Drilling parameter optimization method to improve speed and efficiency

Abstract

The present invention discloses a drilling parameter optimization method for increasing speed and efficiency, and relates to the technical field of oil and gas well engineering. The method comprises the following steps: based on the heat exchange of the formation seepage field and the heat generated by the drill bit breaking the rock, a theoretical heat exchange model of the high-temperature and high-pressure formation is established; based on the measured parameters of the drilling, an actual heat exchange model of the high-temperature and high-pressure formation is established; based on the theoretical heat exchange model and the actual heat exchange model, a heat error is obtained; based on the influence of the drilling parameters on the heat error, the reliability of the heat error is calculated, and based on the reliability threshold, the drilling parameters and their corresponding heat errors are divided into a training set and a data set to be optimized; based on the Tianying optimization algorithm, the training set is trained to output the optimal combination of drilling parameters, and based on the optimal combination of drilling parameters, the drilling parameters in the data set to be optimized are calculated to obtain the optimized drilling parameters. The present invention can optimize the combination of drilling parameters to achieve the purpose of increasing the speed and efficiency of drilling in deep high geothermal gradient formations.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas well engineering, and in particular to a drilling parameter optimization method for increasing drilling speed and efficiency. Background Art

[0002] During the drilling process, optimizing drilling parameters is the key to improving drilling efficiency and reducing costs. By rationally adjusting drilling parameters, we can effectively cope with complex geological conditions, help increase drilling speed, reduce drilling risks, and ensure the smooth progress of drilling operations. At present, the method of optimizing drilling parameters is mainly to use the actual drilling conditions such as drilling speed, torque and drilling pressure as parameters, and to achieve real-time optimization of drilling parameters by establishing complex mathematical models and optimization calculation formulas, so as to achieve the purpose of increasing drilling speed and efficiency.

[0003] However, deep and ultra-deep wells are in a high-temperature and high-pressure geological environment, and face many complex and changeable influencing factors. This results in significant impacts on the performance of drilling fluid and downhole drill bit assembly during the actual drilling process, making it difficult to accurately control drilling parameters, and conventional optimization algorithms and mathematical models cannot achieve effective predictions. For the actual downhole conditions of deep and ultra-deep wells, only an optimization model based on the current actual drilling conditions parameters is established without considering the impact of the actual environment on the various drilling parameters, which will reduce the prediction and optimization effects of the model.

[0004] Patent No. (CN.115293066.A) provides a method for calculating downhole thermal effects, which can reflect the environmental status of drilling. Therefore, based on the characteristics of the existing drilling parameter optimization method, a drilling parameter optimization model based on the actual drilling and completion data of the test well is established, which can provide an optimization method for the drilling parameters of the wells to be drilled in the same block. Summary of the invention

[0005] To solve at least one of the above problems, the present invention proposes a drilling parameter optimization method for increasing drilling speed and efficiency.

[0006] The technical solution of the present invention is: a drilling parameter optimization method for increasing speed and efficiency, comprising the following steps:

[0007] Based on the heat exchange of formation seepage field and the heat generated by drill bit breaking rock, a theoretical heat exchange model of high temperature and high pressure formation is established; based on the measured drilling parameters, an actual heat exchange model of high temperature and high pressure formation is established;

[0008] Based on the theoretical heat transfer model and the actual heat transfer model, the heat error is obtained;

[0009] Based on the influence of drilling parameters on thermal error, the reliability of thermal error is calculated, and based on the reliability threshold, the drilling parameters of the test well and their corresponding thermal errors are divided into a training set and a data set to be optimized;

[0010] The training set is trained based on the Tianying optimization algorithm to output the optimal combination of drilling parameters. Based on the optimal combination of drilling parameters, the compensation values ​​of drilling parameters in the data set to be optimized are calculated.

[0011] The drilling parameter optimization results of the comprehensive test wells are used to form the optimal drilling parameter combination data set for the block.

