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使用优化高斯过程回归的螺栓支钻孔压力的增强预测
Jie Liu1,2,3
1CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan, 030000, China. 357973693@qq.com.
Scientific reports
|January 26, 2024
概括
这项研究优化了高斯过程时间序列回归 (GPR) 用于预测螺栓支系统中的钻井压力. 粒子群集优化-GPR (PSO-GPR) 实现了80%的准确性,增强了地质工程支持系统.
科学领域:
- 地质技术工程 地质技术工程
- 计算智能是一种计算智能.
背景情况:
- 在螺栓支系统中准确预测钻井压力对于地质工程至关重要.
- 高斯过程回归 (GPR) 显示了潜力,但需要对内核函数和历史数据进行优化.
- 在GPR预测结果的可变性需要强大的参数调整.
研究的目的:
- 开发和验证一种新的,优化的GPR方法,用于预测地下螺栓支系统中的钻井压力.
- 为了提高钻井压力预测的准确性和效率.
- 为实际地质技术应用确定最佳的GPR参数.
主要方法:
- 探索了160个GPR方案,结合了10个内核功能和16个历史点.
- 应用的混合优化算法:基因算法-GPR (GA-GPR),粒子群优化-GPR (PSO-GPR) 和殖民地算法-GPR (ACA-GPR).
- 使用混合算法代优化了GPR参数.
主要成果:
- PSO-GPR表现出卓越的性能,在1-2 MPa的偏差下达到80%的预测准确度.
- 确定的最佳GPR配置是RQ内核函数,具有18个历史点.
- 最优的组合产生了0.0047246的根平均平方误差 (RMSE),明显低于最不有效的组合 (RMSE为0.035704).
结论:
- 使用PSO-GPR.开发了一个强大的和高效的预测系统,用于地下螺栓支钻井压力.
- 优化的GPR模型显著提高了地质技术支持系统的准确性和效率.
- 该研究验证了PSO-GPR模型在现实世界钻井操作中的实际适用性.
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