ss The formula for estimating the corresponding load impedance Z0 is as follows:

[0028]

[0029]

[0030] Among them, Z L This is the characteristic impedance of the cable.

[0031] In some embodiments of this specification, the update formula of the coefficient update module is as follows:

[0032] W(n+1)=W(n)+μ*e(n)*Γ(n);

[0033] Where W(n+1) is the weight coefficient of the next time step, W(n) is the weight coefficient of the current time step, μ is the learning rate, e(n) is the error signal, and X(n) is the vector of the input signal.

[0034] The second aspect of the embodiments of this specification discloses an adaptive communication method for ultra-long cables in wells based on LMS, which is implemented by any of the above-mentioned adaptive communication systems for ultra-long cables in wells based on LMS.

[0035] The LMS-based adaptive communication method for ultra-long cables in wells includes:

[0036] The FIR filter module receives reference signals from the cable in the well and performs error calculation processing to obtain the FIR tap coefficients of the cable in the well that are suitable for dynamic changes after adaptive LMS calculation. The coefficients are then entered into the data update module to update the tap coefficients of the FIR filter.

[0037] The FIR filtering module receives data signals from the well equipment via the well cable. After dynamic FIR filtering, the data signals from the well equipment are closer to the actual data detected in the well. The filtered signals are then ready to enter the next module.

[0038] The cable signal is parsed by an OFDM receiver or a UART communication module and then transmitted to the host computer communication module.

[0039] The host computer communication module outputs a command signal based on the received cable signal;

[0040] After the instruction signal is parsed, it is transmitted to the UART communication module for further parsing, and then the training sequence transmission module is controlled to transmit the signal.

[0041] The training sequence transmission module performs BPSK modulation on the command signal after command parsing and outputs the modulated signal to the DA control module.

[0042] The signal is transmitted to the cable and AD control module via a three-adapter through the DA control module;

[0043] The signal is transmitted to the cross-correlation calculation module through the AD control module;

[0044] The reflection coefficient and load impedance are calculated by the cross-correlation calculation module, and the calculated load impedance is transmitted to the impedance matching control module for impedance matching of the terminal system.

[0045] The embodiments described in this specification can achieve at least the following beneficial effects:

[0046] 1. Through real-time filtering and adaptive adjustment of dynamic filter coefficients, this filter helps reduce signal distortion, decrease noise interference, and improve the accuracy of signal waveforms. This ultimately leads to higher quality and more reliable communication signal transmission, reducing communication interruptions and bit error rates, playing a crucial role, especially in complex environments such as deep wells.

[0047] 2. Traditional filters use fixed tap coefficients, making it difficult to adapt to changes in cable channel characteristics. The filter of this invention employs an adaptive LMS algorithm, allowing the filter coefficients to be dynamically adjusted based on real-time signal conditions. This flexibility enables the filter to automatically optimize its performance under different channel conditions without manual intervention.

[0048] 3. Because the filter can automatically adapt to channel changes, reduce signal distortion, and lower noise interference, the need for and frequency of system maintenance are significantly reduced. This helps to reduce maintenance costs and lighten the workload of maintenance personnel.

[0049] 4. By reducing signal distortion and noise interference, this communication system helps improve the reliability of the entire downhole cable communication system. Reliable communication is crucial in critical applications such as deep well exploration, mining, and other remote downhole operations.

[0050] 5. Due to the automatic impedance matching function, interference with transmitted signals is reduced, and the communication quality is effectively improved. The data uploaded to the ground can more accurately reflect the downhole conditions, reducing the workload of logging projects.

[0051] 6. The communication system of this invention is particularly suitable for complex cable channels and harsh environmental conditions, such as deep underground wells. Its adaptive performance enables it to effectively handle signal transmission in these special environments, providing a stable and reliable communication solution. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of the LMS-based adaptive communication system for ultra-long cables in wells involved in this invention.

[0054] Figure 2 This is a schematic diagram illustrating the operation of the communication cable in the well involved in this invention.

[0055] Figure 3 This is a schematic diagram of the FIR filtering module involved in this invention.

[0056] Figure 4 This is a simulation diagram of the LMS adaptive filtering involved in this invention.

