A dynamic prediction method for pipe string life based on corrosion logging

Abstract

The present invention relates to the field of dynamic prediction of tubular string life, and more particularly to a method for dynamic prediction of tubular string life based on corrosion logging. The method comprises: using a single parameter change test to construct a target parameter affecting the corrosion rate of the test tubular string; calculating the acid gas partial pressure, liquid holdup, and fluid flow pattern at each monitoring point; determining the corresponding corrosion rate at the monitoring point using electromagnetic flaw detection logging; establishing a corrosion rate prediction model using a neural network prediction method; and determining future target prediction parameters by predicting the gas production, water production, and formation pressure in each future year. The corrosion rate in each future year is then determined; the remaining wall thickness in the future year is calculated, and the safety factor against internal pressure and the safety factor against external collapse are determined to determine the remaining life of the tubular string to be predicted, and timely warning information is issued; and the warning information and collected data are transmitted to a terminal using wireless communication technology for real-time monitoring of the tubular string. This achieves dynamic prediction of gas well life.

Description

Technical Field

[0001] The present invention relates to the field of dynamic prediction of pipe string life, and in particular to a method for dynamic prediction of pipe string life based on corrosion logging detection. Background Art

[0002] In recent years, the gas produced from marine strata in the Sichuan Basin generally contains acidic gases such as H2S and CO2, and gas wells face serious dual corrosion failure risks. It is a common practice to use economical and applicable pipes to improve economic benefits under the premise of safety. The most widely used oil pipe material on site is high-sulfur-resistant carbon steel such as P110SS, which can better deal with stress corrosion cracking problems. However, corrosion thinning and perforation are still common problems, and subsequent timely intervention and treatment measures must be taken. Therefore, how to determine the service life of pipes in specific blocks is a key technical issue to ensure safe and economical production of gas wells. At present, there is no accurate, economical and applicable life prediction technology, and existing methods are not adaptable.

[0003] Chinese patent application publication number CN103206205A discloses a method for predicting the life of a tubing string. The method includes: calculating the maximum stress of a new tubing string, where σ is the maximum stress of the new tubing string, L is the length of the tubing string, q is the weight of the tubing string per meter, Do is the outer diameter of the new tubing string, and Di is the inner diameter of the new tubing string; calculating the maximum stress of the tubing string at a certain time: Dtx = D i+2vt, where σt is the maximum stress of the tubing string at a certain time, Dtx is the inner diameter of the tubing string at a certain time, Di is the inner diameter of the new tubing, v is the corrosion rate, and t is time; the tensile safety factor of the new tubing string is calculated according to the following formula: where α is the tensile safety factor of the new tubing string, P is the tensile limit load of the new tubing string thread, and g is the acceleration of gravity; the tensile safety factor of the tubing string at a certain time is calculated based on the tensile safety factor of the new tubing string calculated above: where αt is the tensile safety factor of the tubing string at a certain time; when the tensile safety factor of the tubing string at a certain time is equal to the preset safety factor threshold, the decrease time of the tensile safety factor of the tubing string is the tensile safety life of the tubing string.

[0004] In the prior art, the remaining life of the oil tubing string is calculated based on known parameters without analyzing dynamic prediction data, which has certain limitations in predicting the life of the tubing string. Summary of the Invention

[0005] To this end, the present invention provides a dynamic prediction method for tubing string life based on corrosion logging detection. By analyzing the key influencing factors of dynamic parameters and corrosion degree in the production system, the tubing string corrosion rate is obtained efficiently and accurately, thereby solving the problem of effectively predicting the remaining life of the tubing string within fixed parameters.

[0006] To achieve the above objectives, the present invention provides a method for dynamically predicting pipe string life based on corrosion logging detection, comprising:

[0007] A single parameter change test is used to establish several target parameters that affect the corrosion rate of the test string, wherein the target parameters include temperature, acid gas partial pressure, liquid holdup, and fluid flow pattern;

[0008] A plurality of monitoring points are set at intervals of depth along the extension direction of the wellbore, starting from the wellhead, recording the temperature and pressure at each monitoring point, and determining the acid gas partial pressure at each monitoring point based on the pressure and the acid gas ratio;

[0009] The gas-liquid mixture in the wellbore is transmitted using a test string, and the gas-liquid flow rate at each monitoring point is calculated based on the inner diameter of the test string, the wellhead pressure, and the total production of the gas-liquid mixture in the wellbore;

[0010] determining a gas-liquid two-phase flow pattern of the gas-liquid mixture at the monitoring point according to the transmission position of the gas-liquid mixture in the test string;

[0011] Calculating the liquid holdup and fluid flow pattern of each monitoring point according to the gas-liquid flow rate and the gas-liquid two-phase flow pattern;

[0012] Using electromagnetic flaw detection and logging technology to determine the wellbore wall thickness change rate at each monitoring point to determine the corrosion rate corresponding to each monitoring point;

[0013] A corrosion rate prediction model is established using a neural network prediction method according to the target parameters of each monitoring point and the corrosion rate;

[0014] A tubing string to be predicted is set in the gas well to be evaluated, wherein the tubing string to be predicted is used to transport the gas-liquid mixture in the gas well to be evaluated;

[0015] Predicting the gas production, water production, and formation pressure in each future year based on the current parameters of the gas well to be evaluated, and determining target prediction parameters for the gas well in each future year based on the pressure at each location and the total gas-liquid mixture production of the gas well to be evaluated;

[0016] Determining the corrosion rate in each future year based on the target prediction parameters and the corrosion rate prediction model;

[0017] Determine the remaining wall thickness in future years at each location of the gas well to be evaluated based on the corrosion rates in future years;

[0018] Calculating the safety factor against internal pressure and the safety factor against external collapse based on the minimum remaining wall thickness in any year and each target prediction parameter to determine the remaining life of the pipe string to be predicted and issuing a warning message when the pipe string reaches its life;

[0019] A corrosion sensor, a stress sensor and a temperature sensor are installed on the pipe string, and the early warning information and collected data are transmitted to the terminal using wireless communication technology to perform real-time monitoring of the pipe string.

