摘要
随着油气田开发迈向智能化新阶段,机采井精准高效的工况诊断已成为保障油田稳产、优化维护决策与降本增效的核心关键。传统诊断方法依赖人工经验与固定规则匹配,存在诊断效率低、主观性强、可推广性差等局限。尽管以支持向量机、决策树为代表的机器学习方法在智能诊断研究中不断深入,但现有模型仍普遍存在以下问题:模型可解释性不足,难以获得领域专家信任;泛化能力有限,对数据分布变化及类别不平衡问题敏感;技术流程割裂,依赖人工特征工程与复杂调优,且难以与生产管理系统深度融合,制约了其规模化应用。为此,以极限梯度提升算法(XGBoost)为核心构建了一套机采井示功图智能诊断系统,其涵盖数据采集、特征提取、智能诊断与可视化的完整流程。该系统采用B/S架构与“1+N”分布式设计方案,实现多源异构数据的实时接入与统一管理;通过引入XGBoost作为核心分类算法,结合多维特征提取技术与SHAP(shapley additive explanations)可解释性分析框架,在提升分类精度的同时增强诊断过程的透明度与专家可信度。现场试验结果表明,系统对7类典型工况的诊断准确率达90%以上,单井诊断时间由30 min缩短至2 min以内,预警符合率达85.7%。与传统诊断方法相比,该系统在保证诊断精度的前提下,显著提升了诊断效率与结果可解释性。研究结果可为油田机采井智能诊断提供可推广的技术方案。
With the oil and gas field development entering a new intelligent stage,accurate and efficient condition diagnosis of pumping wells has become a core measure for ensuring stable production,optimizing maintenance decisions,and achieving cost reduction and efficiency improvement.Traditional diagnosis methods rely on the matching of personnel experience with fixed rules,making them suffer from limitations such as low diagnosis efficiency,strong subjectivity,and poor scalability.Although machine learning algorithms represented by support vector machine(SVM)and decision tree(DT)have been increasingly applied in intelligent diagnosis research,existing models still face common engineering bottlenecks:(1)insufficient model interpretability,making it difficult to gain the trust of domain experts;(2)limited generalization ability,being sensitive to changes in data distribution and class imbalance issues;and(3)fragmented technical processes,relying on manual feature engineering and complex tuning,while struggling to integrate deeply with production management systems.These constrain large-scale application of the models.This paper constructs an intelligent dynamometer card diagnosis system for pumping wells with eXtreme Gradient Boosting(XGBoost)as its core,covering a complete workflow from data acquisition and feature extraction to intelligent diagnosis and visualization.This system adopts a B/S architecture and a“1+N”distributed design,enabling real-time integration and unified management of multi-source heterogeneous data.By introducing XGBoost as the core classifier and combining it with multi-dimensional feature extraction techniques and the shapley additive explanation(SHAP)interpretability analysis framework,this system enhances classification accuracy while improving the transparency of the diagnosis process and expert credibility.Field test results show that the system achieves a diagnosis accuracy of over 90%for seven typical working conditions,reduces single-well diagnosis time from 30 minutes to less than 2 minutes,and attains an early warning alignment rate of 85.7%.Compared with traditional diagnosis methods,the proposed system significantly improves diagnosis efficiency and result interpretability while ensuring high diagnosis accuracy,providing a promotable technical solution for the intelligent diagnosis of pumping wells.
作者
王萍
林佩怡
吴杰
程伟
Wang Ping;Lin Peiyi;Wu Jie;Cheng Wei(School of Economics and Management,Yangtze University;Xi'an Peihua University;School of Intelligent Manufactur-ing,Jingchu University of Technology)
出处
《石油机械》
北大核心
2026年第3期23-31,共9页
China Petroleum Machinery
基金
国家教育部人文社会科学研究规划基金项目“工匠精神对‘专精特新’企业高质量发展的影响机理及培育对策研究”(24YJA630090)
油气钻完井技术国家工程研究中心科学研究基金项目“深层超深层岩石力学抗钻特性描述及智能演算方法”(NERCDCT202319)。
