Comprehensive detection system and method for petroleum logging sensor

Abstract

The invention relates to the technical field of sensor detection, in particular to a comprehensive detection system and method for a petroleum logging sensor. According to the method, the temperature and pressure data in the well logging shaft are obtained, and the temperature change rate and the pressure change rate are calculated, so that the influence of the environment on the measurement data can be accurately evaluated, the compensation of the temperature-pressure coupling influence is further realized, and the measurement precision is remarkably improved. On the basis, the amplitude change and the phase deviation of the measurement signal are further combined, the interference characteristics of the electromagnetic environment on the measurement signal are extracted, and signal optimization is carried out by calculating interference influence parameters, so that the reliability and the anti-interference capability of the measurement data are enhanced. Meanwhile, the influence of load change on the sensor in the logging process is analyzed, the deviation condition of a pressure measurement value is identified, and the measurement error is corrected by using an influence coefficient, so that the measurement data is more stable. Multi-cycle measurement data are compared and analyzed, and the long-term drifting condition of the data can be identified.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor detection, and in particular to a comprehensive detection system and method for a petroleum well logging sensor. Background Art

[0002] The field of oil logging sensor detection technology includes the relevant technologies for performance testing, calibration and evaluation of various sensors used in the oil logging process. The core content of this technical field includes sensitivity detection of logging sensors, signal stability analysis, environmental adaptability testing and data accuracy evaluation. Oil logging sensor detection technology involves physical quantity measurement, electrical signal analysis, data acquisition and storage, etc. By systematically testing the working characteristics of the sensor, it is ensured that it can accurately reflect the physical parameters of the underground formation during the logging process.

[0003] Among them, the comprehensive detection system for oil logging sensors refers to a system used to conduct all-round testing and calibration of oil logging sensors. The system compares and analyzes the electrical signals collected by the sensors through high-precision measuring equipment, and evaluates their sensitivity, linearity and stability based on standard measurement models. Using multi-dimensional environmental simulation technology, the system can test the performance of sensors under different temperature, pressure and electromagnetic interference conditions, and calibrate the measurement deviation of the sensors in combination with specific logging parameters.

[0004] In actual applications, logging sensors are affected by multiple factors such as temperature, pressure, electromagnetic environment and load changes. The existing technology mainly relies on single-dimensional compensation methods, resulting in large deviations in measurement data under complex environmental conditions. Temperature and pressure compensation is usually based on static temperature and pressure data, while ignoring the temperature and pressure coupling effect, resulting in poor compensation effect under dynamic changing environments. At the same time, the influence of electromagnetic interference is often processed by a simple filtering method, which cannot accurately identify the characteristics of interference in different frequency bands, resulting in the measurement signal still being affected by certain noise interference, affecting the measurement accuracy. In addition, the existing method does not effectively correct the pressure measurement error caused by load changes, resulting in sensor measurement data drifting easily when the downhole equipment vibrates or the pressure fluctuates greatly, affecting the stability of the logging results. In terms of measurement data consistency analysis, the existing technology mainly relies on single measurement data for evaluation, ignoring the change trend of long-term measurement data, making the reliability of measurement data in long-term logging tasks low, and it is difficult to effectively determine the long-term adaptability of logging equipment. In a complex logging environment, due to the lack of systematic analysis of the measurement characteristics of different logging stages, it is difficult for the existing technology to accurately evaluate the adaptability of logging sensors under different working conditions, resulting in the difficulty in ensuring the credibility of measurement data. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a comprehensive detection system and method for a petroleum logging sensor.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A comprehensive detection system for oil well logging sensors comprises: The environmental parameter calibration module obtains the temperature and pressure data in the logging wellbore, calculates the temperature-pressure coupling influence factor according to the temperature change rate and the pressure change rate, performs temperature-pressure sensitivity compensation according to the influence factor, and obtains the temperature-pressure sensitivity compensation result; The interference feature decomposition module analyzes the amplitude change and phase shift of the measurement signal of the oil pressure sensor based on the temperature and pressure sensitivity compensation result, calculates the compensation parameters of the interference influence according to the change and shift, and iteratively adjusts the measurement pressure to obtain the electromagnetic interference compensation result; The load change adjustment module obtains the load change of the oil pressure sensor during the logging process based on the electromagnetic interference compensation result, calculates the influence coefficient of the load change on the measured pressure, corrects the measured pressure according to the influence coefficient, and obtains the corrected oil pressure measurement result; The measurement data consistency analysis module analyzes the stability of the oil pressure measurement data of the oil pressure sensor in multiple logging cycles based on the corrected oil pressure measurement results to obtain oil pressure data analysis results; The comprehensive detection module evaluates the adaptability of the oil pressure sensor in each well logging environment based on the oil pressure data analysis result, and obtains the oil pressure comprehensive detection result.

[0007] As a further scheme of the present invention, the temperature and pressure sensitivity compensation results are specifically temperature compensation coefficient, pressure compensation coefficient, sensitivity adjustment parameter, and voltage correction parameter; the electromagnetic interference compensation results include interference frequency distribution parameters, signal phase offset value, amplitude correction coefficient, and measurement pressure correction value; the corrected oil pressure measurement results specifically refer to load influence correction amount, measurement error correction value, and dynamic adjustment parameter; the oil pressure data analysis results include measurement pressure fluctuation range, time series stability parameter, data drift coefficient, and periodic deviation judgment value; the comprehensive oil pressure detection results are specifically measurement response rate, environmental adaptability parameter, oil pressure measurement reliability index, and working condition evaluation parameter.

