A wellbore trajectory optimization method and system based on an improved whale optimization algorithm

Abstract

The application discloses a wellbore trajectory optimization method based on an improved whale optimization algorithm, and comprises the following steps: defining a target function and constraint conditions of a wellbore trajectory optimization problem; then performing optimization by using an improved whale optimization algorithm (IWOA); and finally outputting an optimization result by taking an optimal solution obtained as the optimization result. The method can simultaneously consider the optimization problem of the target function and the constraint conditions, and obtain a more optimal wellbore trajectory design scheme, thereby improving oil and gas exploration efficiency and reducing cost. The wellbore trajectory optimization method has high practical value and economic benefits, can simultaneously optimize the target function and the constraint conditions, obtain an optimized solution meeting actual requirements, adopts the improved whale optimization algorithm, has global optimization capability and fast convergence speed, and can be applied to different types of wellbore trajectory optimization problems and has good versatility.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of well trajectory optimization, and in particular to a well trajectory optimization method and system based on an improved whale optimization algorithm. BACKGROUND

[0002] With the continuous development of oil and gas exploration technology, well trajectory optimization has become an important research direction. In the process of oil and gas drilling, designing a reasonable well trajectory can improve the efficiency of oil and gas exploration and reduce costs, so the optimization of well trajectory has attracted much attention. Traditional optimization methods mainly include genetic algorithm, particle swarm optimization, simulated annealing algorithm, etc., but these methods have limitations, such as being easily trapped in local optimal solution, being unable to consider target function and constraint conditions simultaneously, etc.

[0003] In recent years, as a new optimization algorithm, the whale optimization algorithm has the advantages of strong parallelism and strong global search ability, and has gradually become a research hotspot in well trajectory optimization. However, the traditional whale optimization algorithm has certain problems in dealing with constraint conditions, which can easily lead to illegal solutions. Therefore, it is necessary to improve the whale optimization algorithm to improve its constraint processing ability. SUMMARY

[0004] This section aims to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0005] In view of the above and / or existing problems that only double objective function optimization is used in traditional well trajectory optimization and the intelligent algorithm is not improved, the present application is proposed.

[0006] Therefore, the problem to be solved by the present application is how to improve the intelligent algorithm.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In a first aspect, the embodiments of the present application provide a well trajectory optimization method based on an improved whale optimization algorithm, which includes defining the objective function and constraint conditions of the well trajectory optimization problem; using the improved whale optimization algorithm for optimization; taking the obtained optimal solution as the optimization result and outputting.

[0009] As a preferred scheme of the well trajectory optimization method based on the improved whale optimization algorithm, the improved whale optimization algorithm is used for optimization, including the following steps: the hunting behavior of the whale in the whale optimization algorithm is divided into surrounding prey, bubble net attacking prey and searching prey; the whale optimization algorithm is improved, non-dominated sorting is introduced, and Levy flight strategy is adopted.

[0010] As a preferred scheme of the well trajectory optimization method based on the improved whale optimization algorithm, the target prey surrounded by the prey is the current optimal solution, and other humpback whales will update their positions to the humpback whale in the position of the target prey, and the specific formula is as follows:

[0011] D = |CX * (t)-X(t)|

[0012] X(t+1) = X * (t)-AD

[0013] Wherein, t is the current iteration number, X(t) is the current whale position vector, X*(t) is the position vector of the current optimal solution, D is the distance between the current whale individual and the current optimal solution, A and C are coefficient vectors.

[0014] As a preferred scheme of the well trajectory optimization method based on the improved whale optimization algorithm, the bubble net attacking prey includes: spiral updating position: the humpback whale swims to the prey in a spiral manner, and the specific formula is as follows:

[0015]

[0016] Wherein, D' is the distance between the current position vector X(t) and the position vector X*(t) of the current optimal solution, b is a constant defining the shape of the logarithmic spiral, t is the current iteration number, D is the distance between the current whale individual and the current optimal solution, p is a random number in [0,1], A and C are coefficient vectors, and l is a random number in [-1,1].

[0017] As a preferred scheme of the well trajectory optimization method based on the improved whale optimization algorithm, the searching prey has the specific formula as follows:

[0018] D = |CX rand (t)-X(t)|

[0019] X(t+1) = X rand (t)-AD

[0020] Wherein, X rand (t) is a randomly selected whale position vector, A and C are coefficient vectors, X(t) is the current whale position vector, and D is the distance between the current whale individual and the current optimal solution.

