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流失的循环预测方法基于一个改进的果算法支持矢量机器优化
Song Deng1, Chunyu Pei1, Xiaopeng Yan1
1School of Petroleum Engineering, Changzhou University, Changzhou 213100, China.
ACS omega
|September 18, 2023
概括
这项研究引入了一种有效的方法,用于预测钻探期间流失的循环. 将主要组件分析与改进的果优化算法和支持矢量机相结合,可以实现高精度,提高运营安全性和效率.
科学领域:
- 石油工程是石油工程中的一个.
- 钻井操作 钻井操作
- 预测建模预测建模
背景情况:
- 钻井过程中流失的循环事件很难控制,因为它们的突然性质.
- 传统的诊断方法是主观的,经验性的,导致预测延迟或不准确.
- 对于高效,及时和准确的流失循环预测方法有着极大的需求.
研究的目的:
- 开发一种新的,高效和准确的方法来预测流失的循环事件.
- 与现有技术相比,提高预测准确度和速度.
- 提高钻井操作的可靠性和性能.
主要方法:
- 使用主要组件分析 (PCA) 和经验分析进行尺寸缩小.
- 完善的果优化算法 (IFOA) 与支持矢量机 (SVM) 技术的整合.
- 纳入帐地图策略,以实现平衡的人口分布和正弦-正弦直线搜索以实现优化.
主要成果:
- 拟议的IFOA-SVM模型实现了特殊的性能指标:96.8%的准确性,97%的精度,96%的回忆率和96%的F1得分.
- 该方法在减少代次数时表现出高精度,表明了效率和速度.
- 综合方法有效地克服了诸如人口不规则分布和过早融合等挑战.
结论:
- 开发的IFOA-SVM方法是一种非常准确和高效的流失流通预测方法.
- 这种新的方法显著改善了传统的预测技术.
- 在钻井操作中实施可以提高效率,可靠性和整体性能.
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