[0008] As a further solution of the present invention, the environmental parameter calibration module includes: The data acquisition submodule obtains the temperature data and pressure data in the logging wellbore based on the temperature sensor and the oil pressure sensor, calculates the temperature change rate and the pressure change rate in the specified time period, and obtains the temperature change rate and the pressure change rate data set; The temperature-pressure coupling effect calculation submodule is based on the temperature change rate and pressure change rate data set and uses the formula: ; Calculate the temperature-pressure coupling influence factor ; in, It is The temperature value at a time point, The previous time point The temperature value, is the pressure value at the previous time point, is the number of data points in the selected time window; The compensation parameter calculation submodule is based on the temperature-pressure coupling influencing factor and adopts the formula: ; Calculate the pressure compensation parameters of the oil pressure sensor , apply the compensation parameters to the current oil pressure sensor measurement reference pressure value to obtain the temperature and pressure sensitivity compensation result; in, It is The pressure measurement offset value within a time window, It is The temperature-pressure coupling influencing factor of the time window is: is the total number of selected time windows.

[0009] As a further solution of the present invention, the interference feature decomposition module includes: The signal frequency division processing submodule obtains the measurement signal and electromagnetic environment parameters of the oil pressure sensor based on the temperature and pressure sensitivity compensation result, analyzes the spectrum information of the measurement signal, divides the measurement signal into multiple frequency bands, calculates the amplitude change and phase shift of each frequency band, compares the energy change of each frequency band, quantifies the influence of the external electromagnetic environment on the oil pressure sensor signal, and generates the spectrum characteristics of the measurement signal; The interference compensation parameter calculation submodule adopts the formula based on the spectrum characteristics of the measurement signal: ; Calculation of interference effect compensation parameters ; in, Representative The measured signal amplitude of the frequency band, Representative The reference signal amplitude of the frequency band, Representative The measured signal phase angle of the frequency band, Representative The reference signal phase angle of the frequency band, Representative The center frequency of the band, Represents the total number of frequency bands; The measurement pressure adjustment submodule calls the interference influence compensation parameter, combines the current oil pressure sensor measurement value, gradually corrects the measurement pressure value, suppresses the signal interference component, optimizes the measurement signal quality, and obtains the electromagnetic interference compensation result.

[0010] As a further solution of the present invention, the load change adjustment module includes: The load change rate and pressure offset analysis submodule obtains the load change rate of the oil pressure sensor during the logging process based on the electromagnetic interference compensation result, calculates the pressure offset caused by the equipment load change during the downhole pressure transmission process within a specified time, and obtains the load change rate and pressure offset information; The load influence coefficient calculation submodule uses the formula based on the load change rate and pressure offset information: ; Calculate the load change influence factor ; in, Indicates the pressure measurement value With load change rate The covariance between Indicates the variance of the load change rate; The measurement pressure error correction submodule corrects the measurement pressure error through the load change influence coefficient to obtain a corrected oil pressure measurement result.

[0011] As a further solution of the present invention, the measurement data consistency analysis module includes: The pressure fluctuation calculation submodule obtains the measured pressure sequence of the oil pressure sensor in multiple logging cycles based on the corrected oil pressure measurement results, extracts the measured pressure values ​​at multiple times at the same depth, and uses the formula: ; Calculate the pressure fluctuation value at the current measuring depth ; in, Representative The measured pressure at all times, Represents the average pressure at a specified measurement depth, Represents the number of measurements within the measurement cycle; The oil pressure data stability analysis submodule calls the pressure fluctuation value of the current measurement depth, analyzes the stability of the oil pressure measurement data in the time dimension, determines whether the measured pressure in the logging cycle changes continuously over time, and obtains the oil pressure data analysis result.

[0012] As a further solution of the present invention, the comprehensive detection module includes: The oil pressure measurement response calculation submodule obtains the oil pressure sensor measurement data in the whole logging process based on the oil pressure data analysis results, extracts the oil pressure measurement values ​​of all measurement points, organizes the data according to the logging time sequence, divides the measurement stages according to various logging conditions, calculates the oil pressure change rate in each stage, and obtains the oil pressure measurement response rate data set; The oil pressure sensor adaptability evaluation submodule analyzes the response rate changes under various logging environments based on the oil pressure measurement response rate data set, screens the measuring point data within the adaptation range, compares the oil pressure measurement response rate in combination with the logging condition category, and obtains the comprehensive oil pressure detection result.

