Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Improved multi-target particle swarm optimization-based complicated well track optimization method

A multi-target particle swarm and borehole trajectory technology, applied in the fields of instrumentation, adaptive control, control/regulation system, etc.

Active Publication Date: 2019-08-16
XI'AN PETROLEUM UNIVERSITY
View PDF3 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Improved multi-target particle swarm optimization-based complicated well track optimization method
  • Improved multi-target particle swarm optimization-based complicated well track optimization method
  • Improved multi-target particle swarm optimization-based complicated well track optimization method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[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 the inertial weights.

[...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

An improved multi-target particle swarm optimization-based complicated well track optimization method comprises the steps of (1) setting a parameter of a multi-target particle swarm optimization MOPSO; (2) initializing population; (3) calculating a target function value; (4) updating a position and a speed of each generation of particle; (5) performing mutation operation on the particle; (6) calculating a target function value of each particle in the population; (7) updating the process of individual optimal algorithm from iteration beginning to a current optimal position; (8) sorting a non-domination set nd; (9) sequencing non-inferior solutions in external document of the MOPOS according to a target function value in a descending order; (11) deleting subsequent remaining individuals by an intercept method; (11) performing global optimization; and (12) obtaining an optimal solution set with algorithm optimization, wherein the actual measurement length of the well track and actual control torque reach optimization relatively. By the improved multi-target particle swarm optimization-based complicated well track optimization method, multi-target well track parameter optimization under an actual well drilling condition is achieved, the drilling success rate is improved, and a theoretical decision foundation is laid for reduction of drilling cost.

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G05B13/02
CPCG05B13/024
Inventor 沙林秀李文燕张奇志李琳
Owner XI'AN PETROLEUM UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products