[0020] Furthermore, the process of constructing several target parameters that affect the corrosion rate of the test string using a single parameter change test includes:

[0021] Using high-temperature autoclave equipment, a weight loss corrosion test of the sample was carried out to determine the corrosion rate of the test string;

[0022] According to the corrosion rate, one target parameter is changed in the test string respectively, and the remaining target parameters are taken to the most extreme value within the research range. The influence of each target parameter on the corrosion rate is evaluated one by one, so as to determine the target parameter that affects the corrosion rate of the test string.

[0023] Furthermore, the process of determining the acid gas partial pressure at each monitoring point according to the pressure and the acid gas proportion includes:

[0024] Determine the molar percentage of acid gas in the mixed gas based on experiments or chemical analysis;

[0025] Using a pressure gauge to measure the total pressure of the mixed gas, multiplying the molar content percentage of the acid gas by the total pressure to obtain the partial pressure of the acid gas;

[0026] Repeat the above steps at each monitoring point along the wellbore extension direction to determine the acid gas partial pressure at each point.

[0027] Furthermore, the process of calculating the liquid holdup and fluid flow pattern at each monitoring point according to the gas-liquid flow rate and the gas-liquid two-phase flow pattern includes:

[0028] The flow rate of the gas-liquid mixture in the wellbore is measured by a flow meter, and the wellhead pressure is reduced. The gas-liquid flow rate at each monitoring point is calculated according to the inner diameter of the pipe string;

[0029] By collecting the flow data of the gas-liquid mixture in the test string, analyzing the properties of the gas and liquid in the gas-liquid mixture, and measuring the ratio of the volume or mass of the gas phase to the volume or mass of the entire gas-liquid mixture using an offline instrument, the ratio is used as the gas holdup;

[0030] Determining the gas-liquid flow pattern at the monitoring point based on the gas-liquid flow rate, fluid properties and gas content of the gas-liquid mixture in the column and with reference to a gas-liquid two-phase flow pattern diagram;

[0031] According to the gas-liquid flow rate and the gas-liquid flow pattern, the liquid holdup and fluid flow pattern of each monitoring point are calculated using multiphase flow PIPESIM software.

[0032] Furthermore, the process of determining the corrosion rate corresponding to each of the monitoring points includes:

[0033] Use electromagnetic flaw detection and logging technology to detect the remaining wall thickness of the wellbore and compare it with the wall thickness detected last time;

[0034] The corrosion rate corresponding to the monitoring point is determined based on the comparison result and the time interval between two detections.

[0035] Furthermore, the process of establishing a corrosion rate prediction model according to the target parameters of each monitoring point and the corrosion rate includes:

[0036] The corrosion target parameter values ​​of each monitoring point are evenly divided and used as training data and test data of the neural network model respectively;

[0037] A neural network prediction method is adopted, wherein the corrosion target parameter value of each monitoring point is used as input, the corrosion rate corresponding to the monitoring point is used as output data, and the training data is used to complete the training of the neural network model;

[0038] The test data is input into the neural network model, and the predicted corrosion rate result output by it is compared with the actual corrosion rate in the test data to ensure that the error is within the standard range. Otherwise, the number of training samples is increased and the neural network model is retrained until the standard range requirements are met, thereby establishing a corrosion rate prediction model.

[0039] Furthermore, the process of determining the corrosion rate in each future year based on the target prediction parameter and the corrosion rate prediction model includes:

[0040] The target prediction parameters are input into the corrosion rate prediction model to determine the corrosion rate in each future year.

[0041] Furthermore, the process of determining the remaining wall thickness in future years at each location of the gas well to be evaluated based on the corrosion rates in future years includes:

[0042] Based on the corrosion rates in each future year, determining the corroded wall thickness value at each corresponding position in the future;

[0043] The future remaining wall thickness of each position is determined based on the current wall thickness of each position of the gas well and the corroded wall thickness value of each corresponding position in the future.

[0044] Furthermore, the process of calculating the safety factor against internal pressure and the safety factor against external collapse based on the minimum remaining wall thickness in any year and the target prediction parameters to determine the remaining life of the pipe string to be predicted, and issuing a warning message in a timely manner when the pipe string reaches its life includes:

[0045] Determine the minimum remaining wall thickness in any year based on the future remaining wall thickness at each location and the corrosion rate in each future year;

[0046] Calculating a safety factor against internal pressure or a safety factor against external collapse based on the minimum remaining wall thickness;

[0047] When the safety factor against internal pressure or the safety factor against external collapse is lower than a target threshold, it is determined that the pipe string life is reached;

[0048] An early warning value is set according to a target threshold value, and an early warning message is triggered when the safety factor against internal pressure or the safety factor against external collapse is lower than the target threshold value.

