A method for predicting borehole expansion rate in fractured formations considering seepage

Abstract

The present invention discloses a method for predicting borehole expansion rate in fractured formations considering seepage, including: S1, determining all independent parameters involved in the prediction of borehole expansion rate; S2, simplifying the independent parameters and determining their basic dimensions; S3, constructing dimensionless parameters based on the simplified independent parameters and basic dimensions; S4, using a constrained mixed multi-factor orthogonal test method to determine the correlation model between dimensionless parameters; S5, substituting the unknown coefficient values ​​and dimensionless parameters into the correlation model to obtain a multi-parameter correlation model of borehole expansion rate, and predicting the borehole expansion rate based on the correlation model. The present invention makes up for the current situation that there are few existing methods for predicting borehole expansion rate, provides a new method for predicting borehole expansion rate, and provides a new idea for predicting wellbore instability. The provided expansion rate prediction method has high prediction accuracy and reliability.

Description

Technical Field

[0001] The invention belongs to the technical field of drilling engineering, and in particular relates to a method for predicting the borehole diameter expansion rate of a fractured formation taking seepage into consideration. Background Art

[0002] In drilling projects, the phenomenon of wellbore instability caused by drilling into broken formations is very common. As oil exploration and development gradually advances to deep, ultra-deep and complex geological conditions, more and more fractured formations are encountered, and the stability of wellbore in fractured formations has received more and more attention. The instability mechanism of wellbore in fractured formations is complex, and the intrusion of drilling fluid into the formation inevitably causes the strength of the surrounding rock to decrease, often causing complex accidents such as wellbore collapse and drill stuck, which greatly hinders the normal progress of on-site drilling work. Understanding the mechanism of wellbore instability and predicting wellbore collapse can provide a reference for safe drilling and drilling fluid optimization design.

[0003] The borehole expansion rate, also known as the wellbore expansion rate, is an important feature of the wellbore stability, and its size can reflect the stability of the wellbore. There are two main traditional methods for measuring the borehole expansion rate. One is to measure it through logging tools: using special logging instruments, such as caliper logging instruments, to go into the well after drilling is completed, and measure the wellbore dimensions at different positions along the depth direction of the well to obtain detailed data on the wellbore. The second is to use downhole imaging technology: such as downhole ultrasonic imaging, downhole optical imaging and other technologies, which can intuitively obtain images of the wellbore wall and determine the size of the wellbore through image processing and analysis. This type of traditional measurement method requires a lot of time and cost, and can only measure wells that have been drilled, and cannot be predicted in advance. It has weak guiding significance for wells that have been drilled. There are few existing prediction models for borehole expansion rate. Sinopec's invention patent with publication number CN115438823A provides a method for predicting borehole expansion rate, which uses safety risk association parameters as input parameters and borehole expansion rate as output parameters to form a wellbore instability analysis and prediction model, and then uses the model to perform wellbore instability analysis and prediction. However, this method does not take into account the degree of fracture of fractured formation rocks, seepage conditions, etc.

[0004] The present invention aims to establish a multi-parameter correlation model of borehole expansion rate in fractured formations considering seepage based on existing geomechanical parameters, drilling fluid parameters and adjacent well data through dimensional analysis method, to predict and analyze the borehole expansion rate, and then to evaluate the wellbore stability. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the method for predicting the borehole expansion rate of fractured formations considering seepage provided by the present invention solves the problems of high cost of traditional prediction methods and failure to consider the fracture performance and rock conditions of the formation, which affects the prediction efficiency.

[0006] In order to achieve the above-mentioned purpose of the invention, the technical solution adopted by the present invention is: a method for predicting the borehole expansion rate of a fractured formation considering seepage, comprising the following steps:

[0007] S1. Determine all independent parameters involved in the prediction of wellbore expansion rate;

[0008] S2. Simplify the independent parameters and determine their basic dimensions;

[0009] S3. Construct dimensionless parameters based on simplified independent parameters and basic dimensions;

[0010] S4, using the constrained mixed multi-factor orthogonal test method to determine the correlation model between dimensionless parameters;

[0011] S5. Substitute the unknown coefficient values ​​and dimensionless parameters into the correlation model to obtain a multi-parameter correlation model of the borehole diameter expansion rate, and predict the borehole diameter expansion rate based on the multi-parameter correlation model.

