A calculation method for quickly controlling the temperature of the wellbore

Abstract

The present invention discloses a calculation method for quickly controlling the wellbore temperature, which relates to the field of oil exploration drilling. The present invention establishes a downhole temperature model of drilling fluid during circulation, and then discretizes its mathematical model. The bottom-hole drilling fluid temperature can be solved by using the finite difference method according to its boundary conditions and initial conditions. At the same time, based on the machine learning method, the influence degree of each parameter on the wellbore temperature during the drilling process is analyzed to obtain the influence degree of each parameter on the wellbore temperature. At the same time, the CART algorithm is used for multi-parameter optimization design. Combining the correlation analysis of each parameter and the wellbore temperature, various relevant parameter combinations are adjusted to obtain the lowest bottom-hole temperature value, and then the key drilling parameters and fluid performance parameters are changed to achieve the purpose of quickly controlling the bottom-hole wellbore temperature, ensuring the safety of drilling operations, and providing a fast calculation method for downhole temperature control drilling technology.

Description

Technical Field

[0001] The present invention relates to the field of oil drilling, and specifically relates to a calculation method for quickly controlling the temperature of a wellbore. Background Art

[0002] During the drilling process, drilling fluid is injected into the drill string from a surface drilling pump, and then flows out of the wellbore through the annular space formed by the drill string and the casing after passing through the bit nozzle. While flowing, it will exchange heat with the formation, causing the temperature of the drilling fluid to rise. Especially during the drilling process of deep wells and ultra-deep wells, this will lead to a decrease in the plastic viscosity of the drilling fluid, a decrease in density, and a consequent decrease in its rock-carrying performance. High temperature not only affects the performance of the drilling fluid, but also has an impact on the service life of downhole operation tools, mechanical rotation speed, wellbore stability, etc. Therefore, it is necessary to control the temperature of the wellbore-formation to a reasonable range to meet the production requirements. Summary of the Invention

[0003] Aiming at the above deficiencies in the prior art, a calculation method for quickly controlling the temperature of a wellbore provided by the present invention can achieve quick control of the wellbore temperature.

[0004] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0005] Provide a calculation method for quickly controlling the temperature of a wellbore, which includes the following steps:

[0006] S1. Determine the initial conditions of the wellbore;

[0007] S2. Based on the heat generation due to fluid friction in the wellbore and the heat exchange mechanism of the fluid in the radial and axial directions, establish a transient cyclic temperature calculation model for each control region of the wellbore-formation;

[0008] S3. Discretely solve the transient cyclic temperature calculation model based on the initial conditions of the wellbore to obtain the wellbore temperature at different time nodes;

[0009] S4. Judge whether the temperature difference between two adjacent time nodes of the wellbore meets the preset accuracy. If so, calculate the wellbore temperature at the next moment and enter step S5; otherwise, return to step S3;

[0010] S5. Use the Pearson correlation coefficient method to analyze the correlation of drilling parameters to obtain the correlation coefficient between the drilling parameters and the wellbore temperature;

[0011] S6. Based on the correlation degree between the drilling parameters and the wellbore temperature, establish a temperature field regression tree model through the CART algorithm to obtain a wellbore cooling scheme;

[0012] S7. Based on the calculated wellbore temperature, control the wellbore temperature according to the wellbore cooling scheme.

[0013] Furthermore, the initial wellbore conditions in step S1 include the drill pipe, casing levels, dimensions of the cement sheath, thermophysical parameters, rheology of the drilling fluid, density of the drilling fluid, specific heat capacity of the drilling fluid, and the pumped displacement.

[0014] Furthermore, the transient cyclic temperature calculation model in step S2 includes a sub-model for the temperature of the drilling fluid inside the drill string, a sub-model for the temperature of the drill string wall, a sub-model for the temperature of the drilling fluid in the annulus, and a near-wellbore heat transfer sub-model; among them, the expression of the sub-model for the temperature of the drilling fluid inside the drill string is:

[0015]

[0016] In the sub-model for the temperature of the drilling fluid inside the drill string, the boundary condition expression between the drilling fluid and the inner wall of the drill pipe is:

[0017]