[0021] As a preferred scheme of the wellbore trajectory optimization method based on the improved whale optimization algorithm, the Levy flight strategy is used, and the specific formula is:

[0022]

[0023] Wherein, D' is the distance between the current position vector X(t) and the position vector X*(t) of the current optimal solution, b is a constant defining the shape of the logarithmic spiral, Levy(λ) is a random search path, and λ is a scale parameter representing the step size; the smaller the value of λ, the smaller the step size, and the higher the search accuracy; the larger the value of λ, the larger the step size, and the wider the search range.

[0024] As a preferred scheme of the wellbore trajectory optimization method based on the improved whale optimization algorithm, the optimal solution obtained is used as the optimization result, and the optimization result is outputted.

[0025] In a second aspect, the embodiments of the present application provide a wellbore trajectory optimization system, which comprises: a data input module for inputting data required for wellbore trajectory optimization; an algorithm optimization module for designing population size, dimension and domain, and algorithm iteration number; and an output module for outputting wellbore trajectory optimization output result.

[0026] In a third aspect, the embodiments of the present application provide a computer device, which comprises a memory and a processor, and the memory stores a computer program, wherein the processor implements any step of the above method when executing the computer program.

[0027] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement any step of the above method.

[0028] The wellbore trajectory optimization method based on the improved whale optimization algorithm provided by the present application has the advantages of global search capability, self-adaptability, avoidance of local optimal solution, high parallelism, simplicity and ease of use, applicability to various optimization problems, and excellent performance and benefits in processing high-dimensional optimization problems. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only represent some of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings. Among them:

[0030] Figure 1 The overall flowchart of the wellbore trajectory optimization method based on the improved whale optimization algorithm in embodiment 1.

[0031] Figure 2 The flowchart of the improved whale optimization algorithm of the wellbore trajectory optimization method based on the improved whale optimization algorithm in embodiment 1.

[0032] Figure 3 The vertical projection diagram of the trajectory optimization of the wellbore trajectory optimization method based on the improved whale optimization algorithm in embodiment 2.

[0033] Figure 4 The horizontal projection diagram of the trajectory optimization of the wellbore trajectory optimization method based on the improved whale optimization algorithm in embodiment 2. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below in combination with the drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should belong to the protection scope of the present application.

[0035] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details that are set forth in the following description, and it is understood that the present application is not limited to the embodiments described herein and can be practiced with or without the other implementations of the present application. Accordingly, the present application is not limited to the embodiments described herein, but rather, the scope of the present application is to be determined with reference to the appended claims.

[0036] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or selective embodiment that excludes other embodiments.

[0037] The present application is described in detail in combination with the schematic diagram. In the detailed description of the embodiments of the present application, the cross-sectional view of the device structure is locally enlarged without the general proportion for the convenience of description, and the schematic diagram is only an example, which should not limit the scope of protection of the present application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual manufacture.

[0038] Meanwhile, in the description of the present application, it should be noted that the terms "upper, lower, inner and outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0039] Unless otherwise expressly specified and limited, the terms "mounting, connecting, connecting" in the present application should be understood broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0040] Embodiment 1

[0041] Reference Figure 1 and Figure 2 The first embodiment of the present application provides a wellbore trajectory optimization method and system based on improved whale algorithm, which comprises:

[0042] S1: defining the objective function and constraint conditions of the wellbore trajectory optimization problem.

[0043] Further, the objective function includes trajectory length, well profile energy and target error, and the constraint conditions include that the distance between the trajectory endpoint and the target point should be within a certain range.

[0044] Further, the specific range requirement depends on the target and requirement of wellbore trajectory design, but the distance requirement between the trajectory endpoint and the target point is usually between a few meters and tens of meters.

[0045] It should be noted that the trajectory length refers to the total length of the wellbore trajectory, and the optimization algorithm minimizes the trajectory length to reduce the drill bit wear and drilling fluid consumption during drilling, thereby reducing the drilling cost and improving the drilling efficiency; the well profile energy refers to the energy of the curvature, twist and bending characteristics of the wellbore trajectory, and the optimization algorithm minimizes the well profile energy to reduce the bending and deformation of the wellbore trajectory, improve the wellbore penetration rate and reduce the wellbore deviation, thereby improving the drilling efficiency and reducing the drilling risk; the target error refers to the distance error between the wellbore trajectory and the target layer, and the optimization algorithm minimizes the target error to improve the wellbore penetration rate and reduce the wellbore deviation, thereby further improving the drilling efficiency and reducing the drilling risk.

