Logging debris late simulation method based on hydrodynamic model and experimental calibration

Abstract

The invention discloses a logging debris late simulation method based on a fluid dynamic model and experimental calibration. The method comprises the following steps: S1, forming an input data set with a unified scale and format; s2, an improved Navier-Stokes equation is used for establishing a drilling fluid flow model in a shaft; s3, forming a unified rock debris late depth prediction model; s4, dynamically adjusting key parameters in the rock debris late depth prediction model by using a feedback correction mechanism; s5, performing simulation calculation on the calibrated rock debris late depth prediction model by adopting a numerical solution method; and S6, docking and comparing a simulation prediction result with real-time logging data, further optimizing model parameters according to data feedback, and outputting a final rock debris late depth prediction result. According to the method, the key parameters of the model are dynamically corrected by combining the field real-time logging data, so that the flow model can adapt to different drilling working conditions, and the prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logging cuttings, and in particular to a logging cuttings late arrival simulation method based on a fluid dynamics model and experimental calibration. Background Art

[0002] In oil and gas drilling projects, the transportation and delay of cuttings in the wellbore have always been important issues affecting drilling efficiency and safety. Timely cleaning of cuttings is of great significance for maintaining bottomhole cleanliness, reducing wellbore kick risks, and improving drilling efficiency. However, due to the complex flow characteristics of drilling fluid, the multi-scale movement behavior of cuttings, and the non-steady-state characteristics of the wellbore environment, existing technologies still have many shortcomings in predicting the depth of cuttings delay.

[0003] Currently, the prediction of cuttings delay mainly relies on empirical formulas, semi-empirical models, or numerical simulation methods based on simplified flow equations. For example, conventional drilling fluid flow models usually assume that the flow in the wellbore is a stable single-phase flow or a simple two-phase flow, and calculate the settling velocity and return velocity of the cuttings through empirical relationships. However, they ignore the multi-scale coupling between drilling fluid and cuttings, the turbulent diffusion effect, and the influence of unsteady shear force, resulting in low prediction accuracy and difficulty in adapting to different drilling conditions.

[0004] In some studies, scholars have attempted to introduce the Navier-Stokes equations to describe the flow of drilling fluid and adjust the flow parameters through experimental calibration. However, existing methods often use fixed parameters and lack a dynamic adjustment mechanism, making it difficult to reflect the dynamic changes in the rheological parameters of drilling fluid and the physical properties of cuttings during drilling. In addition, due to the complex wellbore flow environment, existing models often cannot fully consider the impact of wellbore inclination, bottom hole pressure field and temperature changes on the cuttings transportation process, resulting in a large deviation between the predicted results and the actual logging data.

[0005] In recent years, some numerical simulation methods have introduced multi-scale two-phase flow theory, describing the settling and return behavior of cuttings by establishing more sophisticated flow calculation models. However, the huge amount of computation required in the calculation process makes it difficult to apply it to actual drilling sites in real time. At the same time, the experimental calibration of existing models mostly relies on laboratory data, which lacks adaptability to field conditions, making it difficult for the models to achieve high-precision predictions in actual drilling operations.

[0006] In summary, the existing technologies for predicting late cuttings arrival mainly have the following problems: the applicability of traditional empirical models and semi-empirical models is limited, and they lack a description of the nonlinear coupling relationship between drilling fluid and cuttings; the existing numerical simulation methods have high computational complexity and lack real-time performance, making it difficult to meet the needs of on-site drilling operations; the experimental calibration method cannot fully consider the dynamic changes of the drilling environment, resulting in limited model prediction accuracy. Therefore, there is an urgent need for a more accurate, efficient and adaptable cuttings arrival prediction method for complex drilling conditions to improve the stability and safety of the drilling process. Summary of the Invention

[0007] One purpose of the present invention is to propose a method for simulating late arrival of logging cuttings based on a fluid dynamics model and experimental calibration. The present invention dynamically corrects the key parameters of the model by combining real-time logging data on site, so that the flow model can adapt to different drilling conditions and improve the prediction accuracy.

[0008] According to an embodiment of the present invention, a method for simulating delayed arrival of logging cuttings based on a fluid dynamics model and experimental calibration includes the following steps:

[0009] S1. Collect drilling operation parameters, drilling fluid rheological parameters, and cuttings physical properties, and clean, remove outliers, and standardize the collected data to form an input data set with a unified scale and format;

[0010] S2. Based on a standardized input data set, a wellbore drilling fluid flow model is established using the modified Navier-Stokes equations. Correction parameters reflecting the coupling effect between drilling fluid and cuttings, turbulent diffusion, and shear force are introduced into the wellbore drilling fluid flow model to ensure that the wellbore drilling fluid flow model parameters match the actual drilling conditions.

[0011] S3. Based on the cuttings generation, suspension, sedimentation, and return mechanisms, the cuttings transport model is coupled with an improved wellbore drilling fluid flow model to form a unified cuttings late arrival depth prediction model.

