Koala correction device and method for comprehensive mud logging instrument thermal resistance temperature sensor output

Abstract

The application relates to the field of correction technology, and discloses a Koala correction device and method for the output of a thermal resistance temperature sensor of a comprehensive mud logging instrument, which comprises a data acquisition module, a temperature gradient calculation module, a bias gradient calculation module, a bias value derivation module and a correction value calculation module; and further comprises a thermal resistance temperature sensor and an auxiliary temperature sensor.The temperature gradient calculation module calculates a temperature gradient according to the temperature data measured by the auxiliary temperature sensor, and calculates an average temperature according to the temperature gradient.The bias gradient calculation module calculates a bias value gradient according to the temperature gradient, the average temperature and a temporary bias value of the thermal resistance temperature sensor.The correction value calculation module calculates the bias value of the thermal resistance temperature sensor according to the bias value gradient and the temperature gradient, and further corrects the output temperature of the thermal resistance temperature sensor of the comprehensive mud logging instrument.

Description

Technical Field

[0001] This invention relates to the field of calibration technology applications, specifically to a Koala calibration device and method for the output of a comprehensive logging instrument's resistance temperature sensor. Background Technology

[0002] Resistance temperature sensors (RTS) utilize the characteristic that the resistance of a conductor changes with temperature to detect the temperature of a target and transmit the data signal to the control system. This allows for real-time temperature measurement, and RTS sensors have long been widely used in integrated logging instruments in the oil industry. However, the non-linear relationship between conductor resistance and temperature means that the measured temperature cannot be simply determined from the resistance value, increasing the complexity of temperature signal display and processing. Therefore, this invention addresses this problem by disclosing a Koala correction device and method for the output of an RTS sensor in an integrated logging instrument. This device can correct the non-linearity, improving the accuracy of RTS detection in integrated logging instruments and significantly enhancing the application of RTS sensors in the oil industry. Summary of the Invention

[0003] The present invention aims to provide a Koala calibration device and method for the output of the resistance temperature sensor of a comprehensive logging instrument, so as to solve the problem of inaccurate measurement caused by the nonlinear relationship of the resistance temperature sensor in the comprehensive logging instrument.

[0004] To achieve the above objectives, the basic solution of the present invention is as follows: A Koala calibration device for the output of a comprehensive logging tool's resistance temperature sensor (RTS sensor), comprising a data acquisition module, a temperature gradient calculation module, a bias gradient calculation module, a bias value derivation module, and a correction value calculation module; further comprising an RTS sensor and an auxiliary temperature sensor; both the RTS sensor and the auxiliary temperature sensor acquire temperature data; the data acquisition module acquires voltage data and collects temperature data acquired by the two sensors; the temperature gradient calculation module calculates the temperature gradient based on the temperature data measured by the auxiliary temperature sensor, and calculates the average temperature based on the temperature gradient; the bias value derivation module derives the temporary bias value of the RTS sensor using the voltage data and the temperature data obtained by the data acquisition module; the bias gradient calculation module calculates the bias value gradient based on the temperature gradient, average temperature, and temporary bias value of the RTS sensor; and the correction value calculation module calculates the bias value of the RTS sensor based on the bias value gradient and the temperature gradient.

[0005] The bias correction module includes a temperature gradient calculation module, which uses the temperature data obtained by the acquisition module to calculate the temperature gradient and uses the temperature gradient to calculate the average temperature; and a bias gradient calculation module, which uses the temperature gradient, the average temperature calculated by the temperature gradient calculation module, and the temporary bias value of the thermal resistance temperature sensor derived by the bias derivation module to calculate the bias gradient.

[0006] A Koala correction method for the output of a thermal resistance temperature sensor in a well logging tool is characterized by: a condition estimation module that estimates the target conditions for the acquired voltage and temperature data, wherein when the condition estimation module cannot estimate the conditions, it outputs a temporary bias value of the thermal resistance temperature sensor calculated by the bias value derivation module as the bias value of the thermal resistance temperature sensor.

