Shaft online monitoring and early warning system based on optical fiber sensing network

Abstract

The invention provides a shaft online monitoring and early warning system based on an optical fiber sensing network, and the system comprises the optical fiber sensing network which comprises a plurality of optical fiber Bragg grating sensors which are distributed at different positions in a shaft and are used for obtaining optical signals corresponding to real-time monitoring data; the data acquisition module is used for converting an optical signal corresponding to the real-time monitoring data into an electric signal corresponding to the real-time monitoring data; the data processing module is used for acquiring real-time monitoring data, processing the real-time monitoring data through a neurodynamics algorithm and then outputting the real-time monitoring data; the early warning module is used for comparing and analyzing the real-time monitoring data output by the data processing module with historical monitoring data, and dividing early warning levels and generating early warning information according to a comparison and analysis result; accurate real-time data support can be provided for wellbore state evaluation, distributed monitoring can be achieved, the monitoring efficiency is greatly improved, and the coverage range is greatly widened.

Description

Technical Field

[0001] This invention relates to the field of oil and gas wellbore monitoring, and in particular to an online wellbore monitoring and early warning system based on fiber optic sensor networks. Background Technology

[0002] Oil and natural gas resources occupy an extremely important position in the global energy structure. With the ever-increasing demand for these resources, ensuring the safety and efficiency of their extraction process has become a major challenge for the industry. Wellbores, as the key structure connecting surface and underground oil and gas storage, directly affect the safety and economic benefits of the entire extraction operation. However, wellbores are susceptible to various factors during long-term extraction, such as changes in geological pressure, corrosion, and wear, which may lead to serious problems such as wellbore structural damage, abnormal pressure, or even wellbore collapse. Once such problems occur, they not only interrupt production and increase repair costs, but may also trigger serious safety accidents and cause irreversible environmental damage.

[0003] Traditional wellbore monitoring methods include periodic physical inspections, chemical analyses, and various electronic instrument monitoring. These methods have significant limitations in practice: First, they typically cannot provide real-time monitoring, leading to delays in understanding the wellbore's condition and making it difficult to promptly detect and respond to potential risks; second, these methods often require substantial manpower and resources, increasing mining costs; and finally, due to the unique characteristics of the wellbore environment, the application of these methods may be severely restricted, affecting the accuracy and reliability of the monitoring data. Summary of the Invention

[0004] To address at least one technical problem in the prior art, embodiments of the present invention provide an online wellbore monitoring and early warning system based on a fiber optic sensor network. This system can provide accurate real-time data support for wellbore condition assessment and also enables distributed monitoring, significantly improving monitoring efficiency and coverage. To achieve the above technical objectives, the technical solution adopted in the embodiments of the present invention is as follows:

[0005] This invention provides an online monitoring and early warning system for wellbore based on an optical fiber sensor network, comprising:

[0006] The fiber optic sensing network includes multiple fiber Bragg grating sensors, which are distributed at different locations within the wellbore to acquire optical signals corresponding to real-time monitoring data.

[0007] The data acquisition module is used to convert the optical signal corresponding to the real-time monitoring data into an electrical signal corresponding to the real-time monitoring data;

[0008] The data processing module is used to acquire real-time monitoring data and process the real-time monitoring data using a neurodynamics algorithm before outputting the data.

[0009] The early warning module is used to compare and analyze the real-time monitoring data output by the data processing module with historical monitoring data, and to classify the early warning level and generate early warning information based on the comparison and analysis results.

[0010] The communication module is used to send early warning information;

[0011] The human-machine interface is used to visualize real-time monitoring data, historical monitoring data, and warning levels and information, and to provide a human-machine interactive interface.

[0012] Furthermore, the data acquisition module employs a photodetector.

[0013] 3. The wellbore online monitoring and early warning system based on fiber optic sensor network as described in claim 1, characterized in that,

[0014] The neural dynamics algorithm is shown in Equation (2):

[0015]

[0016] Where z(t)∈R n Let A be a vector of real-time monitoring data as input, with dimension n; A∈R m×n Let y be the observation matrix, which has dimensions m×n; y∈R m Let E be the measurement matrix and E be the identity matrix; α∈(0,1),β∈(1,∞);

[0017] x(t)∈R n This is a vector of the output real-time monitoring data;

[0018] a(t) = T λ (z(t))∈R n The soft threshold function is defined as shown in formula (3):

[0019] T λ (z i )=max(|z i |-λ,0)·sign(z i (3)

[0020] Where λ>0, z i Let be the i-th element of z(t).

[0021] Even better, 0.01 < λ < 10.

