An integrated detection system and method for oil well logging sensors

Abstract

The present invention relates to the technical field of sensor detection, and specifically to an integrated detection system and method for oil well logging sensors. In the present invention, by acquiring the temperature and pressure data in the well logging wellbore and calculating the temperature change rate and pressure change rate, the influence of the environment on the measurement data can be accurately evaluated, and then the compensation for the coupling influence of temperature and pressure can be realized, significantly improving the measurement accuracy. On this basis, further combining the amplitude change and phase shift of the measurement signal, extracting the interference characteristics of the electromagnetic environment on the measurement signal, and optimizing the signal by calculating the interference influence parameter, the reliability and anti-interference ability of the measurement data are enhanced. At the same time, the influence of the change in load on the sensor during the well logging process is analyzed, the deviation of the pressure measurement value is identified, and the measurement error is corrected using the influence coefficient, making the measurement data more stable. The comparison and analysis of multi-period measurement data can identify the long-term drift of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor detection, and in particular to an integrated detection system and method for oil well logging sensors. Background Art

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

[0003] Among them, an integrated detection system for oil well logging sensors refers to a system used for comprehensive testing and calibration of oil well logging sensors. The system conducts comparative analysis on the electrical signals collected by the sensors through high-precision measurement equipment, and evaluates its sensitivity, linearity, and stability based on a standard measurement model. By adopting multi-dimensional environmental simulation technology, the system can test the sensor performance under different temperature, pressure, and electromagnetic interference conditions, and at the same time calibrate the measurement deviation of the sensor in combination with specific well logging parameters.

[0004] Well logging sensors are affected by multiple factors such as temperature, pressure, electromagnetic environment, and load changes in actual applications. The existing technologies mainly rely on single-dimensional compensation means, resulting in large deviations in measurement data under complex environmental conditions. Temperature and pressure compensation is usually based on static temperature and pressure data, ignoring the temperature-pressure coupling effect, resulting in poor compensation effect in a dynamically changing environment. At the same time, the influence of electromagnetic interference is often processed by simple filtering methods, 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 measurement accuracy. In addition, the existing methods do not effectively correct the pressure measurement error caused by load changes, resulting in sensor measurement data being prone to drift under the conditions of large vibration or pressure fluctuation of downhole equipment, affecting the stability of well logging results. In terms of the analysis of measurement data consistency, the existing technologies mainly rely on single measurement data for evaluation, ignoring the change trend of long-term measurement data, making the reliability of measurement data in long-term well logging tasks relatively low, and it is difficult to effectively determine the long-term adaptability of well logging equipment. In a complex well logging environment, due to the lack of systematic analysis of the measurement characteristics in different well logging stages, the existing technologies are difficult to accurately evaluate the adaptability of well logging sensors under different working conditions, resulting in the credibility of measurement data being difficult to guarantee. Summary of the Invention

[0005] The object of the present invention is to solve the disadvantages existing in the prior art, and to propose an integrated detection system and method for oil well logging sensors.

[0006] To achieve the above object, the present invention adopts the following technical solution: An integrated detection system for oil well logging sensors includes:

[0007] 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, and performs temperature-pressure sensitivity compensation according to the influence factor to obtain the temperature-pressure sensitivity compensation result;

[0008] 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-pressure sensitivity compensation result, calculates the compensation parameter for the interference influence according to the change and shift conditions, and performs iterative adjustment of the measured pressure to obtain the electromagnetic interference compensation result;

[0009] The load change adjustment module obtains the load change suffered by the oil pressure sensor during logging based on the electromagnetic interference compensation result, calculates the influence coefficient of the load change on the measured pressure, and corrects the measured pressure through the influence coefficient to obtain the corrected oil pressure measurement result;

[0010] 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 result to obtain the oil pressure data analysis result;

[0011] The comprehensive detection module evaluates the adaptability of the oil pressure sensor in each logging environment based on the oil pressure data analysis result to obtain the oil pressure comprehensive detection result.

[0012] As a further solution of the present invention, the temperature-pressure sensitivity compensation result specifically includes a temperature compensation coefficient, a pressure compensation coefficient, a sensitivity adjustment parameter, and a voltage correction parameter. The electromagnetic interference compensation result includes interference frequency distribution parameters, signal phase shift values, amplitude correction coefficients, and measured pressure correction values. The corrected oil pressure measurement result specifically refers to a load influence correction amount, a measurement error correction value, and a dynamic adjustment parameter. The oil pressure data analysis result includes a measured pressure fluctuation range, a time series stability parameter, a data drift coefficient, and a periodic deviation determination value. The oil pressure comprehensive detection result specifically includes a measurement response rate, an environmental adaptability parameter, an oil pressure measurement reliability index, and a working condition evaluation parameter.