[0012] The beneficial effect of the present invention is that: the present invention is based on deep high-temperature and high-pressure formations, and establishes a theoretical heat exchange and actual heat exchange calculation model of the drilling temperature field under such conditions. Then, the difference between the theoretical heat exchange and the actual heat exchange is used to establish a reliability calculation model, and the drilling parameter combination greater than the reliability threshold is used as a training set, and the drilling parameters less than the drilling parameter reliability threshold is used as a data set to be optimized. The optimal drilling parameter combination is output according to the training set and the Sky Eagle algorithm, and the drilling parameters in the data set to be optimized are optimized according to the optimal drilling parameter combination, so as to achieve the purpose of speeding up and improving the efficiency of drilling in deep high geothermal gradient formations. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of the present invention;

[0014] Figure 2 The flowchart of the Skyhawk algorithm of the embodiment of the present invention is

[0015] Figure 3 The drilling pressure logging data of the well selected in the embodiment of the present invention;

[0016] Figure 4 The rotation speed logging data of the well selected in the embodiment of the present invention;

[0017] Figure 5 Torque path data of the well selected for the embodiment of the present invention;

[0018] Figure 6 The reliability calculation result of the selected block in the embodiment of the present invention. DETAILED DESCRIPTION

[0019] The specific implementation modes of the present invention will be described clearly and completely below with reference to examples and drawings. Obviously, the described examples are only some embodiments of the present invention, rather than all embodiments.

[0020] First, based on the actual situation of drilling engineering, the heat exchange of radial convection of drilling fluid is much greater than the axial heat exchange. Therefore, the embodiment of the present invention makes the following assumptions:

[0021] (1) The drilling fluid is regarded as a single-phase, incompressible fluid. Its physical properties such as density, viscosity, specific heat capacity and thermal conductivity do not change with drilling depth, and heat is transferred only in the radial direction.

[0022] (2) The formation rock is a homogeneous, isotropic entity. Its physical properties such as density, permeability, porosity and thermal conductivity do not change with depth, and heat is only transferred along the pores.

[0023] (3) The frictional heat loss between the drill string system and the drilling fluid is not considered.

[0024] like Figure 1 As shown, a drilling parameter optimization method for improving speed and efficiency comprises the following steps:

[0025] Based on the heat exchange of formation seepage field and the heat generated by drill bit breaking rock, a theoretical heat exchange model of high temperature and high pressure formation is established;

[0026] Specifically, in high geothermal gradient formations, the geothermal gradient varies nonlinearly with depth, and it is difficult to obtain an accurate law of geothermal gradient variation. At the same time, literature research shows that high geothermal gradient formations have the characteristics of associated abnormal formation pressure phenomena. Therefore, the seepage heat transfer of formation fluids is limited by the formation seepage field. The key is to calculate the distribution of formation pressure. The formation pressure P can be described by Darcy's law and fluid mechanics equations to describe the pressure distribution in the formation. First, the fluid in the formation seepage field satisfies Darcy's formula: Where v is the velocity of the fluid in the formation seepage field, m / s; k is the formation permeability, mD; μ is the formation fluid viscosity, mPa·s; P is the formation pressure, MPa.

[0027] Under high temperature and high pressure conditions, the apparent viscosity of drilling fluid will change with temperature and pressure, showing a nonlinear relationship. The change of drilling fluid viscosity with temperature and pressure is expressed by the empirical formula: Where μ0 is the viscosity of the drilling fluid at the wellhead temperature, which is measured by instruments, mPa·s; T m is the measured temperature of the drilling fluid outlet during the test, °C; P m is the fixed pressure on the drilling fluid during the test, MPa; C1 and C2 are drilling fluid characteristic constants, which are determined according to the properties of the drilling fluid and are dimensionless.

[0028] In this embodiment, since the drilling fluid displacement q is constant, the drilling fluid displacement q can be expressed as Where q is the drilling fluid displacement; r is the distance from the calculation point to the wellbore axis, m; H is the vertical depth, m.

[0029] The calculation formula of formation pressure P can be derived from the above formula: Where P w is the bottom hole pressure, MPa; r w is the wellbore radius, m.