[0018] where ρ1 is the density of the drilling fluid; C p1 is the specific heat capacity of the drilling fluid; T1 is the temperature of the fluid inside the drill pipe; t represents time; z represents the axial direction; v z1 is the axial flow velocity of the drilling fluid inside the drill pipe; λ1 is the thermal conductivity of the drilling fluid; r represents the distance from the axis of the drill string; r1 is the radius of the drill string; h1 is the convective heat transfer coefficient of the inner wall of the drill pipe; T2 is the temperature of the drill string wall;

[0019] The expression of the sub-model for the temperature of the drill string wall is:

[0020]

[0021] In the sub-model for the temperature of the drill string wall, the boundary condition expression between the fluid inside the drill pipe and the drill string wall is:

[0022]

[0023] In the sub-model for the temperature of the drill string wall, the boundary condition expression between the annulus fluid and the drill string wall is:

[0024]

[0025] where ρ2 is the density of the drill string; C p2 is the specific heat capacity of the drill string; λ2 is the thermal conductivity of the drill string; r2 is the outer radius of the drill string; h3 is the convective heat transfer coefficient of the outer wall of the drill pipe; T3 is the temperature of the annulus fluid;

[0026] The expression of the sub-model for the temperature of the drilling fluid in the annulus is:

[0027]

[0028]

[0029] In the annulus drilling fluid temperature sub-model, the heat transferred from the formation to the wellbore by heat conduction is equal to the heat flowing into the annulus from the wellbore fluid through heat convection and heat conduction. Its expression is:

[0030]

[0031] where ρ3 is the density of the annulus fluid; C p3 is the specific heat capacity of the annulus fluid; T3 is the temperature of the annulus fluid; v z3 is the axial velocity of the annulus drilling fluid; λ3 is the thermal conductivity of the annulus fluid; v r is the velocity of the fluid in the formation; h ef is the convective heat transfer coefficient of the wellbore surface considering the influence of formation porosity; T4 is the wellbore temperature; λ ef is the effective thermal conductivity of the formation considering the influence of porous fluid; T f is the formation temperature; r3 is the wellbore radius;

[0032] The expression of the near-wellbore heat transfer sub-model is:

[0033]

[0034] (ρc p ) ef = φ(ρc p ) s +(1 - φ)(ρc p ) l

[0035] v r = f(φ, m, m fu , ρ in , A l )

[0036]

[0037] In the near-wellbore heat transfer sub-model, the initial conditions of the drilling fluid inlet and wellbore temperature are:

[0038] T1(r, 0, t) = T in

[0039] T k (r, z, t) = T s + GD

[0040] where (ρC p ) ef is the heat capacity of the area surrounding the wellbore; T i is the temperature of different units in the formation porous medium; r i is the radius of the formation porous medium; φ is the formation porosity; (ρc p )s is the heat capacity of the rock; (ρc p ) l is the heat capacity of the pore fluid; K is the absolute permeability of the isotropic porous medium; μ is the dynamic viscosity of the fluid in the formation; p is the intrinsic average pressure of the fluid in the formation; m is the mass flow rate of the drilling fluid; m fu is the mass flow rate of the fluid in the formation; ρ in is the density of the fluid in the formation; A l is the lateral flow area; T in is the inlet temperature of the drilling fluid; T1(r, 0, t) represents the fluid temperature in the drill pipe at a distance r from the axis, a distance 0 from the pipe mouth, and at time t; T k (r, z, t) is the temperature of the wellbore and the formation at a distance r from the axis, an axial distance z from the wellhead, and at time t; T s is the surface temperature; G is the geothermal gradient; D is the well depth.

[0041] Furthermore, the expression for discrete solution in step S3 is:

[0042]

[0043] where is the wellbore temperature at the (N + 1)-th time node at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the (v + 1)-th iteration; ω is the relaxation parameter; B i,j , F i,j , A i,j , C i,j , D i,j , E i,j are the corresponding temperature node coefficients respectively; is the wellbore temperature at the N-th time node at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at the (N + 1)-th time node at the (i - 1)-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the (v + 1)-th iteration; is the wellbore temperature at the (N + 1)-th time node at the (i + 1)-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at the (N + 1)-th time node at the i-th spatial grid node in the radial direction and the (j - 1)-th spatial grid node in the axial direction during the (v + 1)-th iteration; is the wellbore temperature at the (N + 1)-th time node at the i-th spatial grid node in the radial direction and the (j + 1)-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at the (N + 1)-th time node at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration.