[0013] As a further solution of the present invention, the environmental parameter calibration module includes:

[0014] The data acquisition sub-module, based on temperature sensors and oil pressure sensors, obtains temperature data and pressure data in the logging wellbore, calculates the temperature change rate and pressure change rate within a specified time period, and obtains a data set of temperature change rate and pressure change rate;

[0015] The temperature-pressure coupling influence calculation sub-module, based on the data set of the temperature change rate and pressure change rate, uses the formula:

[0016] ;

[0017] Calculate the temperature-pressure coupling influence factor ;

[0018] where, is the temperature value at the th time point, is the temperature value at the previous time point ; is the pressure value at the previous time point, is the number of data points within the selected time window;

[0019] The compensation parameter calculation sub-module, based on the temperature-pressure coupling influence factor, uses the formula:

[0020] ;

[0021] Calculate the pressure compensation parameter of the oil pressure sensor , and apply the compensation parameter to the current measured reference pressure value of the oil pressure sensor to obtain the temperature-pressure sensitivity compensation result;

[0022] where, is the pressure measurement offset value within the th time window, is the temperature-pressure coupling influence factor of the th time window, is the total number of selected time windows.

[0023] As a further solution of the present invention, the interference feature decomposition module includes:

[0024] The signal frequency division processing sub-module, based on the temperature-pressure sensitivity compensation result, obtains the measurement signal of the oil pressure sensor and the electromagnetic environment parameters, 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 influence of the external electromagnetic environment on the signal of the oil pressure sensor, and generates the spectrum feature of the measurement signal;

[0025] The interference compensation parameter calculation sub-module, based on the spectrum feature of the measurement signal, uses the formula:

[0026] ;

[0027] Calculate the interference impact compensation parameter ;

[0028] Wherein, represents the amplitude of the measurement signal in the th frequency band, represents the amplitude of the reference signal in the th frequency band, represents the phase angle of the measurement signal in the th frequency band, represents the phase angle of the reference signal in the th frequency band, represents the center frequency of the th frequency band, represents the total number of frequency bands;

[0029] The measurement pressure adjustment sub-module calls the interference impact compensation parameter, combines it with the current oil pressure sensor measurement value, gradually corrects the measurement pressure value, optimizes the measurement signal quality by suppressing the signal interference component, and obtains the electromagnetic interference compensation result.

[0030] As a further solution of the present invention, the load change adjustment module includes:

[0031] The load change rate and pressure offset analysis sub-module obtains the load change rate suffered by the oil pressure sensor during the logging process based on the electromagnetic interference compensation result, calculates the pressure offset generated by the equipment load change during the downhole pressure transmission within a specified time, and obtains the load change rate and pressure offset information;

[0032] The load impact coefficient calculation sub-module is based on the load change rate and pressure offset information, and uses the formula:

[0033] ;

[0034] Calculate the load change impact coefficient ;

[0035] Wherein, represents the pressure measurement value and the load change rate covariance between them, represents the variance of the load change rate;

[0036] The measurement pressure error correction sub-module corrects the measurement pressure error through the load change impact coefficient to obtain the corrected oil pressure measurement result.

[0037] As a further solution of the present invention, the measurement data consistency analysis module includes:

[0038] Based on the corrected oil pressure measurement results, the pressure fluctuation calculation sub-module obtains the measurement pressure sequence of the oil pressure sensor within multiple logging cycles, extracts the measurement pressure values at multiple moments at the same depth, and uses the formula:

[0039] ;

[0040] Calculate the pressure fluctuation value at the current measurement depth ;

[0041] where, represents the measured pressure at the th moment, represents the average pressure at the specified measurement depth, represents the number of measurements within the measurement cycle;

[0042] The oil pressure data stability analysis sub-module calls the pressure fluctuation value at the current measurement depth, analyzes the stability of the oil pressure measurement data in the time dimension, determines whether the measured pressure during the logging cycle changes continuously over time, and obtains the oil pressure data analysis result.