[0057] Figure 5 This is a schematic diagram of the amplitude-frequency response of an FIR filter applicable to cables in wells, as described in this invention.

[0058] Figure 6 This is a schematic diagram of the amplitude-frequency response of the cable in the well involved in this invention. Detailed Implementation

[0059] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0060] The following disclosure provides many different implementations or examples for carrying out different structures of the embodiments of the present invention. To simplify the disclosure of the embodiments of the present invention, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the embodiments of the present invention. Furthermore, reference numerals and / or reference letters may be repeated in different examples of the embodiments of the present invention; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various implementations and / or arrangements discussed.

[0061] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0062] like Figure 1 As shown, the first aspect of this specification discloses an adaptive communication system for ultra-long cables in wells based on LMS, comprising:

[0063] FIR filter module, used to receive signals from the cable in the well;

[0064] The OFDM receiver and the UART communication module are respectively connected to the FIR filtering module;

[0065] The host computer communication module is connected to the OFDM receiver and the UART communication module respectively;

[0066] The training sequence transmission module is connected to the host computer communication module;

[0067] The DA control module is connected to the training sequence transmission module;

[0068] The cross-correlation calculation module is connected to the host computer communication module;

[0069] The AD control module and the impedance matching control module are respectively connected to the cross-correlation calculation module.

[0070] In some embodiments of this specification, the FIR filtering module includes:

[0071] An FIR filter is used to receive signals from the cable in the well via RAM;

[0072] The communication module is connected to the FIR filter;

[0073] The error calculation module is connected to the FIR filter;

[0074] A DDS frequency synthesizer is connected to the error calculation module;

[0075] The coefficient update module is connected to the communication module, the error calculation module, and the FIR filter, respectively.

[0076] In some embodiments of this specification, the cross-correlation calculation module includes a correlation operator, and the reflection coefficient formula of the correlation operator is:

[0077]

[0078] Among them, R ss R is the peak value of the autocorrelation of the reference signal. 12 The peak value of the cross-correlation between the reflected signal and the reference signal is max(R). 12 ) represents the maximum value of the cross-correlation peak under different loads, min(R) 12 ) represents the minimum peak value of cross-correlation under different loads, and Γ represents the reflection coefficient;

[0079] R is obtained using the reflection coefficient formula. ss The formula for estimating the corresponding load impedance Z0 is as follows:

[0080]

[0081]

[0082] Among them, Z L This is the characteristic impedance of the cable.

[0083] In some embodiments of this specification, the update formula of the coefficient update module is as follows:

[0084] W(n+1)=W(n)+μ*e(n)*X(n);

[0085] Where W(n+1) is the weight coefficient of the next time step, W(n) is the weight coefficient of the current time step, μ is the learning rate, e(n) is the error signal, and X(n) is the vector of the input signal.

[0086] The second aspect of the embodiments of this specification discloses an adaptive communication method for ultra-long cables in wells based on LMS, which is implemented by any of the above-mentioned adaptive communication systems for ultra-long cables in wells based on LMS.

[0087] The LMS-based adaptive communication method for ultra-long cables in wells includes:

[0088] The FIR filter module receives the signal from the cable in the well and performs filtering to obtain the filtered cable signal.

[0089] The cable signal is parsed by an OFDM receiver and a UART communication module and then transmitted to the host computer communication module.

[0090] The host computer communication module outputs a command signal based on the received cable signal;

[0091] After the instruction signal is parsed, it is transmitted to the UART communication module and the training sequence transmission module;

[0092] The training sequence transmission module performs BPSK modulation on the command signal after command parsing and outputs the modulated signal to the DA control module.

[0093] The signal is transmitted to the cable and AD control module via a three-adapter through the DA control module;

[0094] The signal is transmitted to the cross-correlation calculation module through the AD control module;

[0095] The reflection coefficient and load impedance are calculated by the cross-correlation calculation module, and the calculated load impedance is transmitted to the impedance matching control module for impedance matching.

