Monitoring Method and System for New Energy Power Generation Equipment Based on Big Data

Abstract

The present invention discloses a monitoring method and system for new energy power generation equipment based on big data, which relates to the technical field of new energy. By analyzing the geothermal operation condition data, the influence degree of the formation pressure change is calculated, the influence coefficient Yxyz is constructed, and the geothermal operation condition data within the safe range is screened out to further ensure that the formation pressure fluctuates within the safe range. Based on the geothermal operation condition data within the safe range, the water injection activity of the reinjection well is dynamically adjusted to further ensure the rationality of the water injection rate and the water injection volume. At the same time, the formation condition data information in the geothermal power station is monitored and recorded in real time, the wellbore stability index Jwzs is constructed, and through comparison and analysis with the preset evaluation threshold W, the stability degree of the formation in the current geothermal power station is predicted, and corresponding injection and production measures are taken in time to prevent the risks of formation instability or wellbore instability.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy, and particularly to a monitoring method and system for new energy power generation equipment based on big data. Background Art

[0002] With the increasing global demand for renewable energy, geothermal power generation, as a stable and environmentally friendly energy form, has received extensive attention. At the same time, monitoring methods based on big data have increasingly become a key means to ensure the stability and efficient operation of the system. In a geothermal power plant, production wells and injection wells are key components. The former is responsible for extracting underground heat energy, while the latter injects the treated cooling water back into the ground to maintain the balance of formation pressure and temperature.

[0003] During the water injection activity in the injection well, changes in the injection rate and injection volume will directly affect the formation pressure, and further affect the stability of the formation. However, there are many deficiencies in the current monitoring and management of this process. Traditional methods often rely on regular manual measurements and empirical judgments, and it is difficult to reflect the dynamic changes of formation pressure and wellbore stability in real time. In addition, during the injection process, the influence degrees of different injection states on the formation pressure may be different. If not monitored in time, it may lead to risks such as formation fracture or wellbore instability that may occur during the development of geothermal resources. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a monitoring method and system for new energy power generation equipment based on big data, which solves the problems in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A monitoring method for new energy power generation equipment based on big data, including the following steps,

[0006] S1. Determine the geothermal exploitation range in advance, and based on big data technology, real-time monitor the operation condition data in the production well and the injection well within the geothermal exploitation range. After feature extraction, construct a geothermal operation set;

[0007] S2. Construct an injection state coefficient Zzxs based on the geothermal operation set. If the injection state coefficient Zzxs exceeds the condition threshold, send out a warning instruction. After receiving the warning instruction, collect the trend of the difference in the formation pressure value Dc to obtain a pressure change set, and analyze and calculate the influence degree of the geothermal operation condition data on the formation pressure change to construct an influence coefficient Yxyz, and screen out the geothermal operation condition data within the safe range according to the numerical value of the influence coefficient Yxyz;

[0008] S3. Based on the geothermal operation condition data within the safe range, adjust the operation status of the water injection activity in the reinjection well. At the same time, monitor and record the formation condition data information in the geothermal power station, and construct a wellbore stability index Jwzs based on the formation condition data information.

[0009] S4. Preset an evaluation threshold W, and compare and analyze the wellbore stability index Jwzs with the evaluation threshold W to comprehensively predict and analyze the formation stability degree in the current geothermal power station. According to the prediction and analysis results, and combined with the geothermal operation condition data within the safe range in step S2, take corresponding injection and production measures.

[0010] Preferably, S1 specifically includes the following steps:

[0011] S11. Pre-lock the geothermal power station to be monitored, and obtain the overall layout of the geothermal power station. Among them, the overall layout includes the positions of the generator room, boiler room, substation, production well and reinjection well. After feature extraction, obtain the spacing Gssj between the reinjection well and the production well.

[0012] Preferably, S1 also includes:

[0013] S12. Based on big data technology, real-time monitor the operation condition data in the production well and reinjection well within the geothermal exploitation range. Among them, the operation condition data includes the extraction rate and extraction volume in the production well at different monitoring time periods, and the water injection rate Zssz and water injection volume Zsz in the reinjection well. At the same time, combined with wireless communication technology, transmit the operation condition data to the operation platform.

[0014] S121. Perform data preprocessing on the operation condition data in the operation platform, detect and delete the repeatedly collected data information, and identify the missing data to detect and repair the outliers.

[0015] S122. Use the dimensionless processing technology to standardize the relevant data information preprocessed in step S121, and eliminate the unit and dimension differences in the data.

[0016] Preferably, S2 specifically includes the following steps:

[0017] S21. Correlate the water injection rate Zssz and water injection volume Zsz in the reinjection well according to the geothermal operation set. After dimensionless processing, map the corresponding data values within the interval [0,1], and then obtain the water injection status coefficient Zzxs according to the following formula:

[0018]

[0019] In the formula, n represents the monitoring period, i == 1, 2, 3,..., n, Zsszi denoted as the water injection rate at the \(i\)-th monitoring time node denoted as the average value of the water injection rate within the monitoring period, \(Zszi\) is denoted as the water injection volume at the \(i\)-th monitoring time node denoted as the average value of the water injection volume within the monitoring period, both \(\alpha\) and \(\beta\) are denoted as weight coefficients

[0020] Preferably, S2 further includes:

[0021] S22. Preset a condition threshold, and compare and analyze the condition threshold with the water injection state coefficient \(Zzxs\). If the water injection state coefficient \(Zzxs\) exceeds the condition threshold, it is estimated that the current water blocking activity of the recharge well is in an abnormal state at this time, and a warning instruction is sent outwards;

[0022] If the water injection state coefficient \(Zzxs\) does not exceed the condition threshold, no additional warning instruction is sent outwards for the time being;

[0023] S23. After receiving the warning instruction, collect the trend of the difference in the formation pressure value \(Dc\) to obtain a pressure change set, and the pressure change set includes the formation pressure values \(Dc\) at different monitoring time nodes;

[0024] S24. After determining the geothermal exploitation range, carry out drilling operations in the formation through drilling equipment, and use mud during the drilling process to maintain the stability of the wellbore, and at the same time carry the drill cuttings back to the surface. Among them, the drilling equipment includes but is not limited to drill bits, drill pipes and drilling rigs. After the drilling is completed, a pressure gauge is placed in the well, and then the wellhead is closed, the fluid injection is stopped, the static pressure of the formation is monitored, and the static pressure of the formation is used as the reference pressure \(Bz\).

[0025] Preferably, S2 further includes:

[0026] S25. According to the least squares method, and combined with the operating condition data and the pressure change set, analyze and calculate the influence degree of the geothermal operating condition data on the formation pressure change. The specific analysis content is as follows: Use the operating condition data corresponding to the water injection state coefficient \(Zzxs\) as the independent variable, and use the formation pressure values \(Dc\) at each monitoring time node as the dependent variable to perform linear regression analysis and obtain the corresponding regression equation;

[0027] Use the regression coefficient corresponding to the operating condition data in the regression equation as the influence factor to construct the influence coefficient \(Yxyz\). The specific way to obtain it is as follows:

[0028]

[0029] In the formula, \(\theta\) z denoted as the influence factor of the water injection rate, \(\theta\) sDenoted as the influencing factor of the water injection volume, both a1 and a2 are weight coefficients.

[0030] Preferably, S2 further includes:

[0031] S26. Preset an influence threshold. If the influence coefficient Yxyz exceeds the influence threshold, at this time, the water injection rate Zssz and the water injection volume Zsz within the corresponding monitoring time node are marked as abnormal condition data; if the influence coefficient Yxyz does not exceed the influence threshold, at this time, the water injection rate Zssz and the water injection volume Zsz within the corresponding monitoring time node are marked as non-abnormal condition data.

[0032] S261. According to the monitoring time nodes corresponding to several groups of non-abnormal condition data, extract the corresponding formation pressure values Dc from the pressure change set, and by comparing the several groups of extracted formation pressure values Dc with the reference pressure Bz, filter out the geothermal operation condition data within the safe range. The specific filtering content is as follows:

[0033] If the formation pressure value Dc within the corresponding monitoring time node ≤ the reference pressure Bz, then incorporate the water injection rate Zssz and the water injection volume Zsz within the corresponding monitoring time node into the safe range of the water injection activity.

[0034] If the formation pressure value Dc within the corresponding monitoring time node > the reference pressure Bz, then do not incorporate the water injection rate Zssz and the water injection volume Zsz within the corresponding monitoring time node into the safe range of the water injection activity.

[0035] Preferably, S3 specifically includes the following steps:

[0036] S31. Monitor and record the formation condition data information within the geothermal power station. The formation condition data information includes the pressure change value Ybz during extraction and reinjection and the water pressure Syy in the formation pores.

[0037] S32. Based on the formation condition data information, by correlating the pressure change value Ybz during extraction and reinjection and the water pressure Syy in the formation pores, and after dimensionless processing, calculate and obtain the wellbore stability index Jwzs. The wellbore stability index Jwzs is obtained through the following formula:

[0038]

[0039] In the formula, Gssj represents the distance between the reinjection well and the production well, v1, v2, and v3 are all weight coefficients, and P represents a correction constant.

[0040] Preferably, S4. According to the result of comparing and analyzing the wellbore stability index Jwzs with the evaluation threshold W, the formation stability degree in the current geothermal power station is comprehensively predicted and analyzed. The specific analysis content is as follows:

[0041] If the wellbore stability index Jwzs ≥ the evaluation threshold W, it is determined that the formation stability in the current geothermal power station is in an abnormal state at this time. At this time, in combination with the geothermal operation condition data within the safe range, the injection rate Zssz and the injection volume Zsz of the cooling water injected into the underground reservoir through the recharge well are adjusted;

[0042] If the wellbore stability index Jwzs < the evaluation threshold W, it is determined that the formation stability degree in the current geothermal power station is not in an abnormal state at this time. At this time, no additional injection and production means will be taken.

[0043] The monitoring system of new energy power generation equipment based on big data includes a condition monitoring module, a condition impact analysis module, a geothermal stability analysis module and a decision-making management module;

[0044] The condition monitoring module determines the geothermal exploitation range in advance, and based on big data technology, it monitors the operation condition data in the production well and the recharge well within the geothermal exploitation range in real time. After feature extraction, a geothermal operation set is constructed;

[0045] The condition impact analysis module constructs an injection state coefficient Zzxs based on the geothermal operation set. If the injection state coefficient Zzxs exceeds the condition threshold, a warning instruction is sent outwards. After receiving the warning instruction, the trend of the difference in the formation pressure value Dc is collected to obtain a pressure change set, and the influence degree of the geothermal operation condition data on the formation pressure change is analyzed and calculated to construct an influence coefficient Yxyz, and according to the numerical value of the influence coefficient Yxyz, the geothermal operation condition data within the safe range is screened out;

[0046] The geothermal stability analysis module adjusts the operation state of the injection activity in the recharge well based on the geothermal operation condition data within the safe range, and at the same time monitors and records the formation condition data information in the geothermal power station, and constructs a wellbore stability index Jwzs based on the formation condition data information;

[0047] The decision-making management module sets an evaluation threshold W in advance, and compares and analyzes the wellbore stability index Jwzs with the evaluation threshold W to comprehensively predict and analyze the formation stability degree in the current geothermal power station. According to the prediction and analysis result, and in combination with the geothermal operation condition data within the safe range, corresponding injection and production means are taken.

