Method and device for predicting drilling fluid loss in fractured formations using DFN

Abstract

The present invention relates to a method for predicting drilling fluid loss in fractured formations by using DFN, comprising the following steps: determining rock mechanical parameters; determining a downhole fracture distribution function model; generating a random fracture model based on a Monte-Carlo method; obtaining drilling stress parameters and fluid parameters; establishing a drilling fluid seepage wellbore model by using two-dimensional discrete element software; inputting the obtained rock mechanical parameters, random fracture model, geostress parameters, and fluid parameters into the drilling fluid seepage wellbore model, assigning a wellbore liquid column pressure and a pore pressure to the wellbore, setting a certain drilling fluid loss flow rate, and observing and statistically analyzing the seepage of drilling fluid in fractures near the wellbore after reaching equilibrium through calculation using the two-dimensional discrete element software, wherein the seepage condition includes the flow velocity of the drilling fluid and the magnitude of the fluid pressure; calculating the seepage flow rate output by the wellbore model, and comparing and analyzing the seepage flow rate with the drilling fluid loss flow rate to verify the accuracy of the wellbore model.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas development, and in particular to a method and device for predicting drilling fluid loss in fractured formations by utilizing DFN (discrete fracture network). Background Art

[0002] As oil and gas exploration increases in depth, drilling fluid losses, caused by the complex geological characteristics of deep and unconventional formations, become a frequent problem. During the drilling process, the primary cause of drilling fluid losses is fracture-induced losses caused by the opening of drilling-induced fractures and natural fractures. The presence of fractures exacerbates the loss of drilling fluid from the wellbore into the formation, contaminating nearby formations and resulting in significant economic losses and reduced recovery rates.

[0003] Most of the existing prediction and simulation methods are based on conventional uniform fracture distribution models, which cannot reflect the actual situation of underground fracture formation leakage. Summary of the Invention

[0004] In view of the above problems, the purpose of the present invention is to provide a method and device for predicting drilling fluid loss in fractured formations using DFN, which can accurately and truly reflect the loss situation.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The method for predicting drilling fluid loss in fractured formations using DFN according to the present invention comprises the following steps:

[0007] Determine rock mechanical parameters based on well logging data;

[0008] Determine the downhole fracture distribution function model based on imaging logging parameters of adjacent wells;

[0009] Based on the Monte-Carlo method, the downhole fracture distribution function model is converted into a random fracture model;

[0010] Acquiring drilling engineering parameters, wherein the engineering parameters include ground stress parameters and fluid parameters;

[0011] Using 2D discrete element software, a drilling fluid seepage wellbore model considering seepage-stress coupling was established;

[0012] Inputting the obtained rock mechanics parameters, the random fracture model, the in-situ stress parameters, and the fluid parameters into a drilling fluid seepage wellbore model, assigning a wellbore fluid column pressure and a pore pressure to the wellbore and setting a certain drilling fluid loss flow rate, and after calculation using two-dimensional discrete element software and reaching equilibrium, observing and statistically analyzing the seepage of drilling fluid in the fractures near the wellbore, wherein the seepage conditions include the flow rate and fluid pressure of the drilling fluid;

[0013] The seepage flow rate output by the wellbore model is calculated by observing the statistical drilling fluid flow rate, and the seepage flow rate is compared and analyzed with the set drilling fluid loss flow rate to verify the accuracy of the wellbore model.

[0014] In the method, preferably, the rock mechanical parameters are determined according to the following method:

[0015] Based on well site data or core sampling, triaxial rock failure tests are performed to obtain the rock mechanical parameters of fractured formations.

[0016] In the method, preferably, the random crack model is a combination of two groups of random crack models that obey normal distribution.

[0017] In the method, preferably, the seepage flow rate output by the wellbore model is calculated using the following formula:

[0018]

[0019] Where Q 计算 is the drilling fluid seepage rate output by the calculated wellbore model; n is the number of fractures intersecting the wellbore; S is the cross-sectional area of ​​the fracture; D is the aperture of the fracture; v is the drilling fluid flow rate in each detected fracture; and i is the sequence number of the summation.

