基于DeepSets-TD3的多障碍井约束井眼轨迹智能控制方法
CSTR:
作者:
作者单位:

中国地质大学(北京)工程技术学院,北京,100083

作者简介:

通讯作者:

中图分类号:

P634.7

基金项目:

地球深部探测与矿产资源勘查国家科技重大专项“绿色高效精准智能化钻探技术与装备”所属专题课题“高效精准智能化钻探技术集成研究” 2024ZD1003105.4地球深部探测与矿产资源勘查国家科技重大专项“绿色高效精准智能化钻探技术与装备”所属专题课题“高效精准智能化钻探技术集成研究”(编号:2024ZD1003105.4)


Intelligent wellbore trajectory control method for multiple obstacle-well constraints based on DeepSets-TD3
Author:
Affiliation:

School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China

Fund Project:

地球深部探测与矿产资源勘查国家科技重大专项“绿色高效精准智能化钻探技术与装备”所属专题课题“高效精准智能化钻探技术集成研究” 2024ZD1003105.4地球深部探测与矿产资源勘查国家科技重大专项“绿色高效精准智能化钻探技术与装备”所属专题课题“高效精准智能化钻探技术集成研究”(编号:2024ZD1003105.4)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对密集井网条件下井眼轨迹控制面临的多邻井约束、障碍物输入顺序敏感和连续动作稳定控制问题,本文提出DeepSets?TD3多障碍井约束井眼轨迹智能控制方法。该方法以最小曲率法递推井眼轨迹,将井斜角和方位角增量作为连续控制动作;以靶点相对位移、井斜角及方位角正余弦构成基础状态,以障碍井表面净距及相对方位正余弦构成集合元素,并通过共享编码网络与均值?最大值对称池化形成置换不变表征。在固定四障碍布置测试中,DeepSets?TD3的平均目标误差为7.52 m、物理碰撞率为0。随机四障碍测试中,DeepSets?TD3的平均目标误差为11.2 m,物理碰撞率为1.2%,近失误缓冲区率为1.9%,狗腿严重度合规率为99.6%,且四项指标均优于普通TD3。结果表明,集合表征能够减弱障碍井排列变化引起的策略退化,提高复杂邻井约束下轨迹控制的精度、安全性与泛化能力。

    Abstract:

    Aiming at the challenges in borehole trajectory control under dense well patterns, including constraints from multiple adjacent wells, sensitivity to the input order of obstacles, and stable control of continuous actions, this paper proposes an intelligent control method for multi-obstacle well constrained borehole trajectory named DeepSets-TD3. The method recursively calculates the borehole trajectory using the minimum curvature method, and takes the increments of inclination and azimuth as continuous control actions. The basic state consists of the relative displacement of the target, inclination angle, as well as the sine and cosine values of azimuth angle. Each element in the set is constructed by the surface clear distance of obstacle wells and the sine and cosine values of relative azimuth. A permutation-invariant representation is formed via a shared encoder network and mean-max symmetric pooling. In the test with four fixed obstacles, the DeepSets-TD3 achieves an average target error of 7.52 m and zero physical collision rate. In the random four-obstacle test, the average target error is 11.2 m, the physical collision rate is 1.2%, the near-miss buffer rate is 1.9%, and the compliance rate of dogleg severity reaches 99.6%. All four indicators outperform the conventional TD3. The results demonstrate that the set representation can mitigate policy degradation caused by changes in the arrangement of obstacle wells, and improve the accuracy, safety and generalization capacity of trajectory control under complex constraints of adjacent wells.

    参考文献
    相似文献
    引证文献
引用本文

马伟民,黄新武,王瑜.基于DeepSets-TD3的多障碍井约束井眼轨迹智能控制方法[J].钻探工程,2026,53(S1):102-111.
MA Weimin, HUANG Xinwu, WANG Yu. Intelligent wellbore trajectory control method for multiple obstacle-well constraints based on DeepSets-TD3[J]. Drilling Engineering, 2026,53(S1):102-111.

复制
分享
相关视频

文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2026-07-31
  • 最后修改日期:2026-09-15
  • 录用日期:2026-09-16
  • 在线发布日期: 2026-10-08
  • 出版日期:
文章二维码