[0013] A comprehensive detection method for a petroleum well logging sensor is provided. The comprehensive detection method for a petroleum well logging sensor is performed based on the above-mentioned comprehensive detection system for a petroleum well logging sensor and comprises the following steps: S1: Obtain the temperature and pressure data in the logging wellbore, calculate the temperature-pressure coupling influence factor according to the temperature change rate and the pressure change rate, perform temperature-pressure sensitivity compensation according to the influence factor, and obtain the temperature-pressure sensitivity compensation result; S2: Based on the temperature and pressure sensitivity compensation result, analyze the amplitude change and phase shift of the measurement signal of the oil pressure sensor, calculate the compensation parameters of the interference influence according to the change and shift, and iteratively adjust the measurement pressure to obtain the electromagnetic interference compensation result; S3: based on the electromagnetic interference compensation result, obtaining the load change of the oil pressure sensor during the logging process, calculating the influence coefficient of the load change on the measured pressure, correcting the measured pressure according to the influence coefficient, and obtaining the corrected oil pressure measurement result; S4: Based on the corrected oil pressure measurement result, analyzing the stability of the oil pressure measurement data of the oil pressure sensor in multiple logging cycles to obtain an oil pressure data analysis result; S5: Based on the oil pressure data analysis results, the adaptability of the oil pressure sensor in each logging environment is evaluated to obtain a comprehensive oil pressure detection result.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by obtaining the temperature and pressure data in the logging wellbore and calculating the temperature change rate and pressure change rate, the impact of the environment on the measurement data can be accurately evaluated, thereby realizing the compensation of the temperature-pressure coupling effect and significantly improving the measurement accuracy. On this basis, the interference characteristics of the electromagnetic environment on the measurement signal are further extracted by combining the amplitude change and phase offset of the measurement signal, and the signal is optimized by calculating the interference influence parameters, so that the reliability and anti-interference ability of the measurement data are enhanced. At the same time, the influence of the load change on the sensor during the logging process is analyzed, the offset of the pressure measurement value is identified, and the measurement error is corrected using the influence coefficient, so that the measurement data is more stable. The comparative analysis of multi-cycle measurement data can identify the long-term drift of the data, avoid the accumulation of measurement result errors due to environmental changes, and effectively ensure the measurement stability. In addition, based on the data analysis of the entire measurement process, the oil pressure change characteristics of different logging stages are extracted, and the measurement response rate and sensor adaptability are evaluated in combination with the logging environment parameters, so that the reliability of the system under complex working conditions is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the environmental parameter calibration module of the present invention; Figure 3 This is a flow chart of the interference feature decomposition module of the present invention; Figure 4 This is a flow chart of the load change adjustment module of the present invention; Figure 5 This is a flow chart of the measurement data consistency analysis module of the present invention; Figure 6 This is a flow chart of the comprehensive detection module of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 The present invention provides a technical solution: a comprehensive detection system for petroleum logging sensors includes: The environmental parameter calibration module obtains the temperature and pressure data in the logging wellbore through the temperature sensor and the oil pressure sensor and calculates the temperature change rate and the pressure change rate in the specified time period, calculates the temperature-pressure coupling influence factor according to the change characteristics of the temperature change rate and the pressure change rate, analyzes the effect of the influence factor on the pressure sensor measurement sensitivity, calls the sliding window to calculate the offset value of the pressure measurement in the time series, calculates the compensation parameter according to the temperature-pressure coupling influence factor and the offset value of the pressure measurement in the time series, applies the compensation parameter to the current oil pressure sensor measurement reference pressure value, and obtains the temperature-pressure sensitivity compensation result; Based on the temperature and pressure sensitivity compensation results, the interference feature decomposition module obtains the measurement signal of the oil pressure sensor and the corresponding electromagnetic environment parameters, analyzes the spectrum information of the measurement signal, performs frequency division processing on the measurement signal, analyzes the amplitude changes and phase shifts of multiple frequency bands, calculates the compensation parameters of the interference influence according to the changes and shifts, and iteratively adjusts the measurement pressure of the oil pressure sensor to obtain the electromagnetic interference compensation result; The load change adjustment module obtains the load change rate of the oil pressure sensor during the logging process based on the electromagnetic interference compensation result, calculates the pressure offset caused by the equipment load change during the downhole pressure transmission process within a specified time, determines the correlation between the load change rate and the oil pressure sensor measured pressure, calculates the influence coefficient of the load change on the oil pressure sensor measured pressure based on the correlation, corrects the measured pressure error through the influence coefficient, and obtains the corrected oil pressure measurement result; The measurement data consistency analysis module obtains the measured pressure sequence of the oil pressure sensor in multiple logging cycles based on the corrected oil pressure measurement results, calculates the pressure fluctuations measured at multiple times at the same depth, analyzes the stability of the oil pressure measurement data in the time dimension, determines whether there is data drift or periodic deviation, and obtains the oil pressure data analysis results; Based on the oil pressure data analysis results, the comprehensive detection module obtains the oil pressure sensor measurement pressure sequence during the whole logging process, calculates the oil pressure measurement response rate under various working conditions during the logging process, evaluates the adaptability of the oil pressure sensor in each logging environment, and obtains the comprehensive oil pressure detection results; The results of temperature and pressure sensitivity compensation specifically include temperature compensation coefficient, pressure compensation coefficient, sensitivity adjustment parameters, and voltage correction parameters. The results of electromagnetic interference compensation include interference frequency distribution parameters, signal phase offset value, amplitude correction coefficient, and measurement pressure correction value. The corrected oil pressure measurement results specifically refer to load influence correction amount, measurement error correction value, and dynamic adjustment parameters. The oil pressure data analysis results include measurement pressure fluctuation range, time series stability parameters, data drift coefficient, and periodic deviation judgment value. The comprehensive oil pressure detection results specifically include measurement response rate, environmental adaptability parameters, oil pressure measurement reliability index, and working condition evaluation parameters.