[0046] Further, by setting the three targets, the wellbore trajectory optimization algorithm can comprehensively consider requirements of drilling cost, drilling efficiency and drilling quality and the like, so as to obtain an optimal wellbore trajectory design scheme.

[0047] Further, the trajectory length is calculated as follows:

[0048] L = L1 + L2

[0049] Wherein, L is the trajectory length, L1 is the first segment trajectory length, and L2 is the second segment trajectory length.

[0050] It should be noted that the wellbore trajectory is divided into two segments for length calculation, because in the drilling process, the drill bit will usually first drill into a geological layer with relatively soft stratum (referred to as a saturated segment), and then enter a geological layer with relatively hard stratum (referred to as a non-saturated segment). The properties and characteristics of the two geological layers are different, so the trajectory lengths of the saturated segment and the non-saturated segment need to be calculated respectively.

[0051]

[0052] Wherein, k α,1 , k α,2 are the inclination angle change rates of each well segment, and Δα is the inclination angle difference.

[0053]

[0054] Wherein, are the azimuth angle change rates of each well segment, and Δβ is the azimuth angle difference.

[0055] Further, the well profile energy is calculated as follows:

[0056]

[0057] Wherein, E ω is the well profile energy, k1 and k2 are the first segment curvature and the second segment curvature respectively, and τ1 and τ2 are the first segment torsion and the second segment torsion respectively.

[0058] It should be noted that the curvature refers to the degree of bending of the wellbore trajectory at a point, which is usually described by the curvature radius. The smaller the curvature radius, the greater the degree of bending of the wellbore trajectory at the point. Therefore, considering the curvature in the well profile energy can effectively reflect the bending condition of the wellbore trajectory, thereby reducing the degree of bending of the wellbore trajectory and improving the wellbore penetration rate and reducing the wellbore deviation.

[0059] ​It should be noted that the twist refers to the rotation degree of the well trajectory in the vertical direction, the greater the twist degree of the well trajectory, the more the well trajectory deviates from the target layer, thereby affecting the drilling quality and efficiency, therefore, considering the twist in the well profile energy can effectively reduce the twist degree of the well trajectory, improve the well penetration rate and reduce the well deviation.

[0060] Further, the two indexes of curvature and twist are set in the well profile energy, in order to comprehensively consider the bending degree and twist degree of the well trajectory, improve the well penetration rate and reduce the well deviation, thereby improving the drilling efficiency and reducing the drilling risk.

[0061]

[0062] wherein k α,i , is the azimuth angle change rate of each well section, is the average value of the inclination angle.

[0063]

[0064] wherein k α,i , is the azimuth angle change rate of each well section, is the average value of the inclination angle.

[0065] Further, the calculation of the on-target error is as follows:

[0066]

[0067] wherein H max , D max is the maximum width and height of the target window, D ω is the on-target error, N T , E T , D T is the north coordinate, east coordinate and vertical depth of the target point.

[0068] Further, the calculation of the constraint condition is as follows:

[0069]

[0070]

[0071] wherein N B , E B , D B is the north coordinate, east coordinate and vertical depth of the trajectory end point, N T , E T , D T is the north coordinate, east coordinate and vertical depth of the target point, H max, D max are the maximum width and height of the target window, respectively, g1(x), g2(x) are the position constraints of the trajectory end point, x is the decision variable, x lb is the lower bound of the decision variable, x ub is the upper bound of the decision variable.

[0072] S2: optimization is performed using the improved whale algorithm.

[0073] Further, the hunting behavior of the whale in the whale algorithm is divided into three stages: surrounding prey, bubble net attacking prey and searching for prey.

[0074] Further, the position of the spiral is improved according to the whale algorithm, non-dominated sorting is introduced, and the Levy flight strategy is adopted.

[0075] It should be noted that, as Figure 2 shown, the use of the improved whale algorithm includes:

[0076] S2.1 Surround the prey.

[0077] Assuming that the target prey is the current optimal solution, other humpback whales will update their positions in the direction of the humpback whale located at the target prey position, wherein the whale position update formula is as follows:

[0078] D = |CX * (t)-X(t)|

[0079] X(t+1) = X * (t)-AD

[0080] Where t is the current iteration number, X(t) is the current whale position vector, X*(t) is the position vector of the current optimal solution, D is the distance between the current whale individual and the current optimal solution, and A and C are coefficient vectors.