[0012] S4. Through laboratory simulations and real-time field data collection, the cuttings return velocity and delay parameters are measured. The experimental data are compared with the prediction results of the cuttings delay depth prediction model. The key parameters of the cuttings delay depth prediction model are dynamically adjusted using a feedback correction mechanism.

[0013] S5. Use a numerical solution method to simulate the calibrated cuttings late arrival depth prediction model, simulating the two-phase flow process of drilling fluid and cuttings in the wellbore, and predicting the cuttings late arrival depth under different drilling conditions;

[0014] S6. Compare the simulation prediction results with the real-time logging data, further optimize the model parameters based on the data feedback, and output the final cuttings late arrival depth prediction results.

[0015] Optionally, the S1 specifically includes the following steps:

[0016] S11. Collect drilling operation related data and construct a data input set D, which is composed of a drilling operation parameter set D. p , drilling fluid rheological parameter set D m and rock cuttings physical property parameter set D c composition:

[0017] D={D p ,D m ,D c};

[0018] S12. Clean the collected data input set D, remove outliers, and construct a cleaned data input set;

[0019] S13. Standardize the cleaned data input set and format the normalized data input set to make it meet the model input requirements, and output the final standard input data set D for simulation. final .

[0020] Optionally, the step S2 specifically includes the following steps:

[0021] S21. Based on the standard input data set D final A drilling fluid flow model in the wellbore is established. The drilling fluid flow model in the wellbore is constructed based on the improved Navier-Stokes equations that introduce multi-scale coupling correction terms and unsteady turbulent momentum transfer functions:

[0022]

[0023] Among them, ρ f is the drilling fluid density, which represents the inertial effect of the drilling fluid in the wellbore, u is the drilling fluid velocity vector, which represents the flow state of the drilling fluid in the wellbore, p is the drilling fluid pressure field, which represents the pressure distribution of the drilling fluid in the wellbore, μ is the dynamic viscosity of the drilling fluid, which represents the viscous effect during the flow of the drilling fluid, and F MC is the multi-scale cuttings-drilling fluid coupling correction term, which represents the multi-scale dynamic effect of cuttings in the wellbore on the momentum distribution of the drilling fluid. NT is the unsteady turbulent momentum transfer function, which represents the time-space variation effect of momentum under unsteady turbulent conditions in the wellbore;

[0024] S22. Multi-scale cuttings-drilling fluid coupling correction term F MC The specific ones are:

[0025]

[0026] Among them, β i is the momentum coupling intensity factor of the i-th scale cuttings, which represents the nonlinear coupling effect of cuttings scale difference on drilling fluid flow, ρ c is the rock debris density, which represents the inertia of rock debris particles, C c,i is the volume fraction of the i-th scale rock fragments, u c,i is the velocity vector of the i-th scale cuttings, Ψ(d c,i ,φ c,i ) is the cuttings scale factor function, which depends on the cuttings diameter d c,i and cuttings sphericity φ c,i , represents the effect of cuttings size and shape on the drilling fluid coupling behavior, N is the number of cuttings size classification;

[0027] S23. Unsteady turbulent momentum transfer function F NT The specific ones are:

[0028]

[0029] Among them, ∈ t (t) is the time-varying turbulent diffusion coefficient, which represents the dynamic change characteristics of the turbulent diffusion of the drilling fluid during the drilling process; γ(t) is the unsteady momentum adjustment factor, which represents the dynamic influence of the turbulent state fluctuation on the time response of the momentum distribution during the drilling process;

[0030] S24. Using the real-time logging data and laboratory simulation data, the above parameters β i ,∈ t (t) and γ(t) are initially determined, a drilling fluid flow model that meets the complex flow characteristics of the drilling site is established, and the drilling fluid flow model parameters that match the actual drilling conditions are output.

[0031] Optionally, the S3 specifically includes the following steps:

[0032] S31. Based on the improved Navier-Stokes equations and two-phase flow theory, a cuttings transport model is constructed using the multi-scale dynamic coupling characteristics of drilling fluid and cuttings:

[0033]

[0034] Among them, C c is the cuttings concentration, which represents the volume fraction of cuttings in the wellbore, u c is the cuttings velocity vector, indicating the flow state of cuttings in the wellbore, D c is the rock debris diffusion coefficient, S cis the cuttings generation source term, which characterizes the cuttings generation rate during the rock breaking process of the drill bit;

[0035] S32. Combined with the improved multi-scale cuttings-drilling fluid coupling correction term F MC , calculate the cuttings movement speed, cuttings movement speed u c Affected by drilling fluid flow field, gravity, buoyancy and turbulence:

[0036]

[0037] Among them, λ is the rock cutting response coefficient, which represents the motion response characteristics of rock cuttings after being subjected to force. i Correction, ρ c is the rock debris density, ρ f is the drilling fluid density, g is the acceleration due to gravity, d c is the cuttings diameter, μ is the drilling fluid viscosity, is the drilling fluid pressure gradient;