[0012] Furthermore, in step S1, the independent parameters involved in the prediction of the borehole expansion rate include rock parameters, joint fracture parameters, fluid parameters, other initial parameters, and the pressure transfer coefficient k obtained by discrete element simulation based on the rock parameters, joint fracture parameters, fluid parameters, and other initial parameters. p and wellbore expansion rate E p ;

[0013] Among them, rock parameters include Young's modulus E, Poisson's ratio υ, uniaxial compressive strength σ c , tensile strength σ t and rock crushing degree V f ;

[0014] Joint crack parameters include internal friction angle cohesion c, crack inclination α;

[0015] Fluid parameters include drilling fluid density ρ f , drilling fluid bulk modulus B u and drilling fluid viscosity μ;

[0016] Other initial parameters include well depth H, initial pore pressure p0 and drill bit diameter D b .

[0017] Furthermore, in step S2, the independent parameters are simplified as follows:

[0018] The internal friction angle and the cohesion c is expressed using the shear strength τ, which is expressed as

[0019] In step S2, the basic dimensions include length L, mass M and time T.

[0020] Furthermore, in step S3, the number of dimensionless parameters is the difference between the number of simplified independent parameters and the number of basic dimensions.

[0021] Furthermore, the step S3 is specifically as follows:

[0022] S31, arranging the simplified dimensional coefficients of each independent parameter into a dimensional coefficient matrix;

[0023] S32, dividing the dimensional coefficient matrix into a core matrix and a residual matrix;

[0024] S33, obtaining a dimensionless coefficient matrix according to the core matrix and the residual matrix and in combination with the diagonal unit matrix;

[0025] S34. Construct corresponding dimensionless parameters according to the dimensionless parameter coefficient matrix.

[0026] Furthermore, the dimensionless parameters (Π1, Π2, ..., Π 12 ) Specifically:

[0027] П1=E p , П2=k p , П4=υ, П7=V f D b , П9=α、

[0028] Furthermore, the step S4 is specifically as follows:

[0029] S41, simulate and select values ​​of rock parameters, joint fracture parameters, fluid parameters and other parameters at different levels;

[0030] S42. Based on the simulated values, a constrained hybrid multi-factor orthogonal test method is used to consider the constraint relationship between various parameters, and a simulation test of the wellbore expansion rate is carried out;

[0031] S43, substituting the result data of each independent parameter obtained from the borehole expansion rate simulation test into the corresponding dimensionless parameter to obtain the specific value of each dimensionless parameter;

[0032] S44. Based on the influence of each single factor on the wellbore expansion rate, a multivariate nonlinear regression fitting is performed on the specific values ​​of each dimensionless parameter to determine the correlation model between the dimensionless parameters.

[0033] Furthermore, the step S43 also includes: performing non-zero processing on Π2, Π7 and Π9.

[0034] Furthermore, in step S44, multiple fitting functions are used to perform multivariate nonlinear regression fitting on the specific values ​​of each dimensionless parameter, and the optimal fitting function is determined according to the correlation and fitting accuracy of the unknown coefficients, and is determined as the association model between the dimensionless parameters.

[0035] Furthermore, in step S5, the wellbore expansion rate multi-parameter correlation model is a model obtained by converting the shear strength back into cohesion and internal friction angle and performing simplification.

[0036] The beneficial effects of the present invention are:

[0037] (1) The present invention makes up for the current situation that there are few methods for predicting wellbore expansion rate, provides a new method for predicting wellbore expansion rate, and provides a new idea for predicting wellbore instability.

[0038] (2) The present invention uses the pressure transfer coefficient as an indicator and considers the influence of the pressure transfer degree caused by seepage on the wellbore stability;

[0039] (3) The present invention uses the volume density of rock fractures as a parameter and considers the influence of different rock crushing degrees on the wellbore expansion rate.

[0040] (4) The wellbore expansion rate prediction method provided by the present invention has high prediction accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flow chart of a method for predicting borehole expansion rate in fractured formations taking seepage into consideration provided by the present invention.

[0042] Figure 2 Schematic diagram of the dimensionless coefficient matrix provided by the present invention.

[0043] Figure 3 Comparison between the wellbore expansion rate predicted by the correlation model provided by the present invention and the 3DEC simulation results.

[0044] Figure 4 A density-plugging wellbore expansion rate rapid identification chart provided by the present invention.