[0048] The present invention provides a monitoring method and system for new energy power generation equipment based on big data, which has the following beneficial effects:

[0049] (1) Based on the geothermal operation set, construct the water injection status coefficient Zzxs, and real-time monitor whether the water injection status coefficient exceeds the set conditional threshold. Once it exceeds the threshold, the system issues a warning instruction and collects the formation pressure values Dc at different monitoring time nodes to form a pressure change set. By analyzing the geothermal operation condition data, calculate the influence degree of the formation pressure change, construct the influence coefficient Yxyz, and screen out the geothermal operation condition data within the safe range to further ensure that the formation pressure fluctuates within the safe range. Based on the geothermal operation condition data within the safe range, dynamically adjust the water injection activities of the recharge well to further ensure the rationality of the water injection rate and the water injection volume. At the same time, real-time monitor and record the formation condition data information in the geothermal power station, construct the wellbore stability index Jwzs, and compare and analyze it through the preset evaluation threshold W to predict the stability degree of the formation in the current geothermal power station, and timely take corresponding injection and production measures to prevent the risks of formation instability or wellbore instability; through comprehensive prediction and analysis of the formation stability degree of the current geothermal power station, combined with the geothermal operation condition data within the safe range, adopt scientific and reasonable injection and production measures to optimize the exploitation and utilization of geothermal resources, ensure the safe operation of the geothermal power station, improve the resource utilization efficiency, reduce the equipment failure rate, and further extend the service life of the equipment. In short, this method uses big data technology to real-time monitor and analyze the operation conditions of the geothermal power station, constructs a scientific monitoring and warning system, effectively improves the exploitation efficiency of geothermal resources and the safety of equipment operation, and provides reliable technical support for the intelligent management of new energy power generation equipment.

[0050] (2) By real-time obtaining the operation data of the production well and the recharge well, construct the water injection status coefficient Zzxs and the wellbore stability index Jwzs, and then conduct trend analysis and prediction on the formation pressure change. This can not only improve the accuracy and timeliness of monitoring, but also discover potential risks in advance through the warning mechanism, so as to optimize the water injection activities, ensure the safe operation of the geothermal power station, and at the same time, through the benchmark setting of the static pressure test, further enhance the accurate prediction ability of the formation pressure change.

[0051] (3) Through the influence coefficient Yxyz, the system can real-time monitor the influence degree of the water injection activity on the formation pressure. Through the screening and management steps, it can not only effectively identify and respond to the abnormal conditions in the geothermal operation conditions, but also ensure that the geothermal power generation equipment operates within the safe range, improve the stability and reliability of the equipment, and then optimize the utilization efficiency of energy resources. Description of the Drawings

[0052] Figure 1 It is a block diagram of the monitoring method for new energy power generation equipment based on big data of the present invention;

[0053] Figure 2Schematic diagram of the monitoring system for new energy power generation equipment based on big data according to the present invention. Detailed implementation manners

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0055] Embodiment 1

[0056] Please refer to Figure 1 , the present invention provides a monitoring method for new energy power generation equipment based on big data, including the following steps,

[0057] S1. Determine the geothermal extraction range in advance, and based on big data technology, monitor the operation condition data in production wells and reinjection wells within the geothermal extraction range in real time. After feature extraction, construct a geothermal operation set;

[0058] S2. Construct an injection state coefficient Zzxs based on the geothermal operation set. If the injection state coefficient Zzxs exceeds the condition threshold, send out a warning instruction. After receiving the warning instruction, collect the trend of the difference in formation pressure value Dc to obtain a pressure change set, and analyze and calculate the influence degree of the geothermal operation condition data on the formation pressure change to construct an influence coefficient Yxyz. And according to the numerical value of the influence coefficient Yxyz, screen out the geothermal operation condition data within the safe range;

[0059] S3. Based on the geothermal operation condition data within the safe range, adjust the operation state of the injection activity in the reinjection well, and at the same time monitor and record the formation condition data information in the geothermal power station, and construct a wellbore stability index Jwzs based on the formation condition data information;

[0060] S4. Set an evaluation threshold W in advance, and compare and analyze the wellbore stability index Jwzs with the evaluation threshold W to comprehensively predict and analyze the formation stability degree in the current geothermal power station. According to the prediction and analysis results, and combined with the geothermal operation condition data within the safe range in step S2, take corresponding injection and production measures.