[0019] See also Figure 2 , the environmental parameter calibration module includes: The data acquisition submodule obtains the temperature data and pressure data in the logging wellbore based on the temperature sensor and the oil pressure sensor, calculates the temperature change rate and the pressure change rate in the specified time period, and obtains the temperature change rate and the pressure change rate data set; Based on temperature sensors and oil pressure sensors, multiple measurement points are deployed in the logging wellbore. Each measurement point records the temperature and pressure data at a specific time interval. For example, data is collected every 5 seconds to obtain complete time series data. The collected data is stored in the data management system, where the initial temperature and pressure of each measurement point are used as reference baseline values. During the data collection process, the sensor may be disturbed by the environment, such as the impact of fluid flow in the wellbore, so the data needs to be smoothed, such as using the sliding average method to reduce data noise. When calculating the temperature change rate and pressure change rate within a specified time period, 10 minutes is used as a time window to calculate the temperature change between multiple consecutive time points. , and pressure change , and then calculate the temperature change rate and pressure change rate , which is calculated as follows: ,in, : Temperature change rate, which indicates the average rate of change of temperature per unit time; : Pressure change rate, which indicates the average rate of change of pressure per unit time; : No. The temperature change at each time point; : No. The pressure change at a time point; : No. The temperature value at a time point; : No. The pressure value at a time point; : The number of data points in the selected time window. For example, if data is collected every 5 seconds in a 10-minute window, then If the initial temperature is 80°C and the temperature rises to 85°C after 10 minutes, calculate the temperature change rate , similarly calculate the pressure change rate. If the initial pressure is 20MPa and the pressure rises to 22MPa after 10 minutes, then , and finally obtain the temperature change rate and pressure change rate data sets.

[0020] The temperature-pressure coupling effect calculation submodule is based on the temperature change rate and pressure change rate data sets, using the formula: ; Calculate the temperature-pressure coupling influence factor ; in, It is The temperature value at a time point; The previous time point Temperature value; It is an adjacent time point and The absolute value of the normalized temperature change between is the pressure value at the previous time point; It is an adjacent time point and The absolute value of the normalized pressure change between is the number of data points in the selected time window.

[0021] If the temperature range of the logging data is 70-90℃ and the pressure range is 15-25MPa. At a certain moment, the temperature is 85℃ and the pressure is 22MPa. Perform normalization calculation: , .

[0022] Select data from five time points to calculate the temperature-pressure coupling influence factor. Assume that the normalized temperatures at these time points are 0.70, 0.72, 0.74, 0.76, and 0.78, and the normalized pressures are 0.65, 0.67, 0.69, 0.72, and 0.74, respectively. Calculate the absolute values ​​of changes at adjacent time points: , .

[0023] Calculate the temperature-pressure coupling influence factor: ; Finally, the temperature-pressure coupling influence factor is calculated , used for compensation calculation.

[0024] The compensation parameter calculation submodule is based on the temperature-pressure coupling influencing factor and uses the formula: ; Calculate the pressure compensation parameters of the oil pressure sensor , apply the compensation parameters to the current oil pressure sensor measurement reference pressure value to obtain the temperature and pressure sensitivity compensation result; in, represents the weighted average of the pressure measurement offset in different time windows; It is The pressure measurement offset value within a time window; It is The temperature-pressure coupling influencing factors of each time window; is the total number of selected time windows.

[0025] Call the temperature-pressure coupling influencing factor data, and calculate the offset value of the pressure measurement in the time series based on the sliding window. The sliding window size is set to 30 minutes, that is, the pressure measurement offset is calculated within this time period. ,in, It is The measured pressure value within a time window is represented by the pressure at the time window. The pressure value recorded by the sensor at the end is in MPa; is the previous time window The measured pressure value at the end, i.e. the time window The pressure value at the beginning, in MPa. For example, in the first 30-minute window, the pressure changes from 18 MPa to 18.5 MPa. , if the subsequent window pressure becomes 19.2MPa, then Assuming that three time windows are selected, the pressure offsets are 0.5MPa, 0.7MPa, and 0.4MPa, respectively, and the corresponding temperature-pressure coupling influence factors are 0.8, 0.6, and 0.9, respectively. The compensation parameters are calculated as follows: , calculate the pressure compensation value. Assuming that the pressure measured by the current oil pressure sensor is 20MPa, the pressure after compensation is calculated as follows: The final pressure value of the oil pressure sensor after calibration is 19.487MPa, which reduces the influence of temperature on the measurement and improves the data accuracy.

[0026] See also Figure 3 , the interference feature decomposition module includes: Based on the temperature and pressure sensitivity compensation results, the signal frequency division processing submodule obtains the measurement signal and electromagnetic environment parameters of the oil pressure sensor, analyzes the spectrum information of the measurement signal, splits the measurement signal into multiple frequency bands, calculates the amplitude change and phase shift of each frequency band, compares the energy change of each frequency band, quantifies the impact of the external electromagnetic environment on the oil pressure sensor signal, and generates the spectrum characteristics of the measurement signal; The measurement signal of the oil pressure sensor and the relevant parameters of the electromagnetic environment in which it is located are obtained. The original waveform data of the measurement signal is recorded using high-precision data acquisition equipment. The sampling frequency is set to 100kHz to ensure sufficient time resolution for subsequent spectrum analysis. At the same time, the real-time electromagnetic field strength of the external electromagnetic environment is synchronously recorded. The measurement signal is decomposed by short-time Fourier transform (STFT) and split into multiple different frequency components. The time window length is set to 20ms and the window moving step is set to 5ms to ensure that the signal spectrum analysis can obtain sufficient frequency detail information in different time periods. The energy of the decomposed frequency signal is calculated to obtain the energy distribution of each frequency band. The amplitude change rate of each frequency band is obtained by calculating the change trend of the signal amplitude over time, and the phase offset is obtained by calculating the time change of the phase angle. The amplitude change and phase drift of different frequencies are normalized to eliminate the difference in energy levels of signals in different frequency bands. The normalized amplitude change trend and phase drift trend are calculated. Finally, the influence of the external electromagnetic environment on the measurement signal of the oil pressure sensor is quantified to obtain the spectrum characteristics of the measurement signal.