[0007] Based on the derived temporary bias value of the resistance temperature sensor and the obtained temperature data, the bias value of the resistance temperature sensor is calculated. The derivation of the bias value includes: calculating the temperature gradient based on the obtained temperature data, calculating the average temperature based on the temperature gradient, and deriving the bias value of the resistance temperature sensor using voltage data and temperature data.

[0008] The bias gradient is calculated based on the calculated temperature gradient and average temperature, as well as the derived temporary bias value of the RTD temperature sensor. The bias value of the RTD temperature sensor is then calculated based on the calculated bias gradient and temperature gradient, thereby correcting the bias value of the RTD temperature sensor in the integrated logging tool. The formula for calculating the bias temperature Q of the RTD temperature sensor is as follows:

[0009]

[0010] Vo is the output voltage of the resistance temperature sensor, Vf is the bias value of the resistance temperature sensor, and Y is the sensitivity coefficient of the resistance temperature sensor.

[0011] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the thermal resistance temperature sensor calculation device according to an embodiment of this application.

[0013] Figure 2 This is a schematic diagram of the bias value temperature correlation correction module according to an embodiment of this application.

[0014] Figure 3 This is a diagram showing the relationship between the temperature gradient calculation module and time in an embodiment of this application.

[0015] Figure 4This is a graph showing the relationship between the bias of the resistance temperature sensor and the temperature in an embodiment of this application.

[0016] Figure 5 This is a flowchart illustrating the derivation of bias values ​​using a thermal resistance temperature sensor calculation device according to an embodiment of this application. Detailed Implementation

[0017] The following detailed description illustrates the specific implementation method:

[0018] The purpose of this invention is to introduce a calibration device and method for the output of a thermal resistance temperature sensor in a comprehensive logging instrument.

[0019] To achieve the above objectives, the present invention adopts the following technical solution:

[0020] The basic implementation examples are as follows: Figure 1 Appendix Figure 2 Appendix Figure 3 Appendix Figure 4 and attached Figure 5 As shown: A Koala correction device for the output of a thermal resistance temperature sensor in a well logging tool includes a data acquisition module, a temperature gradient calculation module, an offset gradient calculation module, an offset value derivation module, and a correction value calculation module; it also includes a thermal resistance temperature sensor and an auxiliary temperature sensor;

[0021] Both the resistance temperature sensor (RTS) and the auxiliary temperature sensor acquire temperature data. The data acquisition module acquires voltage data and collects the temperature data from both sensors. The temperature gradient calculation module calculates the temperature gradient based on the temperature data measured by the auxiliary temperature sensor and calculates the average temperature based on the temperature gradient. The bias value derivation module derives the temporary bias value of the RTS using the voltage data and the temperature data obtained by the data acquisition module. The bias gradient calculation module calculates the bias value gradient based on the temperature gradient, average temperature, and temporary bias value of the RTS. The correction value calculation module calculates the bias value of the RTS based on the bias value gradient and the temperature gradient.

[0022] The bias temperature Q measured by the resistance temperature sensor can be derived from the following expression:

[0023]

[0024] In the formula, Vo is the output voltage of the resistance temperature sensor, Vf is the bias value of the resistance temperature sensor, and Y is the sensitivity coefficient of the resistance temperature sensor.

[0025] To accurately determine the temperature, it is necessary to determine the bias value and sensitivity coefficient of the resistance temperature sensor (RTS). The sensitivity coefficient of an RTS generally depends on the differences between individual RTSs and their specific installation position on the integrated logging instrument, taking into account electromagnetic influences. The bias value may vary with temperature. In related technologies, the bias value of the RTS is corrected by the voltage output of the RTS; when the operating temperature of the integrated logging instrument is too high, the temperature and humidity become "0". The logging instrument equipment performs bias value correction during normal use to bring the RTS output voltage back to normal, but this is also affected by environmental and control conditions. Therefore, it is difficult to perform accurate bias value correction periodically. The sensitivity coefficient of the RTS is calculated from the change in the measured quantity per unit time and the output voltage of the RTS. Therefore, the sensitivity coefficient of the RTS is affected by bias value errors.