[0030] High temperature formations are often accompanied by high pressure. The formation fluid flows into the wellbore. The formation seepage heat transfer is divided into two parts: formation fluid seepage heat transfer and formation rock solid phase heat transfer. Therefore, the formation seepage heat transfer Q f The calculation formula is expressed as;

[0031]

[0032] In the formula, λ l ,λ r are the thermal conductivity of formation fluid and the thermal conductivity of formation rock solid phase, W / (m℃); φ is the formation porosity, %; q is the drilling fluid displacement, L / s; ρ r is the core density, measured by indoor test, g / cm3; T j The jth meter is the formation temperature near the well wall, measured by the downhole sensor, ℃; T0 is the temperature at the inlet, measured by the temperature measuring instrument, ℃.

[0033] In high temperature and high pressure formations, the heat generated by the drill bit breaking the rock is mainly related to the drill bit design parameters and drilling parameter settings, and can be calculated by an empirical formula. b The empirical calculation formula is:

[0034]

[0035] Where: w is the rotating speed of the turntable, r / min; τ is the shear strength of the formation rock, MPa; h c is the cutting depth of the drill bit, m; b c Drill cutting width, m; λ c is the thermal conductivity of the drill bit cutting tooth material, W / (m·K); is the shear angle of the drill bit cutting teeth during drilling, °; β is the forward rake angle of the drill bit cutting teeth, °; l is the wear length of the drill bit cutting teeth, m; u is the jet velocity of the drill bit water hole under a certain jet pressure, cm3 / s; T is the torque of the drill bit during rotary drilling, N·m; H c is the hardness of the drill bit cutting teeth, MPa; N is the average drilling pressure during drilling, kN.

[0036] Heat exchange Q through formation seepage field f and the heat generated by the drill bit breaking rock Q b , we can get the theoretical heat transfer model of the formation: Q T =Q f +Q b , where Q T The theoretical heat exchange capacity of drilling fluid.

[0037] Based on the measured drilling parameters, the actual heat exchange model of high temperature and high pressure formation is established; based on the theoretical heat exchange model and the actual heat exchange model, the heat error is obtained;

[0038] In high temperature and high pressure formations, by measuring the actual drilling fluid temperature and the drilling fluid displacement per unit time, the actual heat exchange of the drilling fluid after passing through the downhole drill string system and formation seepage exchange can be calculated. The calculation formula for the actual heat exchange is expressed as:

[0039] In the formula, Q A is the actual heat exchange, J; q is the drilling fluid displacement, L / s; r w is the borehole radius, m; H is the vertical depth, m; C p is the specific heat capacity of drilling fluid, kJ / (kg·℃); T out is the drilling fluid outlet temperature, recorded and measured by the sensor of the first outlet mud pipe, ℃; T in is the drilling fluid inlet temperature, recorded by the sensor at the last inlet mud pipe, ℃.

[0040] According to the actual heat transfer and theoretical heat transfer, the heat error ΔQ can be obtained: ΔQ = Q A -Q T .

[0041] Based on the influence of drilling parameters on thermal error, the reliability of thermal error is calculated, and based on the reliability threshold, the drilling parameters and their corresponding thermal errors are divided into a training set and a data set to be optimized;

[0042] In this embodiment, all the data of drilling parameters are recorded in a time step of 5s and numbered in sequence. The drilling parameters include drilling pressure, torque, rotation speed, drilling fluid displacement, drilling fluid well inlet temperature and drilling fluid outlet temperature. Among them, drilling mechanical parameter data such as drilling pressure, torque and rotation speed are directly measured by logging data, and temperature data are recorded by temperature sensors at the drilling site. Drilling parameters are recorded in time steps, so drilling parameters can be regarded as time series combinations, so the matrix X can be used. i,j Represents the recorded drilling parameter data, expressed as:

[0043]

[0044] In the formula, x i,j (i=1,2,3,...,n,j=1,2,3,...,m) represents the value of the i-th well parameter at the j-th moment.