[0027] The interference compensation parameter calculation submodule is based on the spectrum characteristics of the measured signal and uses the formula: ; Calculation of interference effect compensation parameters ; in, Representative The measured signal amplitude of the frequency band (in V); Representative The reference signal amplitude of the frequency band (in V); Representative The measured signal phase angle of the frequency band (in degrees); Representative The reference signal phase angle of the frequency band (in degrees); Representative The center frequency of the band (in Hz); Represents the total number of frequency bands; Represents the square of the amplitude deviation between the measured signal and the reference signal; Represents the square of the phase shift between the measured signal and the reference signal; Represents the normalized frequency band interference impact factor.

[0028] Set the analysis frequency range to 10Hz to 10MHz, divide it into 10 different frequency bands, and calculate the center frequency of each frequency band And the corresponding signal change trend, the spectrum amplitude and phase are adjusted by normalization method to reduce the excessive influence of individual frequency bands, and the interference correction coefficient is calculated using the formula: ; Set N=10 and enter actual values ​​for calculation. Assuming that the measured signal amplitude of a certain frequency band is 0.85V, the reference signal amplitude is 0.80V, the measured signal phase angle is 45°, the reference signal phase angle is 40°, and the center frequency is 1kHz, the interference impact factor of this frequency band is calculated as follows: ; Sum the calculation results of all frequency bands to obtain the total interference compensation parameters ,Finally, based on the calculation results of different frequency bands, the interference impact distribution of the entire frequency band is established, and the interference impact compensation parameter data is generated.

[0029] The measurement pressure adjustment submodule calls the interference impact compensation parameters, combines the current oil pressure sensor measurement value, gradually corrects the measurement pressure value, suppresses the signal interference components, optimizes the measurement signal quality, and obtains the electromagnetic interference compensation result; Call the interference compensation parameter data, combine it with the current oil pressure sensor measurement value, establish a dynamic adjustment mechanism, use iterative calculation method to correct the measured pressure value, and adjust the interference compensation parameter Applied to the correction of the oil pressure sensor measurement data, the correction formula is set as follows: Assuming that the current pressure value measured by the oil pressure sensor is 20.5MPa, the interference compensation parameter calculated by the previous submodule is , then the corrected pressure value is calculated as follows: The filtering algorithm is used to suppress specific interference components, adjust the influence weights of different frequency bands, smooth the correction values ​​at multiple time points, set the sliding window size to 5 minutes, calculate the pressure mean in the current window, avoid the influence of instantaneous interference on the measurement results, and finally obtain the electromagnetic interference compensation results.

[0030] See also Figure 4 , the load change adjustment module includes: The load change rate and pressure offset analysis submodule obtains the load change rate of the oil pressure sensor during the logging process based on the electromagnetic interference compensation results, calculates the pressure offset caused by the equipment load change during the downhole pressure transmission process within a specified time, and obtains the load change rate and pressure offset information; Obtain the measurement data of the oil pressure sensor during the logging process, including the pressure measurement value and equipment load data in the time series, set the sampling interval to 5s, ensure sufficient sampling density, record the equipment load value at each time point and calculate the load change at adjacent time points. Set the observation window to 10 minutes (600s), collect a total of 120 load data points, extract the load change at adjacent time points, calculate the cumulative change value and normalize it to the number of measurement points, and calculate the load change rate Using the formula: ,in, Represents the average load change rate within the measurement window (N / s), Representative The equipment load value at the moment (N), Representative The equipment load value at the moment (N), Represents the number of measurement points within the measurement window. Assuming the initial load , final load , calculated as follows: After calculating the load change rate, analyze the transmission process of downhole pressure under the condition of equipment load change, extract the measured pressure data of the oil pressure sensor, and combine it with the load change rate Calculate the pressure offset . Set the transfer time of the logging equipment to , the pressure offset calculation formula is defined as follows: ,in, Represents the pressure offset (MPa), is the calculated load change rate (N / s), is the pressure transmission time of the downhole equipment (s). Assume that the transmission time during logging is , the pressure offset is calculated as follows: , and finally the load change rate and pressure offset are calculated for subsequent calculation of the load influence coefficient.

[0031] The load influence coefficient calculation submodule uses the formula based on the load change rate and pressure offset information: ; Calculate the load change influence factor ; in, Indicates the pressure measurement value With load change rate The covariance between Indicates the variance of the load change rate.

[0032] To calculate the influence coefficient of load change on the oil pressure sensor's measured pressure, the formula is used: ; in Representative The measured pressure value at the moment (MPa), Represents the average pressure value within the measurement window (MPa), Representative Load change rate at the moment (N / s), Represents the average load change rate within the measurement window (N / s), Represents the total number of measurement points.

[0033] If within the 10-minute window, measure the average pressure MPa, mean load change rate N / s, set the data of some measuring points as follows: MPa, N / s; MPa, N / s; MPa, N / s. Calculation , calculate the deviation term for each measurement point and substitute it into the formula: ; Finally, the load influence coefficient is calculated , which is used for error correction of subsequent pressure measurements to obtain load influence coefficient data.

[0034] The measurement pressure error correction submodule corrects the measurement pressure error through the load change influence coefficient to obtain the corrected oil pressure measurement result; Call the load influence coefficient data, combine it with the current measured pressure of the oil pressure sensor, and perform error correction. Calculate pressure correction value , adjust the current measurement value, using the formula: ,in, : Corrected measured pressure (MPa), i.e. the actual oil pressure measurement value after correction due to load changes; : Original measured pressure (MPa), that is, the pressure value recorded by the oil pressure sensor before error correction. Assuming that the current measured pressure is 20.5MPa, the calculated load change influence coefficient is , load change rate N / s, the corrected pressure value is calculated as follows: , and finally obtain the corrected oil pressure measurement result, which is used to improve the measurement accuracy of the oil pressure sensor under load change conditions.