[0096] The technical concept of this invention is as follows:

[0097] The LMS-based adaptive communication system for ultra-long cables in wells includes an FIR filtering module, an OFDM receiver, a UART communication module, a host computer communication module, a training sequence transmission module, a DA control module, a cross-correlation calculation module, an AD control module, and an impedance matching control module. The overall block diagram of its digital circuit modules is shown below. Figure 1 As shown.

[0098] The FIR filter module receives reference signals from the cable in the well and performs error calculation processing to obtain the FIR tap coefficients of the cable in the well that are suitable for dynamic changes after adaptive LMS calculation. The coefficients are then entered into the data update module to update the tap coefficients of the FIR filter.

[0099] The FIR filtering module receives data signals from the well equipment via the well cable. After dynamic FIR filtering, the data signals from the well equipment are closer to the actual data detected in the well. The filtered signals are then ready to enter the next module.

[0100] The cable signal is parsed by an OFDM receiver or a UART communication module and then transmitted to the host computer communication module.

[0101] The host computer communication module outputs a command signal based on the received cable signal;

[0102] After the instruction signal is parsed, it is transmitted to the UART communication module for further parsing, and then the training sequence transmission module is controlled to transmit the signal.

[0103] The training sequence transmission module performs BPSK modulation on the command signal after command parsing and outputs the modulated signal to the DA control module.

[0104] The signal is transmitted to the cable and AD control module via a three-adapter through the DA control module;

[0105] The signal is transmitted to the cross-correlation calculation module through the AD control module;

[0106] The reflection coefficient and load impedance are calculated by the cross-correlation calculation module, and the calculated load impedance is transmitted to the impedance matching control module for impedance matching of the terminal system.

[0107] In a cable communication system within a well, the communication diagram is as follows: Figure 2 The signal shown is affected by various interferences and channel characteristics, including but not limited to signal distortion, noise interference, and waveform distortion. The digital circuitry of the adaptive communication system of this invention is generated by an FPGA (Field-Programmable Gate Array), specifically including the following:

[0108] FIR filter module (LMS adaptive filter): This module plays a crucial role after the signal enters the FPGA from the AD (Analog-to-Digital Converter) chip. A schematic block diagram of the entire module is shown below. Figure 3 As shown, it is responsible for real-time filtering of signals received from the cable in the well. Unlike traditional fixed-tap coefficient filters, the filter coefficients of this module are dynamically adjustable. This feature allows the filter to automatically adjust under real-time signal conditions to adapt to the constantly changing cable channel, thereby providing optimal signal processing results.

[0109] Error Calculation Module: This module analyzes the error between the received cable signal and the reference signal. By comparing it with the desired signal, it calculates and quantifies the error signal, reflecting the accuracy and quality of the received signal. The output of the error calculation module is used for subsequent adaptive adjustments.

[0110] Coefficient Update Module: This module implements the core function of LMS adaptive filtering. It automatically adjusts the filter coefficients of the FIR filter based on feedback information from the error signal. By continuously updating these coefficients, the filter can adapt to changes in the characteristics of the cable channel in real time, thereby minimizing the error signal and improving communication quality.

[0111] Training Sequence Transmission Module: This module implements adaptive impedance matching for cable detection. It generates pseudo-random numbers based on a set sequence, performs frequency modulation, and produces a sinusoidal signal suitable for the cable. The length of the generated signal can be controlled according to instructions sent from the host computer to adapt to cables of different lengths. These signals are output via a DA converter and then from the three-way adapter to the cable and AD converter. The AD converter first receives the reference signal and then the reflected signal.

[0112] Cross-correlation calculation module: This module implements adaptive impedance matching cable state analysis. It identifies the signal input from the AD converter, first filtering out irrelevant signals using an averaging filter, and then storing the reference signal and the reflection signal in two separate sets of RAM on the FPGA. The data in the RAM is then analyzed to obtain the cross-correlation peak value and the signal reflection delay time.

[0113] Impedance Matching Control Module: This module implements the core function of adaptive impedance matching. After the cross-correlation calculation module finishes parsing the initial cable state, this module changes the digital potentiometer on the circuit periphery to 50Ω, and repeats the above to obtain a new cross-correlation value. By calculating the two sets of cross-correlation values, the cable termination impedance is obtained, and the digital potentiometer is adjusted to the most suitable value.

