基于ARIMA模型的抽油机井清防蜡时序预测方法

A Time-Series Prediction Method for Dewaxing and Anti-Waxing in Pumping Wells Based on ARIMA Model

摘要: 抽油机井井筒结蜡会引发机械负载增加、泵效持续性降低以及非计划性卡泵停产等问题，传统“固定周期”维护范式普遍面临过度维保导致资源冗余消耗，或延误维保窗口期，诱发突发性经济受损等多维度难题。为此，本研究提出一种基于自回归移动平均(ARIMA)模型的抽油机井清防蜡动态时序反演与前瞻预警方案。方案立足于结蜡演变的多维主控热力学机理，提炼出可由SCADA系统高频采撷的载荷差等核心表征指标作为特征输入矩阵，通过协同实施工业噪声预处理、平稳性校验、规范化定阶与参数估计，深度掘进复杂工况下结蜡演化的线性时序依赖特征。实证研究与油田现场工程应用结果表明，所构建的ARIMA(2,1,2)模型在时序波动信号的拟合上展现出优异的泛化鲁棒性。相较于传统经验决策法及标准ARMA模型，该方法成功将结蜡风险的预判时窗前置3~7天，实现了由“固定周期”向“按需动态”施策的精细化管护模式转变，同时在削减无效作业频次、压减开采综合能耗以及整治卡泵安全隐患方面成效显著，为油田井筒维保数字化转型及高效开发提供了坚实的数理依据与数字化支撑底座。

Abstract: To address the technical bottlenecks of downhole wax deposition in pumping wells—such as step-increases in mechanical loads, stall-attenuation of pump efficiency, and unplanned pump-jamming shutdowns—traditional “fixed-cycle” maintenance paradigms commonly face severe dynamic incompatibilities, resulting in either resource redundancy via over-maintenance or catastrophic economic losses via delayed interventions. Therefore, this study proposes a dynamic time-series inversion and proactive warning scheme for wax removal and prevention in pumping wells based on the Autoregressive Integrated Moving Average (ARIMA) model. Rooted in the multi-dimensional dominant thermodynamic mechanisms of wax evolution, core characteristic indicators including load difference, submergence depth, and wellhead temperature, which are highly retrievable via field SCADA systems, are extracted to construct the feature input matrix. Through the collaborative implementation of industrial noise preprocessing, stationarity verification, standardized identification, and parameter estimation, the linear time-series dependency of wax evolution under complex operating conditions is deeply mined. Empirical research and on-site oilfield engineering applications demonstrate that the established ARIMA(2,1,2) model exhibits excellent generalization robustness in fitting time-series fluctuation signals. Compared with traditional empirical decision-making methods and standard ARMA models, the proposed method successfully advances the prognostic horizon of wax deposition risk by 3 to 7 days, realizing a paradigm shift from “fixed-cycle” to “on-demand dynamic” precise asset management. It achieves prominent performance in reducing the frequency of invalid operations, cutting comprehensive lifting energy consumption, and throttling safety hazards like pump-jamming, thereby providing a solid mathematical foundation and a digital support pedestal for the digital transformation and efficient development of oilfield wellbore maintenance.