Prediction and evaluation method for freezing and blocking of shaft natural gas hydrate

Abstract

The invention relates to the field of natural gas hydrate freezing blockage prediction, and provides a shaft natural gas hydrate freezing blockage prediction evaluation method which comprises the following steps: acquiring real-time environment data in a shaft, and substituting the real-time environment data into a thermodynamic equation set to determine the generation rate of natural gas hydrate, simulating according to the generation rate to obtain the simulated generation amount of the natural gas hydrate; acquiring historical environmental data in a shaft collected by a sensor and the historical generation amount of the natural gas hydrate to construct sample vector data, training a neural network model by using the sample vector data, and substituting the real-time environmental data into the neural network model to obtain the model generation amount of the natural gas hydrate; and according to the simulated generation amount and the predicted generation amount, the predicted generation amount and the predicted blocking rate of the natural gas hydrate at multiple positions and multiple moments in the shaft are determined. According to the method, physical constraints in the generation process of the thermodynamic equation set and the prediction capacity of the neural network model for unknown conditions are combined, so that the prediction value is more accurate and reliable.

Description

Technical Field

[0001] The invention relates to the field of natural gas hydrate freezing prediction, and in particular to a method for predicting and evaluating natural gas hydrate freezing in a wellbore. Background Art

[0002] Natural gas hydrate (combustible ice) is an ice-like crystalline substance formed by natural gas and water under high pressure and low temperature conditions. Since Davy first discovered natural gas hydrate in 1810, people have conducted a lot of research on the thermodynamic phase equilibrium formation conditions of natural gas hydrate. At present, there are four main methods for predicting the formation conditions of natural gas hydrate: one is to use experiments to obtain the required model based on the thermodynamic phase equilibrium theory of natural gas hydrate formation; the second is to establish a statistical thermodynamic model of natural gas hydrate based on actual site conditions; the third is to obtain a semi-empirical or empirical model based on outdoor sampling, indoor experiments or field tests of natural gas hydrate formation pressure and temperature; the fourth is the graphical method. The theoretical model of the phase equilibrium of natural gas hydrate has a strong theoretical basis and high calculation accuracy, but the calculation is complex and difficult to master; the statistical thermodynamic model is the most widely used model, but its generalizability is poor; the graphical method and the empirical semi-empirical model have the advantages of simplicity and convenient calculation, but the scope of use is narrow and the calculation accuracy is not high.

[0003] In the actual natural gas extraction process, due to the complex reservoir conditions often accompanied by high pressure and low temperature environment, and the presence of a large amount of natural gas in the wellbore, there is a high probability of natural gas hydrates being generated in the wellbore. The formation and accumulation of natural gas hydrates will cause freezing of the wellbore, thereby hindering the flow of gas, increasing the internal pressure of the wellbore, causing wellbore rupture, equipment damage, and even causing serious accidents such as blowouts, which will also significantly reduce the efficiency of natural gas extraction and increase production costs. Even if subsequent freezing treatment is carried out, the gas well will be temporarily shut down, wasting production time and causing a lot of economic losses.

[0004] For example, the patent document with application number 202110736243.1 discloses a method for predicting the formation conditions of natural gas hydrates based on polynomial neural networks, which accurately predicts the formation conditions of natural gas hydrates by establishing a polynomial neural network algorithm. However, this method is limited to natural gas transmission pipelines, and the production conditions of natural gas hydrates are single, which is not suitable for the complex natural gas hydrate formation environment in the wellbore.

[0005] Therefore, there is currently no accurate and applicable natural gas hydrate freezing prediction technology for gas well bores. Summary of the invention

[0006] To this end, the present invention provides a method for predicting and evaluating the freezing and plugging of natural gas hydrate in a wellbore, which determines the predicted generation amount and predicted blockage rate of natural gas hydrate through a group of thermodynamic equations and a neural network model, and combines the predictive ability of the neural network model for unknown situations with the physical constraints of the natural gas hydrate generation process of the thermodynamic equation group, making the predicted values ​​more accurate and reliable, and enhancing the robustness of the prediction system.

[0007] To achieve the above object, the present invention proposes a method for predicting and evaluating wellbore natural gas hydrate freezing and plugging, comprising:

[0008] Acquire real-time environmental data in the wellbore collected by sensors, establish a group of thermodynamic equations for representing the conditions for the formation of natural gas hydrates, substitute the real-time environmental data into the group of thermodynamic equations to obtain the formation rate of natural gas hydrates, and perform simulation according to the formation rate to obtain the simulated formation amount of natural gas hydrates within a set time period;

[0009] Acquire historical data and historical generation amount of natural gas hydrate in the wellbore collected by sensors, establish a neural network model for determining the relationship between the historical data and the historical generation amount, use the historical data and the historical generation amount as sample vector data to train the neural network model, and then substitute the real-time environmental data into the neural network model to obtain the model generation amount of natural gas hydrate;

[0010] The predicted generation amount and predicted blockage rate of natural gas hydrate at multiple locations in the wellbore are determined according to the simulated generation amount and the predicted generation amount.