[0061] In this embodiment, by pre-determining the geothermal exploitation range and based on big data technology to monitor the operation condition data in production wells and reinjection wells in real time, this method can quickly and accurately extract geothermal operation characteristics during the actual operation process, construct a geothermal operation set. This ability of real-time monitoring and data feature extraction enables the geothermal power station to timely understand and master the formation pressure and wellbore stability conditions, effectively improving the accuracy and real-time performance of monitoring. According to the geothermal operation set, a water injection state coefficient Zzxs is constructed, and a condition threshold is set. When the water injection state coefficient Zzxs exceeds the threshold, the system automatically issues a warning instruction. After receiving the warning instruction, the trend of the difference in the formation pressure value Dc is collected, a pressure change set is obtained, and the influence degree of the geothermal operation condition data on the formation pressure change is calculated to construct an influence coefficient Yxyz. This intelligent warning mechanism can discover potential risks in advance, further ensuring that while the geothermal power station operates efficiently, the possibility of accidents is minimized; according to the numerical value of the influence coefficient Yxyz, the geothermal operation condition data within the safe range is screened out, and based on these data, the operation state of the water injection activity in the reinjection well is adjusted. At the same time, continuously monitor and record the formation condition data information in the geothermal power station, construct a wellbore stability index Jwzs, and through comparing the wellbore stability index Jwzs with a pre-set evaluation threshold W, comprehensively predict and analyze the stability degree of the current formation in the geothermal power station. This way of optimizing the water injection activity helps to maintain the balance of the formation pressure, ensuring the long-term stability of the formation and the safety of the wellbore. In short, this method can not only monitor and control the operation state of geothermal power generation equipment in real time and accurately, but also effectively ensure the stability of the formation and the safety of the wellbore through intelligent warning and optimized water injection activities, thereby improving the overall operation efficiency and safety level of the geothermal power station.

[0062] Embodiment 2

[0063] Please refer to Figure 1 , specifically: S1 specifically includes the following steps:

[0064] S11. Pre-lock the geothermal power station to be monitored, and obtain the overall layout of the geothermal power station. Among them, the overall layout includes the positions of the generator room, boiler room, substation, production wells and reinjection wells. After feature extraction, the distance Gssj between the reinjection well and the production well is obtained.

[0065] S1 also includes:

[0066] S12. Based on big data technology, in real time monitor the operation condition data in production wells and reinjection wells within the geothermal exploitation range. Among them, the operation condition data includes the extraction rate and extraction volume in the production well during different monitoring periods, and the water injection rate Zssz and water injection volume Zsz in the reinjection well. At the same time, combined with wireless communication technology, transmit the operation condition data to the operation platform;

[0067] S121. Preprocess the operation condition data in the operation platform, detect and delete the repeatedly collected data information to avoid deviation in analysis caused by data repetition, and identify missing data to detect and repair outliers.

[0068] S122. Use the dimensionless processing technology to standardize the relevant data information preprocessed in step S121, and eliminate the differences in units and dimensions in the data.

[0069] In this embodiment, the geothermal power station to be monitored is pre-locked, and the overall layout of the geothermal power station is obtained. The accurate acquisition of this layout information ensures the accuracy of the basic data for monitoring and management, and provides reliable data support for subsequent monitoring and optimization. Based on big data technology, the operation condition data of production wells and reinjection wells within the geothermal exploitation range are monitored in real time. These data include the extraction rate and extraction volume in the production well during different monitoring periods, as well as the injection rate Zssz and injection volume Zsz in the reinjection well. And the operation condition data is transmitted to the operation platform. The real-time monitoring and efficient data transmission further ensure the timeliness and accuracy of the monitoring data, and provide a reliable basis for the operation optimization of the geothermal power station. Data preprocessing and standardization effectively eliminate the noise and deviation in the data, and further improve the accuracy and reliability of data analysis. In short, through the accurate acquisition of the overall layout, the real-time monitoring and efficient transmission of operation condition data, and data preprocessing and standardization, the high quality and high reliability of the operation data of the geothermal power station are ensured.

[0070] Embodiment 3

[0071] Please refer to Figure 1 , specifically: S2 specifically includes the following steps:

[0072] S21. According to the geothermal operation set, associate the injection rate Zssz and injection volume Zsz in the reinjection well. After dimensionless processing, map the corresponding data values within the interval [0, 1], and then obtain the injection state coefficient Zzxs according to the following formula:

[0073]

[0074] In the formula, n represents the monitoring period, i = 1, 2, 3,..., n, Zssz i represents the injection rate at the i-th monitoring time node, represents the average value of the injection rate during the monitoring period, Zszi represents the injection volume at the i-th monitoring time node, represents the average value of the injection volume during the monitoring period, and both α and β represent weight coefficients, where 0 < α ≤ 1, 0 < β ≤ 1, and α + β = 1.

[0075] The above-mentioned water injection rate Zssz and water injection volume Zsz can both be monitored and obtained through a flow sensor;

[0076] S2 also includes:

[0077] S22. Preset a condition threshold, and compare and analyze the condition threshold with the water injection state coefficient Zzxs. If the water injection state coefficient Zzxs exceeds the condition threshold, it is estimated that the current water blocking activity of the recharge well is in an abnormal state at this time, and a warning instruction is sent outwards;

[0078] If the water injection state coefficient Zzxs does not exceed the condition threshold, no additional warning instruction is sent outwards for the time being;

[0079] S23. After receiving the warning instruction, collect the trend of the difference in formation pressure value Dc to obtain a pressure change set, and the pressure change set includes the formation pressure value Dc at different monitoring time nodes;

[0080] S24. After determining the geothermal exploitation range, carry out drilling operations in the formation through drilling equipment, and use mud during the drilling process to maintain the stability of the wellbore, and at the same time carry the drill cuttings back to the surface. Among them, the drilling equipment includes but is not limited to drill bits, drill pipes and drilling rigs. After the drilling is completed, a pressure gauge is placed in the well, and then the wellhead is closed and the fluid injection is stopped to make the fluid in the well static, monitor the static pressure of the formation, and use the static pressure of the formation as the reference pressure Bz. This process is a static pressure test, and the commonly used measuring tools can be electronic pressure gauges or mechanical pressure gauges.