[0044] Further, the specific method for performing the correlation analysis in step S5 is as follows:

[0045] According to the formula:

[0046]

[0047] Obtain the correlation coefficient R between the drilling parameter x and the wellbore temperature y x,y ; where x i and y i are respectively the observed values of the drilling parameter x and the wellbore temperature y; x and y respectively represent the average values of the drilling parameter x and the wellbore temperature y; n represents the number of samples.

[0048] The beneficial effects of the present invention are as follows:

[0049] 1. The present invention considers the heat transfer mechanisms in the radial and axial directions of each control region, and establishes a two-dimensional transient cyclic temperature calculation model for each control region of the wellbore - formation, making the model more comprehensively characterize the heat exchange efficiency of each control region of the wellbore - formation;

[0050] 2. After the discretization processing of the mathematical model, the solution adopts the finite difference calculation method with a combination of implicit and explicit semi - time steps in the radial and axial directions. It has a faster convergence effect than the conventional explicit difference algorithm and can be more quickly applied to on - site practice.

[0051] 3. The present invention uses the Pearson correlation coefficient method to analyze the influence degree of each single factor on the wellbore temperature; on this basis, the CART algorithm method is used to optimize multiple parameters, combining the method of machine learning with traditional theoretical research, comprehensively considering the mutual influence between multiple drilling parameters, determining the key parameters affecting the wellbore temperature, and thus minimizing the wellbore temperature to form a calculation method for quickly controlling the wellbore temperature. Description of the Drawings

[0052] Figure 1 is a flow schematic diagram of this method;

[0053] Figure 2 is a result graph of the correlation coefficients between each drilling parameter and the wellbore temperature;

[0054] Figure 3 is a contour map of the relationship between the annulus temperature and time obtained by using the recommended parameters;

[0055] Figure 4 is a comparison graph of the annulus temperature before and after optimizing various drilling parameters. Detailed Embodiments

[0056] The specific embodiments of the present invention will be described below to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are within the scope of protection.

[0057] As Figure 1 shown, the calculation method for quickly controlling the wellbore temperature includes the following steps:

[0058] S1. Determine the initial conditions of the wellbore;

[0059] S2. Based on the heat generation due to fluid friction in the wellbore and the heat exchange mechanisms in the radial and axial directions of the fluid, establish a transient cyclic temperature calculation model for each control region of the wellbore - formation;

[0060] S3. Discretely solve the transient cyclic temperature calculation model based on the initial conditions of the wellbore to obtain the wellbore temperature at different time nodes;

[0061] S4. Determine whether the temperature difference between two adjacent time nodes of the wellbore meets the preset accuracy. If so, calculate the wellbore temperature at the next moment and proceed to step S5; otherwise, return to step S3;

[0062] S5. Use the Pearson correlation coefficient method to analyze the correlation of drilling parameters to obtain the correlation coefficient between the drilling parameters and the wellbore temperature;

[0063] S6. Based on the degree of correlation between the drilling parameters and the wellbore temperature, establish a temperature field regression tree model through the CART algorithm to obtain a wellbore cooling scheme;

[0064] S7. Based on the calculated wellbore temperature, implement wellbore temperature control according to the wellbore cooling scheme.

[0065] In the specific implementation process, the fluid enters the drill pipe at a velocity v z1 with an initial temperature of T in . When the fluid passes through the drill pipe along the z - direction, its temperature T1 is affected by the heat convection of the fluid inside the drill pipe and the heat conduction in the radial direction with the drill string wall. In addition, the circulating frictional pressure drop will also generate frictional heat, and at this time, a sub - model of the drilling fluid temperature inside the drill string is formed.

[0066] The temperature T2 of the drill pipe wall is affected by the heat convection between the drilling fluid flowing inside the drill pipe and the drilling fluid flowing in the annulus, as well as the heat conduction inside the drill pipe wall, and a sub - model of the drill string wall temperature is formed accordingly.

[0067] When the fluid flows out of the drill pipe through the bit and enters the annulus, the temperature of the fluid in the drill pipe at the bottom hole is equal to the temperature of the annulus. The temperature T3 of the fluid in the annulus is affected by the heat brought by the axial flow inside the annulus, the convective heat transfer occurring between the annulus fluid and the drill string wall as well as the wellbore wall, and the heat generated by the circulating friction of the annulus fluid, corresponding to the formation of the annulus drilling fluid temperature sub-model.