[0045] Figure 5 The invention provides a density-fragmentation degree wellbore expansion rate rapid identification chart.

[0046] Figure 6 An example of estimating the wellbore expansion rate provided by the present invention.

[0047] Figure 7 This is an example of drilling fluid density estimation provided by the present invention. DETAILED DESCRIPTION

[0048] The specific implementation modes of the present invention are described below to facilitate those skilled in the art to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0049] The embodiment of the present invention provides a method for predicting the borehole expansion rate of fractured formations considering seepage, such as Figure 1 As shown, the following steps are included:

[0050] S1. Determine all independent parameters involved in the prediction of wellbore expansion rate;

[0051] S2. Simplify the independent parameters and determine their basic dimensions;

[0052] S3. Construct dimensionless parameters based on simplified independent parameters and basic dimensions;

[0053] S4, using the constrained mixed multi-factor orthogonal test method to determine the correlation model between dimensionless parameters;

[0054] S5. Substitute the unknown coefficient values ​​and dimensionless parameters into the correlation model to obtain a multi-parameter correlation model of the borehole diameter expansion rate, and predict the borehole diameter expansion rate based on the multi-parameter correlation model.

[0055] In step S1 of the embodiment of the present invention, the independent parameters involved in the prediction of the borehole expansion rate include rock parameters, joint fracture parameters, fluid parameters, other initial parameters, and the pressure transfer coefficient k obtained by discrete element simulation based on the rock parameters, joint fracture parameters, fluid parameters, and other initial parameters. p and wellbore expansion rate E p ;

[0056] Among them, rock parameters include Young's modulus E, Poisson's ratio υ, uniaxial compressive strength σ c , tensile strength σ t and rock crushing degree V f ;

[0057] Joint crack parameters include internal friction angle cohesion c, crack inclination α;

[0058] Fluid parameters include drilling fluid density ρ f , drilling fluid bulk modulus B u and drilling fluid viscosity μ;

[0059] Other initial parameters include well depth H, initial pore pressure p0 and drill bit diameter D b .

[0060] In step S2 of the embodiment of the present invention, since the wellbore expansion rate problem involves too many variables and the problem is relatively complicated, the dimensional analysis method is used in this embodiment to simplify and study the problem, and all the variables involved are expressed as a set of dimensionless numbers, and then the relationship between the variables is analyzed. The dimensional table of the above parameters is shown in Table 1;

[0061] Table 1: Parameter dimension table

[0062]

[0063] In step S2 of this embodiment, the independent parameters are simplified as follows:

[0064] The internal friction angle and the cohesion c is expressed using the shear strength τ, which is expressed as Among them, τ is the shear strength, MPa; c is the cohesion, MPa; c is the normal stress causing failure, MPa; is the internal friction angle, °.

[0065] After the above simplification, the variables involved in the wellbore expansion rate problem are simplified to 15 physical quantities, as shown in Table 2;

[0066] Table 2: Simplified parameter dimension table

[0067]

[0068] The relationship between these parameters is expressed as:

[0069] f(E,υ,σ,σ,V f ,τ,α,H,ρ f ,B u ,μ,p0,D b ,E p ,k p )=0

[0070] Where f represents the unknown function relationship.

[0071] In step S2 of the embodiment of the present invention, the basic dimensions include length L, mass M and time T, as shown in Table 3;

[0072] Table 3: Basic dimension parameters

[0073]

[0074] In step S3 of the embodiment of the present invention, the number of dimensionless parameters is the difference between the number of simplified independent parameters and the number of basic dimensions.

[0075] Specifically, from the Π theorem, we know that the total number of parameters involved in the borehole expansion rate problem is n = 15, the number of basic dimensions is j = 3, and the number of dimensionless parameters that can be constructed is k = 15-3 = 12. Therefore, these 15 parameters can be combined into 12 dimensionless parameters (Π1, Π2, ..., Π 12 ).

[0076] The Π theorem in this embodiment is specifically:

[0077] Mathematical formulas in nature all have dimensional homogeneity, that is, the units on both sides of the equal sign must be consistent. The basis of dimensional analysis is based on dimensional homogeneity. Equations that satisfy this property are called dimensional homogeneous equations. According to the principle of dimensional homogeneity, the basic theorem of dimensional analysis can be derived, namely the П theorem (Buckingham). Suppose a physical process can be expressed by f(x1, x2, ..., x n )=0 means, where x1, x2, ..., x n has different dimensions, among which there are j basic dimensions. We choose these j basic dimensions as independent dimensions, and we can get z = nj dimensionless quantities П1, П2, …, П z Therefore, this set of physical relations can be expressed by z dimensionless quantities, namely f(П1,П2,…,П z )=0.