[0043] As a further solution of the present invention, the comprehensive detection module includes:

[0044] Based on the oil pressure data analysis result, the oil pressure measurement response calculation sub-module obtains the oil pressure sensor measurement data during the entire logging process, 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 working conditions, calculates the oil pressure change rate of each stage, and obtains the oil pressure measurement response rate data set;

[0045] Based on the oil pressure measurement response rate data set, the oil pressure sensor adaptability evaluation sub-module analyzes the change of the response rate in various logging environments, screens the measurement point data within the adaptation range, compares the oil pressure measurement response rate in combination with the logging working condition category, and obtains the oil pressure comprehensive detection result.

[0046] An integrated detection method for oil well logging sensors, the integrated detection method for oil well logging sensors is executed based on the above-mentioned integrated detection system for oil well logging sensors, and includes the following steps:

[0047] 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, and perform temperature-pressure sensitivity compensation according to the influence factor to obtain the temperature-pressure sensitivity compensation result;

[0048] S2: Based on the temperature-pressure sensitivity compensation result, analyze the amplitude change and phase shift of the measurement signal of the oil pressure sensor, calculate the compensation parameters for the interference influence according to the change and shift conditions, and perform iterative adjustment of the measured pressure to obtain the electromagnetic interference compensation result;

[0049] S3: Based on the electromagnetic interference compensation result, obtain the load change suffered by the oil pressure sensor during well logging, calculate the influence coefficient of the load change on the measured pressure, and correct the measured pressure through the influence coefficient to obtain the corrected oil pressure measurement result;

[0050] S4: Based on the corrected oil pressure measurement result, analyze the stability of the oil pressure measurement data of the oil pressure sensor within multiple well logging cycles to obtain the oil pressure data analysis result;

[0051] S5: Based on the oil pressure data analysis result, evaluate the adaptability of the oil pressure sensor in each well logging environment to obtain the comprehensive oil pressure detection result.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0053] In the present invention, by obtaining the temperature and pressure data in the well logging wellbore and calculating the temperature change rate and pressure change rate, it is possible to accurately evaluate the influence of the environment on the measurement data, and then realize the compensation of the temperature-pressure coupling influence, significantly improving the measurement accuracy. On this basis, further combining the amplitude change and phase shift of the measurement signal, extracting the interference characteristics of the electromagnetic environment on the measurement signal, and optimizing the signal by calculating the interference influence parameter, the reliability and anti-interference ability of the measurement data are enhanced. At the same time, analyze the influence of the load change on the sensor during well logging, identify the deviation of the pressure measurement value, and correct the measurement error using the influence coefficient to make the measurement data more stable. The comparative analysis of the measurement data in multiple cycles can identify the long-term drift of the data, avoid the accumulation of measurement result errors caused by environmental changes, and effectively ensure the measurement stability. In addition, based on the data analysis of the entire measurement process, extracting the oil pressure change characteristics in different well logging stages and combining with the well logging environment parameters to evaluate the measurement response rate and sensor adaptability, further improving the reliability of the system under complex working conditions. Description of the Drawings

[0054] Figure 1 is the system flow chart of the present invention;

[0055] Figure 2 is the flow chart of the environmental parameter calibration module of the present invention;

[0056] Figure 3 is the flow chart of the interference feature decomposition module of the present invention;

[0057] Figure 4 is the flow chart of the load change adjustment module of the present invention;

[0058] Figure 5 is the flow chart of the measurement data consistency analysis module of the present invention;

[0059] Figure 6 This is the flowchart of the comprehensive detection module of the present invention. Specific embodiments

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present 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 only used to explain the present invention and are not used to limit the present invention.

[0061] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is 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 should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0062] Please refer to Figure 1 , the present invention provides a technical solution: an integrated detection system for oil well logging sensors includes:

[0063] The environmental parameter calibration module obtains the temperature and pressure data in the logging wellbore through the temperature sensor and the oil pressure sensor, calculates the temperature change rate and the pressure change rate within a 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 action relationship between the influence factor and the measurement sensitivity of the pressure sensor, calls the sliding window to calculate the offset value of the pressure measurement in the time series, calculates the compensation parameter based on 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;

[0064] Based on the temperature-pressure sensitivity compensation result, 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 change and phase shift of multiple frequency bands, calculates the compensation parameter for the interference influence according to the change and shift conditions, and performs iterative adjustment of the measured pressure of the oil pressure sensor to obtain the electromagnetic interference compensation result;

[0065] Based on the electromagnetic interference compensation result, the load change adjustment module obtains the load change rate suffered by the oil pressure sensor during well logging, calculates the pressure offset generated by the equipment load change during the downhole pressure transmission within a specified time, determines the correlation between the load change rate and the pressure measured by the oil pressure sensor, calculates the influence coefficient of the load change on the pressure measured by the oil pressure sensor according to the correlation, corrects the measurement pressure error through the influence coefficient, and obtains the corrected oil pressure measurement result;