[0026] The bias value and sensitivity coefficient of the RTD temperature sensor are calibrated during normal use of the equipment. The correction of the bias value and sensitivity coefficient of the RTD temperature sensor is calculated based on the average value of the RTD temperature sensor's output voltage over a predetermined period of time and the change in the measured quantity. Specifically, the bias value Vf of the RTD temperature sensor is calculated as follows:

[0027]

[0028] In the formula, n is the number of times the output voltage of the resistance temperature sensor is sampled, Δt is the sampling period, and Δθ is the azimuth change. The voltage change is calculated from the voltage data. When the bias change is small, i.e., the change is stable, the sensitivity coefficient Y of the resistance temperature sensor is:

[0029]

[0030] Here, Vf is a constant value under stable conditions, and the change in the corrected Vf value is very small.

[0031] This technique approximates the relationship between temperature data and bias values ​​as a function, and calculates the gradient of the bias value near the measured temperature accordingly.

[0032] Specifically, the bias correction value ΔVf is calculated based on the gradient of the bias value when there is a temperature change.

[0033]

[0034] In the formula, β is the bias gradient, Tn is the currently measured temperature, and To is the previously measured temperature. The current bias value is obtained by adding the bias correction value to the bias value determined when the temperature was previously measured.

[0035] Another embodiment of this application differs from the above embodiments in that, as shown in the appendix... Figure 1 Appendix Figure 3 and attached Figure 5 As shown, it also includes a sensitivity coefficient calculation module and an effectiveness determination module. The sensitivity coefficient calculation module calculates the sensitivity coefficient of the RTD temperature sensor based on voltage data, temperature data, and bias value. If the voltage data is valid, the sensitivity coefficient calculation module calculates the temporary sensitivity coefficient F of the RTD temperature sensor within the sampling interval of the position, as shown below. s .

[0036]

[0037] The calculation method based on voltage data, temperature data, sensitivity coefficient, and temporary bias value is as follows:

[0038]

[0039] Where n is the number of temperature data samples within the directional sampling interval, ∑F 2 o This represents the total value of temperature data within the voltage sampling interval. Δθ(deg) represents the change in the measured quantity, and Fs represents the sensitivity coefficient. Fo corresponds to Vo in equation (), and Fs corresponds to Y in equation (1).

[0040] The temperature gradient calculation module uses the time and temperature data received from the temperature storage module to perform functional approximation to calculate the temperature gradient; it applies a least squares linear function approximation method, that is, plotting time along the x-axis and temperature along the y-axis; using the least squares method, the temperature gradient γTn (℃ / sec) and intercept αEn (℃) are calculated as follows.

[0041]

[0042]

[0043] The suffix "n" indicates the number of samples shown in the figure, Pn indicates the number of samples using the least squares method, and Tn (sec) and tn (°C) represent the time and temperature shown in the figure, respectively. The calculation method for Pn is as follows.

[0044]

[0045] The temperature gradient calculation module calculates the temperature T(t) at a given time point as follows:

[0046]

[0047] The temperature-related attribute storage module sequentially receives temporary bias values, usage condition information, temperature T, and temperature gradient γTn, and creates a temperature-related bias attribute table, displaying the data structure of the table stored in the temperature-related attribute storage module. The data structure includes a temperature range, a validity flag, an average temperature, and an average bias value. First, the temperature-related attribute storage module calculates a sampling interval and samples the average temperature field and the average bias value domain input. The sampling interval Bn is calculated as follows:

[0048]

[0049] Where Ti (°C) represents a portion of the total temperature range. The temperature range of 0°C within the temperature range field is substituted into Bn in the formula to calculate the sampling interval. An upper limit for the temperature gradient γTn is defined to ensure that the sampling interval Bn is not less than the sampling interval of the temporary bias value. Assuming the sampling interval of the temporary bias value is (sec), the upper limit of the absolute value of the temperature gradient γTn is set to 0. (°C / sec). Furthermore, the lower limit of the temperature gradient γTn is set to 0.0 (°C / sec) to ensure that the sampling interval of the temporary bias value does not exceed 0 sec.

