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中文核心期刊

基于声波技术的桥塞泄漏实时监测装置设计方案

DESIGN PROPOSAL FOR A REAL-TIME LEAK MONITORING DEVICE FOR BRIDGE PLUGS BASED ON ACOUSTIC TECHNOLOGY

  • 摘要: 检测桥塞位置及泄漏量是掌握桥塞工况、实现分段多簇水力压裂施工实时量化监测的关键, 对压裂效果评估和工艺优化具有重要意义. 本文以桥塞位置精准检测和泄漏量有效识别为核心目标, 结合数值仿真与数值信号处理的方法开展以下研究, 首先, 分析桥塞施工工况, 提出声波监测思路, 再基于k-ω湍流模型与对流波动理论, 建立桥塞工况实时监测数值模型; 其次, 基于设备工作原理与数值信号处理原理, 使用经验模态分析(Empircal Mode Decomposition-EMD)提取信号特征参数; 最后, 采用主成分分析方法(Principe Component Analysis-PCA)降低维度以及随机森林(Random Forest-RF)判断的数字信号处理方法. 在本文介绍的设备方案和数字信号处理方法支撑下, 桥塞位置检测精度达96%以上; 在有限数据集条件下, 使用随机森林算法识别桥塞泄漏量, 训练所得模型的R2超过0.9, 绝对平均误差仅0.0105m3/min. 该研究成果对于提高压裂施工效率、优化压裂设计、降低施工风险具有重要的理论意义和应用价值, 为实现压裂施工过程的实时监测提供了一种新的思路和方法.

     

    Abstract: Accurately detecting bridge plug position and leakage rate is crucial for mastering plug working conditions and achieving real-time quantitative monitoring of multi-cluster staged hydraulic fracturing operations. It holds significant importance for evaluating fracturing effectiveness and optimizing processes. This paper focuses on the core objectives of precise bridge plug position detection and effective leakage rate identification, employing a combination of numerical simulation and digital signal processing methods to conduct the following research.Firstly, by analyzing the operational conditions of bridge plugs, an acoustic monitoring approach is proposed. Subsequently, based on the k-ω turbulence model and the convective wave theory, a real-time monitoring numerical model for bridge plug working conditions is established. Secondly, leveraging equipment working principles and numerical signal processing theory, Empirical Mode Decomposition (EMD) is utilized to extract signal feature parameters. Finally, a digital signal processing methodology is implemented, employing Principal Component Analysis (PCA) for dimensionality reduction and Random Forest (RF) for classification and regression tasks.Supported by the equipment scheme and digital signal processing methods introduced in this paper, the detection accuracy for bridge plug position achieves over 96%. Under conditions of a limited dataset, using the Random Forest algorithm to identify the bridge plug leakage rate yields a trained model with an R-squared value exceeding 0.9 and a mean absolute error of only 0.0105 m3/min.The findings of this research possess substantial theoretical significance and application value for enhancing fracturing operation efficiency, optimizing fracturing design, and reducing operational risks. Furthermore, it provides a novel approach and methodology for realizing real-time monitoring of the fracturing construction process. This study demonstrates the feasibility and effectiveness of integrating physical modeling with advanced data-driven techniques for addressing key challenges in downhole monitoring, paving the way for more intelligent and data-informed fracturing operations. The proposed framework, combining EMD, PCA, and Random Forest, shows strong potential for handling complex, non-linear acoustic signals in noisy downhole environments to deliver accurate and reliable diagnostics.

     

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