[0038] S33. An improved sedimentation model is used to modify the cuttings sedimentation behavior, which is affected by shearing and turbulent diffusion effects within the wellbore:

[0039]

[0040] Among them, u s is the cuttings settling velocity, f t is the turbulence correction factor;

[0041] S34. Calculate the cuttings late depth L d The cuttings arrival depth is affected by the drilling fluid velocity, cuttings settling velocity and turbulent diffusion characteristics:

[0042]

[0043] Among them, u r is the cuttings return velocity;

[0044] S35. Establish a cuttings late arrival depth distribution model to describe the cuttings concentration at different depths in the wellbore:

[0045] C c (z,t)=C c,0 e -κt ;

[0046] Among them, C c (z,t) is the cuttings concentration at the well depth z, C c,0 is the initial cuttings concentration, and κ is the cuttings transport attenuation coefficient.

[0047] Optionally, the S4 specifically includes the following steps:

[0048] S41. A standardized test environment was established in the laboratory to simulate the wellbore and measure the rheological parameters of different drilling fluids. m and rock cuttings physical properties D c The cuttings movement characteristics under the conditions, including the cuttings return velocity u r and cuttings delay time t d ;

[0049] S42. Using downhole sensors to measure the concentration of rock cuttings C c , drilling fluid flow parameter u, well inclination angle θ key variables, and construct the field measurement data set D real :

[0050] D real ={C c ,u,θ,P,T};

[0051] Where P is the bottom hole pressure and T is the bottom hole temperature;

[0052] S43. Calculate theoretical prediction value based on cuttings late arrival depth prediction model Compared with laboratory values and field logging values Perform error analysis:

[0053]

[0054] Where, ΔL d is the deviation between the model prediction and the field logging data, deviations between model predictions and laboratory test data;

[0055] S44. Use feedback correction mechanism to dynamically adjust the key parameters in the cuttings delay depth prediction model, establish parameter optimization function, and adjust the cuttings coupling correction term F. MC , turbulent diffusion coefficient∈ t (t) and the settlement correction factor f t :

[0056]

[0057] Among them, k1, k2 and k3 are correction coefficients;

[0058] S45. Based on the corrected cuttings coupling correction term F MC , turbulent diffusion coefficient∈ t (t) and the settlement correction factor f t , recalculate the cuttings late depth And perform error analysis:

[0059]

[0060] Finally, the optimized cuttings late arrival depth prediction result is output to match the actual working conditions.

[0061] Optionally, the S5 specifically includes the following steps:

[0062] S51. Based on the standard input data set D final , improve the Navier-Stokes equations and cuttings transport model to build a numerical solution simulation platform, use the finite volume method to discretize the coupled model, and establish a computational grid for the movement of drilling fluid and cuttings in the wellbore;

[0063] S52. Using a time-domain iterative method to solve the calibrated cuttings late arrival depth prediction model, the drilling fluid flow velocity field, the cuttings movement velocity field, and the cuttings concentration distribution field are calculated to obtain the dynamic characteristics of the two-phase flow in the wellbore;

[0064] S53. Based on the cuttings velocity calculation method and the cuttings late arrival depth prediction model, the movement trajectory of the cuttings in the wellbore during the entire process of settling, suspension, and return under different drilling conditions is obtained, and the variation pattern of the cuttings late arrival depth over time is predicted;

[0065] S54. Based on the key parameter optimization results obtained by the feedback correction mechanism, the cuttings coupling correction term, turbulent diffusion coefficient, and sedimentation correction coefficient in the simulation platform are updated in real time, and the simulation calculation is re-iterated to obtain the cuttings late arrival depth prediction result that is highly matched with the on-site working conditions.

[0066] The beneficial effects of the present invention are:

[0067] (1) The present invention adopts a drilling fluid flow model based on the improved Navier-Stokes equations. By introducing the multi-scale cuttings-drilling fluid coupling correction term FMC and the non-steady-state turbulent momentum transfer function FNT, the complex flow relationship between drilling fluid and cuttings is accurately described, and the inertia, shear force and turbulent diffusion effects of cuttings are taken into account, thereby improving the accuracy of the prediction of cuttings motion trajectory. At the same time, by combining the real-time logging data on site to dynamically correct the key parameters of the model, the flow model can adapt to different drilling conditions, thereby improving the prediction accuracy.