[0049] Furthermore, the process of using wireless communication technology to transmit the warning information and collected data to the terminal for real-time monitoring of the pipe string includes:

[0050] Use a data acquisition system to collect sensor data, including corrosion level, formation pressure and temperature;

[0051] Utilize wireless communication technology or LoRaWAN to transmit sensor data from the data acquisition system to a central server or cloud platform;

[0052] The collected data is stored for integration and analysis to view visual data charts and alarm information of the pipe string status.

[0053] Compared with the prior art, the beneficial effect of the present invention lies in that a single parameter change test is used to construct several target parameters that affect the corrosion rate of the test string, thereby enhancing the ability to predict the development trend of corrosion and providing a basis for preventive maintenance; a number of monitoring points are set along the extension direction of the wellbore with the wellhead as the starting point and Δh1 as the depth interval, the temperature and pressure of each monitoring point are recorded, and the acid gas partial pressure of each monitoring point is determined according to the pressure and the acid gas ratio, and the test string is used for transmission according to the gas-liquid mixture in the wellbore, and the liquid holdup and fluid flow state of each monitoring point are calculated to provide a more comprehensive corrosion influencing parameter analysis to ensure that all parameters affecting corrosion are taken into account; the corrosion rate corresponding to each monitoring point is determined by determining the wellbore wall thickness change rate of each monitoring point, which helps to promote the standardization of corrosion technology in the industry and provides a basis for establishing a corrosion rate prediction model; a corrosion rate prediction model is established according to the target parameters and the corrosion rate of each monitoring point, thereby reducing the need for on-site testing. The system reduces reliance on testing, thereby reducing costs and time consumption; predicts the gas production, water production, and formation pressure in future years based on the current parameters of the gas well to be evaluated, determines the target prediction parameters for the gas well in future years, and determines the predicted corrosion rate of the tubing in future years based on the corrosion rate prediction model, assesses the corrosion risk in future years, and takes timely preventive measures; determines the remaining wall thickness at each location of the gas well in future years based on the current parameters of the gas well to be evaluated and the corrosion rate in future years, which can predict the remaining wall thickness in advance and then plan predictive maintenance, reducing the need for emergency repairs; determines the internal pressure safety factor and the external collapse safety factor based on the minimum remaining wall thickness in any year and the target prediction parameters, predicts the remaining life of the tubing, and issues early warning information in a timely manner, thereby effectively improving the accuracy of corrosion prediction. Based on sensors installed on the tubing, wireless communication technology is used to transmit the warning information and collected data to a terminal for real-time monitoring of the tubing, thereby achieving dynamic gas well life prediction.

[0054] In particular, the corrosion rate of the test tubing was tested using a high-temperature, high-pressure autoclave, making the experimental results more valuable for practical applications. For the corrosion rate, one corrosion target parameter was changed in the test tubing, and the remaining target parameters were taken to the most extreme values ​​within the research range. The degree of influence of each target parameter on the corrosion rate was evaluated one by one. By identifying the parameters that affect the corrosion target, the corrosion rate can be more effectively predicted, providing data support for the experiment.

[0055] In particular, accurate temperature and pressure data provided by electromagnetic flaw detection and logging technology provide key parameters for calculating partial pressure bodies. The acid gas partial pressure at each monitoring point is determined by multiplying the molar content percentage by the pressure, which helps to accurately calculate the acid gas content and enhance the accuracy of the tubing string test.

[0056] In particular, by using a test string to transmit the gas-liquid mixture in the wellbore and calculating the liquid holdup and fluid flow pattern, the flow behavior and flow problems of the gas-liquid mixture in the string can be more accurately predicted, which helps to optimize production operation parameters.

[0057] In particular, by comparing the two test results, the corrosion rate can be quantified and the severity and development trend of corrosion can be evaluated.

[0058] In particular, a corrosion rate prediction model is established by combining the target parameters of each monitoring point with the corrosion rate to predict future corrosion rates and help formulate a preventive maintenance plan.

[0059] In particular, determining the corrosion rate for each future year based on the target prediction parameters and the corrosion rate prediction model helps to formulate a preventive maintenance plan, improve response speed and flexibility, and enhance market competitiveness.

[0060] In particular, the remaining wall thickness in future years at each location of the gas well to be evaluated is determined based on current parameters and the corrosion rate in future years, thereby preventing corrosion problems in advance and reducing the risk of wellbore failure due to insufficient wall thickness.

[0061] In particular, the safety factor against internal pressure and the safety factor against external collapse are calculated based on the minimum remaining wall thickness in any year, the remaining life of the predicted pipe string is determined, and when the pipe string reaches its life, early warning information is issued in a timely manner, ensuring the structural safety of the pipe string and reducing the risk of rupture or failure.