[0078] Based on this, Figure 2 As shown, step S3 of this embodiment is specifically as follows:

[0079] S31, arranging the simplified dimensional coefficients of each independent parameter into a dimensional coefficient matrix;

[0080] S32, dividing the dimensional coefficient matrix into a core matrix and a residual matrix;

[0081] S33, obtaining a dimensionless coefficient matrix according to the core matrix and the residual matrix and in combination with the diagonal unit matrix;

[0082] S34. Construct corresponding dimensionless parameters according to the dimensionless parameter coefficient matrix.

[0083] Specifically, in Figure 2 In , the calculation formula of the dimensionless parameter coefficient matrix C is:

[0084] C=-D(A -1 B) T

[0085] Where C represents the dimensionless coefficient matrix; D is the diagonal unit matrix; the core matrix A is the basic dimensional coefficient matrix; and B is the residual dimensional coefficient matrix.

[0086] Based on this, the calculation results of each matrix are shown in Table 4;

[0087] Table 4: Dimensional analysis table

[0088]

[0089] According to the above dimensionless parameter coefficient matrix calculation formula, the calculation results are shown in Table 5;

[0090] Table 5: Dimensionless coefficient matrix calculation table

[0091]

[0092] The dimensionless parameters (Π1, Π2, ..., Π 12 ) Specifically:

[0093] П1=E p , П2=k p , П4=υ, П7=V f D b , П9=α、

[0094] Among the 12 dimensionless parameters mentioned above, since the shear strength can be expressed by cohesion and internal friction angle, П8 is expressed as

[0095] Thus, we get 12 dimensionless parameters involved in the borehole expansion rate problem, which are given by. According to the Π theorem, the physical process of wellbore instability can be expressed as:

[0096] F(П1,П2,...,П 12 )=0

[0097] Based on this, the borehole expansion rate problem in this embodiment is transformed into solving П1, П2, …, П 12 The problem of the correlation function F between these 12 dimensionless numbers greatly simplifies the research ideas and reduces the difficulty of analyzing the wellbore instability problem.

[0098] In step S4 of the embodiment of the present invention, in order to obtain the specific expression form of the dimensionless parameter correlation function, a constrained mixed multi-factor orthogonal experimental design method is used to simplify the design of the multi-parameter mixed level simulation test scheme of the borehole expansion rate. Then, according to the simulation test results, a multivariate nonlinear regression fitting method is used to obtain a specific multi-parameter correlation model of the borehole expansion rate; specifically, step S4 of this embodiment is as follows:

[0099] S41, simulate and select values ​​of rock parameters, joint fracture parameters, fluid parameters and other parameters at different levels;

[0100] S42. Based on the simulated values, a constrained hybrid multi-factor orthogonal test method is used to consider the constraint relationship between various parameters, and a simulation test of the wellbore expansion rate is carried out;

[0101] S43, substituting the result data of each independent parameter obtained from the borehole expansion rate simulation test into the corresponding dimensionless parameter to obtain the specific value of each dimensionless parameter;

[0102] S44. Based on the influence of each single factor on the wellbore expansion rate, a multivariate nonlinear regression fitting is performed on the specific values ​​of each dimensionless parameter to determine the correlation model between the dimensionless parameters.

[0103] Step S43 of this embodiment also includes: performing non-zero processing on Π2, Π7 and Π9.

[0104] Specifically, since П2=k p , k p represents the pressure transfer coefficient. When the pore pressure near the wellbore is equal to the initial pore pressure of the formation, k p can be equal to 0, when k p When it is equal to 0, the whole formula is meaningless, so it is necessary to make П2 non-zero; in addition, П7=V f D b , when the rock is intact, V f When α is equal to 0, the whole formula is meaningless, so П7 also needs to be non-zeroed. Similarly, П9=α, α represents the crack inclination, which can be equal to 0. When α is equal to 0, the whole formula is meaningless and also needs to be non-zeroed.