[0045] Therefore, the increment Δx of each drilling parameter data in the time interval [j, j+1] is i,j It is expressed as:

[0046] Δx i,j =x i,j -x i,j+1

[0047] Further, the incremental data is represented as a matrix ΔX i,j :

[0048]

[0049] Drilling parameters are discrete data recorded in continuous time. Therefore, the established reliability calculation equation can reflect the impact of changes in various drilling parameters on the thermal error ΔQ, providing a basis for guiding drilling parameters. The reliability calculation equation for thermal error is expressed as:

[0050]

[0051] In the formula, R ΔQ is the reliability of the thermal error ΔQ; D is the threshold, dimensionless; Φ(·) is the standard normal distribution function; μ is the drift coefficient, dimensionless; σ is the diffusion coefficient, dimensionless; t is the time, s.

[0052] Furthermore, the threshold value D is expressed as:

[0053]

[0054] Furthermore, the drift coefficient μ is the estimated drilling parameter increment ΔX per unit time. j The diffusion coefficient σ is the average change of the drilling parameter increment ΔX per unit time. j The rate of change of can therefore be used to calculate and estimate the data within a fixed time interval, expressed as:

[0055]

[0056] Where N is the data volume, dimensionless; Δt is the time change, s.

[0057] Combining the above equations, the final calculation formula for thermal error reliability can be obtained:

[0058]

[0059] As for the reliability threshold, it can be set according to the actual situation. For example, it can usually be set to about 0.75 to 0.85. In this embodiment, the reliability threshold is set to 0.8. Therefore, the drilling parameters with a reliability threshold greater than 0.8 are set as the training set, and the drilling parameters with a reliability threshold less than 0.8 are set as the set to be optimized. After optimizing the drilling parameters in the set to be optimized, combined with the drilling parameters in the training set, the complete drilling parameters of different wells in the same block can be obtained.

[0060] The training set is trained based on the Tianying optimization algorithm to output the optimal combination of drilling parameters. Based on the optimal combination of drilling parameters, the compensation values ​​of drilling parameters in the data set to be optimized are calculated.

[0061] like Figure 2 As shown, the Eagle Optimization Algorithm is a common optimization algorithm in this field. Its basic idea is to simulate the behavior of the Eagle catching animals, establish a mathematical equation corresponding to the behavior, and finally achieve optimal selection in a specific space. In this embodiment, the drilling parameter combination is regarded as an animal population, and the Eagle Optimization Algorithm is used to calculate the optimal drilling parameter combination. The specific calculation process is shown below.

[0062] Step S301: Initialize algorithm parameters and initial population size;

[0063] The initialization algorithm parameters include the maximum number of iterations K, which is defined by the user; the initial population is composed of the recorded drilling parameter data matrix X i,j (i=1,2,3,...,n,j=1,2,3,...,m), the number of rows is n, which indicates the number of optimized drilling parameters, and the number of columns is m, which indicates the population size of drilling parameters; the upper bound a and lower bound b of the initial population are defined, expressed as a=max{X i,j}, b = min{X i,j}; Use Logistic mapping to construct a chaotic random number function f(r) to determine the random number, expressed as:

[0064] f(r)=3.99(rr 2 ),r=rand(0,1)

[0065] In the formula, rand(0,1) represents a random number between 0 and 1.

[0066] The quality function of the number of iterations is defined as α(k), expressed as:

[0067]

[0068] Where K is the maximum number of iterations; k is the current number of iterations.

[0069] Step S302: Initialize population fitness and obtain the best individual;

[0070] The reliability R of the heat error in step S2 is ΔQ As the fitness evaluation function, when R ΔQ The larger the value is, the better the current drilling parameter combination is. The function form is expressed as i represents the i-th group of drilling parameter combinations, and the optimal individual is defined as X best , represented as a one-dimensional vector X best =[x1,x2,...,x n ], where n is the number of drilling parameters, x n is the value of the corresponding drilling parameter.