[0068] The near-wellbore is composed of rock, cement sheath, casing, static isolation fluid, drilling fluid, etc. Use T t to represent the temperature of this area (including rock, cement sheath, casing, static isolation fluid and drilling fluid), corresponding to the formation of the near-wellbore heat transfer sub-model.

[0069] In the specific implementation process, the transient cyclic temperature calculation model is discretized and solved by using the fully implicit finite difference method. First, determine the step size division in the axial and radial directions and the time step. The axial direction is determined by the well depth, and the radial direction is determined by the sizes of the casing and the cement sheath, and are divided successively from inside the drill pipe to the formation. Then use the finite difference method to represent the partial differential equation in the form of finite differences for each grid cell. In the implicit form describing the transient heat exchange of each element, this process is completed within half a time step. In the second half of the complete time step, the previous explicit direction becomes implicit, while the other direction becomes explicit. The equation for each node can be expressed as follows:

[0070] Using the implicit method in the radial direction and the explicit method in the axial direction:

[0071]

[0072] Using the explicit method in the radial direction and the implicit method in the axial direction:

[0073]

[0074] where α, β, γ, σ, ζ, and χ are coefficients.

[0075] The matrix form of the finite difference for each node is shown as follows:

[0076]

[0077] The finite difference equation is solved by the successive over-relaxation (SOR) iterative method, and then the expression is obtained:

[0078]

[0079] where is the wellbore temperature at the time node N + 1 at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the (v + 1)-th iteration; ω is the relaxation parameter; B i,j 、Fi,j 、A i,j 、C i,j 、D i,j 、E i,j are the corresponding temperature node coefficients respectively; is the wellbore temperature at time node N at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at time node N + 1 at the (i - 1)-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the (v + 1)-th iteration; is the wellbore temperature at time node N + 1 at the (i + 1)-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at time node N + 1 at the i-th spatial grid node in the radial direction and the (j - 1)-th spatial grid node in the axial direction during the (v + 1)-th iteration; is the wellbore temperature at time node N + 1 at the i-th spatial grid node in the radial direction and the (j + 1)-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at time node N + 1 at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration.

[0080] In an embodiment of the present invention, the calculation data obtained from eight parameters including cycle time, inlet temperature, displacement, drilling fluid density, thermal conductivity, flow behavior index, consistency coefficient, and plastic viscosity are used. Taking 25 m as the axial step unit of each equation of the wellbore - formation, and the number of axial step grids is n = H / 25 (H is the well depth); including j (j = 1, 2, 3, 4, 5...) characteristic attributes, then the well depth data corresponding to each attribute is expressed as and the wellbore temperature y i (1, 2…, n). Taking the parameters of each characteristic attribute as input variables and the wellbore temperature as the output variable, the initial data is defined as

[0081] Using the Pearson correlation coefficient method to calculate the initial data and the wellbore temperature y i correlation, obtaining the correlation coefficients of j variables and the wellbore temperature. Judging the degree of association between two variables according to the value of the correlation coefficient, the range of its value is -1 to 1, and the closer it is to 1, the higher the degree of correlation.

[0082] Combining the degree of correlation between different drilling parameters and the wellbore temperature, using the CART algorithm to establish a regression tree model to explore the influence of the combination and interaction of multiple parameters on the wellbore temperature, and obtaining the relevant parameter combination that maximizes the decrease in the wellbore temperature, so as to achieve the purpose of quickly reducing the wellbore temperature.

[0083] In the specific implementation process, the specific process of establishing a temperature field regression tree model through the CART algorithm to obtain the wellbore cooling solution is as follows:

[0084] Use the correlation coefficient method to analyze the correlation between the input variables and the wellbore temperature in the initial dataset. Calculate the correlation coefficients between the 8 input variables and the wellbore temperature in turn, and sort the input variables according to the magnitude of the correlation coefficients, so as to obtain the training datasets for different opening times, denoted as F2. Then use the CART algorithm (Li Hang. Statistical Learning Methods. 2nd Edition [M], Beijing: Tsinghua University Press, 2019) to establish a regression tree model between the input variables and the wellbore temperature in different training datasets F2. Randomly divide 80% of the data in the training dataset F2 as the training set to train the regression tree model, and use the remaining 20% of the data as the test set to test the trained regression tree model, and obtain the trained regression tree model.

