基于数据挖掘技术的钻井液优化方法研究

Research on Drilling Fluid Optimization Method Based on Data Mining Technology

钻井液的成分和密度会对钻速产生显著的影响,传统的钻井液使用方法主要依赖于专家的个人经验知识,缺乏理论指导。近年来,随着钻井信息化的飞速发展,长庆钻井公司积累了大量的钻井数据,从而使挖掘历史数据发现钻井液的使用规律成为可能。笔者通过使用优化的支持向量回归(Support Vector Regression,SVR)的方法研究钻井液成分与机器钻速之间的联系,建立了钻井液与钻速之间的预测模型。通过采用真实数据的测试,证实了该模型可较准确地预测钻速,可为井队钻井人员选用钻井液提供科学的辅助决策。

The composition and density of the drilling fluid will have a significant impact on the drilling rate.The traditional methods of using drilling fluid mainly rely on the personal experience and knowledge of the experts,and lack theoretical guidance.With the rapid development of drilling informatization in recent years,Changqing Drilling Company has accumulated a large amount of drilling data,which makes it possible to discover the law of drilling fluid usage by mining historical data.This paper uses the optimized support vector regression(SVR)method to study the relationship between drilling fluid composition and machine drilling rate,and establishes a prediction model between drilling fluid and drilling rate.Through testing with real data,it is confirmed that the model can predict the drilling rate more accurately,and can provide scientific auxiliary decision-making for drilling fluid selection of drilling crews.