[0020] The device for predicting drilling fluid loss in fractured formations using DFN according to the present invention comprises:

[0021] A first processing unit is used to determine rock mechanical parameters based on well logging data;

[0022] The second processing unit is used to determine the downhole fracture distribution function model based on the imaging logging parameters of the adjacent wells,

[0023] The third processing unit generates a random fracture model from the downhole fracture distribution function model based on the Monte-Carlo method;

[0024] a fourth processing unit, configured to obtain drilling engineering parameters, wherein the engineering parameters include ground stress parameters and fluid parameters;

[0025] The fifth processing unit is used to establish a drilling fluid seepage wellbore model considering seepage-stress coupling using two-dimensional discrete element software;

[0026] a sixth processing unit, configured to input the obtained rock mechanics parameters, random fracture model, geostress parameters, and fluid parameters into a drilling fluid seepage wellbore model, assign a wellbore fluid column pressure and pore pressure to the wellbore, set a certain drilling fluid loss flow rate, and, after calculation using two-dimensional discrete element software and reaching equilibrium, observe and statistically analyze the seepage of drilling fluid in the fractures near the wellbore, wherein the seepage conditions include the flow rate and fluid pressure of the drilling fluid;

[0027] The seventh processing unit is used to calculate the seepage flow rate output by the wellbore model by observing the statistical drilling fluid flow rate, and compare and analyze the seepage flow rate with the set drilling fluid loss flow rate to verify the accuracy of the wellbore model.

[0028] The present invention also provides a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method steps for predicting drilling fluid loss in fractured formations using DFN are implemented.

[0029] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method steps for predicting drilling fluid loss in fractured formations using DFN are implemented.

[0030] The present invention has the following advantages due to the adoption of the above technical solution:

[0031] The two-dimensional model of the present invention intuitively displays the drilling fluid process of fractured formations and dynamically displays the changes in fluid pressure and flow rate at the fractures during the drilling fluid loss process. Therefore, using DFN to predict drilling fluid loss in fractured formations is more intuitive and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference numerals are used to denote the same components. In the drawings:

[0033] Figure 1 is a flow chart of the present invention;

[0034] Figure 2 This is a wellbore model diagram considering drilling fluid seepage of the present invention;

[0035] Figure 3 is a fluid flow rate-time variation diagram of the present invention;

[0036] Figure 4 is a fluid pressure-time variation diagram of the present invention;

[0037] Figure 5 It is a schematic diagram of the data verification result of the present invention. DETAILED DESCRIPTION

[0038] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0039] The present invention provides a method for predicting drilling fluid loss in fractured formations using DFN. This method intuitively and dynamically displays the process of drilling fluid loss in fractured formations, completes a qualitative analysis of the degree of drilling fluid seepage, and can predict drilling fluid loss in fractured formations, so that on-site engineers can promptly adopt reasonable and efficient plugging technologies based on the formation loss situation, reduce drilling fluid loss, and promptly resolve drilling risks.

[0040] like Figure 1 As shown, the method for predicting drilling fluid loss in fractured formations using DFN provided by the present invention includes the following steps:

[0041] S100: Determine the mechanical parameters of rock based on well logging data;

[0042] First, the Poisson's ratio μ of the rock is calculated using the following formula using the transverse and longitudinal wave time difference data:

[0043]

[0044] Where: Δt c , Δt s is the time difference between the longitudinal wave and the shear wave of the formation;

[0045] The rock cohesion τ and internal friction angle can be calculated using acoustic, density, and gamma logging data.

[0046]

[0047]

[0048] Among them, V sh is the mud content, which can be obtained from the GR logging value; ρ b is the density of the formation rock; M = 58.93-1.785τ.

[0049] According to Poisson's ratio μ and Young's modulus E, the bulk modulus K and shear modulus G of the rock are calculated:

[0050]

[0051]

[0052] S200: Determine a downhole fracture distribution function model based on imaging logging parameters of adjacent wells;

[0053] Resistivity imaging logging is performed using Schlumberger's FMI tool. The tool has eight plates with a total of 192 electrodes. During the measurement process, the eight plates are pushed against the wellbore wall, and the 192 electrodes measure simultaneously. Each electrode can measure the apparent resistivity value of the wellbore wall at that location. The resistivity data of these 192 points are used to adjust the color code to obtain a microresistivity scanning image of the area covered by the wellbore plates. As the tool is raised, data of the entire well section can be measured. After a series of processing, a microresistivity scanning image in the vertical direction of the measured well section is obtained.