[0081] A = 2ar-a

[0082] Where r is a random number in [0, 1].

[0083] S2.2 Bubble net attack prey.

[0084] Further, in order to describe the bubble net hunting behavior of humpback whales, the following two ways are designed.

[0085] S2.2.1 Contraction surrounding mechanism: this surrounding method is realized by controlling the parameter a to linearly decrease from 2 to 0, and A is a random number in [-1, 1].

[0086] Specifically, in the local search stage, the value of the control parameter a starts to linearly decrease from 2 to 0, at this time the search range of the whale gradually shrinks, when the value of a is 0, the search range of the whale becomes the neighborhood range of the current optimal solution, at this time the whale finds a better solution by fine-tuning the solution in the neighborhood, and in this process, the decreasing speed of the value of the control parameter a gradually increases to ensure that the algorithm can converge to the optimal solution as soon as possible.

[0087] S2.2.2 Spiral update position: the humpback whale swims towards the prey in a spiral manner, and the specific formula is as follows:

[0088] D = |X * (t) - X(t)|

[0089] X(t+1) = X * (t) + D'e bl cos(2πl)

[0090] where D' is the distance between the current position vector X(t) and the position vector X*(t) of the current optimal solution, b is a constant that defines the shape of the logarithmic spiral, t is the current iteration number, D is the distance between the current whale individual and the current optimal solution, and l is a random number in [-1, 1].

[0091] It should be noted that in this embodiment, b = 1, and l is a random number in [-1, 1], when l = -1, it means that the whale is closest to the prey, and when l = 1, it means that the whale is farthest from the prey.

[0092] Further, assuming that the probability of each individual selecting the contraction enclosure mechanism and the spiral update position mechanism is 50%, the complete expression of this mathematical model is as follows:

[0093]

[0094] where p is a random number in [0, 1] and l is a random number in [-1, 1].

[0095] S2.3 Searching for prey.

[0096] Further, in order to expand the search range of the whale, in this stage, the whale no longer updates its position according to the current optimal prey, but updates it according to a randomly selected whale, that is, in order to enhance the global search ability of the algorithm and prevent falling into local optimum, when the random probability p ≥ 0.5, if |A| ≥ 1, the distance D is randomly updated.

[0097] It should be noted that the random probability p is an adjustable parameter used to control the randomness of the whale in the search process, and the value of the random probability p is adjusted according to the performance of the algorithm and the search results.

[0098] Further, the mathematical model simulating the whale search can be realized by randomly selecting a whale X rand (t) to guide other whale individuals, and the specific expression is as follows:

[0099] X(t+1) = X rand (t) - AD

[0100] Wherein, X rand (t) is a randomly selected whale position vector, A and C are coefficient vectors, and X(t) is the current whale position vector.

[0101] S2.4 Non-dominated sorting.

[0102] First, find the Pareto optimal solution in the current solution set, store the optimal solution and mark it as level 1, and remove it from the current solution set, then continue to find the Pareto optimal solution in the remaining solution set, mark it as level 2 and remove it, and repeat this step until the level of all Pareto optimal solutions is determined.

[0103] Specifically, the Pareto optimal solution is an important concept in multi-objective optimization problem, which refers to the solution set with the highest optimization degree of other objective functions when a certain objective function cannot be further optimized in the case of multiple objective functions. Non-dominated sorting is a commonly used method in multi-objective optimization algorithm, which can help us select a group of optimal solutions that are not dominated by other solutions in all objective functions, and is a group of optimal solution set. In practical applications, these optimal solutions can help us make more accurate and reasonable decisions, and improve the performance and efficiency of the system.

[0104] S2.5 Levy flight strategy.

[0105] Further, after adopting the Levy flight strategy, the spiral update position calculation formula is as follows:

[0106]

[0107] Wherein, D' is the distance between the current position vector X(t) and the position vector X*(t) of the current optimal solution, b is a constant defining the shape of the logarithmic spiral, Levy(λ) is a random search path, and λ is a scale parameter representing the step size;

[0108] The smaller the value of λ, the smaller the step size, and the higher the search accuracy;

[0109] The larger the value of λ, the larger the step size, and the wider the search range.