[0011] Further, the thermodynamic equations include the Clapeyron equation and a thermodynamic model, and the real-time environmental data includes at least temperature and interface area;

[0012] Substituting the temperature into the Clapeyron equation to determine the equilibrium pressure of natural gas hydrate formation;

[0013] Substituting the temperature into the thermodynamic model to determine the driving force for the formation of natural gas hydrates;

[0014] The rate of natural gas hydrate formation is determined according to the equilibrium pressure, the driving force and the interface area.

[0015] Further, the thermodynamic model is a VWP model;

[0016] The occupancy rate, escape coefficient and adsorption constant of gas molecules in the natural gas hydrate cage are calculated and substituted into the VWP model to determine the difference between the liquid phase chemical potential of water and the natural gas hydrate phase chemical potential, and the difference is the driving force.

[0017] In the above scheme, the Clapeyron equation can accurately describe the behavior of gas during phase change, while the VWP model can accurately simulate the formation process of natural gas hydrates in porous media. Combining the two can derive a more accurate natural gas hydrate generation rate, which is especially suitable for the complex environment of the wellbore.

[0018] Furthermore, the real-time environmental data also includes pressure;

[0019] Determining a corresponding rate coefficient according to the driving force;

[0020] The generation rate is obtained by multiplying the rate coefficient, the interface area, and the difference between the pressure and the equilibrium pressure.

[0021] Further, the neural network model is a multi-layer feedforward neural network model based on an error back propagation algorithm, and the historical data includes environmental parameters in the wellbore, natural gas composition parameters and database parameters;

[0022] The input layer neurons of the neural network model are environmental data, and the output layer nodes are the amount of natural gas hydrate generated, and a loss function with a regularization term is used;

[0023] The normalized historical data and the historical generated amount are used as sample vectors to train the neural network model to generate an objective function;

[0024] Substituting the real-time environmental data into the objective function obtains the model generation amount.

[0025] Further, the historical data includes the volume fraction, temperature, pressure, wellbore depth, interface area, rate constant and flow rate of each gas source component of the natural gas;

[0026] Generate a basis function matrix according to the historical data, wherein the basis function matrix includes each of the historical data, the square of each of the historical data and the product of two different historical data;

[0027] The neural network model is trained by using the normalized basis function matrix and the historical generation amount as sample vectors.

[0028] Furthermore, the wellbore management cloud platform stores the temperature collected by the temperature sensor at different positions of the wellbore in different production areas at different times, the pressure collected by the pressure sensor, the volume fraction of each gas source component of the natural gas collected by the gas sensor and the flow rate of the natural gas, and the wellbore depth collected by the well depth meter to generate a big data database, so as to use the big data to improve the accuracy of the neural network model;

[0029] The wellbore management cloud platform carries an interface area data table, and the temperature and the pressure are substituted into the interface area data table to determine the corresponding interface area, so as to determine the rate of natural gas hydrate formation;

[0030] The wellbore management cloud platform determines the rate constant based on the change in the volume fraction.

[0031] In the above scheme, the neural network model is used to achieve better prediction accuracy and promotion ability of model generation.

[0032] Furthermore, the wellbore management cloud platform simulates and derives a plurality of simulated generation amounts of different set durations according to the generation rate;

[0033] The wellbore management cloud platform determines model generation amounts of multiple different set time lengths according to the initial model generation amount obtained by the objective function;

[0034] The wellbore management cloud platform generates a well depth curve of the predicted generation quantity based on the average value or weighted average value of the multiple simulated generation quantities and the multiple model generation quantities, determines the predicted blockage rate after different time lengths based on the time well depth curve, and generates a time curve for display.

[0035] Furthermore, the wellbore management cloud platform determines and displays a warning freezing and blocking level according to the predicted blocking rate to display the freezing and blocking situation intuitively.

[0036] Furthermore, when the wellbore management cloud platform determines that the duration of the warning freezing and blocking level being at the dangerous level reaches a first duration, the wellbore drainer is controlled to increase the operating power to reduce the moisture content of the natural gas in the wellbore;

[0037] After the wellbore management cloud platform controls to increase the operating power of the drainer, when it is determined that the duration of the warning freezing level being at the dangerous level reaches a second duration, the pipeline doser is controlled to release antifreeze agent into the wellbore.

[0038] In the above scheme, it is achieved by accurately predicting the blockage rate and controlling the wellbore's drainer and dosing device, so as to automatically avoid the natural gas hydrate from completely freezing the wellbore and minimize the impact of freezing on wellbore production.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. The predicted generation amount and blockage rate of natural gas hydrate are determined by thermodynamic equations and neural network models. The prediction ability of the neural network model for unknown situations and the physical constraints of the natural gas hydrate formation process of the thermodynamic equations are combined to make the predicted values ​​more accurate and reliable, and enhance the robustness of the prediction system.