[0068] (2) The present invention measures the cuttings return velocity and delay parameters in real time through laboratory simulation tests and field logging data feedback, compares the experimental data with the predicted results, and uses a feedback correction mechanism to dynamically adjust the cuttings coupling correction term, turbulent diffusion coefficient, and sedimentation correction coefficient to achieve adaptive optimization of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0070] Figure 1 The present invention provides a flow chart of a method for simulating late arrival of logging cuttings based on a fluid dynamics model and experimental calibration. DETAILED DESCRIPTION

[0071] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0072] refer to Figure 1 A method for simulating late arrival of mud logging cuttings based on a fluid dynamics model and experimental calibration includes the following steps:

[0073] S1. Collect drilling operation parameters, drilling fluid rheological parameters, and cuttings physical properties, and clean, remove outliers, and standardize the collected data to form an input data set with a unified scale and format;

[0074] S2. Based on a standardized input data set, a wellbore drilling fluid flow model is established using the modified Navier-Stokes equations. Correction parameters reflecting the coupling effect between drilling fluid and cuttings, turbulent diffusion, and shear force are introduced into the wellbore drilling fluid flow model to ensure that the wellbore drilling fluid flow model parameters match the actual drilling conditions.

[0075] S3. Based on the cuttings generation, suspension, sedimentation, and return mechanisms, the cuttings transport model is coupled with an improved wellbore drilling fluid flow model to form a unified cuttings late arrival depth prediction model.

[0076] S4. Through laboratory simulations and real-time field data collection, the cuttings return velocity and delay parameters are measured. The experimental data are compared with the prediction results of the cuttings delay depth prediction model. The key parameters of the cuttings delay depth prediction model are dynamically adjusted using a feedback correction mechanism.

[0077] S5. Use a numerical solution method to simulate the calibrated cuttings late arrival depth prediction model, simulating the two-phase flow process of drilling fluid and cuttings in the wellbore, and predicting the cuttings late arrival depth under different drilling conditions;

[0078] S6. Compare the simulation prediction results with the real-time logging data, further optimize the model parameters based on the data feedback, and output the final cuttings late arrival depth prediction results.

[0079] In this embodiment, S1 specifically includes the following steps:

[0080] S11. Collect drilling operation related data and construct data input set D, which is composed of drilling operation parameter set D. p , drilling fluid rheological parameter set D m and rock cuttings physical property parameter set D c composition:

[0081] D={D p ,D m ,D c};

[0082] S12. Clean the collected data input set D, remove outliers, and construct a cleaned data input set;

[0083] S13. Standardize the cleaned data input set and format the normalized data input set to make it meet the model input requirements, and output the final standard input data set D for simulation. final .

[0084] In this embodiment, S2 specifically includes the following steps:

[0085] S21. Based on the standard input data set D final A drilling fluid flow model in the wellbore is established. The drilling fluid flow model in the wellbore is constructed based on the improved Navier-Stokes equations that introduce multi-scale coupling correction terms and unsteady turbulent momentum transfer functions:

[0086]

[0087] Among them, ρ f is the drilling fluid density, which represents the inertial effect of the drilling fluid in the wellbore, u is the drilling fluid velocity vector, which represents the flow state of the drilling fluid in the wellbore, p is the drilling fluid pressure field, which represents the pressure distribution of the drilling fluid in the wellbore, μ is the dynamic viscosity of the drilling fluid, which represents the viscous effect during the flow of the drilling fluid, and F MC is the multi-scale cuttings-drilling fluid coupling correction term, which represents the multi-scale dynamic effect of cuttings in the wellbore on the momentum distribution of the drilling fluid. NT is the unsteady turbulent momentum transfer function, which represents the time-space variation effect of momentum under unsteady turbulent conditions in the wellbore;

[0088] S22. Multi-scale cuttings-drilling fluid coupling correction term F MC The specific ones are:

[0089]

[0090] Among them, β iis the momentum coupling intensity factor of the i-th scale cuttings, which represents the nonlinear coupling effect of cuttings scale difference on drilling fluid flow, ρ c is the rock debris density, which represents the inertia of rock debris particles, C c,i is the volume fraction of the i-th scale rock fragments, u c,i is the velocity vector of the i-th scale cuttings, Ψ(d c,i ,φ c,i ) is the cuttings scale factor function, which depends on the cuttings diameter d c,i and rock cuttings sphericity φ c,i , represents the effect of cuttings size and shape on the drilling fluid coupling behavior, N is the number of cuttings size classification;

[0091] S23. Unsteady turbulent momentum transfer function F NT The specific ones are:

[0092]

[0093] Among them, ∈ t (t) is the time-varying turbulent diffusion coefficient, which represents the dynamic change characteristics of the turbulent diffusion of the drilling fluid during the drilling process; γ(t) is the unsteady momentum adjustment factor, which represents the dynamic influence of the turbulent state fluctuation on the time response of the momentum distribution during the drilling process;

[0094] S24. Using the real-time logging data and laboratory simulation data, the above parameters β i ,∈ t (t) and γ(t) are initially determined, a drilling fluid flow model that meets the complex flow characteristics of the drilling site is established, and the drilling fluid flow model parameters that match the actual drilling conditions are output.