[0114] Through this LMS-based adaptive communication system for ultra-long cables in wells, the signal enters the FPGA from the AD converter and undergoes filtering to obtain a more suitable waveform. Its simulation is as follows: Figure 4 As shown. This helps reduce signal distortion, noise interference, and improve the accuracy of signal waveforms. This communication system is designed to improve the signal quality and reliability of downhole cable communication systems, and is particularly suitable for complex cable channels and unstable environmental conditions. Through real-time signal processing and adaptive adjustment, it is expected to play a key role in specialized applications such as deep wells.

[0115] In practical implementation, this invention employs the Least Mean Square (LMS) algorithm to design an LMS-based adaptive communication system for ultra-long cables in wells. This communication system is generated by an FPGA (Field-Programmable Gate Array) and includes the following key modules:

[0116] FIR Filtering Module: The FIR filter is used to filter signals transmitted to the cable in the well. The filter coefficients of this module are dynamically adjustable to adapt to changes in the cable channel. A relatively low order of 16 is chosen for the FIR filter to reduce FPGA logic resource consumption. Its amplitude-frequency response diagram is shown below. Figure 5 As shown. The output signal of the filter module can be expressed as:

[0117] y(n)=∑[W(k)*x(nk)], k=0,1,2,...,N;

[0118] Here, y(n) is the filter output, W(k) is the weighting coefficient, and x(nk) is the delayed version of the input signal. These weighting coefficients are adaptively adjusted according to the LMS algorithm to optimize the filtering effect.

[0119] Error Calculation Module: This module analyzes the error between the received cable signal and the reference signal, calculating an error signal to represent the difference between the received signal and the expected signal. The error signal can typically be represented as:

[0120] e(n) = d(n) - y(n);

[0121] Where e(n) is the error signal, d(n) is the desired output, and y(n) is the actual output of the filter.

[0122] Coefficient Update Module: This module implements the core of LMS adaptive filtering. Based on feedback from the error signal, it adaptively adjusts the weight coefficients of the FIR filter. The update rules are as follows:

[0123] W(n+1)=W(n)+μ*e(n)*X(n);

[0124] Where W(n+1) is the weight coefficient of the next time step, W(n) is the weight coefficient of the current time step, μ is the learning rate, e(n) is the error signal, and X(n) is the vector of the input signal.

[0125] This adaptive filter for ultra-long cable communication in manholes, based on the LMS algorithm, aims to improve the signal quality and reliability of such systems. By continuously and adaptively adjusting the filter's weight coefficients, the filter effectively reduces signal distortion, noise interference, and waveform distortion, thereby providing a more stable and reliable communication service. This is particularly important in complex cable channels and environmental conditions, helping to optimize signal transmission and improve system performance. The amplitude-frequency response of the cable in the manhole is as follows: Figure 6 As shown.

[0126] Cross-correlation calculation module: This module can determine the impedance of the cable under test based on two sets of cross-correlation values. Specifically, it loads the cable with known but different load impedances, and calculates the cross-correlation between the reflected signal and the reference signal under different load impedances. The autocorrelation peak value of the reference signal is R. ss The peak value of the cross-correlation between the reflected signal and the reference signal, R 12 , where max(R) 12 ) represents the maximum value of the cross-correlation peak under different loads, min(R) 12 Γ represents the minimum peak value of the cross-correlation under different loads, and Γ is the reflection coefficient, as shown in the following formula:

[0127]

[0128] R is obtained using the reflection coefficient formula. ss The corresponding formula for estimating the load impedance Z0 is given, where Z... L The characteristic impedance of the cable is estimated using the following formula:

[0129]

[0130]

[0131] The embodiments described above are for illustrative purposes only and are not intended to limit the invention. Therefore, any changes in numerical values ​​or substitutions of equivalent elements should still fall within the scope of this invention.

[0132] The above detailed description will enable those skilled in the art to understand that the present invention can indeed achieve the aforementioned objectives and has complied with the provisions of the Patent Law.