[0066] Based on the corrected oil pressure measurement result, the measurement data consistency analysis module obtains the measurement pressure sequence of the oil pressure sensor in multiple well logging cycles, 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 result;

[0067] Based on the oil pressure data analysis result, the comprehensive detection module obtains the measurement pressure sequence of the oil pressure sensor during the entire well logging process, calculates the oil pressure measurement response rate under various working conditions during well logging, evaluates the adaptability of the oil pressure sensor in each well logging environment, and obtains the oil pressure comprehensive detection result;

[0068] The temperature and pressure sensitivity compensation result specifically includes a temperature compensation coefficient, a pressure compensation coefficient, a sensitivity adjustment parameter, and a voltage correction parameter. The electromagnetic interference compensation result includes interference frequency distribution parameters, signal phase offset values, amplitude correction coefficients, and measurement pressure correction values. The corrected oil pressure measurement result specifically refers to a load influence correction amount, a measurement error correction value, and a dynamic adjustment parameter. The oil pressure data analysis result includes a measurement pressure fluctuation range, time series stability parameters, a data drift coefficient, and a periodic deviation determination value. The oil pressure comprehensive detection result specifically includes a measurement response rate, environmental adaptability parameters, an oil pressure measurement reliability index, and a working condition evaluation parameter.

[0069] Please refer to Figure 2 , the environmental parameter calibration module includes:

[0070] Based on the temperature sensor and the oil pressure sensor, the data acquisition sub-module obtains the temperature data and pressure data in the well logging wellbore, calculates the temperature change rate and pressure change rate within a specified time period, and obtains the temperature change rate and pressure change rate data sets;

[0071] Based on temperature sensors and oil pressure sensors, multiple measurement points are deployed inside the logging wellbore. Each measurement point records temperature and pressure data at specific time intervals, for example, data is collected every 5 seconds, to obtain complete time series data. The collected data is stored in a data management system, where the initial temperature and pressure of each measurement point serve as reference benchmark values. During the data collection process, the sensors may be affected by environmental interference, such as the impact of fluid flow in the wellbore. Therefore, it is necessary to smooth the data, for example, using the moving average method to reduce data noise. When calculating the temperature change rate and pressure change rate within a specified time period, a 10-minute time window is used to calculate the temperature change amount between consecutive multiple time points , and the pressure change amount , and then calculate the temperature change rate and the pressure change rate , and their calculation methods are as follows: , where : Temperature change rate, representing the average change rate of temperature per unit time; : Pressure change rate, representing the average change rate of pressure per unit time; : The temperature change amount at the th time point; : The pressure change amount at the th time point; : The temperature value at the th time point; : The pressure value at the th time point; : The number of data points within the selected time window. For example, if data is collected every 5 seconds under a 10-minute window, then , if the initial temperature is 80°C and the temperature rises to 85°C after 10 minutes, then calculate the temperature change rate , and similarly calculate the pressure change rate. If the initial pressure is 20 MPa and the pressure rises to 22 MPa after 10 minutes, then , and finally obtain the temperature change rate and pressure change rate data sets.

[0072] The temperature-pressure coupling effect calculation sub-module is based on the temperature change rate and pressure change rate data sets and uses the formula:

[0073] ;

[0074] Calculate the temperature-pressure coupling influence factor ;

[0075] where is the temperature value at the th time point; is the temperature value at the previous time point ; is the absolute value of the normalized temperature change between adjacent time points and ; is the pressure value at the previous time point is the absolute value of the normalized pressure change between adjacent time points and ; is the number of data points within the selected time window

[0076] If the temperature range in the logging dataset is 70 - 90 °C and the pressure range is 15 - 25 MPa. At a certain moment, the temperature is 85 °C and the pressure is 22 MPa, perform normalization calculations: , .

[0077] 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, 0.78 in sequence, and the normalized pressures are 0.65, 0.67, 0.69, 0.72, 0.74 in sequence. Calculate the absolute value of the change between adjacent time points: , .