[0035] See also Figure 5 , the measurement data consistency analysis module includes: The pressure fluctuation calculation submodule obtains the measured pressure sequence of the oil pressure sensor in multiple logging cycles based on the corrected oil pressure measurement results, extracts the measured pressure values ​​at multiple times at the same depth, and uses the formula: ; Calculate the pressure fluctuation value at the current measuring depth ; in, Representative The measured pressure at the moment (MPa), Represents the average pressure at the measurement depth (MPa), Represents the number of measurements within a measurement cycle.

[0036] If the measurement depth is 1000m and the measurement period is 24h, the pressure values ​​at the measurement points are MPa, MPa, MPa, and the average pressure at the measuring depth MPa, the calculation is as follows: Finally, the pressure fluctuation value is obtained for subsequent data stability analysis.

[0037] The oil pressure data stability analysis submodule calls the pressure fluctuation value of the current measurement depth, analyzes the stability of the oil pressure measurement data in the time dimension, determines whether the measured pressure in the logging cycle changes continuously over time, and obtains the oil pressure data analysis results; First, the measured pressure data within multiple logging cycles are obtained to construct a time series data set. The measurement cycle is set to 24 hours, and data is extracted at intervals of 1 hour to ensure the integrity and comparability of the data. Then, the pressure change trend within the measurement cycle is calculated, and the sliding window smoothing method is used to eliminate the measurement noise. The sliding window size is set to 3 hours, that is, the measured pressure at each time point is taken as the average of the data before and after 1 hour as the smoothed pressure value to ensure the continuity of the data, and then determine whether there is a drift phenomenon in the measured pressure, and define the drift threshold. MPa, that is, if the measured pressure changes within 5 consecutive hours are greater than the threshold, it is considered that data drift exists. Further analyze the periodic deviation, extract the measured pressure fluctuations in multiple logging cycles, set the period detection threshold to 0.2MPa, calculate the pressure fluctuation amplitudes in different logging cycles, and if the pressure fluctuation amplitude of a certain cycle exceeds 10% compared with the average fluctuation amplitude of other cycles, it is considered that there is an abnormal deviation in the cycle. Then the data comparison method is used to calculate the pressure change trend of different cycles. If the trend has a periodic repetition feature, it is judged that the data has a periodic deviation. Assuming that the measurement depth is 1000m, the logging cycle is 24h, and the initial pressure data of the measurement points are MPa, after sliding window smoothing, the data is adjusted to MPa. By comparing and analyzing the pressure variation trends in different logging cycles, it is found that the pressure fluctuation amplitudes in two consecutive logging cycles are and , the deviation between the two , exceeding the periodic detection threshold of 10%, so it is determined that there is a periodic deviation in the logging cycle, and finally the oil pressure data analysis result is obtained.

[0038] See also Figure 6 , the comprehensive detection module includes: The oil pressure measurement response calculation submodule obtains the oil pressure sensor measurement data during the whole logging process based on the oil pressure data analysis results, extracts the oil pressure measurement values ​​of all measurement points, organizes the data according to the logging time sequence, divides the measurement stages according to various logging conditions, calculates the oil pressure change rate in each stage, and obtains the oil pressure measurement response rate data set; Obtain the oil pressure sensor measurement data during the whole logging process, extract the oil pressure measurement values ​​of all measuring points, sort the data according to the logging time sequence, remove the abnormal measuring point data, select the effective measuring points for calculation, divide the measurement stage according to different logging conditions, calculate the oil pressure change rate of each stage, and obtain the oil pressure measurement response rate. The specific implementation process is as follows: First, during the logging operation, the oil pressure sensor records the pressure value once per second. Assuming that a logging operation lasts for 1000 seconds, 1000 oil pressure data points will be obtained. These data are stored in chronological order to form a complete oil pressure measurement data sequence. During the data sorting process, it is necessary to check whether the measurement data is abnormal. For example, if the pressure value at a certain moment suddenly jumps from 20MPa to 100MPa, and then returns to 20MPa in the next second, then the data point is obviously unreasonable. At this time, the method of setting upper and lower limits can be used to remove abnormal data. For example, the normal pressure range is set to 10MPa to 50MPa, and the measuring points outside this range are considered abnormal and deleted. After the data screening is completed, the measurement phases need to be divided according to the logging conditions. For example, in the drilling phase, the measured oil pressure gradually rises from 15MPa to 30MPa, and the duration is 300 seconds. In the drilling phase, the oil pressure drops from 30MPa to 18MPa, and the duration is 200 seconds. In the drilling stop phase, the oil pressure is basically stable at about 20MPa, and the duration is 500 seconds. Then, the oil pressure change rate of each stage is calculated. For example, in the drilling phase, the pressure rises from 15MPa to 30MPa, and the duration is 300 seconds. Therefore, the oil pressure change rate is (30-15) / 300=0.05MPa / s. In the drilling phase, the oil pressure drops from 30MPa to 18MPa, and the duration is 200 seconds. The change rate is (30-18) / 200=0.06MPa / s. In the drilling stop phase, the pressure remains unchanged at 20MPa, so the change rate is 0MPa / s. Through the above steps, the oil pressure measurement response rate at each stage is obtained.

