钻井泄漏是石油开采中的重大隐患，严重影响工业生产、社会生活和自然环境。井漏时，井内带压流体的压力波动，形成压力波信号，可用于泄漏位置诊断。然而，瞬态压力波信号常受白噪声干扰，传统频域滤波方法难以有效去噪，限制了泄漏特征的准确提取。提出了一种改进的局部特征尺度分解（local characteristicscale decomposition, LCD）方法，结合支持向量回归（support vector regression, SVR），解决了模态混叠和端点效应问题。同时，提出了一种基于马氏距离与累计均值的分量划分方法，以提高信号重构质量。此外，利用邻域差值法分析信号特征变化，实现了漏失位置的精准定位。通过建立模型，搭建室内井漏检测设备，验证了所提方法的有效性。实验研究表明，该方法实验误差低于5%，定位精确度高，显著提高了井漏检测的精度和可靠性，为石油开采中的井漏诊断提供了新的技术支撑。

Drilling leaks are a major hazard in oil extraction, seriously affecting industrial production, social life and the natural environment. When a well leaks, the pressure fluctuation of the pressurised fluid in the well forms a pressure wave signal, which can be used to diagnose the leak location. However, the transient pressure wave signal is often interfered by white noise, and the traditional frequency domain filtering method is difficult to effectively denoise, which limits the accurate extraction of leakage features. An improved LCD (local feature scale decomposition) method, combined with SVR (support vector regression), was proposed to solve the problems of modal aliasing and endpoint effects. Meanwhile, a component division method based on the Marginal distance and cumulative mean was proposed to improve the quality of signal reconstruction. In addition, the neighbourhood difference method was used to analyse the variation of signal characteristics to achieve the accurate positioning of the leakage location. The effectiveness of the proposed method was verified by establishing a model and building an indoor well leakage detection device. The experimental study shows that the experimental error of the method is less than 5% and the positioning accuracy is high, which significantly improves the precision and reliability of well leakage detection and provides a new technical support for well leakage diagnosis in oil extraction.