[0078] Calculate the temperature - pressure coupling influence factor: ;

[0079] Finally, calculate the temperature - pressure coupling influence factor , which is used for compensation calculation

[0080] The compensation parameter calculation sub - module is based on the temperature - pressure coupling influence factor and uses the formula:

[0081] ;

[0082] Calculate the pressure compensation parameter of the oil pressure sensor , and apply the compensation parameter to the current measured reference pressure value of the oil pressure sensor to obtain the temperature - pressure sensitivity compensation result

[0083] Among them, represents the weighted average of the pressure measurement offset within different time windows is the pressure measurement offset value within the th time window is the temperature - pressure coupling influence factor within the th time window is the total number of selected time windows

[0084] Call the data of the temperature-pressure coupling influence factor, and calculate the offset value of the pressure measurement in the time series based on a sliding window. The size of the sliding window is set to 30 minutes, that is, calculate the pressure measurement offset within this time period. , where is the measured pressure value within the th time window, representing the pressure value recorded by the sensor at the end of the time window , with the unit of MPa; is the measured pressure value at the end of the previous time window , that is, the pressure value at the start of the time window , with the unit of MPa. For example, within the first 30-minute window, the pressure changes from 18 MPa to 18.5 MPa, then , if the pressure in the subsequent window changes to 19.2 MPa, then . Assume that three time windows are selected, and their pressure offsets are 0.5 MPa, 0.7 MPa, and 0.4 MPa respectively, and the corresponding temperature-pressure coupling influence factors are 0.8, 0.6, and 0.9 respectively. Calculate the compensation parameter: , calculate the pressure compensation value. Assume that the pressure measured by the current oil pressure sensor is 20 MPa, and the compensated pressure is calculated as follows: , and finally obtain the pressure value of the calibrated oil pressure sensor as 19.487 MPa, reducing the influence of temperature on the measurement and improving the data accuracy.

[0085] Please refer to Figure 3 , the interference feature decomposition module includes:

[0086] The signal frequency division processing sub-module, based on the temperature-pressure sensitivity compensation result, obtains the measurement signal of the oil pressure sensor and the electromagnetic environment parameters, 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 influence of the external electromagnetic environment on the signal of the oil pressure sensor, and generates the spectrum features of the measurement signal;

[0087] Obtain the measurement signal of the oil pressure sensor and the relevant parameters of its electromagnetic environment. Use a high-precision data acquisition device to record the original waveform data of the measurement signal. Set the sampling frequency to 100 kHz to ensure sufficient time resolution for subsequent spectrum analysis. At the same time, synchronously record the real-time electromagnetic field intensity of the external electromagnetic environment. Use the short-time Fourier transform (STFT) to decompose the measurement signal, splitting it into multiple different frequency components. Set the time window length to 20 ms and the window moving step size to 5 ms to ensure that the signal spectrum analysis can obtain sufficient frequency detail information in different time periods. Calculate the energy of the decomposed frequency signals to obtain the energy distribution of each frequency band. By calculating the change trend of the signal amplitude over time, obtain the amplitude change rate of each frequency band, and by calculating the time change of the phase angle, obtain the phase offset. Perform normalization processing on the amplitude changes and phase drifts of different frequencies to eliminate the differences in signal energy levels of different frequency bands. Calculate the normalized amplitude change trend and phase drift trend, and finally quantify the impact of the external electromagnetic environment on the measurement signal of the oil pressure sensor to obtain the spectral characteristics of the measurement signal.

[0088] The interference compensation parameter calculation sub-module, based on the spectral characteristics of the measurement signal, uses the formula:

[0089] ;

[0090] Calculate the interference impact compensation parameter ;

[0091] Where represents the amplitude of the measurement signal in the frequency band (unit: V); represents the amplitude of the reference signal in the frequency band (unit: V); represents the phase angle of the measurement signal in the frequency band (unit: °); represents the phase angle of the reference signal in the frequency band (unit: °); represents the center frequency of the frequency band (unit: Hz); represents the total number of frequency bands; represents the square of the amplitude deviation between the measurement signal and the reference signal; represents the square of the phase offset between the measurement signal and the reference signal; represents the normalized frequency band interference impact factor.

[0092] Set the analysis frequency range from 10 Hz to 10 MHz, divided into 10 different frequency bands, and calculate the center frequency of each frequency band And the corresponding signal change trend, the normalization method is used to adjust the spectral amplitude and phase to reduce the excessive influence of individual frequency bands, calculate the interference correction coefficient, using the formula:

[0093] ;

[0094] Set N = 10 and substitute the actual values for calculation. Assume 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. Then the interference influence factor of this frequency band is calculated as follows:

[0095] ;

[0096] Sum up the calculation results of all frequency bands to obtain the total interference compensation parameter , and finally based on the calculation results of different frequency bands, establish the interference influence distribution of the entire frequency band and generate the interference influence compensation parameter data.