[0095] In this embodiment, S3 specifically includes the following steps:

[0096] S31. Based on the improved Navier-Stokes equations and two-phase flow theory, a cuttings transport model is constructed using the multi-scale dynamic coupling characteristics of drilling fluid and cuttings:

[0097]

[0098] Among them, C c is the cuttings concentration, which represents the volume fraction of cuttings in the wellbore, u c is the cuttings velocity vector, indicating the flow state of cuttings in the wellbore, D c is the rock debris diffusion coefficient, S c is the cuttings generation source term, which characterizes the cuttings generation rate during the rock breaking process of the drill bit;

[0099] S32. Combined with the improved multi-scale cuttings-drilling fluid coupling correction term F MC, calculate the cuttings movement speed, cuttings movement speed u c Affected by drilling fluid flow field, gravity, buoyancy and turbulence:

[0100]

[0101] Among them, λ is the rock cutting response coefficient, which represents the motion response characteristics of rock cuttings after being subjected to force. i Correction, ρ c is the rock debris density, ρ f is the drilling fluid density, g is the acceleration due to gravity, d c is the cuttings diameter, μ is the drilling fluid viscosity, is the drilling fluid pressure gradient;

[0102] S33. An improved sedimentation model is used to modify the cuttings sedimentation behavior, which is affected by shearing and turbulent diffusion effects within the wellbore:

[0103]

[0104] Among them, u s is the cuttings settling velocity, f t is the turbulence correction factor;

[0105] S34. Calculate the cuttings late depth L d The cuttings arrival depth is affected by the drilling fluid velocity, cuttings settling velocity and turbulent diffusion characteristics:

[0106]

[0107] Among them, u r is the cuttings return velocity;

[0108] S35. Establish a cuttings late arrival depth distribution model to describe the cuttings concentration at different depths in the wellbore:

[0109] C c (z,t)=C c,0 e -κt ;

[0110] Among them, C c (z,t) is the cuttings concentration at the well depth z, C c,0 is the initial cuttings concentration, and κ is the cuttings transport attenuation coefficient.

[0111] In this embodiment, S4 specifically includes the following steps:

[0112] S41. A standardized test environment was established in the laboratory to simulate the wellbore and measure the rheological parameters of different drilling fluids. m and rock cuttings physical properties D cThe cuttings movement characteristics under the conditions, including the cuttings return velocity u r and cuttings delay time t d ;

[0113] S42. Using downhole sensors to measure the concentration of rock cuttings C c , drilling fluid flow parameter u, well inclination angle θ key variables, and construct the field measurement data set D real :

[0114] D real ={C c ,u,θ,P,T};

[0115] Where P is the bottom hole pressure and T is the bottom hole temperature;

[0116] S43. Calculate theoretical prediction value based on cuttings late arrival depth prediction model Compared with laboratory values and field logging values Perform error analysis:

[0117]

[0118] Where, ΔL d is the deviation between the model prediction and the field logging data, deviations between model predictions and laboratory test data;

[0119] S44. Use feedback correction mechanism to dynamically adjust the key parameters in the cuttings delay depth prediction model, establish parameter optimization function, and adjust the cuttings coupling correction term F. MC , turbulent diffusion coefficient∈ t (t) and the settlement correction factor f t :

[0120]

[0121]

[0122] Among them, k1, k2 and k3 are correction coefficients;

[0123] S45. Based on the corrected cuttings coupling correction term F MC , turbulent diffusion coefficient∈ t (t) and the settlement correction factor f t , recalculate the cuttings late depth And perform error analysis:

[0124]

[0125] Finally, the optimized cuttings late arrival depth prediction result is output to match the actual working conditions.

[0126] In this embodiment, S5 specifically includes the following steps:

[0127] S51. Based on the standard input data set D final , improve the Navier-Stokes equations and cuttings transport model to build a numerical solution simulation platform, use the finite volume method to discretize the coupled model, and establish a computational grid for the movement of drilling fluid and cuttings in the wellbore;

[0128] S52. Using a time-domain iterative method to solve the calibrated cuttings late arrival depth prediction model, the drilling fluid flow velocity field, the cuttings movement velocity field, and the cuttings concentration distribution field are calculated to obtain the dynamic characteristics of the two-phase flow in the wellbore;

[0129] S53. Based on the cuttings velocity calculation method and the cuttings late arrival depth prediction model, the movement trajectory of the cuttings in the wellbore during the entire process of settling, suspension, and return under different drilling conditions is obtained, and the variation pattern of the cuttings late arrival depth over time is predicted;

[0130] S54. Based on the key parameter optimization results obtained by the feedback correction mechanism, the cuttings coupling correction term, turbulent diffusion coefficient, and sedimentation correction coefficient in the simulation platform are updated in real time, and the simulation calculation is re-iterated to obtain the cuttings late arrival depth prediction result that is highly matched with the on-site working conditions.