[0039] The oil pressure sensor adaptability evaluation submodule analyzes the response rate changes under various logging environments based on the oil pressure measurement response rate data set, selects the measurement point data within the adaptation range, compares the oil pressure measurement response rate in combination with the logging condition category, and obtains the comprehensive oil pressure detection results; First, according to different logging environment classifications, for example, it can be divided into sandstone layer, shale layer and carbonate layer according to the formation type, or divided into different categories such as 0-1000 meters, 1000-2000 meters and 2000-3000 meters according to the well depth range, and then the oil pressure measurement response rate in these categories is calculated respectively. For example, in the sandstone layer, the response rates of multiple measuring points are 0.04MPa / s, 0.05MPa / s, and 0.06MPa / s, and the average value is 0.05MPa / s. In the shale layer, the response rates of multiple measuring points are 0.03MPa / s, 0.04MPa / s, and 0.05MPa / s, and the average value is 0.04MPa / s. In the carbonate layer, the measured response rates are 0.02MPa / s, 0.03MPa / s, and 0.04MPa / s, and the average value is 0.03MPa / s. Then, the adaptability threshold range is set. For example, if the response rate is between 0.03MPa / s and 0.06MPa / s, it is considered to have good adaptability, and the measuring points outside this range are considered to have poor adaptability. The measuring point data in the sandstone layer and shale layer are all within the adaptability range, while the measuring point data of some carbonate rock layers (such as 0.02MPa / s) are in the range of poor adaptability. Next, the filtered oil pressure measurement response rate is compared in combination with the logging condition category to analyze the response characteristics under different working conditions. For example, in the stop drilling stage, the oil pressure change rate of each formation is generally low, between 0.01MPa / s and 0.02MPa / s, while in the drilling stage, the oil pressure change rate is large, generally between 0.04MPa / s and 0.06MPa / s. These data can be used to judge the adaptability of the oil pressure sensor in different formations and different working conditions. Finally, the above calculation results are combined to obtain the comprehensive oil pressure detection results.

[0040] A comprehensive detection method for a petroleum logging sensor is provided. The comprehensive detection method for a petroleum logging sensor is performed based on the above-mentioned comprehensive detection system for a petroleum logging sensor and comprises the following steps: S1: Obtain the temperature and pressure data in the logging wellbore, calculate the temperature-pressure coupling influence factor according to the temperature change rate and the pressure change rate, perform temperature-pressure sensitivity compensation according to the influence factor, and obtain the temperature-pressure sensitivity compensation result; S2: Based on the temperature and pressure sensitivity compensation results, the amplitude change and phase offset of the oil pressure sensor's measurement signal are analyzed, the compensation parameters of the interference influence are calculated according to the change and offset, and the measurement pressure is iteratively adjusted to obtain the electromagnetic interference compensation result; S3: Based on the electromagnetic interference compensation result, the load change of the oil pressure sensor during the logging process is obtained, the influence coefficient of the load change on the measured pressure is calculated, and the measured pressure is corrected by the influence coefficient to obtain the corrected oil pressure measurement result; S4: Based on the corrected oil pressure measurement result, analyzing the stability of the oil pressure measurement data of the oil pressure sensor in multiple logging cycles, and obtaining the oil pressure data analysis result; S5: Based on the oil pressure data analysis results, the adaptability of the oil pressure sensor in each logging environment is evaluated to obtain the comprehensive oil pressure detection results.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A comprehensive detection system for petroleum logging sensors, characterized in that: The system comprises: The environmental parameter calibration module obtains the temperature and pressure data in the logging wellbore, calculates the temperature-pressure coupling influence factor according to the temperature change rate and the pressure change rate, performs temperature-pressure sensitivity compensation according to the influence factor, and obtains the temperature-pressure sensitivity compensation result; The interference feature decomposition module analyzes the amplitude change and phase shift of the measurement signal of the oil pressure sensor based on the temperature and pressure sensitivity compensation result, calculates the compensation parameters of the interference influence according to the change and shift, and iteratively adjusts the measurement pressure to obtain the electromagnetic interference compensation result; The load change adjustment module obtains the load change of the oil pressure sensor during the logging process based on the electromagnetic interference compensation result, calculates the influence coefficient of the load change on the measured pressure, corrects the measured pressure according to the influence coefficient, and obtains the corrected oil pressure measurement result; The measurement data consistency analysis module analyzes the stability of the oil pressure measurement data of the oil pressure sensor in multiple logging cycles based on the corrected oil pressure measurement results to obtain oil pressure data analysis results; The comprehensive detection module evaluates the adaptability of the oil pressure sensor in each well logging environment based on the oil pressure data analysis result, and obtains the oil pressure comprehensive detection result.

2. The comprehensive detection system for petroleum logging sensors according to claim 1 is characterized in that: The temperature and pressure sensitivity compensation results specifically include temperature compensation coefficient, pressure compensation coefficient, sensitivity adjustment parameter, and voltage correction parameter. The electromagnetic interference compensation results include interference frequency distribution parameters, signal phase offset value, amplitude correction coefficient, and measurement pressure correction value. The corrected oil pressure measurement results specifically refer to load influence correction amount, measurement error correction value, and dynamic adjustment parameter. The oil pressure data analysis results include measurement pressure fluctuation range, time series stability parameter, data drift coefficient, and periodic deviation judgment value. The comprehensive oil pressure detection results specifically include measurement response rate, environmental adaptability parameter, oil pressure measurement reliability index, and working condition evaluation parameter.