[0071] Step S303: Searching stage;

[0072] When k≤0.6K, the algorithm enters the search phase. The search phase includes two stages. The first stage is the expansion search phase, that is, searching for the best area within the specified area. The mathematical model is expressed as:

[0073] X1(k+1)=X best (k)×α(k)+(X m (k)-X best )×f(r)

[0074] In the formula, X best (k) is the optimal drilling parameter combination for the current iteration number; X M (k) is the mean value from the first iteration to the current position, expressed as:

[0075]

[0076] The second stage is the narrowing search stage. After finding the best area, it hovers in the sky waiting for prey. The mathematical model is expressed as:

[0077] X2(k+1)=X best (k)×f levy (D m )+X r (k)+(yx)×f(r)

[0078] In the formula, X r For X i,j A random position in levy (·) is the levy flight function, D m ∈[0,m] integer; y, x are the spiral parameters, expressed as:

[0079]

[0080] Where R is the searching period, dimensionless; θ is the searching angle, degrees.

[0081] Step S304: development stage;

[0082] When k>0.6K, the algorithm enters the development stage. The development stage includes two stages. The first stage is the expansion development stage. The mathematical model is expressed as:

[0083] X3(k+1)=0.1×(X best (k)-X M (k))-f(r)+0.1×[(ab)×f(r)+b]

[0084] The second stage is the shrinking development stage, and the mathematical model is expressed as:

[0085] X4(k+1)=QF(k)×X best (k)-[G1×f(r)×X(k)]-[α(k)×(f levy (D m )+f(r))]

[0086] Where QF(·) is the quality function and G1 is the random value function, which are expressed as:

[0087]

[0088] Step S305: Calculate the fitness after each iteration and update the current optimal solution;

[0089] Step S306: repeating steps S301 to S305, and when the maximum number of iterations K is reached, outputting the optimal combination of drilling parameters;

[0090] Step S307: Compare the calculated optimal drilling combination with the drilling parameter combination to be optimized, and the difference is the corresponding drilling parameter compensation value.

[0091] The drilling parameter optimization results of the comprehensive test wells are used to form the optimal drilling parameter combination data set for the block.

[0092] The training set and the adjusted set to be optimized are recombined to obtain the optimal drilling parameter combination for the test well. When drilling in subsequent blocks with the same geological morphology as the test well, adjustments are made according to the optimal drilling parameter combination of the test well, which can effectively reflect the relationship between the formation and the drilling parameters, achieve efficient drilling, and achieve the purpose of speeding up and improving the efficiency of high-temperature and high-pressure formations.

[0093] In order to further enable those skilled in the art to understand the technical solution of this embodiment, specific data are used as an example for explanation below.

[0094] The well section selected in this example is the logging data of a test well in the Y101 well area with a vertical depth of 4000-4500m. The drilling parameters are recorded in 5s steps and numbered in sequence, with a total of 800 data. The drilling parameters to be optimized are drilling pressure, rotation speed and torque.

[0095] like Figure 3 to Figure 5 As shown, it is the drilling parameter logging data selected in this embodiment, corresponding to the drilling pressure, speed and torque logging data with time number 0-800, and the heat exchange Q of the high temperature and high pressure temperature field theory T The actual heat transfer Q of high temperature and high pressure temperature field A , and calculate the reliability R ΔQ The calculation results are as follows Figure 6 Then, RΔQ The drilling parameter combinations with values ​​greater than 0.8 are used as training sets, and the drilling parameter combinations with values ​​less than 0.8 are used as the data set to be optimized. Figure 2 to Figure 4 The data numbered 401-410 in the middle grey area are provided for explanation.

[0096] The logging data and reliability calculation results of the drilling parameters of time numbers 401-410 are shown in Table 1. As shown in the table, the reliability R ΔQ It is less than 0.8, so it is a combination that needs to be optimized.

[0097] Table 1 Drilling parameters and reliability results corresponding to time numbers 404-407

[0098] Time number Drilling pressure (kN) Speed ​​(r / min) Torque (N·m) <![CDATA[Reliability R ΔQ > 401 148.3 66.7 22.5 0.81 402 148.4 62.3 20.9 0.93 403 127.4 65.6 18.0 0.80 404 167.0 65.5 12.9 0.77 405 135.8 65.5 14.6 0.76 406 132.4 65.5 16.1 0.78 407 137.6 65.5 13.2 0.75 408 143.1 65.6 11.4 0.80 409 141.6 65.6 12.4 0.82 410 141.1 65.5 13.3 0.81

[0099] The recommended drilling pressure and lead time of the drilling parameters of time numbers 404-407 calculated based on the Sky Eagle algorithm are shown in Table 2. Taking time number 404 as an example, the meaning of parameter adjustment is further explained. When the speed is 65.5r / min for time number 404, the drilling pressure is 167kN, and the recommended optimal drilling pressure is 166kN, so the compensation value is 1kN; the torque is 12.9N·m, and the optimal value is 11.7N·m, so the compensation value is -1.2N·m. The calculation method of the compensation value of the remaining drilling parameter sets to be optimized in this embodiment is the same as the example, and will not be further explained.