[0041] 2. The Clapeyron equation can accurately describe the behavior of gas during phase change, while the VWP model can accurately simulate the formation process of natural gas hydrates in porous media. Combining the two can derive a more accurate natural gas hydrate generation rate, which is especially suitable for the complex environment of the wellbore.

[0042] 3. By accurately predicting the blockage rate and controlling the wellbore's drainer and dosing device, the impact of freezing on wellbore mining can be avoided as much as possible while automatically avoiding the complete freezing of natural gas hydrates in the wellbore. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the system structure of an embodiment of the present invention;

[0044] Figure 2 It is a schematic flow chart of a method for predicting and evaluating freezing and plugging of natural gas hydrate according to an embodiment of the present invention;

[0045] Figure 3 It is a schematic diagram of a curve of collected data of a method for predicting and evaluating freezing and plugging of natural gas hydrate according to an embodiment of the present invention;

[0046] Figure 4 It is a schematic diagram of prediction results of the natural gas hydrate freezing prediction and evaluation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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 only used to explain the present invention and are not used to limit the present invention.

[0048] 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 protection scope of the present invention.

[0049] 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 drawings. This is merely 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 cannot be understood as a limitation on the present invention.

[0050] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] like Figures 1 to 4 As shown, the present invention provides a method for predicting and evaluating the freezing and plugging of natural gas hydrate in a wellbore, which determines the predicted generation amount and predicted plugging rate of natural gas hydrate through a group of thermodynamic equations and a neural network model, and combines the predictive ability of the neural network model for unknown situations with the physical constraints of the natural gas hydrate generation process of the group of thermodynamic equations, so that the predicted values ​​are more accurate and reliable, and the robustness of the prediction system is enhanced.

[0052] in, Figure 1 A schematic diagram of the system structure of an embodiment of the present invention; Figure 2 It is a schematic flow chart of a method for predicting and evaluating freezing and plugging of natural gas hydrate according to an embodiment of the present invention; Figure 3 It is a schematic diagram of a curve of collected data of a method for predicting and evaluating freezing and plugging of natural gas hydrate according to an embodiment of the present invention; Figure 4 It is a schematic diagram of prediction results of the natural gas hydrate freezing prediction and evaluation method according to an embodiment of the present invention.

[0053] Embodiment 1:

[0054] like Figures 1 to 4 As shown, this embodiment proposes a method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore, including: obtaining real-time environmental data in the wellbore collected by a sensor, establishing a group of thermodynamic equations for representing the conditions for the generation of natural gas hydrates, substituting the real-time environmental data into the group of thermodynamic equations to obtain the generation rate of natural gas hydrates, and simulating and emulating the generation rate to obtain a simulated generation amount of natural gas hydrates within a set time period; obtaining historical data and historical generation amount of natural gas hydrates in the wellbore collected by the sensor, establishing a neural network model for determining the relationship between the historical data and the historical generation amount, training the neural network model with the historical data and the historical generation amount as sample vector data, and then substituting the real-time environmental data into the neural network model to obtain the model generation amount of natural gas hydrates; determining the predicted generation amount and predicted blockage rate of natural gas hydrates at multiple locations in the wellbore according to the simulated generation amount and the predicted generation amount, so as to improve the accuracy of prediction and evaluation.

[0055] It can be understood that robustness refers to the property that the output can remain relatively stable and reliable when facing a certain range of disturbances in the input data. Since the neural network model itself is limited by the richness of the sample vector, and in the complex environment of the wellbore, there are extreme cases that the sample vector cannot accurately cover. At this time, through the physical constraints of the natural gas hydrate formation process of the thermodynamic equations, the prediction results of the neural network model can be corrected, thereby making the prediction results more accurate and more robust in the complex environment of the wellbore.

[0056] Furthermore, the thermodynamic equations include the Clapeyron equation and a thermodynamic model, and the real-time environmental data includes at least temperature and interface area; the temperature is substituted into the Clapeyron equation to determine the equilibrium pressure of natural gas hydrate formation; the temperature is substituted into the thermodynamic model to determine the driving force of natural gas hydrate formation; and the rate of natural gas hydrate formation is determined based on the equilibrium pressure, the driving force and the interface area.

[0057] Furthermore, the thermodynamic model is a VWP model; by calculating the occupancy rate, escape coefficient and adsorption constant of gas molecules in the natural gas hydrate cage and substituting them into the VWP model, the difference between the liquid phase chemical potential of water and the natural gas hydrate phase chemical potential is determined, and the difference is the driving force.

[0058] Furthermore, the real-time environmental data also includes pressure; a corresponding rate coefficient is determined according to the driving force; and the rate coefficient is multiplied by the interface area and the difference between the pressure and the equilibrium pressure to obtain the generation rate.

[0059] Specifically, the real-time environmental data include temperature, pressure, gas constant, wellbore depth, methane volume fraction, empirical constant of Langmuir adsorption constant, gas molar volume, natural gas hydrate molar volume, wellbore temperature gradient, wellbore pressure gradient, etc. of the natural gas hydrate formation environment.