3. The comprehensive detection system for petroleum logging sensors according to claim 2 is characterized in that: The environmental parameter calibration module comprises: The data acquisition submodule obtains the temperature data and pressure data in the logging wellbore based on the temperature sensor and the oil pressure sensor, calculates the temperature change rate and the pressure change rate in the specified time period, and obtains the temperature change rate and the pressure change rate data set; The temperature-pressure coupling effect calculation submodule is based on the temperature change rate and pressure change rate data set and uses the formula: ； Calculate the temperature-pressure coupling influence factor ; in, It is The temperature value at a time point, The previous time point The temperature value, is the pressure value at the previous time point, is the number of data points in the selected time window; The compensation parameter calculation submodule is based on the temperature-pressure coupling influencing factor and adopts the formula: ； Calculate the pressure compensation parameters of the oil pressure sensor , apply the compensation parameters to the current oil pressure sensor measurement reference pressure value to obtain the temperature and pressure sensitivity compensation result; in, It is The pressure measurement offset value within a time window, It is The temperature-pressure coupling influencing factor of the time window is: is the total number of selected time windows.

4. The comprehensive detection system for petroleum logging sensors according to claim 3 is characterized in that: The interference feature decomposition module comprises: The signal frequency division processing submodule obtains the measurement signal and electromagnetic environment parameters of the oil pressure sensor based on the temperature and pressure sensitivity compensation result, analyzes the spectrum information of the measurement signal, divides the measurement signal into multiple frequency bands, calculates the amplitude change and phase shift of each frequency band, compares the energy change of each frequency band, quantifies the influence of the external electromagnetic environment on the oil pressure sensor signal, and generates the spectrum characteristics of the measurement signal; The interference compensation parameter calculation submodule adopts the formula based on the spectrum characteristics of the measurement signal: ； Calculation of interference effect compensation parameters ; in, Representative The measured signal amplitude of the frequency band, Representative The reference signal amplitude of the frequency band, Representative The measured signal phase angle of the frequency band, Representative The reference signal phase angle of the frequency band, Representative The center frequency of the band, Represents the total number of frequency bands; The measurement pressure adjustment submodule calls the interference influence compensation parameter, combines the current oil pressure sensor measurement value, gradually corrects the measurement pressure value, suppresses the signal interference component, optimizes the measurement signal quality, and obtains the electromagnetic interference compensation result.

5. The comprehensive detection system for petroleum logging sensors according to claim 4 is characterized in that: The load change adjustment module comprises: The load change rate and pressure offset analysis submodule obtains the load change rate of the oil pressure sensor during the logging process based on the electromagnetic interference compensation result, calculates the pressure offset caused by the equipment load change during the downhole pressure transmission process within a specified time, and obtains the load change rate and pressure offset information; The load influence coefficient calculation submodule uses the formula based on the load change rate and pressure offset information: ； Calculate the load change influence factor ; in, Indicates the pressure measurement value With load change rate The covariance between Indicates the variance of the load change rate; The measurement pressure error correction submodule corrects the measurement pressure error through the load change influence coefficient to obtain a corrected oil pressure measurement result.

6. The comprehensive detection system for petroleum logging sensors according to claim 5 is characterized in that: The measurement data consistency analysis module includes: The pressure fluctuation calculation submodule obtains the measured pressure sequence of the oil pressure sensor in multiple logging cycles based on the corrected oil pressure measurement results, extracts the measured pressure values ​​at multiple times at the same depth, and uses the formula: ； Calculate the pressure fluctuation value at the current measuring depth ; in, Representative The measured pressure at all times, Represents the average pressure at a specified measurement depth, Represents the number of measurements within the measurement cycle; The oil pressure data stability analysis submodule calls the pressure fluctuation value of the current measurement depth, analyzes the stability of the oil pressure measurement data in the time dimension, determines whether the measured pressure in the logging cycle changes continuously over time, and obtains the oil pressure data analysis result.

7. The comprehensive detection system for petroleum logging sensors according to claim 6 is characterized in that: The comprehensive detection module comprises: The oil pressure measurement response calculation submodule obtains the oil pressure sensor measurement data in the whole logging process based on the oil pressure data analysis results, extracts the oil pressure measurement values ​​of all measurement points, organizes the data according to the logging time sequence, divides the measurement stages according to various logging conditions, calculates the oil pressure change rate in each stage, and obtains the oil pressure measurement response rate data set; The oil pressure sensor adaptability evaluation submodule analyzes the response rate changes under various logging environments based on the oil pressure measurement response rate data set, screens the measuring point data within the adaptation range, compares the oil pressure measurement response rate in combination with the logging condition category, and obtains the comprehensive oil pressure detection result.

8. A comprehensive detection method for petroleum logging sensors, characterized in that: The comprehensive detection system for petroleum logging sensors according to any one of claims 1 to 7 comprises the following steps: S1: Obtain the temperature and pressure data in the logging wellbore, calculate the temperature-pressure coupling influence factor according to the temperature change rate and the pressure change rate, perform temperature-pressure sensitivity compensation according to the influence factor, and obtain the temperature-pressure sensitivity compensation result; S2: Based on the temperature and pressure sensitivity compensation result, analyze the amplitude change and phase shift of the measurement signal of the oil pressure sensor, calculate the compensation parameters of the interference influence according to the change and shift, and iteratively adjust the measurement pressure to obtain the electromagnetic interference compensation result; S3: based on the electromagnetic interference compensation result, obtaining the load change of the oil pressure sensor during the logging process, calculating the influence coefficient of the load change on the measured pressure, correcting the measured pressure according to the influence coefficient, and obtaining the corrected oil pressure measurement result; S4: Based on the corrected oil pressure measurement result, analyzing the stability of the oil pressure measurement data of the oil pressure sensor in multiple logging cycles to obtain an oil pressure data analysis result; S5: Based on the oil pressure data analysis results, the adaptability of the oil pressure sensor in each logging environment is evaluated to obtain a comprehensive oil pressure detection result.

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