[0062] In particular, transmitting the warning information and collected data to the terminal for real-time monitoring of the pipe string based on communication technology can achieve instant data transmission, ensure that the status information of the pipe string can be received in real time, and respond to possible problems in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic flow chart of a method for dynamically predicting tubing string life based on corrosion logging detection provided by an embodiment of the present invention;

[0064] Figure 2 A schematic diagram of several target parameter flow charts of a method for dynamically predicting tubing life based on corrosion logging provided by an embodiment of the present invention;

[0065] Figure 3 A schematic flow chart of the acid gas partial pressure at each monitoring point of the method for dynamically predicting the life of a tubing string based on corrosion logging provided by an embodiment of the present invention;

[0066] Figure 4 A schematic flow chart of the liquid holdup and fluid flow pattern at each monitoring point of the dynamic prediction method for tubing life based on corrosion logging provided by an embodiment of the present invention;

[0067] Figure 5 A schematic diagram of a process for determining the corrosion rate corresponding to each monitoring point in a dynamic prediction method for tubing life based on corrosion logging provided by an embodiment of the present invention;

[0068] Figure 6 A test result diagram showing the corrosion rate change of the dynamic prediction method for tubing life based on corrosion logging provided by an embodiment of the present invention;

[0069] Figure 7 A schematic diagram of a process for establishing a corrosion rate prediction model for a dynamic prediction method for tubing life based on corrosion logging provided by an embodiment of the present invention;

[0070] Figure 8 A schematic diagram of the process flow of the future annual remaining wall thickness of the method for dynamically predicting the pipe string life based on corrosion logging detection provided by an embodiment of the present invention;

[0071] Figure 9 A graph showing the predicted corrosion rate of a tubing string according to a dynamic prediction method for tubing string life based on corrosion logging provided by an embodiment of the present invention;

[0072] Figure 10 A graph showing the remaining wall thickness at various locations of a gas well to be evaluated in the future according to the method for dynamically predicting the life of a tubing string based on corrosion logging provided by an embodiment of the present invention;

[0073] Figure 11 A schematic diagram of a process for determining when the remaining life of a tubing string to be predicted reaches its lifespan and issuing a warning message in a dynamic tubing string lifespan prediction method based on corrosion logging detection provided by an embodiment of the present invention;

[0074] Figure 12 A schematic diagram of a process for transmitting early warning information and collected data to a terminal for monitoring the tubing string by wireless communication in a dynamic prediction method for tubing string life based on corrosion logging detection provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0076] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0077] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as a limitation on the present invention.

[0078] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0079] See also Figure 1 As shown, the present invention provides a method for dynamically predicting the life of a tubing string based on corrosion logging detection, the method comprising:

[0080] Step S100, using a single parameter change test to construct several target parameters that affect the corrosion rate of the test string;

[0081] Step S200: setting a plurality of monitoring points at intervals of depth along the wellbore extension direction starting from the wellhead, recording the temperature and pressure at each monitoring point, and determining the acid gas partial pressure at each monitoring point based on the pressure and the acid gas ratio;

[0082] Step S300, calculating the liquid holdup and fluid flow pattern of each monitoring point according to the gas-liquid mixture in the wellbore transmitted using a test string;

[0083] Step S400, using electromagnetic flaw detection and logging technology to determine the wellbore wall thickness change rate of each monitoring point to determine the corrosion rate corresponding to each monitoring point;

[0084] Step S500, establishing a corrosion rate prediction model using a neural network prediction method according to the target parameters of each monitoring point and the corrosion rate;

[0085] Step S600, predicting the gas production, water production, and formation pressure in each future year based on the current parameters of the gas well to be evaluated, determining the target prediction parameters of the gas well to be evaluated in each future year, and determining the predicted tubing corrosion rate in each future year based on the corrosion rate prediction model;

[0086] Step S700, determining the remaining wall thickness of the gas well to be evaluated in the future year at each position based on the current parameters of the gas well to be evaluated and the corrosion rate in the future year;

[0087] Step S800, calculating the safety factor against internal pressure and the safety factor against external collapse based on the minimum remaining wall thickness in any year and each target prediction parameter to determine the remaining life of the pipe string to be predicted and issuing a warning message when the pipe string reaches its life;

[0088] Step S900: Install a corrosion sensor, a stress sensor, and a temperature sensor on the pipe string, and use wireless communication technology to transmit the warning information and collected data to a terminal for real-time monitoring of the pipe string.

[0089] Specifically, the embodiment of the present invention uses a single parameter change test to construct several target parameters that affect the corrosion rate of the test string, thereby enhancing the ability to predict the development trend of corrosion and providing a basis for preventive maintenance; a number of monitoring points are set along the extension direction of the wellbore with the wellhead as the starting point and Δh1 as the depth interval, the temperature and pressure of each monitoring point are recorded, and the acid gas partial pressure of each monitoring point is determined according to the pressure and the acid gas ratio, and the test string is used for transmission according to the gas-liquid mixture in the wellbore, and the liquid holdup and fluid flow state of each monitoring point are calculated to provide a more comprehensive corrosion influencing parameter analysis to ensure that all parameters affecting corrosion are considered; the wellbore wall thickness change rate of each monitoring point is determined to determine the corrosion rate corresponding to each monitoring point, which helps to promote the standardization of corrosion technology in the industry and provides a basis for establishing a corrosion rate prediction model; a corrosion rate prediction model is established based on the target parameter and the corrosion rate of each monitoring point, thereby reducing dependence on field testing. Reduce costs and time consumption; predict the gas production, water production and formation pressure in future years based on the current parameters of the gas well to be evaluated, determine the target prediction parameters of the gas well to be evaluated in future years, and determine the predicted pipe corrosion rate in future years based on the corrosion rate prediction model, evaluate the corrosion risk in future years, and take timely preventive measures; determine the remaining wall thickness of each location of the gas well to be evaluated in future years based on the current parameters of the gas well to be evaluated and the corrosion rate in future years, so as to predict the remaining wall thickness in advance and then plan predictive maintenance to reduce the need for emergency repairs; determine the internal pressure safety factor and the external collapse safety factor based on the minimum remaining wall thickness in any year and the target prediction parameters, predict the remaining life of the pipe string and issue early warning information in time, thereby effectively improving the accuracy of corrosion prediction; based on the sensors installed on the pipe string, use wireless communication technology to transmit the warning information and collected data to the terminal for real-time monitoring of the pipe string to achieve dynamic gas well life prediction.