[0054] Data preprocessing (bad electrode removal, depth alignment, voltage correction, acceleration correction, orientation correction) and image processing are performed to quantitatively evaluate cracks and holes:

[0055] Weighted calculation of average crack width:

[0056]

[0057] Where W i is the width of the i-th segment of a crack on the FMI image; L i is the length of the i-th segment of a crack on the FMI image;

[0058] Calculation of fracture length (a formula describing the cumulative fracture length per unit area within a well section):

[0059]

[0060] Where, F e is the cumulative fracture length per unit area in the well section; R is the wellbore radius; C is the wellbore coverage of the borehole electrical imaging logging, and its value decreases with the increase of the wellbore radius; H is the length of the evaluation well section (in the two-dimensional model, H = 1); L i is the length of the i-th crack.

[0061] For the interpretation and calculation of the crack orientation: first identify the crack on the imaging map, then select several points on the crack trajectory and fit a sine curve to calculate the inclination and orientation of the crack.

[0062] S300: Based on the Monte-Carlo method, a random fracture model is generated from the downhole fracture distribution function model; the random fracture model is a combination of two groups of random fracture models that obey a normal distribution.

[0063] According to the obtained crack distribution function, a suitable function model is selected, and the Monte-Carlo method is used to generate random numbers that obey the above distribution function in the computer to obtain the geometric parameters of the fracture network. Then, the fracture network in the rock mass is generated by the computer; based on the Monte-Carlo method, a program for generating random fracture networks is written using the Fish language in UDEC.

[0064] S400: Acquire drilling engineering parameters, where the engineering parameters include ground stress parameters and fluid parameters;

[0065] The ground stress parameters and drilling engineering parameters are obtained based on the on-site logging data, wherein the stress parameters and drilling engineering parameters include well diameter, drilling fluid density, viscosity, etc.

[0066] S500: Using 2D discrete element software, a drilling fluid seepage wellbore model considering seepage-stress coupling is established;

[0067] The model is as Figure 2 As shown in Figure 2, in this wellbore model, random cracks are distributed throughout the wellbore. The constitutive model of the rock mass adopts an isotropic linear elastic model, and the constitutive model of the cracks adopts a regional contact elastic joint model under Coulomb slip failure.

[0068] The borehole model is set to 3m × 3m in size, with a circular borehole of 0.3m in diameter. The mesh is set to triangular blocks. To ensure accuracy and computational time, the mesh density is higher near the borehole, while the rest of the mesh is lower and evenly divided.

[0069] For the DFN (random fracture network), two sets of fracture models were added: one with a mean angle of 45 degrees (225 degrees) and a standard deviation of 10 degrees, and the other with a mean angle of 135 degrees (315 degrees) and a standard deviation of 10 degrees. Both sets of models had a mean length of 3.5 meters and a standard deviation of 1 meter. Both angles and lengths were distributed according to a normal distribution.

[0070] The mechanical parameters of the two sets of cracks are the same. Setting the joint normal stiffness of the cracks to a larger value can avoid model errors caused by large block embeddings.

[0071] The following boundary conditions and initial conditions are used in the analysis and calculation of the wellbore model:

[0072] Wellbore model boundary condition setting: all four faces of the model are set as fixed displacement boundaries, and all four faces are set as stress boundaries.

[0073] Initial conditions of the wellbore model: The initial conditions are set to ground stress conditions, with the overburden pressure of 40 MPa, the maximum horizontal principal stress of 25 MPa, and the direction pointing to the model along the X-axis; the minimum horizontal principal stress is 20 MPa, and the direction pointing to the model along the Y-axis (in UDEC, compressive stress is a negative value); the liquid column pressure is 15 MPa, and the pore pressure is 8 MPa.

[0074] S600: Inputting the obtained rock mechanics parameters, random fracture model, geostress parameters, and fluid parameters into a drilling fluid seepage wellbore model, assigning a wellbore fluid column pressure and pore pressure to the wellbore, and setting a certain drilling fluid loss flow rate; and after calculation using two-dimensional discrete element software and reaching equilibrium, observing and statistically analyzing the seepage of drilling fluid in the fractures near the wellbore, wherein the seepage conditions include the flow rate and fluid pressure of the drilling fluid;

[0075] S700: Calculate the seepage flow rate output by the wellbore model by observing the statistical drilling fluid flow rate, and compare and analyze the seepage flow rate with the set drilling fluid loss flow rate to verify the accuracy of the wellbore model.

