| 摘 要: 在石油勘探开发中,油田站场常因设备限制和操作技术影响采集到异常数据,导致数据解释偏差。针对此问题,提出一种基于局部密度改进孤立森林的异常数据检测方法。通过计算数据局部密度改进孤立森林的结构与算法,融入局部密度信息以提升对低密度区域局部异常点的识别能力,并进行了性能与鲁棒性对比验证实验验证了改进孤立森林方法与其他机器学习方法相比的优越性,为油田场站异常数据检测提供新思路。 |
| 关键词: 油田站场 异常数据检测 机器学习 孤立森林 |
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中图分类号: TP391
文献标识码: A
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| Research on Anomaly Data Detection Methods for Oilfield Stations Based on Improved Isolation Forest |
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TONG Wei, ZHANG Juan
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(Information Center, No.1 Oil Production Plant of Daqing Oilfield, Daqing 163000, China)
cyyc_twei@163.com; Juanzhang@petrochina.com
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| Abstract: In petroleum exploration and development, oilfield stations often collect anomalous data due to equipment limitations and operational techniques, leading to deviations in data interpretation. To address this issue, an anomaly detection method based on an improved Isolation Forest using local density is proposed. By calculating the local density of data, the structure and algorithm of Isolation Forest are enhanced, incorporating local density information to improve the identification of local anomalies in low-density regions. Performance and robustness comparison experiments validate the superiority of the improved Isolation Forest method over other machine learning approaches, offering a new perspective for anomaly detection in oilfield stations. |
| Keywords: oilfield station anomaly detection machine learning Isolation Forest |