[0090] See also Figure 2 As shown in FIG, the process of constructing several target parameters that affect the corrosion rate of the test string using a single parameter change test includes:

[0091] Step S110, using a high-temperature autoclave device to conduct a weight loss corrosion test on the sample to determine the corrosion rate of the test string;

[0092] In step S120, based on the corrosion rate, one target parameter is changed in the test string, and the remaining target parameters are set to the most extreme value within the study range. The influence of each target parameter on the corrosion rate is evaluated one by one, thereby determining that the target parameters that affect the corrosion rate of the test string are acid gas partial pressure, wellbore temperature, liquid holdup, and fluid flow state.

[0093] Specifically, the corrosion rate of the test string is tested using a high-temperature autoclave as described in the embodiment of the present invention, making the experimental results more practical. For the corrosion rate, one corrosion target parameter is changed in the test string respectively, and the remaining target parameters are taken to the most extreme value within the research range. The influence of each target parameter on the corrosion rate is evaluated one by one. By identifying the parameters that affect the corrosion target, the corrosion rate can be more effectively predicted, providing data support for the experiment.

[0094] See also Figure 3 As shown, the process of determining the acid gas partial pressure at each monitoring point according to the pressure and the acid gas ratio includes:

[0095] Step S210, determining the molar percentage of the acid gas in the mixed gas based on experiments or chemical analysis;

[0096] Step S220, using a pressure gauge or measuring equipment to measure the total pressure of the mixed gas, converting the molar content percentage of the acid gas into a decimal form, and multiplying it by the total pressure to obtain the partial pressure of the acid gas;

[0097] Step S230 , repeating the above steps at each monitoring point in the extension direction of the wellbore to determine the acid gas partial pressure at each point.

[0098] Specifically, the embodiments of the present invention provide accurate temperature and pressure data based on electromagnetic flaw detection and logging technology, providing key parameters for calculating partial pressure bodies. The acid gas partial pressure at each monitoring point is determined by multiplying the molar content percentage by the pressure, which helps to accurately calculate the acid gas content and enhance the accuracy of the tubing string test.

[0099] See also Figure 4 As shown, the process of calculating the liquid holdup and fluid flow pattern of each monitoring point according to the gas-liquid flow rate and the gas-liquid two-phase flow pattern includes:

[0100] Step S310, using a flow meter to measure the flow rate of the gas-liquid mixture in the wellbore, and performing a pressure reduction process on the wellhead, and calculating the gas-liquid flow rate at each monitoring point according to the inner diameter of the pipe string;

[0101] Step S320, by collecting the gas-liquid mixture flow data in the test string, analyzing the properties of the gas and liquid in the gas-liquid mixture, and measuring the ratio of the volume or mass of the gas phase to the volume or mass of the entire gas-liquid mixture using an offline instrument, and using the ratio as the gas holdup;

[0102] Step S330, determining the gas-liquid flow pattern at the monitoring point based on the gas-liquid flow rate, fluid properties and gas void fraction of the gas-liquid mixture in the column and with reference to a gas-liquid two-phase flow pattern diagram;

[0103] Step S340 , calculating the liquid holdup and fluid flow pattern at each of the monitoring points using multiphase flow PIPESIM software according to the gas-liquid flow pattern.

[0104] Specifically, the embodiments of the present invention use a test string to transmit the gas-liquid mixture in the wellbore, calculate the liquid holdup and fluid flow pattern, and more accurately predict the flow behavior and flow problems of the gas-liquid mixture in the string, which helps to optimize production operation parameters.

[0105] See also Figure 5 As shown, the process of determining the corrosion rate corresponding to each monitoring point includes:

[0106] Step S410, using electromagnetic flaw detection and logging technology to detect the remaining wall thickness of the wellbore and compare it with the wall thickness detected last time;

[0107] Step S420: Determine the corrosion rate corresponding to the monitoring point based on the comparison of the results and the time interval between two detections.

[0108] Specifically, electromagnetic flaw detection and logging technology is used to detect the current wall thickness of the wellbore oil pipe and compare it with the wall thickness test result of the last time (one and a half years ago). Based on the time interval between the two tests, the wall thickness loss rate at different wellbore depths is obtained, that is, the corrosion rate. The data unit is mm / year. Starting from the wellhead depth of 0, the wall thickness thinning value point and the corrosion rate value point are determined with a depth interval of 1m (in meters). The corrosion rate is between 0.08-0.52mm / a, with an average of 0.29mm / a. The test results are as follows: Figure 6 .

[0109] Specifically, the embodiment of the present invention can quantify the corrosion rate and evaluate the severity and development trend of corrosion by comparing the two detection results.