[0060] Specifically, the process of determining the generation rate is:

[0061] Step S1, using the Clapeyron equation to calculate the equilibrium pressure of natural gas hydrate formation at a specific temperature, the Clapeyron equation is:

[0062]

[0063] Wherein, P is the equilibrium pressure, T is the temperature, L is the latent heat, Vg is the gas phase volume, and Vs is the solid phase volume. The temperature T, latent heat L, gas phase volume Vg, and solid phase volume Vs are acquired by sensors.

[0064] Step S2: Calculate the difference between the chemical potential of water in the natural gas hydrate phase and the chemical potential of water in the liquid phase using the VWP (VanderWaal's-Platteeuw) model. The VWP model is:

[0065]

[0066] In the formula, is the chemical potential of water in the gas hydrate phase, is the chemical potential of water in the liquid phase, v i is the occupancy rate of gas molecules in the gas hydrate cage, C ij is the Langmuir adsorption constant, f j is the escape coefficient of gas j in cage i.

[0067] Step S3, using the rate equation to calculate the natural gas hydrate generation rate, through the dynamic simulation and calculation of the generation rate, predicting the amount and location of natural gas hydrate that may be generated in the wellbore within a given time, the rate equation is:

[0068]

[0069] In the formula, is the gas hydrate formation rate, k is the rate coefficient, A is the interfacial area, P eq is the equilibrium pressure for natural gas hydrate formation.

[0070] The above step S2 includes:

[0071] Step S21, calculating the Langmuir adsorption constant of each gas molecule according to an empirical formula, the empirical formula is:

[0072]

[0073] In the formula, K ij is a constant, R is the gas constant, and T is the absolute temperature.

[0074] Step S22: Calculate the occupancy rate of gas molecules in different types of cages, specifically:

[0075]

[0076] In the formula, θ i is the cage occupancy, C i and C j is the summed Langmuir adsorption constant, f i and f jIt is the escape coefficient of gas molecules. The gas escape rate is indirectly evaluated by continuously monitoring the change of gas concentration through electrochemical gas sensors or semiconductor gas sensors, and the escape coefficient is determined based on the linear relationship between the escape rate and the escape coefficient in the wellbore experiment.

[0077] Step S23, substituting the adsorption constant and the occupancy rate into the VWP model, calculating the chemical potential difference, and expressing the driving force of generation by the chemical potential difference of water, wherein the chemical potential difference is specifically:

[0078]

[0079] Where Δμ is the chemical potential difference, is the chemical potential of water in the liquid phase, is the chemical potential of water in the gas hydrate phase.

[0080] In the above scheme, the Clapeyron equation can accurately describe the behavior of gas during phase change, while the VWP model can accurately simulate the formation process of natural gas hydrates in porous media. Combining the two can derive a more accurate natural gas hydrate generation rate, which is especially suitable for complex geological environments in wellbores.

[0081] Furthermore, the neural network model is a feedforward neural network model, and the historical environmental data include the temperature, pressure and depth of the wellbore, the volume fraction of each gas source component of the natural gas, the interface area, the rate constant and the natural gas flow rate; a polynomial basis function matrix is ​​constructed according to each of the historical environmental data, and the quadratic polynomial matrix includes the square of each of the historical environmental data and the product of two different historical environmental data; the basis function matrix and the historical generation amount are used to construct the sample vector data.

[0082] Furthermore, the neural network model is a multi-layer feedforward neural network model (Back Propagation Network) based on the error back propagation algorithm, the historical data includes the environmental parameters in the wellbore, the natural gas composition parameters and the database parameters; the input layer neurons of the neural network model are the environmental data, and the output layer nodes are the generation amount of natural gas hydrates, and a loss function with a regularization term is used to improve the generalization ability of the neural network model; each of the historical data is normalized and the historical generation amount is used as a sample vector to train the neural network model to generate an objective function; the real-time environmental data is substituted into the objective function to obtain the model generation amount. The environmental data includes historical environmental data and real-time environmental data.

[0083] Furthermore, the historical data include the volume fraction, temperature, pressure, wellbore depth, interface area, rate constant and flow rate of natural gas hydrate formation of each gas source component of natural gas; a basis function matrix is ​​generated according to the historical data, and the basis function matrix includes each of the historical data, the square of each of the historical data and the product of two different historical data; the basis function matrix is ​​normalized and used as a sample vector to train the neural network model.

[0084] Furthermore, the wellbore management cloud platform stores and manages the temperatures collected by temperature sensors at different positions of wellbores in different production areas at different times, the pressures collected by pressure sensors, the volume fractions of various gas source components of natural gas collected by gas sensors and the flow rate of natural gas, and the wellbore depth collected by the well depth meter, and generates a big data database that stores at least 10,000 sets of data to improve the accuracy of the neural network model; the wellbore management cloud platform is equipped with an interface area data table, and the temperature and the pressure are substituted into the interface area data table to determine the corresponding interface area to determine the rate of natural gas hydrate formation; the wellbore management cloud platform determines the rate constant based on the change in the volume fraction.