[0133] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention. The above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

[0134] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0135] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0136] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0137] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Therefore, aspects of this application can be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. All of the above hardware or software can be referred to as a “unit,” “module,” or “system.” Furthermore, aspects of this application can take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0138] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, and Python; general programming languages ​​such as C; Visual Basic, Fortran2103, Perl, COBOL2102, PHP, and ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0139] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although some currently considered useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a purely software solution, such as an installation on an existing server or mobile device.

[0140] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this approach of the present application should not be construed as reflecting an intention that the claimed subject matter requires more features than expressly recited in each claim. Rather, the subject of the invention should possess fewer features than in any single embodiment described above.

Claims

1. An adaptive communication system for ultra-long cables in wells based on LMS, characterized in that, include: FIR filter module, used to receive signals from the cable in the well; The OFDM receiver and the UART communication module are respectively connected to the FIR filtering module; The host computer communication module is connected to the OFDM receiver and the UART communication module respectively; The training sequence transmission module is connected to the host computer communication module; The DA control module is connected to the training sequence transmission module; The cross-correlation calculation module is connected to the host computer communication module; The AD control module and the impedance matching control module are respectively connected to the cross-correlation calculation module.

2. The LMS-based adaptive communication system for ultra-long cables in wells according to claim 1, characterized in that, The FIR filtering module includes: An FIR filter is used to receive signals from the cable in the well via RAM; The communication module is connected to the FIR filter; The error calculation module is connected to the FIR filter; A DDS frequency synthesizer is connected to the error calculation module; The coefficient update module is connected to the communication module, the error calculation module, and the FIR filter, respectively.

3. The LMS-based adaptive communication system for ultra-long cables in wells according to claim 1, characterized in that, The cross-correlation calculation module includes a correlation operator, and the reflection coefficient formula of the correlation operator is: Among them, R ss R is the peak value of the autocorrelation of the reference signal. 12 The peak value of the cross-correlation between the reflected signal and the reference signal is max(R). 12 ) represents the maximum value of the cross-correlation peak under different loads, min(R) 12 ) represents the minimum peak value of cross-correlation under different loads, and Γ represents the reflection coefficient; R is obtained from the reflection coefficient formula. ss The formula for estimating the corresponding load impedance Z0 is as follows: Among them, Z L This is the characteristic impedance of the cable.

4. The LMS-based adaptive communication system for ultra-long cables in wells according to claim 2, characterized in that, The update formula for the coefficient update module is as follows: W(n+1)=W(n)+μ*e(n)*X(n); Where W(n+1) is the weight coefficient of the next time step, W(n) is the weight coefficient of the current time step, μ is the learning rate, e(n) is the error signal, and X(n) is the vector of the input signal.

5. An adaptive communication method for ultra-long cables in wells based on LMS, characterized in that, This is achieved through the LMS-based adaptive communication system for ultra-long cables in wells, as described in any one of claims 1 to 4. The LMS-based adaptive communication method for ultra-long cables in wells includes: The FIR filter module receives reference signals from the cable in the well and performs error calculation processing to obtain the FIR tap coefficients of the cable in the well that are suitable for dynamic changes after adaptive LMS calculation. The coefficients are then entered into the data update module to update the tap coefficients of the FIR filter. The FIR filtering module receives data signals from the well equipment via the well cable. After dynamic FIR filtering, the data signals from the well equipment are closer to the actual data detected in the well. The filtered signals are then ready to enter the next module. The cable signal is parsed by an OFDM receiver or a UART communication module and then transmitted to the host computer communication module. The host computer communication module outputs a command signal based on the received cable signal; After the instruction signal is parsed, it is transmitted to the UART communication module for further parsing, and then the training sequence transmission module is controlled to transmit the signal. The training sequence transmission module performs BPSK modulation on the command signal after command parsing and outputs the modulated signal to the DA control module. The signal is transmitted to the cable and AD control module via a three-adapter through the DA control module; The signal is transmitted to the cross-correlation calculation module through the AD control module; The reflection coefficient and load impedance are calculated by the cross-correlation calculation module, and the calculated load impedance is transmitted to the impedance matching control module for impedance matching of the terminal system.

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