[0105] In step S44 of this embodiment, multiple fitting functions are used to perform multivariate nonlinear regression fitting on the specific values ​​of each dimensionless parameter, and the optimal fitting function is determined according to the correlation and fitting accuracy of the unknown coefficients, and is determined as the association model between the dimensionless parameters.

[0106] Exemplarily, the present embodiment provides the following three fitting functions for determining the association model:

[0107] (1)

[0108] (2)

[0109] (3)

[0110] In step S5 of the embodiment of the present invention, the wellbore expansion rate multi-parameter correlation model is a model obtained by converting the shear strength back into cohesion and internal friction angle and performing simplification.

[0111] Specifically, in this embodiment, in order to better understand the process of determining the multi-parameter correlation model of the wellbore expansion rate, this embodiment provides the following specific data and data analysis process examples;

[0112] The simulation values ​​of rock parameters, joint fracture parameters, fluid parameters and other parameters at different levels are as follows:

[0113] (1) Rock related parameters

[0114] Young's modulus E is divided into 5 levels: 15GPa, 20GPa, 25GPa, 30GPa, and 35GPa.

[0115] The Poisson's ratio υ is divided into 4 levels: 0.1, 0.15, 0.2, and 0.25.

[0116] Uniaxial compressive strengthσ c It is divided into 5 levels: 30MPa, 40MPa, 50MPa, 60MPa and 70MPa.

[0117] Tensile strength t It is divided into 4 levels: 2MPa, 3MPa, 4MPa and 5MPa.

[0118] Degree of crushing V f Divided into 4 levels, namely: 0.1m -1 , 0.2m -1 , 0.3m -1 、0.4m -1 .

[0119] (2) Crack-related parameters

[0120] Internal friction angle It is divided into 4 levels: 0°, 15°, 30°, and 45°.

[0121] The cohesion c is divided into 4 levels: 5MPa, 15MPa, 25MPa, and 35MPa.

[0122] The crack inclination angle α is divided into 5 levels: 15°, 30°, 45°, 60°, and 75°.

[0123] (3) Fluid related parameters

[0124] Drilling fluid density ρ f Divided into 5 levels, 1.4g / cm 3 , 1.6g / cm3 , 1.8g / cm 3 , 2.0g / cm 3 .

[0125] The drilling fluid viscosity μ is divided into three levels, namely 0.1Pa·s, 0.2Pa·s, and 0.3Pa·s.

[0126] Drilling fluid bulk modulus B u There are 4 levels: 1GPa, 1.5GPa, 2GPa and 2.5GPa.

[0127] (4) Other parameters

[0128] The well depth H is divided into five levels, namely 3000m, 4000m, 5000m, 6000m and 7000m.

[0129] The initial pore pressure p0 is divided into five levels, namely 40MPa, 60MPa, 80MPa, 100MPa and 120MPa.

[0130] Drill diameter D b It is divided into 3 levels, namely 432mm, 333mm and 241mm.

[0131] The various parameters are summarized in Table 6;

[0132] Table 6: Simulation parameter value table

[0133]

[0134] Since there are too many parameters, if all parameters are fully tested, 576,000,000 groups of experiments will be required. The number of experiments is too many, and the test cycle of a single group is long, which is difficult to carry out. On the other hand, in actual situations, with the change of well depth H and drilling fluid density ρ f , uniaxial compressive strengthσ c , tensile strength σ t , Young's modulus E, Poisson's ratio υ, internal friction angle Cohesion c, drill diameter D b , initial pore pressure p0 and other parameters have constraints. For example, if the well depth is large, the Young's modulus of the formation rock will not be very small, and the density of the drilling fluid to maintain the wellbore expansion rate will not be very small. Due to the above reasons, it is necessary to optimize the simulation test plan to achieve the requirements of multi-factor, high efficiency, full coverage of mixed levels and uniform distribution.

[0135] In this embodiment, a constrained mixed multi-factor orthogonal test method is adopted to fully consider the constraint relationship between various parameters. The simplified wellbore expansion rate simulation test scheme has only 42 groups, and the test scheme design results are shown in Table 7.

[0136] Table 7: Experimental design results table

[0137]

[0138] The 42 sets of constrained multi-parameter mixed orthogonal tests were simulated using 3DEC (3Dimension Distinct Element Code) geotechnical analysis software. The pressure transfer coefficient was used to characterize the plugging property of the rock near the wellbore, and the wellbore expansion rate was used to characterize the degree of wellbore instability. The simulation test results are shown in Table 8.