[0110] See also Figure 7 As shown, the process of establishing a corrosion rate prediction model based on the target parameters of each monitoring point and the corrosion rate includes:

[0111] Step S510, dividing the corrosion target parameter values ​​of each monitoring point into two groups, which are used as training data and test data of the neural network model respectively;

[0112] Step S520, using a neural network prediction method, taking the corrosion target parameter value of each monitoring point as input, taking the corrosion rate corresponding to the monitoring point as output data, and completing training of the neural network model using training data;

[0113] In step S530, the test data is input into the neural network model, and the predicted corrosion rate result output by the neural network model is compared with the actual corrosion rate in the test data to ensure that the error is within the standard range. Otherwise, the number of training samples is increased and the neural network model is retrained until the standard range requirements are met, thereby establishing a corrosion rate prediction model.

[0114] Specifically, the parameters of the main controlling factors of corrosion at different wellbore depths are divided into two parts, which are used as neural network training data and test data, respectively, as shown in Table 1 and Table 2.

[0115] Table 1 BP neural network model training samples

[0116]

[0117]

[0118] The main corrosion influencing parameters at different wellbore depths, namely gas partial pressure, wellbore temperature, liquid holdup, and flow state, are used as input data. In this example, 14 groups of data are randomly selected, and the corrosion rate values ​​obtained from well logging at the corresponding depths are used as output data. The data are input into the neural network model, and the BP neural network parameters are set to implement data sample training. The S-type function is used to establish the transformation function between units, and a nonlinear mapping relationship between the main corrosion influencing parameters and the corrosion rate values ​​at the corresponding depths is established. The training of all input data is completed, and the training sample results are shown in Table 1.

[0119] Table 2 BP neural network model test results

[0120]

[0121] The trained neural network model was validated using test data. Model parameters were continuously adjusted until the expected prediction accuracy was achieved. Five sets of data on key corrosion-influencing parameters were randomly sampled at different wellbore depths as validation data. These data were then fed into the trained BP neural network model. The model-predicted corrosion rates were compared with the actual corrosion rates of the test samples. The results, shown in Table 2, were within 20% error, meeting engineering application requirements. Otherwise, adjustments to the network parameters or an increase in the number of training samples would be necessary, and the neural network model would need to be retrained until the accuracy requirements were met.

[0122] Specifically, in the embodiment of the present invention, a corrosion rate prediction model is established by combining the target parameters of each monitoring point with the corrosion rate to predict future corrosion rates and help formulate a preventive maintenance plan.

[0123] Specifically, the embodiment of the present invention determines the corrosion rate in each future year by inputting target prediction parameters into the corrosion rate prediction model.

[0124] Specifically, in the embodiment of the present invention, the corrosion rate for each future year is determined based on the target prediction parameters and the corrosion rate prediction model, which helps to formulate a preventive maintenance plan, improve response speed and flexibility, and enhance market competitiveness.

[0125] See also Figure 8 As shown, the process of determining the remaining wall thickness in future years at each location of the gas well to be evaluated based on the corrosion rate in future years includes:

[0126] Step S610, based on the corrosion rate in each future year, determining the corroded wall thickness value at each corresponding position in the future;

[0127] Step S620 , determining the future remaining wall thickness at each position based on the current wall thickness at each position of the gas well and the corroded wall thickness value at each position in the future.

[0128] Specifically, the gas production, water production and formation pressure in the next 1-5 years are predicted based on the current parameters of the gas well to be evaluated, as shown in Table 3. The values ​​of the main corrosion influencing factors of the gas well to be evaluated in the next 1-5 years, such as gas partial pressure, wellbore temperature, liquid holdup, and fluid flow state, are determined and input into the corrosion rate prediction model to determine the predicted string corrosion rate in the next 1-5 years. For details, see Figure 9 , according to the predicted string corrosion rate in 1-5 years, determine the corroded wall thickness value of each corresponding position in the future, and according to the wall thickness of each position of the gas well to be evaluated at present minus the corroded value of each corresponding position in the future, determine the future remaining wall thickness of each position of the gas well to be evaluated, such as Figure 10 .

[0129] Table 3 Parameter forecast for the next 1-5 years

[0130]

[0131] Specifically, in the embodiment of the present invention, the future remaining wall thickness of each position of the gas well to be evaluated is determined based on the current wall thickness of each position of the gas well to be evaluated minus the corrosion value of each corresponding position in the future.

[0132] Specifically, in an embodiment of the present invention, the remaining wall thickness in future years at each location of the gas well to be evaluated is determined based on current parameters and the future annual corrosion rate, thereby preventing corrosion problems in advance and reducing the risk of wellbore failure due to insufficient wall thickness.

[0133] See also Figure 11 As shown, the process of calculating the safety factor against internal pressure and the safety factor against external collapse based on the minimum remaining wall thickness in any year and each target prediction parameter to determine the remaining life of the pipe string to be predicted and issuing an early warning message when the pipe string reaches its life includes:

[0134] Step S710, determining the minimum remaining wall thickness in any year based on the future remaining wall thickness at each location and the corrosion rate in each future year;

[0135] Step S720, calculating the safety factor against internal pressure or the safety factor against external collapse according to the minimum remaining wall thickness;

[0136] Step S730: When the safety factor against internal pressure or the safety factor against external collapse is lower than a target threshold of 1.5, it is determined that the pipe string life is reached;

[0137] Step S740: setting a warning value of 1.5 according to the target threshold value, and triggering a warning message when the safety factor against internal pressure or the safety factor against external collapse is lower than the target threshold value of 1.5.