[0050] The bias gradient calculation module uses the average temperature and average bias value to approximate the bias gradient using a function. Here, a method using a least-squares linear function to approximate the gradient is presented, plotting the average temperature along the x-axis and the average bias along the y-axis. The bias gradient γSn (mV / ℃) calculated using the least-squares method is as follows:

[0051]

[0052] Where m represents the number of entries in the valid flag field that are the average temperature and the average bias. If m is greater than (a threshold), the bias gradient calculation module performs function approximation. Tc(°C) is the average temperature input in the average temperature field, and H is the average bias input in the average bias field. In other words, the bias gradient calculation module calculates the gradient of the bias value based on the temperature gradient calculated in the temperature gradient calculation module, and the average temperature and temporary bias value derived in the bias value derivation module.

[0053] The correction value calculation module receives the temporary bias value, usage status information, temperature gradient γTn, and bias gradient γSn, and then calculates the bias value. When the usage status information indicates that the integrated logging tool is being used in an abnormally high-temperature environment, the correction value calculation module outputs the temporary bias value as the bias value. Simultaneously, when the usage condition information indicates that the usage conditions cannot be estimated, the correction value calculation module calculates the bias value based on the bias gradient γSn and the temperature gradient γTn.

[0054]

[0055] The temperature difference is derived from the temperature gradient obtained by function approximation using temperature data, and the bias value is derived from the temperature difference. Furthermore, the range of the functional approximation using temperature data is determined based on the aforementioned derived temperature gradient. Therefore, a high-precision temperature difference can be obtained. When creating the temperature-related characteristic table, the sampling interval of the temperature-related characteristic data is obtained by using the temperature gradient obtained through functional approximation of the temperature data. Temperature-related performance data is obtained by functional approximating the temperature data. Therefore, temperature-related characteristics can be derived with high precision. Furthermore, the temperature-related performance data is weighted according to the field conditions. Therefore, temperature-related characteristics can be derived with high precision.

[0056] The bias gradient is calculated using a functional approximation of the bias value, and the interval for performing this approximation is determined based on the temperature obtained using the functional approximation of the temperature data. Therefore, the bias gradient can be derived with high precision, and the bias value can be corrected with high precision. The time period of the function approximating the sensor temperature changes according to the temperature gradient of the RTD sensor. Thus, high-precision temperature data is obtained, improving the calibration accuracy of the RTD temperature sensor.

[0057] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific structures and / or characteristics in the solutions are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A Koala calibration device for the output of a thermal resistance temperature sensor in a comprehensive logging tool, characterized in that: It includes a data acquisition module, a temperature gradient calculation module, an offset gradient calculation module, an offset value derivation module, and a correction value calculation module, as well as a resistance temperature sensor and an auxiliary temperature sensor; Both the resistance temperature sensor and the auxiliary temperature sensor acquire temperature data. The data acquisition module acquires voltage data and collects temperature data from two sensors. The temperature gradient calculation module calculates the temperature gradient based on the temperature data measured by the auxiliary temperature sensor, and calculates the average temperature based on the temperature gradient. The bias value derivation module uses the voltage data and the temperature data obtained by the data acquisition module to derive the temporary bias value of the thermal resistance temperature sensor. The bias gradient calculation module calculates the bias gradient based on the temperature gradient, average temperature, and temporary bias value of the resistance temperature sensor. The correction value calculation module calculates the bias value of the resistance temperature sensor based on the bias value gradient and the temperature gradient.