[0076] In the above embodiment, preferably, the seepage flow rate output by the wellbore model is calculated using the following formula:

[0077]

[0078] Where Q 计算 is the drilling fluid seepage rate output by the calculated wellbore model; n is the number of fractures intersecting the wellbore; S is the cross-sectional area of ​​the fracture, D is the aperture of the fracture; v is the drilling fluid flow rate in each detected fracture; i is the sequence number of the summation.

[0079] Example 1:

[0080] Mechanical parameters of rock: density is 2.2×10 3 kg / m 3 , Young's modulus is 15GPa, shear modulus is 7.72GPa, internal friction angle is 32 degrees, cohesion is 2MPa. Mechanical parameters of the crack: shear stiffness is 2000GPa, normal stiffness is 5000GPa, joint friction angle is 32 degrees, tensile strength is 5MPa, and crack conductivity is 83.3Pa -1 ·sec -1 , the aperture at zero normal stress is 2.5×10 -4 m, and the residual hydraulic aperture is 1.25×10 -4 m. Drilling fluid parameters: density 1.1×10 - 3 kg / m 3 , the dynamic viscosity is 1×10 -3Pa·sec, and the cohesive force is 0.1MPa.

[0081] After a finite number of iterations, we can see (see Figure 3 and Figure 4 ), as the number of operating steps increases, the fluid flow rate and the fluid pressure in the fracture change dynamically, and the drilling fluid begins to leak along the wall of the fracture.

[0082] for Figure 3 The loss of drilling fluid near the wellbore does not show strong anisotropy. There are two main reasons for this: first, the distribution of cracks near the wellbore is relatively uniform in direction; second, the numerical difference between the horizontal and vertical model directions of the in-situ stress is small.

[0083] for Figure 4 It can be seen that the number of fracture intersections affects the drilling fluid flow rate. The fluid pressure fluctuations on the left side of Steps 2, 3, and 4 are the fastest, indicating the fastest drilling fluid flow rate. This is because there are fewer fracture intersections in this area, allowing the drilling fluid to flow to the left along the fractures and be less affected by the flow in other branches.

[0084] Figure 5 This is the result of five calculations and fitting comparisons of the input drilling fluid flow rate value and the actual measured drilling fluid flow rate around the wellbore. As shown in the figure, the error between the calculated value and the input value is small, and the average error of the five verifications is 9.41%, which shows that UDEC has good accuracy in predicting drilling fluid loss. The verification formula is as follows:

[0085]

[0086] Where n is the number of fractures intersecting the wellbore; S is the cross-sectional area of ​​the fracture; D is the aperture of the fracture; and v is the drilling fluid flow rate in each detected fracture.

[0087] Verification method:

[0088] Sum the flow rates of all fractures in contact with the wellbore;

[0089] The flow velocity and the cross-sectional area of ​​the fracture are multiplied to obtain Q 计算 ;

[0090] Q 输入 With Q 计算 Compare and verify the accuracy of the model.

[0091] Verification example: For the display diagram in the present invention, enter the flow value Q 输入 =1×10 -3 m 3 / s. Figure 3The sum of all fracture velocities in the 4 m / s, and then multiply by the crack cross-sectional area 1.5625×10 -8 m 2 , the total flow velocity multiplied by the total fracture cross-sectional area equals the total flow rate Q 计算 =9.906×10 -4 m 3 / s. Q 输入 With Q 计算 The error is within 10%. In summary, the accuracy of the model in predicting drilling fluid loss in fractured formations can be verified.