[0085] The regression tree model with a binary tree structure obtained by the CART algorithm characterizes the influence strength of the input variables on the wellbore temperature from the top to the bottom. The topmost represents the strongest, and the bottommost represents the weakest. Each layer of nodes gives the optimal recommended values of the input variables. Traverse the node division results of each layer from top to bottom to obtain different recommended values of drilling parameters, and realize wellbore temperature control by using the recommended values of drilling parameters.

[0086] In an embodiment of the present invention, taking the actual wellbore structure of a certain well and each component during the circulation process as parameters, the present invention is further described, but the present invention is not limited to the following examples:

[0087] (1) Determine the wellbore structure and performance parameters of the drilling fluid at a certain location according to the actual situation. The specific wellbore structure is as follows:

[0088] 1. Use a 660.4 mm bit in the first open hole to drill to 600 m, run in a 508 mm casing with a wall thickness of 11.13 mm, return the cement slurry for well cementing to the surface, and install the wellhead for the second open hole.

[0089] 2. Drill with a 444.5 mm bit in the second open hole, drill through the Permian system, enter the lower formation for 50 m and complete the intermediate well, with the intermediate well depth of 4582 m, and run in a 365.13 mm technical casing with a wall thickness of 13.88 mm. Return the cement slurry to the surface.

[0090] 3. Drill with a 333.38 mm bit in the third open hole, drill through the Silurian system, enter the lower formation for 50 m and complete the intermediate well, with the intermediate well depth of 6562 m, and run in a 273.1 mm technical casing with a wall thickness of 12.57 mm. Return the cement slurry to the surface.

[0091] 4. The four - opening drilling uses a 241.3 - mm drill bit. The outer diameter of the drill string is 127 mm, and the inner diameter is 108.6 mm. The intermediate completion is carried out 5 m above the top boundary of the Yijianfang Formation (the designed intermediate completion depth is 7707 m). A 193.7 - mm production casing with a wall thickness of 6.35 mm is run in, and the cement slurry returns to the surface.

[0092] (2) Drilling parameters: Drilling time is 4 h, the inlet temperature is 25 °C, the displacement is 16 L / s, and the geothermal gradient is 1.9 °C / 100 m.

[0093] (3) Fluid property parameters: The density of the drilling fluid is 1.35 g / cm 3 , specific heat capacity is 1600 J / (kg·°C), thermal conductivity is 1.25 J / (m·s·°C), flow behavior index is 0.7, consistency coefficient is 0.58 mPa·s n , and plastic viscosity is 20 Pa·s.

[0094] (4) Determine the axial, radial, and time steps according to the wellbore structure of the actual example. The radial step is determined according to the sizes of the casing and the cement sheath. The axial step is taken as no more than 5% of the well depth of the actual example as the axial step. Calculations show that the axial step of the example well is 25 m and the time step is 60 s.

[0095] (5) Establish a transient cyclic temperature calculation model based on the above data, and set the temperature change value T i over time for each control region of the wellbore - formation, such as the temperature change parameters in the drill string, on the drill string wall, and in the annulus are T1, T2, and T3. Since the annulus temperature has a great influence on the fluid properties in the wellbore, and further has a great influence on the downhole power drill string, instrumentation, and the relationship between annulus pressure and formation pressure. Therefore, the distribution characteristics of the annulus temperature directly affect drilling safety, so the annulus temperature is analyzed, that is, T3.

[0096] (6) Take the time node n as the outer - loop condition and the axial node j as the inner - loop condition, discretize the temperatures of each control region of the wellbore - formation, and merge the temperatures representing the same node.

[0097] (7) Judge whether the difference between the temperature at the (n + 1) - th time node and the temperature at the n - th time node of the wellbore - formation during the cycle is less than or equal to the preset accuracy. If the accuracy is not met, go to (5) for iterative calculation.

[0098] (8) Accurately obtain the bottom - hole temperature during the cycle, that is, T3. It can be seen from Figure 4 that the bottom - hole temperature before optimization is 141 °C.

[0099] (9) The Pearson correlation coefficient method is used to analyze the correlation of various drilling parameters such as displacement, fluid density, fluid rheological parameters, fluid specific heat capacity, fluid thermal conductivity, rotary table speed, and inlet temperature, and the correlation coefficient results diagram of each drilling parameter and the wellbore temperature is obtained as shown in Figure 2 shown below.