[0097] The measurement pressure adjustment sub-module calls the interference influence compensation parameter, combines it with the current measured value of the oil pressure sensor, gradually corrects the measured pressure value, optimizes the measurement signal quality by suppressing the interference component of the signal, and obtains the electromagnetic interference compensation result;

[0098] Call the interference influence compensation parameter data, combine it with the current measured value of the oil pressure sensor, establish a dynamic adjustment mechanism, use the iterative calculation method to correct the measured pressure value, and apply the interference compensation parameter to the correction of the measured data of the oil pressure sensor. The correction formula is set as follows: . Assume that the current measured pressure value of the oil pressure sensor is 20.5MPa, and the interference compensation parameter calculated by the previous sub-module, then the corrected pressure value is calculated as follows: . Use the filtering algorithm to suppress specific interference components, adjust the influence weights of different frequency bands, smooth the corrected values at multiple time points, set the sliding window size to 5min, calculate the average pressure within the current window, avoid the influence of instantaneous interference on the measurement result, and finally obtain the electromagnetic interference compensation result.

[0099] Please refer to Figure 4 , the load change adjustment module includes:

[0100] The load change rate and pressure offset analysis sub-module, based on the electromagnetic interference compensation result, obtains the load change rate suffered by the oil pressure sensor during the logging process, calculates the pressure offset generated by the equipment load change during the downhole pressure transmission within the specified time, and obtains the load change rate and pressure offset information;

[0101] Obtain the measurement data of the oil pressure sensor during the logging process, including the pressure measurement values and equipment load data in the time series. Set the sampling interval to 5 s to ensure sufficient sampling density. Record the equipment load value at each time point and calculate the load change amount between adjacent time points. Set the observation window to 10 minutes (600 s), collect a total of 120 load data points, extract the load changes between adjacent time points, calculate the cumulative change value and normalize it to the number of measurement points, and calculate the load change rate. Use the formula: , where represents the average load change rate (N / s) within the measurement window, represents the equipment load value (N) at the th moment, represents the equipment load value (N) at the th moment, represents the number of measurement points within the measurement window. Assume the initial load , and the final load , the calculation is as follows: After calculating the load change rate, analyze the transmission process of the downhole pressure under the condition of equipment load change, extract the measured pressure data of the oil pressure sensor, and combine the load change rate to calculate the pressure offset . Set the transmission time of the logging equipment to , and define the pressure offset calculation formula as follows: , where represents the pressure offset (MPa), is the calculated load change rate (N / s), is the pressure transmission time (s) of the downhole equipment. Assume the transmission time during the logging process, then the pressure offset is calculated as follows: Finally, calculate the load change rate and pressure offset for subsequent calculation of the load influence coefficient.

[0102] Based on the load change rate and pressure offset information, the load influence coefficient calculation sub-module uses the formula:

[0103] ;

[0104] Calculate the load change influence coefficient ;

[0105] where represents the covariance between the pressure measurement value and the load change rate , represents the variance of the load change rate.

[0106] For calculating the influence coefficient of the change in computational load on the measured pressure of the oil pressure sensor, the formula is used:

[0107] ;

[0108] where represents the measured pressure value (MPa) at the th moment, represents the average pressure value (MPa) within the measurement window, represents the load change rate (N / s) at the th moment, represents the average load change rate (N / s) within the measurement window, represents the total number of measurement points.

[0109] If within a 10-minute window, the average measured pressure MPa, and the average load change rate N / s, the data for some measurement points are set as follows: MPa, N / s; MPa, N / s; MPa, N / s. When calculating , calculate the deviation term for each measurement point and substitute it into the formula: ;

[0110] Finally, calculate the load influence coefficient , which is used for subsequent error correction of the measured pressure to obtain the load influence coefficient data.

[0111] The measured pressure error correction sub-module corrects the measured pressure error through the load change influence coefficient to obtain the corrected oil pressure measurement result;

[0112] Call the load influence coefficient data, combine it with the current measured pressure of the oil pressure sensor, perform error correction, and calculate the pressure correction value according to the load influence coefficient , adjust the current measured value, and use the formula: , where : the corrected measured pressure (MPa), that is, the true oil pressure measurement value after being corrected by the load change influence; : the original measured pressure (MPa), that is, the pressure value recorded by the oil pressure sensor before error correction. Assume that the current measured pressure is 20.5 MPa, the calculated load change influence coefficient , and the load change rate N / s, then the corrected pressure value is calculated as follows: , finally, the corrected oil pressure measurement result is obtained to improve the measurement accuracy of the oil pressure sensor under load change conditions.