[0110] It should be noted that the Levy flight strategy is introduced to help the algorithm explore the search space more effectively. The Levy flight strategy is an optimization algorithm based on Levy flight, which can help the algorithm jump out of the local optimal solution and increase the possibility of global search. In addition, improving the mode of spiral updating position can also help the algorithm avoid the phenomenon of falling into local optimal solution and premature convergence. In the traditional WOA, the mode of spiral updating position is fixed, which is easy to lead the algorithm to fall into local optimal solution in the search process.

[0111] Further, by improving the mode of spiral updating position, the diversity and exploration ability of the algorithm can be increased, and the algorithm can be prevented from falling into local optimal solution and premature convergence.

[0112] S3: The optimal solution of the obtained well inclination angle and azimuth angle change rate of each well section is taken as the optimization result and output.

[0113] Further, the embodiment also provides a wellbore trajectory optimization system based on an improved whale optimization algorithm, comprising: a data input module for inputting data required for wellbore trajectory optimization; an algorithm optimization module for designing population size, dimension and definition domain, and algorithm iteration number; and an output module for outputting wellbore trajectory optimization output result.

[0114] The embodiment also provides a computer device suitable for the case of the wellbore trajectory optimization method, comprising:

[0115] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the power transformer substation and customer relationship identification method proposed in the above embodiment.

[0116] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. In addition, the input device can be an external keyboard, touchpad or mouse, etc.

[0117] The embodiment also provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0118] define a target function and a constraint condition of the wellbore trajectory optimization problem;

[0119] optimization is performed by using the improved whale algorithm;

[0120] the obtained optimal solution is taken as an optimization result and output.

[0121] In conclusion, by using the improved whale algorithm, the wellbore trajectory can be effectively optimized, drilling operation is safer, more efficient, energy-saving and more environmentally friendly, and therefore the quality and benefits of drilling operation are improved, which has high practical value and economic benefits; the improved whale algorithm has global optimization capability, can globally search for an optimal solution in a solution space, avoids falling into a local optimal solution, and therefore improves the accuracy and stability of the optimization result; the improved whale algorithm uses multiple search strategies and an adaptive parameter control method, can converge to the optimal solution in a short time, and has fast convergence speed and high efficiency.

[0122] Embodiment 2

[0123] Reference Figure 3 , Figure 4 and Table 1, a wellbore trajectory optimization method based on an improved whale algorithm is provided in the second embodiment of the present application, and in order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiments are performed for scientific demonstration.

[0124] Further, a population is initialized, the population size is defined as 50, the dimension is defined as 10, the domain is defined as [-500, 500], and an initial population is randomly generated.

[0125] Further, a target function and a constraint condition are calculated, the length of the wellbore trajectory, the well profile energy and the target error are taken as the target function, the distance between the trajectory end point and the target point should be within a certain range as the constraint condition, and an initial optimal solution and an optimal target function are calculated.

[0126] Further, the following iteration process is repeatedly executed until the maximum iteration number 100 is reached.

[0127] Further, the obtained optimal solution is taken as an optimization result, and the optimization result is output.

[0128] Table 1 wellbore trajectory optimization design parameter results of different weight coefficients

[0129]

[0130] Further, as Figure 3 , Figure 4and shown in Table 1, there are four trajectories designed according to different weight factors, Figure 3 The vertical projection of the curve reflects the vertical deviation of the well trajectory, which can be used to evaluate the longitudinal smoothness and vertical deviation of the trajectory, Figure 4 The horizontal projection of the curve reflects the horizontal deviation of the well trajectory, which can be used to evaluate the lateral smoothness and lateral deviation of the trajectory.

[0131] Specifically, L is the length of the trajectory, E ω is the well profile energy, D ω is the hit target, k α,i , are the inclination and azimuth rate of each well section, respectively, w i is the weight factor of the objective function, according to different weight factors, the optimized well trajectory has different characteristics and advantages and disadvantages.

[0132] Preferably, when the weight factor w1=1, w2=0, w3=0, the optimization result shows that the minimum total length is 211.5 meters, the well profile energy is 8.5, but the accuracy of hitting the target is 75%.

[0133] Preferably, when the weight factor w1=0, w2=1, w3=0, the optimization result shows that the trajectory with the minimum well profile energy of 6.9 has a total trajectory length of 212.3 meters, but the accuracy of hitting the target is 0.1%.

[0134] Preferably, when the weight factor w1=0, w2=0, w3=1, the optimization result shows that the trajectory with the maximum value of 212.7 meters has a target hit accuracy of 100%, the length and the well profile energy are 11.8.