[0100] (10) Combining the correlation analysis of each parameter and the wellbore temperature, a temperature field regression tree model is established based on the CART algorithm to explore which combination of multiple parameters can achieve the fastest cooling effect under the condition of mutual influence.

[0101] (11) The optimized drilling parameters are as follows: circulation time 4h, inlet temperature 30°C, displacement 18L / s, and geothermal gradient 1.9°C / 100m. The optimized fluid performance parameters are as follows: drilling fluid density 1.40g / cm 3 , specific heat capacity 2000J / (kg·°C), thermal conductivity 1.25J / (m·s·°C), flow behavior index 0.7, consistency coefficient 0.46mpa.s n , and plastic viscosity 18Pa.s.

[0102] (12) Substituting the recommended drilling parameters into the established wellbore-formation transient temperature field, the contour map of the annulus temperature variation with time under these parameters is obtained as shown in Figure 3 shown below, and the comparison diagram of the annulus temperature before and after optimization is as shown in Figure 4 shown below. It can be seen that the bottom temperature of the optimized annulus is 128°C, and the method of the present invention can achieve the effect of quickly controlling the wellbore temperature field.

[0103] In summary, based on the principle of energy conservation, the present invention takes into account the influence of the actual drill string assembly and wellbore structure on the wellbore-formation temperature distribution, combines the heat transfer mechanism of the wellbore-formation under actual drilling conditions, establishes a downhole temperature model of the drilling fluid during circulation, and then discretizes its mathematical model. According to its boundary conditions and initial conditions, the finite difference method is used for solution, and the bottom-hole drilling fluid temperature can be solved. To distinguish the main and secondary parameters affecting the wellbore temperature, the present invention simultaneously analyzes the influence degree of each parameter on the wellbore temperature during drilling based on machine learning methods, including fluid displacement, drilling time, inlet temperature, and fluid performance, etc., to obtain the influence degree of each parameter on the wellbore temperature. At the same time, the CART algorithm is used for multi-parameter optimization design, combined with the correlation analysis of each parameter and the wellbore temperature, and multiple relevant parameter combinations are adjusted to obtain the lowest bottom-hole temperature value, and then the key drilling parameters and fluid performance parameters are changed to achieve the purpose of quickly controlling the bottom-hole wellbore temperature, ensuring the safety of drilling operations, and providing a fast calculation method for downhole temperature control drilling technology. The present invention has important theoretical significance for optimizing the bottom-hole temperature and meeting the safety production requirements during drilling, and has broad market prospects.

Claims

1. A calculation method for quickly controlling the wellbore temperature, characterized in that, It includes the following steps: S1. Determine the initial conditions of the wellbore; S2. Based on the heat generation due to fluid friction in the wellbore and the heat exchange mechanisms of the fluid in the radial and axial directions, establish a transient cyclic temperature calculation model for each control region of the wellbore - formation; the transient cyclic temperature calculation model includes a drilling fluid temperature sub - model inside the drill string, a drill string wall temperature sub - model, an annulus drilling fluid temperature sub - model, and a near - wellbore heat transfer sub - model; S3. Discretely solve the transient cyclic temperature calculation model based on the initial conditions of the wellbore to obtain the wellbore temperature at different time nodes; S4. Judge whether the temperature difference between two adjacent time nodes of the wellbore meets the preset accuracy. If so, calculate the wellbore temperature at the next moment and enter step S5; otherwise, return to step S3; S5. Use the Pearson correlation coefficient method to conduct a correlation analysis on the drilling parameters to obtain the correlation coefficients between the drilling parameters and the wellbore temperature; S6. Based on the degree of correlation between the drilling parameters and the wellbore temperature, establish a temperature field regression tree model through the CART algorithm to obtain a wellbore temperature reduction plan; S7. Based on the calculated wellbore temperature, implement wellbore temperature control according to the wellbore temperature reduction plan; The specific method for conducting the correlation analysis in step S5 is as follows: According to the formula: ； Obtain the correlation coefficient of drilling parameter x and wellbore temperature y ; where and are the observed values of drilling parameter x and wellbore temperature y respectively; and represent the average values of drilling parameter x and wellbore temperature y respectively; n represents the sample size.