[0138] Specifically, in the embodiment of the present invention, the internal pressure value of the pipe string and the internal pressure resistance strength of the pipe string are calculated to obtain the internal pressure resistance safety factor.

[0139] Column internal pressure value:

[0140]

[0141] Among them, p smax is the upper limit of the pressure in the pipe, unit is MPa; G is the specific gravity of natural gas; h v The vertical depth of the wellbore calculation point, unit is m; H is the vertical depth of the wellbore, unit is m.

[0142] Pipe string internal pressure strength:

[0143]

[0144] Among them, [p i ] is the internal pressure strength, unit is MPa; k t Wellbore wall thickness deviation coefficient is 0.875; f ymn is the minimum yield strength of the string material, in MPa; t cmin is the minimum remaining wall thickness, in mm; D cmax It is the measured maximum outer diameter in mm.

[0145] Safety factor against internal pressure:

[0146] S i =[p i ] / p i

[0147] Specifically, in the embodiment of the present invention, the absolute external extrusion pressure and the external extrusion resistance strength of the casing are calculated to obtain the external extrusion resistance safety factor.

[0148] Safety factor against external collapse:

[0149] S o =[p o ] / p o

[0150] Among them, S o is the safety factor against external collapse, [p o ] is the casing's anti-external collapse strength, p o External extrusion pressure, divided into absolute external extrusion pressure and effective external extrusion pressure;

[0151] The absolute external extrusion pressure is calculated as follows:

[0152] p o =p oh +0.00981ρ c h v

[0153] Among them, p o is the external extrusion pressure, unit MPa, p oh is the annular pressure outside the wellbore, unit MPa, ρ c is the density of the formation water outside the wellbore, which is 1.05 g / cm 3

[0154] The calculation formula for the minimum operating pressure of the wellbore is:

[0155]

[0156] Among them, p i ' is the minimum pressure in the pipe, unit is MPa; p smin is the minimum operating pressure of the wellbore, unit: MPa;

[0157] The calculation formula for effective external pressure is:

[0158] p oe =p o -p′ i

[0159] Among them, p oe It is the effective external extrusion pressure, unit is MPa.

[0160] Specifically, in the embodiment of the present invention, the safety factor against internal pressure and the safety factor against external collapse are calculated based on the minimum remaining wall thickness in any year, the remaining life of the predicted pipe string is determined, and when the pipe string reaches its life, early warning information is issued in a timely manner, thereby ensuring the structural safety of the pipe string and reducing the risk of rupture or failure.

[0161] See also Figure 12 As shown, the process of using wireless communication technology to transmit the warning information and collected data to the terminal for real-time monitoring of the pipe string includes:

[0162] Step S810, using a data acquisition system to collect sensor data, the sensor data including corrosion degree, formation pressure and temperature;

[0163] Step S820, using wireless communication technology or LoRaWAN to transmit sensor data from the data acquisition system to a central server or cloud platform;

[0164] Step S830 : storing the collected data for integration and analysis to view a visual data chart and alarm information of the pipe string status.

[0165] Specifically, the embodiment of the present invention transmits the warning information and collected data to the terminal for real-time monitoring of the pipe string based on communication technology, which can realize instant data transmission, ensure that the status information of the pipe string can be received in real time, and respond to possible problems in a timely manner.

[0166] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0167] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A dynamic prediction method for pipe string life based on corrosion logging detection, characterized in that: include: A single parameter change test is used to establish several target parameters that affect the corrosion rate of the test string, wherein the target parameters include temperature, acid gas partial pressure, liquid holdup, and fluid flow pattern; A plurality of monitoring points are set at intervals of depth along the extension direction of the wellbore, starting from the wellhead, recording the temperature and pressure at each monitoring point, and determining the acid gas partial pressure at each monitoring point based on the pressure and the acid gas ratio; The gas-liquid mixture in the wellbore is transmitted using a test string, and the gas-liquid flow rate at each monitoring point is calculated based on the inner diameter of the test string, the wellhead pressure, and the total production of the gas-liquid mixture in the wellbore; determining a gas-liquid two-phase flow pattern of the gas-liquid mixture at the monitoring point according to the transmission position of the gas-liquid mixture in the test string; Calculating the liquid holdup and fluid flow pattern of each monitoring point according to the gas-liquid flow rate and the gas-liquid two-phase flow pattern; Using electromagnetic flaw detection and logging technology to determine the wellbore wall thickness change rate at each monitoring point to determine the corrosion rate corresponding to each monitoring point; A corrosion rate prediction model is established using a neural network prediction method according to the target parameters of each monitoring point and the corrosion rate; A tubing string to be predicted is set in the gas well to be evaluated, wherein the tubing string to be predicted is used to transport the gas-liquid mixture in the gas well to be evaluated; Predicting the gas production, water production, and formation pressure in each future year based on the current parameters of the gas well to be evaluated, and determining target prediction parameters for the gas well in each future year based on the pressure at each location and the total gas-liquid mixture production of the gas well to be evaluated; Determining the corrosion rate in each future year based on the target prediction parameters and the corrosion rate prediction model; Determine the remaining wall thickness in future years at each location of the gas well to be evaluated based on the corrosion rates in future years; Calculating the safety factor against internal pressure and the safety factor against external collapse based on the minimum remaining wall thickness in any year and each target prediction parameter to determine the remaining life of the pipe string to be predicted and issuing a warning message when the pipe string reaches its life; A corrosion sensor, a stress sensor and a temperature sensor are installed on the pipe string, and the early warning information and collected data are transmitted to the terminal using wireless communication technology to perform real-time monitoring of the pipe string.

2. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 1 is characterized in that: The process of using a single parameter change test to establish several target parameters that affect the corrosion rate of the test string includes: Using high-temperature autoclave equipment, a weight loss corrosion test of the sample was carried out to determine the corrosion rate of the test string; According to the corrosion rate, one target parameter is changed in the test string respectively, and the remaining target parameters are taken to the most extreme value within the research range. The influence of each target parameter on the corrosion rate is evaluated one by one, so as to determine the target parameter that affects the corrosion rate of the test string.

3. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 2, characterized in that: The process of determining the acid gas partial pressure at each monitoring point according to the pressure and the acid gas proportion includes: Determine the molar percentage of acid gas in the mixed gas based on experiments or chemical analysis; Using a pressure gauge to measure the total pressure of the mixed gas, multiplying the molar content percentage of the acid gas by the total pressure to obtain the partial pressure of the acid gas; Repeat the above steps at each monitoring point along the wellbore extension direction to determine the acid gas partial pressure at each point.

4. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 3 is characterized in that: The process of calculating the liquid holdup and fluid flow pattern of each monitoring point according to the gas-liquid flow rate and the gas-liquid two-phase flow pattern includes: The flow rate of the gas-liquid mixture in the wellbore is measured by a flow meter, and the wellhead pressure is reduced. The gas-liquid flow rate at each monitoring point is calculated according to the inner diameter of the pipe string; By collecting the flow data of the gas-liquid mixture in the test string, analyzing the properties of the gas and liquid in the gas-liquid mixture, and measuring the ratio of the volume or mass of the gas phase to the volume or mass of the entire gas-liquid mixture using an offline instrument, the ratio is used as the gas holdup; Determining the gas-liquid flow pattern at the monitoring point based on the gas-liquid flow rate, fluid properties and gas content of the gas-liquid mixture in the column and with reference to a gas-liquid two-phase flow pattern diagram; According to the gas-liquid flow rate and the gas-liquid flow pattern, the liquid holdup and fluid flow pattern of each monitoring point are calculated using multiphase flow PIPESIM software.

5. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 4, characterized in that: The process of determining the corrosion rate corresponding to each of the monitoring points includes: Use electromagnetic flaw detection and logging technology to detect the remaining wall thickness of the wellbore and compare it with the wall thickness detected last time; The corrosion rate corresponding to the monitoring point is determined based on the comparison result and the time interval between two detections.

6. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 5, characterized in that: The process of establishing a corrosion rate prediction model according to the target parameters of each monitoring point and the corrosion rate includes: The corrosion target parameter values ​​of each monitoring point are evenly divided and used as training data and test data of the neural network model respectively; A neural network prediction method is adopted, wherein the corrosion target parameter value of each monitoring point is used as input, the corrosion rate corresponding to the monitoring point is used as output data, and the training data is used to complete the training of the neural network model; The test data is input into the neural network model, and the predicted corrosion rate result output by it is compared with the actual corrosion rate in the test data to ensure that the error is within the standard range. Otherwise, the number of training samples is increased and the neural network model is retrained until the standard range requirements are met, thereby establishing a corrosion rate prediction model.

7. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 6, characterized in that: The process of determining the corrosion rate in each future year based on the target prediction parameters and the corrosion rate prediction model includes: The target prediction parameters are input into the corrosion rate prediction model to determine the corrosion rate in each future year.

8. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 7, characterized in that: The process of determining the future annual remaining wall thickness at each location of the gas well to be evaluated based on the future annual corrosion rates includes: Based on the corrosion rates in each future year, determining the corroded wall thickness value at each corresponding position in the future; The future remaining wall thickness of each position is determined based on the current wall thickness of each position of the gas well and the corroded wall thickness value of each corresponding position in the future.

9. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 8, characterized in that: The process of calculating the safety factor against internal pressure and the safety factor against external collapse based on the minimum remaining wall thickness in any year and the target prediction parameters to determine the remaining life of the pipe string to be predicted and issuing early warning information in a timely manner when the pipe string reaches its life includes: Determine the minimum remaining wall thickness in any year based on the future remaining wall thickness at each location and the corrosion rate in each future year; Calculating a safety factor against internal pressure or a safety factor against external collapse based on the minimum remaining wall thickness; When the safety factor against internal pressure or the safety factor against external collapse is lower than a target threshold, it is determined that the pipe string life is reached; An early warning value is set according to a target threshold value, and an early warning message is triggered when the safety factor against internal pressure or the safety factor against external collapse is lower than the target threshold value.

10. The method for dynamic prediction of pipe string life based on corrosion logging detection according to claim 9, characterized in that: The process of using wireless communication technology to transmit the warning information and collected data to the terminal for real-time monitoring of the pipe string includes: Use a data acquisition system to collect sensor data, including corrosion level, formation pressure and temperature; Utilize wireless communication technology or LoRaWAN to transmit sensor data from the data acquisition system to a central server or cloud platform; The collected data is stored for integration and analysis to view visual data charts and alarm information of the pipe string status.

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