[0131] Example 1:

[0132] On February 15, 2024, while drilling a shale gas well (SC-4200) in a basin in a certain province, field logging engineers noticed an abnormal increase in cuttings return at a depth of 3,820 meters, with a 20% increase in the lag time compared to the previous period. Typically, the return time for cuttings at this depth should be around 75-80 minutes, but the actual return time in this section exceeded 90 minutes. Engineers suspected a cuttings accumulation at the wellbore. However, because the traditional empirical formula still calculated the lag depth of cuttings at 3,811.7 meters, which was significantly different from field observations, they were unsure whether to adjust the drilling fluid rate and rheological parameters.

[0133] In order to further verify the actual lag of cuttings return, engineers immediately used the mud logging cuttings delay simulation method based on the fluid dynamics model and experimental calibration of the present invention to perform calculations.

[0134] First, the on-site engineer uses the real-time data acquisition system to obtain the following key data:

[0135] Drilling fluid density: 1.42g / cm 3Drilling fluid viscosity: 55 mPa·s; rheological parameters (PV / YV): 22 / 10; pump displacement: 32 L / s; wellbore inclination: 67°; current drilling speed: 6.5 m / h; bottomhole temperature: 121°C; cuttings particle size range: 0.3 mm to 4.5 mm.

[0136] Subsequently, the engineer inputs these data into the method of the present invention for simulation calculation.

[0137] The simulation used a modified Navier-Stokes equation, combined with the multi-scale cuttings-drilling fluid coupling correction term FMC and the unsteady turbulent momentum transfer function FNT. During the calculation process, engineers set a time step of 0.1s and 1000 iterations to ensure convergence.

[0138] The simulation results show:

[0139] The predicted late arrival depth of rock cuttings is 3809.3 meters, which is significantly lower than the predicted value of traditional methods (3811.7 meters).

[0140] The average velocity of cuttings returning dropped to 0.68 m / s, a 28% decrease compared with the previous well section, indicating that the cuttings transportation efficiency was significantly reduced.

[0141] The predicted lag time value is 91.5 minutes, which is only 1.5 minutes different from the field measurement value (90 minutes), verifying the high accuracy of the model.

[0142] Based on the simulation results, on-site engineers quickly adjusted the drilling fluid displacement, increasing the pump speed from 32L / s to 38L / s, and appropriately increased the low shear force of the drilling fluid to improve the cuttings suspension capacity. After the adjustment, the system automatically tracked the logging data, showing that the cuttings return lag time was shortened by 20 minutes, successfully returning to the normal range.

[0143] Further testing showed that when drilling to 3920 meters, the cuttings return time stabilized at 70 minutes, with an error of only 2.3% from the corrected result (71.6 minutes) predicted by simulation, proving that the method of the present invention can achieve high-precision adaptive adjustment under different well conditions.

[0144] In order to verify the superiority of the method of the present invention, we used both the traditional empirical model and the method of the present invention for comparative analysis. The test data are as follows:

[0145] index Traditional methods Method of the present invention Predicted cuttings late arrival depth (m) 3811.7 3809.3 Actual logging measurement delay depth (m) 3810 3810 Prediction error (m) +1.7 -0.7 Predicted cuttings return time (min) 75 91.5 Actual cuttings return time (min) 90 90 Prediction error (%) 16.7％ 1.7％ Adapt to different well inclination angles Difference good Adapt to changes in drilling fluid rheological parameters generally excellent

[0146] This experiment shows that the method of the present invention can accurately predict the depth of late arrival of cuttings, provide real-time optimization suggestions, and effectively reduce the risk of cuttings accumulation at the bottom of the well, avoiding the problem of poor cuttings removal due to prediction errors. Especially under the conditions of high well deviation and strong shear drilling fluid, the adaptability of the method of the present invention is far superior to the traditional empirical formula calculation method. Ultimately, the method of the present invention successfully reduced the abnormal waiting time of drilling operations by about 1.5 hours, improved drilling efficiency, and ensured operation safety.

[0147] The present invention adopts a drilling fluid flow model based on the improved Navier-Stokes equations. By introducing the multi-scale cuttings-drilling fluid coupling correction term FMC and the unsteady turbulent momentum transfer function FNT, the complex flow relationship between drilling fluid and cuttings is accurately described. The inertia of cuttings, shear force and turbulent diffusion effects are taken into account, thereby improving the accuracy of cuttings motion trajectory prediction. At the same time, by dynamically correcting the key parameters of the model in combination with real-time on-site logging data, the flow model can adapt to different drilling conditions and improve prediction accuracy.

[0148] The present invention establishes a cuttings transport model, combines the flow field characteristics of the drilling fluid, comprehensively considers the entire process of cuttings generation, sedimentation, suspension, and return, adopts a cuttings motion velocity calculation method, dynamically calculates the motion state of cuttings of different sizes in the drilling fluid, and uses an improved sedimentation model to make corrections.