[0085] Furthermore, the wellbore management cloud platform simulates the generation rate to obtain multiple simulated generation quantities with different set time lengths; the wellbore management cloud platform determines multiple model generation quantities with different set time lengths based on the initial model generation quantity obtained by the objective function; the wellbore management cloud platform generates a well depth curve of the predicted generation quantity based on the average or weighted average of the multiple simulated generation quantities and the multiple model generation quantities, determines the predicted blockage rate after different time lengths based on the time well depth curve and generates a time curve for displaying the predicted freezing and blockage situation.

[0086] In the above scheme, the neural network model is used to achieve better prediction accuracy and promotion ability of model generation.

[0087] Furthermore, the transfer function of the hidden layer neurons of the neural network model is a hyperbolic tangent S-type, and the transfer function of the output layer neurons is a linear transfer function.

[0088] In the above scheme, the neural network model is used to achieve better prediction accuracy and promotion ability of model generation.

[0089] Specifically, the process of determining the model generation amount is:

[0090] Step S4, obtaining the parameters of the neural network model for the formation of natural gas hydrates in the wellbore, wherein the environmental parameters include temperature (T), pressure (P), and wellbore depth (D); the chemical parameters include the natural gas composition (volume fraction of CH, C2H6, etc.); the operating parameters include the interface area (A), rate constant (k), and flow rate (v); and the environmental parameters include temperature gradient and pressure gradient.

[0091] Step S5, use the above parameters as input variables to construct polynomial basis functions. Assuming that we choose quadratic polynomial basis functions, the basis functions can be defined as: φ0=1, φ1=T, φ2=P, φ3=D, φ4=xCH4, φ5=xC2H6, φ6=A, φ7=k, φ8=v, φ9=T2, φ10=P2, φ11=D2, φ12=x2CH4, φ13=x2C2H6, φ14=A2, φ15=k2, φ16=v2, φ17=TP, φ18=TD, φ19=TxCH4, φ20=TxC2H6, φ21=TA, φ22=Tk, φ23=Tv, φ24=PD, φ25=PxCH4, φ26=PxC2H6, φ27=PA, φ28=Pk, φ29=Pv...

[0092] Step S6, construct a basis function matrix with the polynomial basis function, specifically: for each sample i, the i-th line of the design matrix is: Φ(xi)=[φ0(xi),φ1(xi),φ2(xi),…,φn(xi)]. For example, assuming there is a sample data (T i,Pi,Di,xCH4,i,xC2H6,i,Ai,ki,vi)(Ti,Pi,D i,xCH4,i,xC2H6,i,Ai,ki,vi), the corresponding design matrix line is: Φ(xi)=[1,Ti,Pi,Di,xCH4,i,xC2H6,i,Ai,ki,vi,T i2,Pi2,Di2,xCH4,i2,xC2H6,i2,Ai2,ki2,vi2,TiPi,T iDi,…] i,xCH4,i,xC2H6,i,Ai,ki,vi,Ti2,Pi2,Di2,xCH4,i2,xC2H6,i2,Ai2,ki2,vi2,Ti].

[0093] Step S7: Use the constructed basis function matrix Φ and target variable y (such as natural gas hydrate generation rate, historical generation amount of natural gas hydrate) to train the neural network model.

[0094] Step S8: After the neural network model is trained, the model can be used for prediction to obtain the model generation amount. new , first convert it into the design matrix form Φ(xnew ), and then calculate the predicted value: y = Φ(x new )w, where y is the predicted value and w is the weight obtained by the neural network.

[0095] Preferably, the collected natural gas hydrate formation environment data includes: temperature (T), the temperature at different positions in the wellbore, in Kelvin (K); pressure (P), the pressure at different positions in the wellbore, in megapascal (MPa); gas constant (R), 8.314 J / (mol·K); wellbore depth (D), in meters (m). More preferably, the different quantities of collected data imported are the actual test pressure, temperature, gas source components and other related data under different working conditions in different production areas.

[0096] Specifically, the wellbore management cloud platform Figure 1 The sensor shown acquires environmental data, real-time environmental data and historical environmental data, and specifically utilizes the synchronous X-ray imaging method of the X-ray imager to perform three-dimensional imaging of the natural gas hydrate, and performs technical processing on the image to obtain the numerical value of the interface area of ​​the natural gas hydrate.

[0097] Step S9: Determine the predicted generation amount and predicted blockage rate of natural gas hydrate at multiple locations and multiple times in the wellbore according to the average value or weighted average value of the simulated generation amount and the predicted generation amount, and draw the following diagram: Figure 3 and 4 The curve shown.

[0098] Preferably, the polynomial network basis function constructed based on the collected data is converted into input data volume and generated results by importing pressure data, temperature data and gas source data into a computer to establish a generation environment model.