[0092] The present invention also provides a device for predicting drilling fluid loss in fractured formations using DFN, comprising:

[0093] A first processing unit is used to determine rock mechanical parameters based on well logging data;

[0094] The second processing unit is used to determine the downhole fracture distribution function model based on the imaging logging parameters of the adjacent wells,

[0095] The third processing unit generates a random fracture model from the downhole fracture distribution function model based on the Monte-Carlo method;

[0096] a fourth processing unit, configured to obtain drilling engineering parameters, wherein the engineering parameters include ground stress parameters and fluid parameters;

[0097] The fifth processing unit is used to establish a drilling fluid seepage wellbore model considering seepage-stress coupling using two-dimensional discrete element software;

[0098] a sixth processing unit, configured to input the obtained rock mechanics parameters, random fracture model, geostress parameters, and fluid parameters into a drilling fluid seepage wellbore model, assign a wellbore fluid column pressure and pore pressure to the wellbore, set a certain drilling fluid loss flow rate, and, after calculation using two-dimensional discrete element software and reaching equilibrium, observe and statistically analyze the seepage of drilling fluid in the fractures near the wellbore, wherein the seepage conditions include the flow rate and fluid pressure of the drilling fluid;

[0099] The seventh processing unit is used to calculate the seepage flow rate output by the wellbore model by observing the statistical drilling fluid flow rate, and compare and analyze the seepage flow rate with the set drilling fluid loss flow rate to verify the accuracy of the wellbore model.

[0100] The present invention also provides a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method steps for predicting drilling fluid loss in fractured formations using DFN are implemented.

[0101] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method steps for predicting drilling fluid loss in fractured formations using DFN are implemented.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for predicting drilling fluid loss in fractured formations using DFN, characterized in that: The steps include: Determine rock mechanical parameters based on well logging data; Determine the downhole fracture distribution function model based on imaging logging parameters of adjacent wells; Based on the Monte-Carlo method, the downhole fracture distribution function model is converted into a random fracture model; Acquiring drilling engineering parameters, wherein the engineering parameters include ground stress parameters and fluid parameters; Using 2D discrete element software, a drilling fluid seepage wellbore model considering seepage-stress coupling was established; Inputting the obtained rock mechanics parameters, the random fracture model, the in-situ stress parameters, and the fluid parameters into a drilling fluid seepage wellbore model, assigning a wellbore fluid column pressure and a pore pressure to the wellbore and setting a certain drilling fluid loss flow rate, and after calculation using two-dimensional discrete element software and reaching equilibrium, observing and statistically analyzing the seepage of drilling fluid in the fractures near the wellbore, wherein the seepage conditions include the flow rate and fluid pressure of the drilling fluid; The seepage flow rate output by the wellbore model is calculated by observing the statistical drilling fluid flow rate, and the seepage flow rate is compared and analyzed with the set drilling fluid loss flow rate to verify the accuracy of the wellbore model.

2. The method according to claim 1, characterized in that The rock mechanical parameters are determined according to the following method: Based on well site data or core sampling, triaxial rock failure tests are performed to obtain the rock mechanical parameters of fractured formations.

3. The method according to claim 1, characterized in that The random crack model is a combination of two groups of random crack models that obey normal distribution.

4. A device for predicting drilling fluid loss in fractured formations using DFN, characterized in that: include: A first processing unit is used to determine rock mechanical parameters based on well logging data; The second processing unit is used to determine the downhole fracture distribution function model based on the imaging logging parameters of the adjacent wells, The third processing unit generates a random fracture model from the downhole fracture distribution function model based on the Monte-Carlo method; a fourth processing unit, configured to obtain drilling engineering parameters, wherein the engineering parameters include ground stress parameters and fluid parameters; The fifth processing unit is used to establish a drilling fluid seepage wellbore model considering seepage-stress coupling using two-dimensional discrete element software; a sixth processing unit, configured to input the obtained rock mechanics parameters, random fracture model, geostress parameters, and fluid parameters into a drilling fluid seepage wellbore model, assign a wellbore fluid column pressure and pore pressure to the wellbore, set a certain drilling fluid loss flow rate, and, after calculation using two-dimensional discrete element software and reaching equilibrium, observe and statistically analyze the seepage of drilling fluid in the fractures near the wellbore, wherein the seepage conditions include the flow rate and fluid pressure of the drilling fluid; The seventh processing unit is used to calculate the seepage flow rate output by the wellbore model by observing the statistical drilling fluid flow rate, and compare and analyze the seepage flow rate with the set drilling fluid loss flow rate to verify the accuracy of the wellbore model.

5. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method steps for predicting drilling fluid loss in fractured formations using DFN according to any one of claims 1 to 3 are implemented.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method steps for predicting drilling fluid loss in fractured formations using DFN as described in any one of claims 1 to 3 are implemented.

Images