[0149] The present invention measures the cuttings return velocity and delay parameters in real time through laboratory simulation tests and field logging data feedback, compares the experimental data with the predicted results, and adopts a feedback correction mechanism to dynamically adjust the cuttings coupling correction term, turbulent diffusion coefficient and sedimentation correction coefficient to achieve adaptive optimization of the model.

[0150] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for simulating late arrival of logging cuttings based on a fluid dynamics model and experimental calibration, characterized in that: The steps include: S1. Collect drilling operation parameters, drilling fluid rheological parameters, and cuttings physical properties, and clean, remove outliers, and standardize the collected data to form an input data set with a unified scale and format; S2. Based on a standardized input data set, a wellbore drilling fluid flow model is established using the modified Navier-Stokes equations. Correction parameters reflecting the coupling effect between drilling fluid and cuttings, turbulent diffusion, and shear force are introduced into the wellbore drilling fluid flow model to ensure that the wellbore drilling fluid flow model parameters match the actual drilling conditions. S3. Based on the cuttings generation, suspension, sedimentation, and return mechanisms, the cuttings transport model is coupled with an improved wellbore drilling fluid flow model to form a unified cuttings late arrival depth prediction model. S4. Through laboratory simulations and real-time field data collection, the cuttings return velocity and delay parameters are measured. The experimental data are compared with the prediction results of the cuttings delay depth prediction model. The key parameters of the cuttings delay depth prediction model are dynamically adjusted using a feedback correction mechanism. S5. Use a numerical solution method to simulate the calibrated cuttings late arrival depth prediction model, simulating the two-phase flow process of drilling fluid and cuttings in the wellbore, and predicting the cuttings late arrival depth under different drilling conditions; S6. Compare the simulation prediction results with the real-time logging data, further optimize the model parameters based on the data feedback, and output the final cuttings late arrival depth prediction results.

2. The method for simulating delayed arrival of mud logging cuttings based on a fluid dynamics model and experimental calibration according to claim 1, characterized in that: The S1 specifically includes the following steps: S11. Collect drilling operation related data and construct a data input set D, which is composed of a drilling operation parameter set D. p , drilling fluid rheological parameter set D m and rock cuttings physical property parameter set D c composition: D={D p ,D m ,D c }; S12. Clean the collected data input set D, remove outliers, and construct a cleaned data input set; S13. Standardize the cleaned data input set and format the normalized data input set to make it meet the model input requirements, and output the final standard input data set D for simulation. final .

3. The method for simulating delayed arrival of mud logging cuttings based on a fluid dynamics model and experimental calibration according to claim 1, characterized in that: The S2 specifically includes the following steps: S21. Based on the standard input data set D final A drilling fluid flow model in the wellbore is established. The drilling fluid flow model in the wellbore is constructed based on the improved Navier-Stokes equations that introduce multi-scale coupling correction terms and unsteady turbulent momentum transfer functions: Among them, ρ f is the drilling fluid density, which represents the inertial effect of the drilling fluid in the wellbore, u is the drilling fluid velocity vector, which represents the flow state of the drilling fluid in the wellbore, p is the drilling fluid pressure field, which represents the pressure distribution of the drilling fluid in the wellbore, μ is the dynamic viscosity of the drilling fluid, which represents the viscous effect during the flow of the drilling fluid, and F MC is the multi-scale cuttings-drilling fluid coupling correction term, which represents the multi-scale dynamic effect of cuttings in the wellbore on the momentum distribution of the drilling fluid. NT is the unsteady turbulent momentum transfer function, which represents the time-space variation effect of momentum under unsteady turbulent conditions in the wellbore; S22. Multi-scale cuttings-drilling fluid coupling correction term F MC The specific ones are: Among them, β i is the momentum coupling intensity factor of the i-th scale cuttings, which represents the nonlinear coupling effect of cuttings scale difference on drilling fluid flow, ρ c is the rock debris density, which represents the inertia of rock debris particles, C c,i is the volume fraction of the i-th scale rock fragments, u c,i is the velocity vector of the i-th scale cuttings, Ψ(d c,i ,φ c,i ) is the cuttings scale factor function, which depends on the cuttings diameter d c,i and rock cuttings sphericity φ c,i , represents the effect of cuttings size and shape on the drilling fluid coupling behavior, N is the number of cuttings size classification; S23. Unsteady turbulent momentum transfer function F NT The specific ones are: Among them, ∈ t (t) is the time-varying turbulent diffusion coefficient, which represents the dynamic change characteristics of the turbulent diffusion of the drilling fluid during the drilling process; γ(t) is the unsteady momentum adjustment factor, which represents the dynamic influence of the turbulent state fluctuation on the time response of the momentum distribution during the drilling process; S24. Using the real-time logging data and laboratory simulation data, the above parameters β i ,∈ t (t) and γ(t) are initially determined, a drilling fluid flow model that meets the complex flow characteristics of the drilling site is established, and the drilling fluid flow model parameters that match the actual drilling conditions are output.