[0022] Specifically, the step of comparing and analyzing the real-time monitoring data output by the data processing module with historical monitoring data, and classifying the warning level and generating warning information based on the comparison and analysis results, specifically includes:

[0023] Calculate the error P between real-time monitoring data and historical monitoring data, and compare the error P with the first error threshold θ1 and the second error threshold θ2.

[0024] If P < θ1, the warning level is low risk;

[0025] If θ1≤P<θ2, then the warning level is medium risk;

[0026] If P≥θ2, then the warning level is high risk;

[0027] When the warning level is medium risk or high risk, the corresponding warning information is generated.

[0028] Furthermore, the wellbore online monitoring and early warning system based on fiber optic sensor networks also includes:

[0029] The log module is used to record warning logs and communication logs. The warning logs include the warning time, warning level, and warning information content. The communication logs include the warning information recipient, warning information sending time, and sending status.

[0030] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows:

[0031] 1) It can quickly and accurately identify abnormal conditions in the wellbore, and its real-time monitoring and instant analysis capabilities greatly reduce response time and improve the efficiency of handling potential risks.

[0032] 2) Strong anti-interference capability, effectively filtering out noise and transient interference signals.

[0033] 3) Distributed monitoring is achieved, which greatly improves the efficiency and coverage of monitoring.

[0034] 4) Reduced maintenance costs and enhanced economic and environmental benefits. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the structure of the wellbore online monitoring and early warning system in an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0037] In the description of the embodiments of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0038] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0039] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0040] like Figure 1 As shown, this embodiment of the invention proposes an online monitoring and early warning system for wellbore based on an optical fiber sensor network, comprising:

[0041] The fiber optic sensing network includes multiple fiber Bragg grating sensors, which are distributed at different locations within the wellbore to acquire optical signals corresponding to real-time monitoring data.

[0042] The data acquisition module is used to convert the optical signal corresponding to the real-time monitoring data into an electrical signal corresponding to the real-time monitoring data;

[0043] The data processing module is used to acquire real-time monitoring data and process the real-time monitoring data using a neurodynamics algorithm before outputting the data.

[0044] The early warning module is used to compare and analyze the real-time monitoring data output by the data processing module with historical monitoring data, and to classify the early warning level and generate early warning information based on the comparison and analysis results.

[0045] The communication module is used to send early warning information;

[0046] The human-machine interface is used to visualize real-time monitoring data, historical monitoring data, and warning levels and information, and to provide a human-machine interactive interface.

[0047] The following details the components of the wellbore online monitoring and early warning system;

[0048] (I) Fiber Optic Sensor Network

[0049] Fiber Bragg grating sensors reflect light of a specific wavelength by creating periodic refractive index changes in an optical fiber. This reflected wavelength changes with temperature or strain, enabling precise temperature and strain measurements. Fiber Bragg grating sensors can also be used to assess the safety status of wellbores in real time, promptly detect and warn of potential risks, and thus effectively ensure the safe operation of wellbores and even oil fields.

[0050] The wavelength variation of the fiber Bragg grating sensor is shown in equation (1):

[0051]

[0052] Where, λ B Δλ is the center wavelength of the fiber Bragg grating sensor. B For the change in center wavelength, P e Where C is the photoelastic coefficient, T is the temperature, and ΔT is the temperature change.

[0053] The output of the fiber Bragg grating sensor changes when the temperature or strain changes.

[0054] (II) Data Acquisition Module

[0055] The data acquisition module uses a photodetector, which contains a photodiode and an amplifier circuit, and can convert the optical signal corresponding to the real-time monitoring data into an electrical signal corresponding to the real-time monitoring data.

[0056] (III) Data Processing Module

[0057] The data processing module processes the real-time monitoring data using a neurodynamic algorithm and then outputs the results; the neurodynamic algorithm is shown in formula (2):

[0058]

[0059] Where z(t)∈R n Let A be a vector of real-time monitoring data as input, with dimension n; A∈R m×n Let y be the observation matrix, which has dimensions m×n; y∈R m Let E be the measurement matrix and E be the identity matrix; α∈(0,1),β∈(1,∞);

[0060] x(t)∈R n This is a vector of the output real-time monitoring data;

[0061] a(t) = T λ (z(t))∈R n The soft threshold function is defined as shown in formula (3):

[0062] T λ (z i )=max(|z i |-λ,0)·sign(z i (3)

[0063] Where λ>0, z i Let z(t) be the i-th element;

[0064] Preferably, 0.01 < λ < 10;

[0065] By processing real-time monitoring data using neurodynamic algorithms, noise and transient interference signals can be effectively removed.