2. The calculation method for quickly controlling the wellbore temperature according to claim 1, characterized in that, The initial conditions of the wellbore in step S1 include the drill pipe, casing layers, dimensions of the cement sheath, thermophysical parameters, rheology of the drilling fluid, density of the drilling fluid, specific heat capacity of the drilling fluid, and the pumped displacement.

3. The calculation method for quickly controlling the wellbore temperature according to claim 1, characterized in that, The expression of the drilling fluid temperature sub - model inside the drill string is: ； In the drilling fluid temperature sub - model inside the drill string, the boundary condition expression between the drilling fluid and the inner wall of the drill pipe is: ； wherein is the density of the drilling fluid; is the specific heat capacity of the drilling fluid; is the temperature of the fluid inside the drill pipe; t represents time; z represents the axial direction; is the axial flow velocity of the drilling fluid inside the drill pipe; is the thermal conductivity of the drilling fluid; r represents the distance from the axis of the drill string; is the radius of the drill string; is the convective heat transfer coefficient of the inner wall of the drill pipe; is the temperature of the drill string wall; The expression of the drill string wall temperature sub - model is: ； In the drill string wall temperature sub - model, the boundary condition expression between the fluid inside the drill pipe and the drill string wall is: ； In the drill string wall temperature sub - model, the boundary condition expression between the annulus fluid and the drill string wall is: ； wherein is the density of the drill string; is the specific heat capacity of the drill string; is the thermal conductivity of the drill string; is the outer radius of the drill string; is the convective heat transfer coefficient on the outer wall of the drill pipe; is the temperature of the annulus fluid; The expression of the annulus drilling fluid temperature sub - model is: ； ； In the annulus drilling fluid temperature sub - model, the heat transferred from the formation to the wellbore by heat conduction is equal to the heat flowing into the annulus by heat convection and heat conduction from the wellbore fluid, and its expression is: ； wherein is the density of the annulus fluid; is the specific heat capacity of the annulus fluid; is the temperature of the annulus fluid; is the axial velocity of the annulus drilling fluid; is the thermal conductivity of the annulus fluid; is the velocity of the fluid in the formation; is the convective heat transfer coefficient of the wellbore wall considering the influence of formation porosity; is the wellbore wall temperature; is the effective thermal conductivity of the formation considering the influence of porous fluid; is the formation temperature; is the wellbore radius; The expression of the near - wellbore heat transfer sub - model is: ； ； ； ； In the near - wellbore heat transfer sub - model, the initial conditions of the drilling fluid inlet and the wellbore temperature are: ； ； Wherein is the heat capacity around the wellbore region; is the temperature of different units in the formation porous medium; is the radius of the formation porous medium; is the formation porosity; is the heat capacity of the rock; is the heat capacity of the pore fluid; K is the absolute permeability of the isotropic porous medium; is the dynamic viscosity of the fluid in the formation; is the intrinsic average pressure of the fluid in the formation; is the mass flow rate of the drilling fluid; is the mass flow rate of the fluid in the formation; is the density of the fluid in the formation; is the lateral flow area; is the inlet temperature of the drilling fluid; represents the fluid temperature at a distance r from the axis, a distance 0 from the rod mouth, and at time t in the drill pipe; is the temperature of the wellbore and the formation at a distance r from the axis, an axial distance z from the wellhead, and at time t; is the surface temperature; is the geothermal gradient; is the well depth.

4. A calculation method for quickly controlling the temperature of a wellbore according to claim 3, characterized in that, The expression for discrete solution in step S3 is: ； Wherein is the wellbore temperature at time node N + 1 at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the (v + 1)-th iteration; is the relaxation parameter; , , , , , are the corresponding temperature node coefficients respectively; is the wellbore temperature at time node N at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at time node N + 1 at the (i - 1)-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the (v + 1)-th iteration; is the wellbore temperature at time node N + 1 at the (i + 1)-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at time node N + 1 at the i-th spatial grid node in the radial direction and the (j - 1)-th spatial grid node in the axial direction during the (v + 1)-th iteration; is the wellbore temperature at time node N + 1 at the i-th spatial grid node in the radial direction and the (j + 1)-th spatial grid node in the axial direction during the v-th iteration; is the wellbore temperature at time node N + 1 at the i-th spatial grid node in the radial direction and the j-th spatial grid node in the axial direction during the v-th iteration.

Images