4. The method for simulating delayed arrival of mud logging cuttings based on a fluid dynamics model and experimental calibration according to claim 1, characterized in that: The S3 specifically includes the following steps: S31. Based on the improved Navier-Stokes equations and two-phase flow theory, a cuttings transport model is constructed using the multi-scale dynamic coupling characteristics of drilling fluid and cuttings: Among them, C c is the cuttings concentration, which represents the volume fraction of cuttings in the wellbore, u c is the cuttings velocity vector, indicating the flow state of cuttings in the wellbore, D c is the rock debris diffusion coefficient, S c is the cuttings generation source term, which characterizes the cuttings generation rate during the rock breaking process of the drill bit; S32. Combined with the improved multi-scale cuttings-drilling fluid coupling correction term F MC , calculate the cuttings movement speed, cuttings movement speed u c Affected by drilling fluid flow field, gravity, buoyancy and turbulence: Among them, λ is the rock cutting response coefficient, which represents the motion response characteristics of rock cuttings after being subjected to force. i Correction, ρ c is the rock debris density, ρ f is the drilling fluid density, g is the acceleration due to gravity, d c is the cuttings diameter, μ is the drilling fluid viscosity, is the drilling fluid pressure gradient; S33. An improved sedimentation model is used to modify the cuttings sedimentation behavior, which is affected by shearing and turbulent diffusion effects within the wellbore: Among them, u s is the cuttings settling velocity, f t is the turbulence correction factor; S34. Calculate the cuttings late depth L d The cuttings arrival depth is affected by the drilling fluid velocity, cuttings settling velocity and turbulent diffusion characteristics: Among them, u r is the cuttings return velocity; S35. Establish a cuttings late arrival depth distribution model to describe the cuttings concentration at different depths in the wellbore: C c (z,t)=C c,0 e -κt ; Among them, C c (z,t) is the cuttings concentration at the well depth z, C c,0 is the initial cuttings concentration, and κ is the cuttings transport attenuation coefficient.

5. The method for simulating delayed arrival of mud logging cuttings based on a fluid dynamics model and experimental calibration according to claim 1, characterized in that: The S4 specifically includes the following steps: S41. A standardized test environment was established in the laboratory to simulate the wellbore and measure the rheological parameters of different drilling fluids. m and rock cuttings physical properties D c The cuttings movement characteristics under the conditions, including the cuttings return velocity u r and cuttings delay time t d ; S42. Using downhole sensors to measure the concentration of rock cuttings C c , drilling fluid flow parameter u, well inclination angle θ key variables, and construct the field measurement data set D real : D real ={C c ,u,θ,P,T}; Where P is the bottom hole pressure and T is the bottom hole temperature; S43. Calculate theoretical prediction value based on cuttings late arrival depth prediction model Compared with laboratory values and field logging values Perform error analysis: Where, ΔL d is the deviation between the model prediction and the field logging data, deviations between model predictions and laboratory test data; S44. Use feedback correction mechanism to dynamically adjust the key parameters in the cuttings delay depth prediction model, establish parameter optimization function, and adjust the cuttings coupling correction term F. MC , turbulent diffusion coefficient∈ t (t) and the settlement correction factor f t : Among them, k1, k2 and k3 are correction coefficients; S45. Based on the corrected cuttings coupling correction term F MC , turbulent diffusion coefficient∈ t (t) and the settlement correction factor f t , recalculate the cuttings late depth And perform error analysis: Finally, the optimized cuttings late arrival depth prediction result is output to match the actual working conditions.

6. The method for simulating delayed arrival of logging cuttings based on a fluid dynamics model and experimental calibration according to claim 1, characterized in that: The S5 specifically includes the following steps: S51. Based on the standard input data set D final , improve the Navier-Stokes equations and cuttings transport model to build a numerical solution simulation platform, use the finite volume method to discretize the coupled model, and establish a computational grid for the movement of drilling fluid and cuttings in the wellbore; S52. Using a time-domain iterative method to solve the calibrated cuttings late arrival depth prediction model, the drilling fluid flow velocity field, the cuttings movement velocity field, and the cuttings concentration distribution field are calculated to obtain the dynamic characteristics of the two-phase flow in the wellbore; S53. Based on the cuttings velocity calculation method and the cuttings late arrival depth prediction model, the movement trajectory of the cuttings in the wellbore during the entire process of settling, suspension, and return under different drilling conditions is obtained, and the variation pattern of the cuttings late arrival depth over time is predicted; S54. Based on the key parameter optimization results obtained by the feedback correction mechanism, the cuttings coupling correction term, turbulent diffusion coefficient, and sedimentation correction coefficient in the simulation platform are updated in real time, and the simulation calculation is re-iterated to obtain the cuttings late arrival depth prediction result that is highly matched with the on-site working conditions.

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