[0066] (iv) Early Warning Module

[0067] The process of comparing and analyzing real-time monitoring data output by the data processing module with historical monitoring data, and classifying early warning levels and generating early warning information based on the comparison and analysis results, specifically includes:

[0068] Calculate the error P between real-time monitoring data and historical monitoring data, and compare the error P with the first error threshold θ1 and the second error threshold θ2.

[0069] If P < θ1, the warning level is low risk;

[0070] If θ1≤P<θ2, then the warning level is medium risk;

[0071] If P≥θ2, then the warning level is high risk;

[0072] When the warning level is medium risk or high risk, corresponding warning information is generated;

[0073] (V) Communication Module

[0074] The communication module is a WiFi module, an Ethernet module, or a 4G / 5G wireless communication module; the communication module can send early warning information to the user terminal; the user terminal can be a mobile phone, a dedicated monitoring computer, etc.

[0075] (vi) Human-computer interface

[0076] The real-time monitoring data, historical monitoring data, and the visualization of early warning levels and information are displayed in the form of charts and / or curves and / or heatmaps;

[0077] The human-computer interaction interface provides interactive interface elements such as buttons, sliders, and drop-down menus, which users can configure, such as setting error thresholds, and also allowing users to select and view real-time monitoring data, historical monitoring data, and early warning information.

[0078] Furthermore, the wellbore online monitoring and early warning system also includes a log module;

[0079] (vii) Log module

[0080] The log module is used to record warning logs and communication logs. The warning logs include the warning time, warning level, and warning information content. The communication logs include the warning information recipient, warning information sending time, and sending status.

[0081] The aforementioned data processing module, early warning module, communication module, human-machine interface, and log module are all located on the server.

[0082] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A wellbore online monitoring and early warning system based on fiber optic sensor networks, characterized in that, include: The fiber optic sensing network includes multiple fiber Bragg grating sensors, which are distributed at different locations within the wellbore to acquire optical signals corresponding to real-time monitoring data. The data acquisition module is used to convert the optical signal corresponding to the real-time monitoring data into an electrical signal corresponding to the real-time monitoring data; The data processing module is used to acquire real-time monitoring data and process the real-time monitoring data using a neurodynamics algorithm before outputting the data. The early warning module is used to compare and analyze the real-time monitoring data output by the data processing module with historical monitoring data, and to classify the early warning level and generate early warning information based on the comparison and analysis results. The communication module is used to send early warning information; The human-machine interface is used to visualize real-time monitoring data, historical monitoring data, and warning levels and information, and to provide a human-machine interactive interface.

2. The wellbore online monitoring and early warning system based on fiber optic sensor network as described in claim 1, characterized in that, The data acquisition module uses a photodetector.

3. The wellbore online monitoring and early warning system based on fiber optic sensor network as described in claim 1, characterized in that, The neurodynamic algorithm is shown in Equation (2): Where z(t)∈R n Let A be a vector of real-time monitoring data as input, with dimension n; A∈R m×n Let y be the observation matrix, which has dimensions m×n; y∈R m Let E be the measurement matrix and E be the identity matrix; α∈(0,1),β∈(1,∞); x(t)∈R n This is a vector of the output real-time monitoring data; a(t) = T λ (z(t))∈R n The soft threshold function is defined as shown in formula (3): T λ (z i )=max(|z i |-λ,0)·sign(z i ) (3) Where λ>0, z i Let be the i-th element of z(t).

4. The wellbore online monitoring and early warning system based on fiber optic sensor network as described in claim 3, characterized in that, 0.01 < λ < 10.

5. The wellbore online monitoring and early warning system based on fiber optic sensor network as described in claim 3, characterized in that, The process of comparing and analyzing real-time monitoring data output by the data processing module with historical monitoring data, and classifying early warning levels and generating early warning information based on the comparison and analysis results, specifically includes: Calculate the error P between real-time monitoring data and historical monitoring data, and compare the error P with the first error threshold θ1 and the second error threshold θ2. If P < θ1, the warning level is low risk; If θ1≤P<θ2, then the warning level is medium risk; If P≥θ2, then the warning level is high risk; When the warning level is medium risk or high risk, the corresponding warning information is generated.

6. The wellbore online monitoring and early warning system based on fiber optic sensor networks as described in any one of claims 1 to 5, characterized in that, Also includes: The log module is used to record warning logs and communication logs. The warning logs include the warning time, warning level, and warning information content. The communication log includes the recipient of the warning information, the time when the warning information was sent, and the sending status.

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