[0099] More specifically, the optimal generation conditions, generation quantity and time model are compared with the optimal data volume in the comparison based on the polynomial network basis function results, and more accurate wellbore natural gas hydrate production data volume and generation time data are obtained.

[0100] More specifically, step S5 and step S6 include: collecting wellbore natural gas hydrate formation environment data, the next step of collecting wellbore natural gas hydrate formation environment data is to establish a wellbore generation prediction model, the next step of the computer establishing the wellbore generation prediction model is to construct a polynomial network basis function matrix based on the collected data, the next step of constructing the polynomial network basis function matrix based on the collected data is to import different amounts of collected data, the collected data is connected to the database through JDBC, the database related parameters are configured, and the index table space is created to define and collect initial parameters and data.

[0101] For example, the collected data in the following table:

[0102] T(K) P(MPa) D(m) <![CDATA[x_CH4]]> <![CDATA[x_C2H6]]> <![CDATA[A(m 2 )]]> k v(m / s) 275 10 1000 0.9 0.05 0.1 0.01 0.5 280 12 1200 0.85 0.1 0.12 0.02 0.6

[0103] For each sample i, construct the i-th row of the design matrix:

[0104] Sample 1:

[0105] Φ(x1)=[1,275,10,1000,0.9,0.05,0.1,0.01,0.5,275 2 ,10 2 ,1000 2 ,0.92,0.052,0.1 2 ,0.01 2 ,0.5 2 ,275·10,275·1000,275·0.9,…]

[0106] Sample 2:

[0107] Φ(x2)=[1,280,12,1200,0.85,0.1,0.12,0.02,0.6,280 2 ,12 2 ,1200 2 ,0.85 2 ,0.12,0.12 2 ,0.02 2 ,0.6 2 ,280·12,280·1200,280·0.85,…]

[0108] More specifically, step S8 includes: for the gas wells that need to be predicted, firstly, pressure data, temperature data, and gas source data are collected, and the collected data are stored in a temporary table of the database through a JDBC connection. The data cleaning program processes the format, anomaly, redundancy, etc. of the collected data and then organizes them. The relevant data is classified according to statistical conditions in the database to generate a table and save it. The collected data is imported into the optimized prediction model, that is, different quantities of collected data are imported.

[0109] More specifically, step S8 includes: analyzing and comparing the prediction results of the neural network model, that is, comparing the results of the polynomial network basis functions; by comparing the calculation results, determining the optimal pressure data, temperature data, gas source data, the amount of natural gas hydrate generated data and the generation time data, that is, the most accurate generation conditions and generation quantity and time model.

[0110] Since it is quite difficult to make a comprehensive judgment on the factors affecting natural gas hydrates in the wellbore, the above-mentioned seven historical environmental factors that are easy to accurately measure are selected as the input of the feedforward neural network model, which is divided into 35 input layer neuron nodes, namely φ0(xi) to φ35(xi), where φ(xi) corresponds to the data of multiple moments of the neuron, so that it does not include the case where the input neuron is zero. The output layer takes a node, which is the model generation amount or model generation rate of natural gas hydrates under the corresponding component and pressure. The choice of hidden layer is related to the system itself. For more complex systems, selecting a hidden layer is a three-layer feedforward neural (BP) network. The number of nodes in the hidden layer is 2 to 3 times the number of input nodes, preferably 70.

[0111] Common transfer functions of neural networks include linear transfer function, hyperbolic tangent sigmoid transfer function, logarithmic sigmoid transfer function, etc. Preferably, the hyperbolic tangent sigmoid (tans ig) is used as the transfer function of the hidden layer neuron.

[0112] The learning method of the feedforward neural network, that is, the adjustment rules of weights and thresholds, adopts the error back propagation algorithm. The thresholds and weights are adjusted along the negative gradient direction of the network error change, and the network error is finally minimized, that is, the error gradient is zero at this point. Due to the inherent defects of the gradient descent algorithm, the standard feedforward neural algorithm usually has the disadvantages of slow convergence and easy to fall into local minima, so the regularization method is selected to improve the generalization ability of the network.

[0113] In order to avoid the influence of the distance between the maximum and minimum values ​​in the network input vector on the network error propagation, the input data of each neuron in the input layer are uniformly normalized. The maximum number of network training times is set to 20,000 times, and the network performance target is set to 1×10 —6 Since the model uses a fast and effective learning algorithm, after 936 trainings, the network performance target is 8.82844×10 —7 That is, the accuracy required by the model is obtained. The maximum absolute error between the historical generation and the model generation is calculated to be 0.41, and the minimum absolute error is 0.06. It can be proved that the neural network model established by the selected samples accurately predicts the generation conditions of natural gas hydrates in the wellbore, and it can also be proved that the selection of the 35 input neurons required by the neural network model is appropriate, and a satisfactory prediction effect can be achieved.