[0081] In this embodiment, through the geothermal operation set, the water injection state coefficient Zzxs is calculated and obtained, and further considering the water injection rate Zssz, water injection volume Zsz and their mean values within the monitoring period, this method further ensures that the calculation of the water injection state coefficient Zzxs is more scientific and reasonable, can accurately reflect the state of the water injection activity, and provides reliable basic data for subsequent warning and management. By comparing and analyzing the water injection state coefficient Zzxs with the condition threshold, if the water injection state coefficient Zzxs exceeds the condition threshold, the system immediately issues a warning instruction to indicate that the current water injection activity of the recharge well is in an abnormal state. If it does not exceed the condition threshold, no warning instruction is issued for the time being. This warning mechanism can timely detect abnormal situations, further improve the safety and reliability of the operation of the geothermal power station, and reduce potential risks and losses.

[0082] By collecting and analyzing the formation pressure values Dc at different monitoring time nodes, the dynamic changes of the formation pressure can be accurately grasped. This trend analysis method helps to timely adjust the water injection activities of the recharge wells, optimize the utilization of geothermal resources, improve the power generation efficiency, and further reduce the equipment failure rate. At the same time, through the static pressure test method, the accuracy of the reference data of the formation pressure is further ensured, which is helpful for the accurate monitoring and analysis of subsequent pressure changes and improves the reliability of the overall monitoring system. In short, through big data technology, this method realizes the real-time monitoring and analysis of the operating conditions of production wells and recharge wells in geothermal power stations. Through scientific calculation of the water injection state coefficient Zzxs, effective early warning mechanisms, and accurate static pressure tests, the safety and efficiency of the operation of geothermal power stations are further improved, providing strong technical support for the sustainable utilization of geothermal resources.

[0083] Example 4

[0084] Please refer to Figure 1 , specifically: S2 also includes:

[0085] S25. Based on the least squares method and combined with the operating condition data and the pressure change set, analyze and calculate the influence degree of the geothermal operating condition data on the formation pressure change. The specific analysis content is as follows: Take the operating condition data corresponding to the water injection state coefficient Zzxs as the independent variable, and take the formation pressure value Dc at each monitoring time node as the dependent variable, perform a linear regression analysis, and obtain the corresponding regression equation;

[0086] Take the regression coefficient corresponding to the operating condition data in the regression equation as the influence factor, and construct the influence coefficient Yxyz. The specific way to obtain it is as follows:

[0087]

[0088] In the formula, θ z represents the influence factor of the water injection rate, θ s represents the influence factor of the water injection volume. Both a1 and a2 are weight coefficients, where 0 < a1 ≤ 1, 0 < a2 ≤ 1, and a1 + a2 = 1.

[0089] It should be noted that the corresponding regression equation is obtained through linear regression analysis. Among them, the regression equation is a simple binary linear equation, that is, y = a + b1x1 + b2x2 + c. Here, a is the intercept term, indicating the value of the dependent variable when the independent variable is 0; b1 and b2 are both slope terms, representing the influence degree of the independent variable on the dependent variable, and c is the error term, indicating the factors that the model cannot fully explain; moreover, b1 specifically represents the expected change in the dependent variable y when the independent variable x1 increases by one unit, assuming that other independent variables remain unchanged; b2 specifically represents the expected change in the dependent variable y when the independent variable x2 increases by one unit, assuming that other independent variables remain unchanged.

[0090] In this embodiment, in step S25, the least squares method is used to analyze the influence degree of geothermal operation condition data on the formation pressure change. First, the operation condition data associated with the water injection state coefficient Zzxs is used as the independent variable, and the formation pressure value Dc at each monitoring time node is used as the dependent variable for linear regression analysis. The regression equation obtained through the regression analysis characterizes the quantitative relationship between the operation condition data and the formation pressure. This step effectively quantifies the specific influence degree of the water injection activity on the formation pressure change. Through these analyses and calculations, this method not only improves the understanding and management of the influencing factors of the operation state of geothermal power generation equipment, but also can, according to the numerical value of the influence coefficient Yxyz, lay a foundation for screening out the geothermal operation condition data within the safe range in the follow-up, further ensuring the stable operation of the equipment and the effective utilization of resources.

[0091] Embodiment 5

[0092] Please refer to Figure 1 , specifically: S2 also includes:

[0093] S26. Preset an influence threshold. If the influence coefficient Yxyz exceeds the influence threshold, at this time, the water injection rate Zssz and the water injection volume Zsz at the corresponding monitoring time node are marked as abnormal condition data; if the influence coefficient Yxyz does not exceed the influence threshold, at this time, the water injection rate Zssz and the water injection volume Zsz at the corresponding monitoring time node are marked as non-abnormal condition data;

[0094] S261. According to the monitoring time nodes corresponding to several groups of non-abnormal condition data, extract the corresponding formation pressure values Dc from the pressure change set, and by comparing the several groups of extracted formation pressure values Dc with the reference pressure Bz, screen out the geothermal operation condition data within the safe range. The specific screening content is as follows:

[0095] If the formation pressure value Dc at the corresponding monitoring time node ≤ the reference pressure Bz, then the water injection rate Zssz and the water injection volume Zsz at the corresponding monitoring time node are included in the safe range of the water injection activity;

[0096] If the formation pressure value Dc within the corresponding monitoring time node is greater than the reference pressure Bz, then the water injection rate Zssz and the water injection volume Zsz within the corresponding monitoring time node are not included in the safe range of the water injection activity.