[0113] Please refer to Figure 5 , the measurement data consistency analysis module includes:

[0114] Based on the corrected oil pressure measurement result, the pressure fluctuation calculation sub-module obtains the measurement pressure sequence of the oil pressure sensor within multiple logging cycles, extracts the measurement pressure values at multiple moments at the same depth, and uses the formula:

[0115] ;

[0116] Calculate the pressure fluctuation value at the current measurement depth ;

[0117] Among them, represents the measured pressure (MPa) at the th moment, represents the average pressure (MPa) at this measurement depth, represents the number of measurements within the measurement cycle.

[0118] If the measurement depth is 1000m and the measurement cycle is 24h, and the pressure values at the measurement points are MPa, MPa, MPa, and the average pressure MPa at the measurement depth, then the calculation is as follows: . Finally, the pressure fluctuation value is obtained for subsequent data stability analysis.

[0119] The oil pressure data stability analysis sub-module calls the pressure fluctuation value at the current measurement depth, analyzes the stability of the oil pressure measurement data in the time dimension, judges whether the measured pressure changes continuously over time, and obtains the oil pressure data analysis result;

[0120] First, obtain the measured pressure data within multiple logging cycles, construct a time series data set, set the measurement cycle to 24 hours, extract data at 1-hour intervals to ensure the integrity and comparability of the data. Subsequently, calculate the pressure change trend within the measurement cycle, use the sliding window smoothing method to eliminate measurement noise, set the sliding window size to 3h, that is, take the average value of the data 1 hour before and after each time point of the measured pressure as the smoothed pressure value to ensure the continuity of the data, and then judge whether there is a drift phenomenon in the measured pressure. Define the drift threshold MPa. That is, if the measured pressure changes within 5 consecutive hours are all greater than this threshold, it is considered that there is a data drift phenomenon. Further analyze the periodic deviation, extract the measured pressure fluctuations within multiple logging cycles, set the periodic detection threshold to 0.2 MPa, calculate the pressure fluctuation amplitude within different logging cycles. If the pressure fluctuation amplitude of a certain cycle exceeds 10% of the average fluctuation amplitude of other cycles, it is considered that there is an abnormal deviation within this cycle. Subsequently, use the data comparison method to calculate the pressure change trend in different cycles. If the trend has a periodic repetition feature, it is judged that the data has a periodic deviation. Assume that at a measurement depth of 1000 m, the logging cycle is 24 h, and the initial pressure data of the measurement points are respectively MPa. After being smoothed by a sliding window, the data is adjusted to MPa. By comparing and analyzing the pressure change trends of different logging cycles, it is found that the pressure fluctuation amplitudes within two consecutive logging cycles are respectively and , and the deviation between them is , exceeding 10% of the periodic detection threshold. Therefore, it is determined that there is a periodic deviation within this logging cycle, and finally the oil pressure data analysis result is obtained.

[0121] Please refer to Figure 6 , the comprehensive detection module includes:

[0122] Based on the oil pressure data analysis result, the oil pressure measurement response calculation sub-module obtains the oil pressure sensor measurement data during the entire logging process, 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 of each stage, and obtains the oil pressure measurement response rate data set;

[0123] Obtain the measured data of the oil pressure sensor during the whole process of well logging, extract the oil pressure measurement values of all measurement points, organize the data according to the well logging time sequence, eliminate the data of abnormal measurement points, select the effective measurement points for calculation, divide the measurement stages according to different well logging working 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 well logging operation, the oil pressure sensor records the pressure value once per second. Assuming that a well 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 organization process, it is necessary to check whether there are any abnormalities in the measurement data. For example, if the pressure value suddenly jumps from 20 MPa to 100 MPa at a certain moment and then returns to 20 MPa the next second, this data point is obviously unreasonable. At this time, the method of setting upper and lower limits can be used to eliminate abnormal data. For example, the normal pressure range is set to 10 MPa to 50 MPa, and the measurement points outside this range are regarded as abnormal and deleted. After the data screening is completed, the measurement stages need to be divided according to the well logging working conditions. For example, during the drilling stage, the measured oil pressure gradually rises from 15 MPa to 30 MPa, lasting for 300 seconds. During the tripping stage, the oil pressure drops from 30 MPa to 18 MPa, lasting for 200 seconds. And during the drilling stop stage, the oil pressure is basically stable at about 20 MPa, lasting for 500 seconds. Subsequently, calculate the oil pressure change rate of each stage. For example, during the drilling stage, the pressure rises from 15 MPa to 30 MPa, lasting for 300 seconds. Therefore, its oil pressure change rate is (30 - 15) / 300 = 0.05 MPa / s. During the tripping stage, the oil pressure drops from 30 MPa to 18 MPa, lasting for 200 seconds, then the change rate is (30 - 18) / 200 = 0.06 MPa / s. During the drilling stop stage, the pressure remains unchanged at 20 MPa, so the change rate is 0 MPa / s. Through the above steps, the oil pressure measurement response rate of each stage is obtained.

