三维侧钻井钻进轨迹多目标智能优化算法的应用实验及结果分析

Experiment and result analysis of multi-object intelligent optimization algorithm for 3D sidetracking trajectory

摘要:

钻进轨迹多目标优化是实现定向井高效、安全钻进的关键之一。随着优化算法性能的不断改进，求解轨迹优化问题能够得到具有更小轨迹长度、复杂度和中靶误差的轨迹设计方案，然而算法的实用性尚未得到验证。本文利用结合自适应罚函数的多目标分解进化算法、基于最小模糊熵的综合评价方法，解决三维侧钻井轨迹优化问题。将所提出的优化与决策方法在钻进过程智能控制实验系统中进行应用，验证了所提方法的实用性，能够为工程实际中的轨迹设计提供借鉴和指导，并为后续轨迹跟踪控制提供参考。

Abstract:

Multi-object optimization of drilling trajectory is one of the keys to achieve efficient and safe drilling. The improvement of the optimization algorithms can get smaller trajectory length and less complexity in finding solutions to the trajectory optimization questions. However， the practicability of the trajectory optimization algorithm has not been verified. In this paper， the multi-object decomposition evolutionary algorithm combined with the adaptive penalty function， and the comprehensive evaluation method based on minimum fuzzy entropy are used to study the 3D sidetracking well trajectory optimization problem. The proposed methods were verified for their practicability with application in the drilling process intelligent control experimental system. They can provide reference and guidance for trajectory design in engineering practice， and provide reference for drilling trajectory tracking control.