[0135] Preferably, when the weight factor is 0.5, the optimization result shows that the most appropriate trajectory is obtained, which has a relatively short length of 211.6 meters, a small well profile energy of 8.2, and a very high hit rate of 99.6%.

[0136] In summary, according to different combinations of weight factors, the optimization results of the well trajectory have different advantages and disadvantages, and the optimal weight factor combination needs to be selected according to the actual demand and application scenario to obtain the best optimization result.

[0137] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, which should be covered by the claims of the present application.

Claims

1. A wellbore trajectory optimization method based on an improved whale optimization algorithm, characterized in that: Comprising, defining an objective function and constraint conditions of a wellbore trajectory optimization problem; wherein the objective function comprises a trajectory length, a well profile energy and a target error, and the constraint conditions comprise that a distance between a trajectory end point and a target point should be within a certain range; the calculation of the trajectory length is specifically as follows: L = L1 + L2 wherein L is the trajectory length, L1 is the first segment trajectory length, and L2 is the second segment trajectory length; the calculation of the well profile energy is specifically as follows: wherein E ω is the well profile energy, k1, k2 are the first segment curvature, second segment curvature, τ1, τ2 are the first segment twist, second segment twist, respectively; the calculation of the target error is specifically as follows: Wherein, H max , D max are the maximum width and height of the target window, respectively, D ω is the centering error, N T , E T , D T are the north coordinate, east coordinate and vertical depth of the target point. an improved whale optimization algorithm is used for optimization; wherein the hunting behavior of a whale in the whale optimization algorithm is divided into surrounding prey, bubble net attacking prey and searching prey; the whale optimization algorithm is improved, non-dominated sorting is introduced, and a Levy flight strategy is used; the Levy flight strategy is specifically as follows: wherein D' is a distance between a current position vector X(t) and a position vector X*(t) of a current optimal solution, b is a constant defining a logarithmic spiral shape, Levy(λ) is a random search path, and λ is a scale parameter representing a step length; the smaller the λ value, the smaller the step length, and the higher the search accuracy; the larger the λ value, the larger the step length, and the wider the search range; the obtained optimal solution is taken as an optimization result and output.

2. The well trajectory optimization method based on improved whale optimization algorithm of claim 1, wherein: the surrounding prey comprises: a target prey is a current optimal solution, and other humpback whales will update their positions towards the humpback whale at the target prey position, and the specific formula is as follows: D = |CX * (t)-X(t)| X(t+1) = X * (t)-AD wherein t is a current iteration number, X(t) is a current whale position vector, X*(t) is a position vector of a current optimal solution, D is a distance between a current whale individual and a current optimal solution, and A and C are coefficient vectors.

3. The well trajectory optimization method based on improved whale optimization algorithm of claim 2, wherein: the bubble net attacking prey comprises: spiral updating position: a humpback whale swims towards prey in a spiral manner, and the specific formula is as follows: wherein D' is a distance between a current position vector X(t) and a position vector X*(t) of a current optimal solution, b is a constant defining a logarithmic spiral shape, t is a current iteration number, D is a distance between a current whale individual and a current optimal solution, p is a random number in [0, 1], A and C are coefficient vectors, and l is a random number in [-1, 1].

4. The well trajectory optimization method based on improved whale optimization algorithm of claim 2, wherein: the searching prey comprises: D = ||CX rand (t)-X(t)| X(t+1) = X rand (t)-AD where X rand (t) is a randomly selected whale position vector, A and C are coefficient vectors, X(t) is the current whale position vector, and D is the distance between the current whale individual and the current best solution.

5. The well trajectory optimization method based on improved whale optimization algorithm of claim 2, wherein: the optimal solution obtained as the optimization result comprises the following steps: initializing a population, including population size, dimension and definition domain; calculating an objective function and constraint conditions to obtain an initial optimal solution and an optimal objective function; repeatedly executing the following iteration process until a maximum iteration number is reached; the optimal solution obtained is taken as the optimization result, and the optimization result is output.

6. A wellbore trajectory optimization system based on improved whale optimization algorithm, the wellbore trajectory optimization method based on improved whale optimization algorithm according to any one of claims 1-5, characterized in that: Comprising, a data input module for inputting data required for wellbore trajectory optimization; an algorithm optimization module for designing population size, dimension and definition domain and algorithm iteration number; an output module for outputting a wellbore trajectory optimization output result.

7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: The processor executes the computer program to implement the steps of the method of any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the method of any one of claims 1-5.

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