Complex borehole trajectory optimization method based on improved multi-objective particle swarm optimization algorithm
A multi-target particle swarm and wellbore trajectory technology, which is applied to instruments, adaptive control, control/regulation systems, etc., to achieve the effects of improving drilling success rate, reducing drilling cost and maintaining distribution
Active Publication Date: 2022-04-29
XI'AN PETROLEUM UNIVERSITY
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[0004] In order to overcome the defects of the above-mentioned prior art, the object of the present invention is to provide a complex borehole trajectory optimization method based on the improved multi-objective particle swarm optimization algorithm, using the improved multi-objective particle swarm optimization algorithm (Multi-objectiveParticle Swarm Optimization Algorithm), for individual The improvement of the selection method of the optimal and global optimal position, the application of a disturbance mutation operator to the particles, so as to avoid falling into the local optimum, the generation of external sets, the selection of non-dominated sets, etc., the algorithm improves the particle swarm algorithm, for optimizing multi-objective problems
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[0093] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0094] The complex wellbore trajectory optimization method based on the improved multi-objective particle swarm algorithm includes the following steps:
[0095] (1) Set the parameters of the multi-objective particle swarm optimization algorithm MOPSO, including the maximum value and maximum value of the dynamic inertia weight, acceleration factor, population size, and the maximum number of iterations GEN.
[0096] Constraint conditions and independent variable constraint boundary conditions are shown in Table 2.
[0097] Table 1 Parameter settings of MOPSO
[0098]
[0099] In Table 1, POP is the population size, exPOP is the external file size, generally take POP=exPOP; GEN is the maximum number of iterations; in MOPSO, psoc1 and psoc2 are acceleration factors; psow max 、psow min are the maximum and minimum values of inertia weights.
[0100]...
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Complex borehole trajectory optimization method based on the improved multi-objective particle swarm optimization algorithm, (1) set the parameters of the multi-objective particle swarm optimization algorithm MOPSO, (2) initialize the population; (3) calculate the objective function value, (4) update each generation of particles (5) Perform mutation operations on particles; (6) Calculate the objective function value of each particle in the population; (7) Updating the individual optimum is the process from the beginning of the algorithm iteration to the current optimal position, (8 ) Sort the non-dominated set nd, (9) sort the non-inferior solutions in the external archives in MOPSO in descending order according to the objective function value; (10) adopt the truncation method to delete the redundant individuals behind; (11) the global optimal (12) Obtain the optimal solution set of algorithm optimization, that is, the actual measured length of the wellbore trajectory and the actual control torque reach the relative optimum; the present invention realizes the multi-objective wellbore trajectory parameter optimization under actual drilling conditions, and improves the drilling success rate and reduce drilling costs to lay a theoretical basis for decision-making.
Description
technical field [0001] The invention relates to the technical field of borehole trajectory optimization, in particular to a complex borehole trajectory optimization method based on an improved multi-objective particle swarm algorithm. Background technique [0002] With more and more oil and gas exploration shifting from inland to deep sea, desert and other areas, coupled with the increasing number of unconventional, deep water, deep layer, polar and other oil and gas fields, the corresponding drilling technology and data acquisition technology while drilling , Logging data comprehensive interpretation methods and intelligent optimization algorithms have also made great progress. In addition, during oilfield development, the requirements for well pattern layout are also increasing day by day, and interwell scanning and anti-collision have been paid more and more attention by people. Secondly, in order to further increase the production of oilfields, the development of thin o...
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IPC IPC(8): G05B13/02
CPCG05B13/024
Inventor沙林秀李文燕张奇志李琳
OwnerXI'AN PETROLEUM UNIVERSITY