[0139] Table 8: Constrained mixed multi-factor orthogonal test calculation data table

[0140]

[0141] Substituting the above simulation results into the dimensionless parameters determined above, we can obtain all dimensionless numbers П1~П 12 The specific values ​​are shown in Table 9.

[0142] Table 9: Dimensionless number calculation results

[0143]

[0144]

[0145] Combined with the influence of each single factor on the borehole expansion rate, the following three fitting functions are used to perform multivariate linear regression processing on the simulation results:

[0146] (1)

[0147] (2)

[0148] (3)

[0149] The above three fitting formulas are used to perform nonlinear regression fitting on dimensionless parameters, and the values ​​of the undetermined coefficients in three functional forms are shown in Tables 10 to 12;

[0150] Table 10: Fitting results of the first function's unknown coefficients

[0151]

[0152] Table 11: Fitting results of the second function's unknown coefficients

[0153]

[0154] Table 12: Fitting results of the third function's unknown coefficients

[0155]

[0156] Generally, in order to meet engineering requirements, the accuracy of the fitting results must reach at least 90%. From the fitting results, it can be seen that the fitting accuracy of the above three function forms can meet the requirements. However, considering the correlation and fitting accuracy of the undetermined coefficients, the second fitting result is preferred as the final correlation model. The 15 undetermined coefficient values ​​and 12 dimensionless numbers are substituted into the second fitting function formula to obtain the multi-parameter correlation model of wellbore expansion rate:

[0157]

[0158] For the convenience of calculation, the shear strength is converted back to cohesion and internal friction angle, and the multi-parameter correlation model of wellbore expansion rate is simplified to obtain:

[0159]

[0160] In the formula, E p —maximum borehole expansion rate, dimensionless; k p —pressure transmission coefficient, dimensionless; E—Young's modulus, Pa; υ—Poisson's ratio, dimensionless; σ c —Uniaxial compressive strength, Pa; σ t —tensile strength, Pa; c—cohesion, Pa; —internal friction angle, dimensionless; p0—initial pore pressure, Pa; ρ f —Drilling fluid density, kg / m 3 ; α—crack inclination, dimensionless; V f —Crack volume density, m -1 ; B u —drilling fluid bulk modulus, Pa; μ—drilling fluid viscosity, Pa·s; D b —Drill bit diameter, m.

[0161] In this embodiment, in order to verify the established correlation model, the simulation results of 3DEC software are compared with the wellbore expansion rate predicted by the correlation model, such as Figure 3 shown.

[0162] Depend on Figure 3 It can be seen that the borehole expansion rate predicted by the correlation model is in good agreement with the results obtained by 3DEC simulation. The relative errors between the borehole expansion rate predicted by the 3DEC correlation model and the simulation results are mostly within 15%, with an average error of only 3.08% and a maximum error of 28.03%. The results show that the established multi-parameter correlation model for borehole expansion rate has high accuracy and strong reliability.

[0163] In the embodiment of the present invention, the multi-parameter correlation model is an empirical formula including bottom hole pressure and borehole expansion rate, which can be used to predict the borehole expansion rate and estimate the bottom hole pressure to maintain the borehole expansion rate when the relevant parameters are known. Among the relevant parameters, rock and fracture parameters such as Young's modulus, Poisson's ratio, uniaxial compressive strength, tensile strength, degree of crushing, internal friction angle, cohesion, fracture inclination, pressure transfer coefficient, etc. can be obtained through field sampling and core experiments; fluid parameters such as viscosity and bulk modulus can also be obtained through field sampling tests.

[0164] The correlation model is drawn into a chart for estimating the borehole expansion rate. The borehole expansion rate can be estimated more quickly on site. Positive means expansion, negative means contraction. The plugging property is characterized by the pressure transfer coefficient. The chart of drilling fluid density-plugging property is as follows: Figure 4 shown.

[0165] The fracture volume density is used to characterize the degree of rock fragmentation, and a quick identification chart of drilling fluid density-rock fragmentation degree and borehole expansion rate is drawn, such as Figure 5 shown.