[0124] Based on the oil pressure measurement response rate data set, the oil pressure sensor adaptability evaluation sub-module analyzes the change of the response rate in various well logging environments, screens the measured point data within the adaptation range, compares the oil pressure measurement response rate in combination with the well logging working condition category, and obtains the comprehensive oil pressure detection result;

[0125] First, classify according to different logging environments. For example, it can be divided into sandstone layers, shale layers, and carbonate rock layers according to formation types, or divided into different categories such as 0 - 1000 meters, 1000 - 2000 meters, and 2000 - 3000 meters according to well depth ranges. Then, calculate the oil pressure measurement response rates in these categories respectively. For example, in the sandstone layer, the response rates of multiple measurement points are 0.04 MPa / s, 0.05 MPa / s, and 0.06 MPa / s respectively, and the calculated average value is 0.05 MPa / s. In the shale layer, the response rates of multiple measurement points are 0.03 MPa / s, 0.04 MPa / s, and 0.05 MPa / s respectively, and the average value is 0.04 MPa / s. In the carbonate rock layer, the measured response rates are 0.02 MPa / s, 0.03 MPa / s, and 0.04 MPa / s respectively, and the average value is 0.03 MPa / s. Subsequently, set the adaptive threshold range. For example, if the response rate is between 0.03 MPa / s and 0.06 MPa / s, it is considered to have good adaptability, and the measurement points outside this range are considered to have poor adaptability. Then, the measurement point data in the sandstone layer and the shale layer are within the adaptive range, while some measurement point data in the carbonate rock layer (such as 0.02 MPa / s) is in the range of poor adaptability. Next, compare the filtered oil pressure measurement response rates in combination with the logging working condition categories, and analyze the response characteristics under different working conditions. For example, during the drill - stop stage, the oil pressure change rates in each formation are generally low, between 0.01 MPa / s and 0.02 MPa / s, while during the drilling stage, the oil pressure change rates are larger, generally between 0.04 MPa / s and 0.06 MPa / s. Through these data, the adaptability of the oil pressure sensor under different formations and different working conditions can be judged. Finally, based on the above - mentioned calculation results, obtain the comprehensive oil pressure detection result.

[0126] A comprehensive detection method for oil well logging sensors. The comprehensive detection method for oil well logging sensors is executed based on the above - mentioned comprehensive detection system for oil well logging sensors, and includes the following steps:

[0127] 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, and perform temperature - pressure sensitivity compensation according to the influence factor to obtain the temperature - pressure sensitivity compensation result;

[0128] S2: Based on the temperature - pressure sensitivity compensation result, analyze the amplitude change and phase shift of the measurement signal of the oil pressure sensor, calculate the compensation parameters for the interference influence according to the change and shift conditions, and perform iterative adjustment of the measured pressure to obtain the electromagnetic interference compensation result;

[0129] S3: Based on the electromagnetic interference compensation result, obtain the load change suffered by the oil pressure sensor during well logging, calculate the influence coefficient of the load change on the measured pressure, and correct the measured pressure through the influence coefficient to obtain the corrected oil pressure measurement result;

[0130] S4: Based on the corrected oil pressure measurement result, analyze the stability of the oil pressure measurement data of the oil pressure sensor within multiple well logging cycles to obtain the oil pressure data analysis result;

[0131] S5: Based on the oil pressure data analysis result, evaluate the adaptability of the oil pressure sensor in each well logging environment to obtain the comprehensive oil pressure detection result.

[0132] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall 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 amplitude change and phase shift of the signal, 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 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 by using the load change influence coefficient to obtain a 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 1 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.

6. The comprehensive detection system for petroleum logging sensors according to claim 5 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.

7. 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 6 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, the amplitude change and phase shift of the measurement signal of the oil pressure sensor are analyzed, the compensation parameters of the interference influence are calculated according to the amplitude change and phase shift of the signal, and the measurement pressure is iteratively adjusted 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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