[0114] Furthermore, the wellbore management cloud platform determines and displays the early warning freezing and blocking level according to the predicted blocking rate to display the intuitive freezing and blocking situation. It is understandable that construction personnel can conveniently evaluate the safety of the wellbore and judge whether the wellbore construction process needs to be improved by visually observing the freezing and blocking situation of the wellbore, and then reasonably determine the diameter of the freezing hole arrangement circle, correctly select pipe materials and connection methods, control the excavation section height and the exposure time of the well wall, etc., so as to reduce the occurrence of problems such as freezing pipe breakage and well wall rupture, thereby improving the construction quality, reducing construction risks, and laying the foundation for the long-term stable production of gas wells.

[0115] Furthermore, when the wellbore management cloud platform determines that the warning freezing level has been at a dangerous level for a first period of time, it controls the wellbore drainer to increase the operating power to reduce the moisture content of the natural gas in the wellbore; after the wellbore management cloud platform controls the increase of the operating power of the drainer, when it determines that the warning freezing level has been at a dangerous level for a second period of time, it controls the pipeline doser to release antifreeze agent into the wellbore.

[0116] Specifically, see Figure 4 When the freezing rate is between 0.05 and 0.15, the warning freezing level is slight; when the freezing rate is between 0.15 and 0.25, the warning freezing level is medium; when the freezing rate is between 0.25 and 0.4, the warning freezing level is severe, wherein the severe warning freezing level is the dangerous level. The first time length is preferably 30 minutes, and the second time length is preferably 20 minutes. That is, the freezing rate is between 0.25 and 0.4 for more than 30 minutes. Considering that the introduction of antifreeze agents will have a slight impact on the quality of natural gas extraction, at this time the wellbore management cloud platform only controls the wellbore drainer to increase the operating power and extract the moisture from the natural gas in the wellbore. If the speed at which natural gas hydrates are generated in the complex geological environment of the wellbore is faster than the speed at which the drainer extracts water, the drainer alone may not be able to effectively prevent the formation of natural gas hydrates. As a result, after the operating power of the drainer is increased, the freezing rate is between 0.25 and 0.4 for more than 20 minutes. In this case, the wellbore management cloud platform controls the drainer to maintain high-power operation while controlling the pipeline doser to introduce antifreeze agents into the wellbore to completely prevent the formation of natural gas hydrates.

[0117] It can be understood that increasing the operating power of the drainage machine can increase the drainage volume, thereby improving the liquid carrying capacity of the wellbore, so that the produced water of the gas in the formation can be brought out of the wellbore more quickly, reducing the formation of liquid accumulation in the wellbore, thereby reducing the pressure and temperature gradient of the wellbore, and thereby reducing the amount of natural gas hydrate formed.

[0118] Specifically, organic chemical inhibitors such as methanol, ethylene glycol, diethylene glycol, etc. are used. These inhibitors form solutions with very low freezing points with water condensed during the cooling process. High-concentration inhibitor solutions can absorb water, resulting in a significant decrease in the formation temperature of natural gas hydrates, thereby preventing the formation of natural gas hydrates.

[0119] In the above scheme, it is achieved by accurately predicting the blockage rate and controlling the wellbore's drainer and dosing device, so as to automatically avoid the natural gas hydrate from completely freezing the wellbore and minimize the impact of freezing on wellbore production.

[0120] See also Figure 3 and Figure 4 The prediction results of this embodiment can intuitively study the factors affecting the formation of natural gas hydrates through the curve, and timely intervention can be carried out to ensure that the drainage effect and antifreeze blocking agent have sufficient time to reach the bottom of the wellbore where the freezing is most serious.

[0121] Furthermore, in this embodiment, the data acquisition process uses a two-dimensional Gaussian function and an inverse tangent function to determine a duty cycle disturbance step function, and the disturbance step function is:

[0122]

[0123] Where G(x,y) is the function value at point (x,y), A is the amplitude, i.e. the maximum value, and μ x and μ y are the means in the x and y directions respectively; σ x and σ y is the standard deviation in the x and y directions respectively. arctan2(y,x), the result range is -π to π, it calculates the angle of the point (x,y) relative to the origin. In this function, the Gaussian function is selected because the Gaussian function can affect the amplitude of the disturbance step. When the external environment changes drastically, the Gaussian function can be used to avoid excessive changes in the duty cycle. The inverse tangent function is selected because the inverse tangent function can increase or decrease the change in the disturbance step, thereby improving the dynamic response speed of the MPPT. In addition, it can also limit the oscillation near the MPP to improve the steady-state performance of the system.

[0124] In this embodiment, the predicted generation amount and predicted blockage rate of natural gas hydrate are determined by thermodynamic equations and neural network models, which combines the prediction ability of the neural network model for unknown situations and the physical constraints of the natural gas hydrate generation process of the thermodynamic equations, making the predicted values ​​more accurate and reliable, and enhancing the robustness of the prediction system. The Clapeyron equation can accurately describe the behavior of gas during phase change, while the VWP model can accurately simulate the formation process of natural gas hydrate in porous media. Combining the two can derive a more accurate natural gas hydrate generation rate, which is particularly suitable for complex geological environments in wellbores. It is achieved by accurately predicting the blockage rate and controlling the wellbore's drainer and dosing device, while automatically avoiding the complete freezing of natural gas hydrates in the wellbore, and minimizing the impact of freezing on wellbore mining.