[0100] Table 2 Optimal drilling parameter combinations and compensation values ​​corresponding to time numbers 404-407.

[0101]

[0102] Finally, the drilling parameter optimization results of 800 data points of the test wells with a vertical depth of 4000-4500m were integrated to form the optimal drilling parameter combination data set for the block at the same vertical depth, which guided the subsequent drilling of new development wells in the block.

[0103] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A drilling parameter optimization method for increasing drilling speed and efficiency, characterized in that: The following steps are involved: Based on the heat exchange of formation seepage field and the heat generated by drill bit breaking rock, a theoretical heat exchange model of high temperature and high pressure formation is established; Based on the measured drilling parameters, the actual heat exchange model of high temperature and high pressure formation is established, and the heat error is obtained based on the theoretical heat exchange model and the actual heat exchange model; Based on the influence of drilling parameters on thermal error, the reliability of thermal error is calculated. Based on the reliability threshold, the drilling parameters of the test wells in the block and their corresponding thermal errors are divided into a training set and a data set to be optimized. Based on the Tianying optimization algorithm, the training set is trained, the optimal combination of drilling parameters is output, and the compensation value of drilling parameters in the data set to be optimized is calculated; The drilling parameter optimization results of the comprehensive test wells are used to form the optimal drilling parameter combination data set for the block; The theoretical heat transfer model is as follows: Q T =Q f +Q b In the formula, Q T is the theoretical heat exchange of drilling fluid; Q f represents the heat transfer of formation seepage field; Q b Indicates the heat generated by the drill bit breaking rock; l ,λ r are the thermal conductivity of formation fluid and the thermal conductivity of formation rock solid phase respectively; φ is the formation porosity; q is the drilling fluid displacement; ρ r is the core density; T j is the formation temperature near the wellbore wall at the jth meter; T0 is the temperature at the inlet; r is the distance from the calculation point to the wellbore axis; P w is the bottom hole pressure; H is the vertical depth; P is the formation pressure; w is the rotating speed of the rotary table; τ is the shear strength of the formation rock; h c b is the cutting depth of the drill bit; c Drill cutting width; λ c is the thermal conductivity of the drill bit cutting tooth material; is the shear angle of the drill bit cutting teeth during drilling; β is the forward rake angle of the drill bit cutting teeth; l is the wear length of the drill bit cutting teeth; u is the jet velocity of the drill bit water hole under a certain jet pressure; T is the torque of the drill bit during rotary drilling; H c is the hardness of the drill bit cutting teeth; N is the average drilling pressure during drilling; The actual heat transfer model is as follows: In the formula, Q A is the actual heat exchange; r w is the wellbore radius; C p is the specific heat capacity of drilling fluid; T out is the drilling fluid outlet temperature; T in is the drilling fluid inlet temperature.

2. The method according to claim 1, characterized in that The measured parameters of the drilling fluid include drilling pressure, rotation speed, torque, and drilling fluid temperature.

3. The method according to claim 1, characterized in that The heat error is: ΔQ = Q A -Q T , where ΔQ is the heat error.

4. The method according to claim 1, characterized in that: The reliability of the thermal error is calculated by the following formula: In the formula, R ΔQ is the reliability of the heat error ΔQ; Φ(·) is the standard normal distribution function; t is the time; Δx i,j =x i,j -x i,j+1 , x i,j represents the value of the i-th well parameter at the j-th time, i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., m, n is the maximum number of drilling parameters, and m represents the maximum number of time steps; Denotes ΔX i,j The average value of .

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