[0097] In this embodiment, according to the comparison result between the influence coefficient Yxyz and the influence threshold, it is determined whether the data under the corresponding conditions is in an abnormal state, and the system marks this data as abnormal condition data for subsequent further processing and analysis. For the monitoring time nodes that do not exceed the influence threshold, the corresponding formation pressure values Dc are extracted from the pressure change set, and these formation pressure values are compared with the pre-set reference pressure Bz. Through these screening and marking processes, the system can monitor and identify the geothermal operation condition data within the safe range in real time, and take adjustment measures in a timely manner to ensure the stable operation of the equipment and the effective utilization of formation resources. At the same time, the identification and processing of abnormal condition data help to prevent potential formation pressure problems and further reduce possible operation risks and losses.

[0098] Embodiment 6

[0099] Please refer to Figure 1 , specifically: S3 specifically includes the following steps:

[0100] S31. Monitor and record the formation condition data information within the geothermal power station. The formation condition data information includes the pressure change value Ybz during the extraction and reinjection processes and the water pressure Syy in the formation pores.

[0101] S32. Based on the formation condition data information, by correlating the pressure change value Ybz during the extraction and reinjection processes and the water pressure Syy in the formation pores, and after dimensionless processing, calculate and obtain the wellbore stability index Jwzs. The wellbore stability index Jwzs is obtained through the following formula:

[0102]

[0103] In the formula, Gssj represents the distance between the reinjection well and the production well, and v1, v2, and v3 are all weighting coefficients, where 0 < v1 ≤ 1, 0 < v2 ≤ 1, 0 < v3 ≤ 1, and v1 + V2 + V3 = 1, and P represents a correction constant.

[0104] The distance Gssj between the above-mentioned reinjection well and the production well can be monitored on-site at the geothermal power station through a laser rangefinder or a GPS ranging device.

[0105] The pressure change value Ybz during the extraction and reinjection processes can be used to monitor the pressure change in real time through a pressure gauge installed in the well, such as an electronic pressure gauge and a mechanical pressure gauge.

[0106] The water pressure Syy in the formation pores can be monitored and obtained through a pore pressure gauge. Common types include pneumatic pore pressure gauges and hydraulic pore pressure gauges.

[0107] Among them, when hot water is extracted from the production well, the water in the underground reservoir is removed, which may cause the pore pressure near the production well to drop. This pressure drop is mainly concentrated around the production well, forming a pressure funnel effect; when cooling water is injected into the underground reservoir through the injection well, the water in the underground reservoir increases, which may cause the pore pressure near the injection well to rise. This pressure rise is also mainly concentrated around the injection well, forming a pressure highland effect.

[0108] S4. According to the comparison and analysis results of the wellbore stability index Jwzs and the evaluation threshold W, comprehensively predict and analyze the stability degree of the formation in the current geothermal power station. The specific analysis content is as follows:

[0109] If the wellbore stability index Jwzs ≥ the evaluation threshold W, it is determined that the formation stability in the current geothermal power station is in an abnormal state. At this time, combined with the geothermal operation condition data within the safe range, adjust the water injection rate Zssz and the water injection volume Zsz when cooling water is injected into the underground reservoir through the injection well;

[0110] If the wellbore stability index Jwzs < the evaluation threshold W, it is determined that the formation stability degree in the current geothermal power station is not in an abnormal state. At this time, no additional injection and production means will be taken.

[0111] In this embodiment, the system continuously monitors and records the formation condition data in the geothermal power station. These data provide a basis for subsequent stability evaluation. Based on the formation condition data information, the wellbore stability index Jwzs is calculated. This index evaluates the stability of the wellbore by comprehensively considering parameters such as the spacing Gssj between the injection well and the production well and the water pressure Syy in the formation pores, further ensuring the safety of the wellbore during operation; the system compares and analyzes the wellbore stability index Jwzs with the pre-set evaluation threshold W. According to the comparison and analysis results, the system can accurately judge the stability degree of the formation in the current geothermal power station. If the wellbore stability index Jwzs ≥ the evaluation threshold W, the system determines that the formation stability in the geothermal power station is abnormal. The system will combine the geothermal operation condition data within the safe range and adjust the water injection rate Zssz and the water injection volume Zsz when cooling water is injected into the underground reservoir through the injection well to reduce the risk of formation instability. If the wellbore stability index Jwzs < the evaluation threshold W, the system determines that the formation stability degree in the geothermal power station is normal. The system will keep the current injection and production means unchanged to ensure the equipment operates in a stable state and improve the energy production efficiency and equipment reliability.