2. The Koala calibration device for the output of the thermal resistance temperature sensor of the integrated logging instrument according to claim 1, characterized in that: The bias temperature Q can be derived from the following expression. Vo is the output voltage of the resistance temperature sensor, Vf is the bias value mentioned above, and Y is the sensitivity coefficient of the resistance temperature sensor.

3. The Koala calibration device for the output of the thermal resistance temperature sensor of the integrated logging instrument according to claim 2, characterized in that: The bias value Vf is calculated as follows: n is the number of times the output voltage of the resistance temperature sensor is sampled, Δt is the sampling period, and Δθ is the azimuth change.

4. The Koala correction device for the output of the thermal resistance temperature sensor of the integrated logging tool according to claim 3, when the change is stable, the sensitivity coefficient Y of the thermal resistance temperature sensor is, Vf is a constant value under stable conditions.

5. The Koala calibration device for the output of the thermal resistance temperature sensor of the integrated logging instrument according to claim 4, characterized in that: Based on the gradient of the bias value, the bias correction value ΔVf is calculated when there is a temperature change. β is the bias gradient, Tn is the currently measured temperature, and To is the previously measured temperature; The current bias value is obtained by adding the bias correction value ΔVf to the bias value determined when measuring the temperature previously.

6. The Koala calibration device for the output of the thermal resistance temperature sensor of the integrated logging instrument according to claim 5, characterized in that: It also includes a sensitivity coefficient calculation module and an effectiveness determination module. The sensitivity coefficient calculation module calculates the sensitivity coefficient of the resistance temperature sensor based on voltage data, temperature data, and bias value.

7. The Koala calibration device for the output of the thermal resistance temperature sensor of the integrated logging instrument according to claim 6, characterized in that: The sensitivity coefficient calculation module calculates the temporary sensitivity coefficient F of the resistance temperature sensor within the sampling interval at the location, as shown below. s , The calculation method based on voltage data, temperature data, sensitivity coefficient, and temporary bias value is as follows. n is the number of temperature data samples taken within the directional sampling interval, ∑F 2 o The total value of temperature data within the voltage sampling interval; Δθ(deg) represents the change in the measured quantity, Fs represents the sensitivity coefficient; Fo corresponds to Vo in equation (), and Fs corresponds to Y in equation (). The temperature gradient γTn (°C / sec) and intercept αEn (°C) are calculated using the least squares method. The suffix "n" indicates the sample size, Pn represents the sample size using the least squares method, and Tn (sec) and tn (°C) represent time and temperature, respectively. The calculation method for Pn is as follows: The temperature gradient calculation module calculates the temperature T(t) at a given time point as follows.

8. The Koala calibration device for the output of the thermal resistance temperature sensor of the integrated logging instrument according to claim 7, characterized in that: It also includes a temperature-related attribute storage module, which sequentially receives temporary bias values, usage condition information, temperature T, and temperature gradient γTn, and creates and stores a temperature-related bias attribute table, displaying the data structure of the table stored in the temperature-related attribute storage module; the data structure includes a temperature range, a validity flag, an average temperature, and an average bias value.

9. The Koala calibration device for the output of the thermal resistance temperature sensor of the integrated logging instrument according to claim 8, characterized in that: The bias gradient γSn (mV / ℃) is calculated using the least squares method.

10. The Koala calibration device for the output of the thermal resistance temperature sensor of the integrated logging instrument according to claim 9, characterized in that: When the usage condition information indicates that the usage conditions cannot be estimated, the correction value calculation module calculates the bias value based on the bias gradient γSn and the temperature gradient γTn. The temperature gradient is obtained by approximating the temperature data using a function, the temperature difference is derived, and the bias value is derived based on the temperature difference.

Images