[0166] The multi-parameter correlation model in this embodiment can be directly used to predict the borehole expansion rate, so as to estimate the wellbore instability according to the prediction results. For example, the field data of a well is shown in Table 13;

[0167] Table 13: Field parameters of a well

[0168]

[0169] Generally speaking, the absolute value of the borehole expansion rate is above 10%, which is considered as wellbore instability. As shown in Table 13, the pressure transfer coefficient k p is 0.14, the drilling fluid density ρ f 2.2g / cm 3 , substitute Figure 4 The wellbore expansion rate E can be obtained p Probably around 13.83%, such as Figure 6 As shown in the figure, the wellbore is beyond the range of stability and is in an unstable state. The data obtained by 3DEC simulation is 11.95%, which is not much different from the estimated value, and the estimate is relatively accurate.

[0170] In this embodiment, the approximate rock crushing degree can be obtained through core experiments or on-site calculations, such as V f Substituting it into Figure 5 It can be seen that in order to maintain the wellbore expansion rate within the acceptable range of 10%, the drilling fluid density should be between 1.7 and 1.9, as shown in 7.

[0171] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0172] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for predicting the borehole expansion rate in fractured formations considering seepage, It is characterized in that The following steps are involved: S1. Determine all independent parameters involved in the prediction of wellbore expansion rate; S2. Simplify the independent parameters and determine their basic dimensions; S3. Construct dimensionless parameters based on simplified independent parameters and basic dimensions; S4, using the constrained mixed multi-factor orthogonal test method to determine the correlation model between dimensionless parameters; S5, substituting the undetermined coefficient value and the dimensionless parameter into the correlation model to obtain a multi-parameter correlation model of the wellbore expansion rate, and predicting the wellbore expansion rate based on the multi-parameter correlation model; In step S1, the independent parameters involved in the prediction of the borehole expansion rate include rock parameters, joint fracture parameters, fluid parameters, other initial parameters, and the pressure transfer coefficient k obtained by discrete element simulation based on the rock parameters, joint fracture parameters, fluid parameters, and other initial parameters. p and wellbore expansion rate E p ; Among them, rock parameters include Young's modulus E, Poisson's ratio υ, uniaxial compressive strength σ c , tensile strength σ t and rock crushing degree V f ; Joint crack parameters include internal friction angle cohesion c, crack inclination α; Fluid parameters include drilling fluid density ρ f , drilling fluid bulk modulus B u and drilling fluid viscosity μ; Other initial parameters include well depth H, initial pore pressure p 0 and drill diameter D b ; In step S2, the independent parameters are simplified as follows: The internal friction angle and the cohesion c is expressed using the shear strength τ, which is expressed as In step S2, the basic dimensions include length L, mass M and time T; In step S3, the number of dimensionless parameters is the difference between the number of simplified independent parameters and the number of basic dimensions; The step S3 is specifically as follows: S31, arranging the simplified dimensional coefficients of each independent parameter into a dimensional coefficient matrix; S32, dividing the dimensional coefficient matrix into a core matrix and a residual matrix; S33, obtaining a dimensionless coefficient matrix according to the core matrix and the residual matrix and in combination with the diagonal unit matrix; S34, constructing corresponding dimensionless parameters according to the dimensionless parameter coefficient matrix; The dimensionless parameter (Π 1 ,Π 2 ,…,Π 12 ) Specifically: P 1 =E p 、P 2 =k p 、 P 7 =V f D b 、 P 9 α、 The step S4 is specifically as follows: S41, simulate and select values ​​of rock parameters, joint fracture parameters, fluid parameters and other parameters at different levels; S42. Based on the simulated values, a constrained hybrid multi-factor orthogonal test method is used to consider the constraint relationship between various parameters, and a simulation test of the wellbore expansion rate is carried out; S43, substituting the result data of each independent parameter obtained from the borehole expansion rate simulation test into the corresponding dimensionless parameter to obtain the specific value of each dimensionless parameter; S44, combining the influence of each single factor on the borehole expansion rate, performing multivariate nonlinear regression fitting on the specific values ​​of each dimensionless parameter, and determining the correlation model between the dimensionless parameters; The step S43 also includes: 2 , Π 7 and Π 9 Perform non-zero processing; In the step S44, multiple fitting functions are used to perform multivariate nonlinear regression fitting on the specific values ​​of each dimensionless parameter, and the optimal fitting function is determined according to the correlation and fitting accuracy of the undetermined coefficients, and is determined as the correlation model between the dimensionless parameters; In step S5, the wellbore expansion rate multi-parameter correlation model is a model obtained by converting the shear strength back into cohesion and internal friction angle and performing simplification.

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