[0112] Example 7

[0113] Please refer to Figure 1 and Figure 2 , specifically: A monitoring system for new energy power generation equipment based on big data, including a condition monitoring module, a condition impact analysis module, a geothermal stability analysis module, and a decision-making management module;

[0114] The condition monitoring module pre-determines the geothermal exploitation range, and based on big data technology, it monitors the operation condition data in production wells and reinjection wells within the geothermal exploitation range in real time. After feature extraction, it constructs a geothermal operation set;

[0115] The condition impact analysis module constructs a water injection state coefficient Zzxs based on the geothermal operation set. If the water injection state coefficient Zzxs exceeds the condition threshold, it sends out a warning instruction. After receiving the warning instruction, it collects the trend of the difference in formation pressure value Dc to obtain a pressure change set, and analyzes and calculates the influence degree of the geothermal operation condition data on the formation pressure change to construct an influence coefficient Yxyz, and filters out the geothermal operation condition data within the safe range according to the numerical value of the influence coefficient Yxyz;

[0116] The geothermal stability analysis module adjusts the operation state of the water injection activity in the reinjection well based on the geothermal operation condition data within the safe range, and simultaneously monitors and records the formation condition data information in the geothermal power station, and constructs a wellbore stability index Jwzs based on the formation condition data information;

[0117] The decision-making management module pre-sets an evaluation threshold W, and compares and analyzes the wellbore stability index Jwzs with the evaluation threshold W to comprehensively predict and analyze the current formation stability degree in the geothermal power station. According to the prediction and analysis results, and combined with the geothermal operation condition data within the safe range, corresponding injection and production measures are taken.

[0118] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A monitoring method for new energy power generation equipment based on big data, characterized in that: The following steps are included: S1. Determine the geothermal exploitation scope in advance, and based on big data technology, monitor the operating condition data of production wells and reinjection wells within the geothermal exploitation scope in real time, and construct a geothermal operation set after feature extraction; S2. Construct the water injection state coefficient Zzxs based on the geothermal operation set. If the water injection state coefficient Zzxs exceeds the condition threshold, issue an early warning command. After receiving the early warning command, collect trends of the differences in the formation pressure values ​​Dc to obtain a pressure change set, and analyze and calculate the influence of the geothermal operation condition data on the formation pressure change to construct the influence coefficient Yxyz, and screen out the geothermal operation condition data within a safe range according to the value of the influence coefficient Yxyz. S2 also includes: S26, pre-set the impact threshold, if the impact coefficient Yxyz exceeds the impact threshold, then the water injection rate Zssz and water injection volume Zsz in the corresponding monitoring time node are marked as abnormal condition data; if the impact coefficient Yxyz does not exceed the impact threshold, then the water injection rate Zssz and water injection volume Zsz in the corresponding monitoring time node are marked as non-abnormal condition data; S261. According to the monitoring time nodes corresponding to the several groups of non-abnormal condition data, the corresponding formation pressure value Dc is extracted from the pressure change set, and the several groups of extracted formation pressure values ​​Dc are compared with the reference pressure Bz to screen out the geothermal operation condition data within the safe range. The specific screening contents are as follows: If the formation pressure value Dc at the corresponding monitoring time node is ≤ the reference pressure Bz, the water injection rate Zssz and water injection volume Zsz at the corresponding monitoring time node are included in the safety range of the water injection activity; If the formation pressure value Dc in the corresponding monitoring time node is greater than the reference pressure Bz, the water injection rate Zssz and water injection volume Zsz in the corresponding monitoring time node will not be included in the safety range of the water injection activity; S3. Based on the geothermal operating condition data within the safe range, the operating state of the water injection activity in the reinjection well is adjusted, and at the same time, the formation condition data information in the geothermal power station is monitored and recorded, and the wellbore stability index Jwzs is constructed based on the formation condition data information; S4. Pre-set an evaluation threshold W, and compare and analyze the wellbore stability index Jwzs with the evaluation threshold W to comprehensively predict and analyze the stability of the formation in the current geothermal power station. According to the prediction and analysis results, combined with the geothermal operating condition data within the safe range in step S2, take corresponding injection and production measures.

2. The monitoring method of new energy power generation equipment based on big data according to claim 1 is characterized in that: S1 specifically includes the following steps: S11. Pre-lock the geothermal power station to be monitored and obtain the overall layout of the geothermal power station, where the overall layout includes the locations of the generator room, boiler room, substation, production well and reinjection well. After feature extraction, obtain the distance Gssj between the reinjection well and the production well.

3. The monitoring method of new energy power generation equipment based on big data according to claim 1 is characterized in that: S1 also includes: S12. Based on big data technology, real-time monitoring of the operating condition data in the production wells and reinjection wells within the geothermal exploitation range, wherein the operating condition data includes the extraction rate and extraction volume in the production wells during different monitoring periods, and the water injection rate Zssz and water injection volume Zsz in the reinjection wells, and the wireless communication technology is combined to transmit the operating condition data to the operation platform; S121, preprocessing the operating condition data in the operating platform, detecting and deleting the repeatedly collected data information, and identifying the missing data to detect and repair abnormal values; S122. Utilize dimensionless processing technology to standardize the relevant data information preprocessed in step S121 to eliminate unit and dimension differences in the data.

4. The monitoring method of new energy power generation equipment based on big data according to claim 3 is characterized in that: S2 specifically includes the following steps: S21. According to the geothermal operation set, the injection rate Zssz and the injection volume Zsz in the reinjection well are associated, and after dimensionless processing, the corresponding data values ​​are mapped in the interval [0,1], and then the injection state coefficient Zzxs is obtained according to the following formula: Where n represents the monitoring period, i = 1, 2, 3, ..., n, Zssz i It is represented as the water injection rate at the i-th monitoring time node, It is expressed as the mean value of water injection rate during the monitoring period, Zsz i It is represented as the water injection volume at the i-th monitoring time node, It is expressed as the mean value of water injection volume during the monitoring period, and α and β are expressed as weight coefficients.