[0125] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting and evaluating freezing of natural gas hydrate in a wellbore, characterized in that: include: Acquire real-time environmental data in the wellbore collected by sensors, establish a group of thermodynamic equations for representing the conditions for the formation of natural gas hydrates, substitute the real-time environmental data into the group of thermodynamic equations to obtain the formation rate of natural gas hydrates, and perform simulation according to the formation rate to obtain the simulated formation amount of natural gas hydrates within a set time period; Acquire historical data and historical generation amount of natural gas hydrate in the wellbore collected by sensors, establish a neural network model for determining the relationship between the historical data and the historical generation amount, use the historical data and the historical generation amount as sample vector data to train the neural network model, and then substitute the real-time environmental data into the neural network model to obtain the model generation amount of natural gas hydrate; The predicted generation amount and predicted blockage rate of natural gas hydrate at multiple locations in the wellbore are determined according to the simulated generation amount and the predicted generation amount.

2. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to claim 1, characterized in that: The thermodynamic equations include the Clapeyron equation and a thermodynamic model, and the real-time environmental data include at least temperature and interface area; Substituting the temperature into the Clapeyron equation to determine the equilibrium pressure of natural gas hydrate formation; Substituting the temperature into the thermodynamic model to determine the driving force for the formation of natural gas hydrates; The rate of natural gas hydrate formation is determined according to the equilibrium pressure, the driving force and the interface area.

3. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to claim 2, characterized in that: The thermodynamic model is a VWP model; The occupancy rate, escape coefficient and adsorption constant of gas molecules in the natural gas hydrate cage are calculated and substituted into the VWP model to determine the difference between the liquid phase chemical potential of water and the natural gas hydrate phase chemical potential, and the difference is the driving force.

4. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to claim 2, characterized in that: The real-time environmental data also includes pressure; Determining a corresponding rate coefficient according to the driving force; The generation rate is obtained by multiplying the rate coefficient, the interface area, and the difference between the pressure and the equilibrium pressure.

5. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to any one of claims 1 to 4, characterized in that: The neural network model is a multi-layer feedforward neural network model based on an error back propagation algorithm, and the historical data includes environmental parameters in the wellbore, natural gas composition parameters and database parameters; The input layer neurons of the neural network model are environmental data, and the output layer nodes are the amount of natural gas hydrate generated, and a loss function with a regularization term is used; The normalized historical data and the historical generated amount are used as sample vectors to train the neural network model to generate an objective function; Substituting the real-time environmental data into the objective function obtains the model generation amount.

6. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to claim 5, characterized in that: The historical data include the volume fraction, temperature, pressure, wellbore depth, interface area, rate constant and flow rate of each gas source component of natural gas; Generate a basis function matrix according to the historical data, wherein the basis function matrix includes each of the historical data, the square of each of the historical data and the product of two different historical data; The neural network model is trained by using the normalized basis function matrix and the historical generation amount as sample vectors.

7. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to claim 6, characterized in that: The wellbore management cloud platform stores data based on the different positions of wellbores in different production areas, the temperatures collected by temperature sensors at different times, the pressures collected by pressure sensors, the volume fractions of various gas source components and the flow rate of natural gas collected by gas sensors, and the wellbore depth collected by well depth meters to generate a big data database, so as to improve the accuracy of the neural network model with big data; The wellbore management cloud platform carries an interface area data table, and the temperature and the pressure are substituted into the interface area data table to determine the corresponding interface area, so as to determine the rate of natural gas hydrate formation; The wellbore management cloud platform determines the rate constant based on the change in the volume fraction.

8. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to claim 7, characterized in that: The wellbore management cloud platform simulates and emulates the generation rate to obtain a plurality of simulated generation amounts with different set durations; The wellbore management cloud platform determines model generation amounts of multiple different set time lengths according to the initial model generation amount obtained by the objective function; The wellbore management cloud platform generates a well depth curve of the predicted generation quantity based on the average value or weighted average value of the multiple simulated generation quantities and the multiple model generation quantities, determines the predicted blockage rate after different time lengths based on the time well depth curve, and generates a time curve for display.

9. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to claim 8, characterized in that: The wellbore management cloud platform determines and displays the early warning freezing and blocking level according to the predicted blocking rate.

10. The method for predicting and evaluating freezing and plugging of natural gas hydrate in a wellbore according to claim 9, characterized in that: When the wellbore management cloud platform determines that the duration of the warning freezing and blocking level being at the dangerous level reaches a first duration, the wellbore drainer is controlled to increase the operating power to reduce the moisture content of the natural gas in the wellbore; After the wellbore management cloud platform controls to increase the operating power of the drainer, when it is determined that the duration of the warning freezing level being at the dangerous level reaches a second duration, the pipeline doser is controlled to release antifreeze agent into the wellbore.

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