5. The monitoring method of new energy power generation equipment based on big data according to claim 1 is characterized in that: S2 also includes: S22, presetting a condition threshold, and comparing and analyzing the condition threshold with the water injection state coefficient Zzxs. If the water injection state coefficient Zzxs exceeds the condition threshold, it is estimated that the current water blocking activity of the recharge well is in an abnormal state, and an early warning instruction is issued externally; If the water injection state coefficient Zzxs does not exceed the condition threshold, no additional warning instructions will be issued; S23, after receiving the early warning instruction, collecting the trend of the difference of the formation pressure value Dc to obtain a pressure change set, wherein the pressure change set includes the formation pressure values ​​Dc at different monitoring time nodes; S24. After the scope of geothermal exploitation is determined, drilling operations are carried out in the formation by means of drilling equipment, and mud is used during the drilling process to maintain the stability of the well wall and carry drill cuttings back to the surface, wherein the drilling equipment includes but is not limited to a drill bit, a drill pipe and a drilling rig, and after the drilling is completed, a pressure gauge is placed in the well, the wellhead is closed, fluid injection is stopped, the static pressure of the formation is monitored, and the static pressure of the formation is used as the reference pressure Bz.

6. The monitoring method of new energy power generation equipment based on big data according to claim 5 is characterized in that: S2 also includes: S25. Based on the least square method, combined with the operating condition data and the pressure change set, the influence of the geothermal operating condition data on the formation pressure change is analyzed and calculated. The specific analysis content is as follows: taking the operating condition data corresponding to the water injection state coefficient Zzxs as the independent variable and the formation pressure value Dc at each monitoring time node as the dependent variable, a linear regression analysis is performed, and the corresponding regression equation is obtained; The regression coefficient corresponding to the operating condition data in the regression equation is used as the influencing factor to construct the influencing coefficient Yxyz, which is obtained in the following way: In the formula, θ z Expressed as the influencing factor of water injection rate, θ s It is expressed as the influencing factor of water injection volume, and a1 and a2 are weight coefficients.

7. The monitoring method of new energy power generation equipment based on big data according to claim 2 is characterized in that: S3 specifically includes the following steps: S31, monitoring and recording formation condition data information in the geothermal power station, wherein the formation condition data information includes the pressure change value Ybz during extraction and reinjection and the water pressure Syy in the formation pores; S32. According to the formation condition data information, the pressure change value Ybz during the extraction and reinjection process and the water pressure Syy in the formation pores are correlated, and after dimensionless processing, the wellbore stability index Jwzs is calculated and obtained. The wellbore stability index Jwzs is obtained by the following formula: Where Gssj is the distance between the reinjection well and the production well, v1, v2 and v3 are weight coefficients, and P is the correction constant.

8. The monitoring method of new energy power generation equipment based on big data according to claim 1 is characterized in that: S4. Based on the result of comparing and analyzing the wellbore stability index Jwzs with the evaluation threshold W, a comprehensive prediction and analysis is performed to determine the stability of the formation in the current geothermal power station. The specific analysis contents are as follows: If the wellbore stability index Jwzs ≥ the evaluation threshold W, it is judged that the formation stability in the current geothermal power station is in an abnormal state. At this time, the injection rate Zssz and the injection volume Zsz of cooling water injected into the underground reservoir through the reinjection well will be adjusted in combination with the geothermal operation condition data within the safe range; If the wellbore stability index Jwzs is less than the evaluation threshold W, it is determined that the stability of the formation in the current geothermal power station is not in an abnormal state, and no additional injection and production measures will be taken.

9. A monitoring system for new energy power generation equipment based on big data, using the monitoring method for new energy power generation equipment based on big data as described in any one of claims 1 to 8, characterized in that: It includes condition monitoring module, condition impact analysis module, geothermal stability analysis module and decision management module; The condition monitoring module determines the geothermal exploitation range in advance, and based on big data technology, monitors the operating condition data of production wells and reinjection wells within the geothermal exploitation range in real time, and constructs a geothermal operation set after feature extraction; The conditional impact analysis module constructs a water injection state coefficient Zzxs based on the geothermal operation set. If the water injection state coefficient Zzxs exceeds the condition threshold, an early warning instruction is issued. After receiving the early warning instruction, the trend of the difference in the formation pressure value Dc is collected to obtain the pressure change set, and the influence of the geothermal operation condition data on the formation pressure change is analyzed and calculated to construct the influence coefficient Yxyz, and the geothermal operation condition data within the safe range is screened out according to the value of the influence coefficient Yxyz; The geothermal stability analysis module adjusts the operation status of the water injection activity in the reinjection well based on the geothermal operation condition data within the safe range, monitors and records the formation condition data information in the geothermal power station, and constructs the wellbore stability index Jwzs based on the formation condition data information; The decision management module pre-sets an evaluation threshold W, and compares and analyzes the wellbore stability index Jwzs with the evaluation threshold W to comprehensively predict and analyze the stability of the formation in the current geothermal power station, and adopts corresponding injection and production measures based on the prediction and analysis results and combined with geothermal operating condition data within a safe range.

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