摘要
近年来,国内外对钻井液性能自动检测技术的研究取得了长足进步.针对现有钻井液自动检测仪器成本高、结构复杂和工作环境受限等问题,研制了钻井液漏斗黏度和密度自动在线检测仪.使用可编程逻辑控制器、物联网模组、压力传感器、超声传感器、蠕动泥浆泵、电动球阀和隔膜泵等,搭建取浆模块、检测模块、清洗模块和控制模块,实现了对钻井液漏斗黏度和密度的自动检测、移动端设备数据查看和操作等功能.采用传感器数据融合和滤波算法等方法提高仪器精度和稳定性,对不同钻井液体系性能测试验证,漏斗黏度误差控制在±1 s,密度误差控制在±0.01 g/cm3,且可连续地对钻井液性能进行自动测试.室内测试和现场试验结果表明,该仪器具有操作方便、测试结果准确、可重复性高、人为测量误差小和节约人工等优点,能满足实际钻井施工过程中对钻井液性能检测的要求,具有较强的普适性和广泛的应用前景.
Abstract
In recent years,the research on the automatic performance testing technology for drilling fluids has achieved significant progress both domestically and internationally.However,automatic drilling fluid testing instruments face challenges,such as high costs,complicated structures,and restricted working environments.Given these challenges,this study developed an automatic online testing instrument for the funnel viscosity and density of drilling fluids.The testing instrument consists of mud pumping,testing,cleaning,and control modules,which are built using a programmable logic controller(PLC),an Internet of things(IoT)module,a pressure sensor,an ultrasonic sensor,a peristaltic mud pump,an electric ball valve,and a diaphragm pump.It can fulfill functions including the automatic funnel viscosity and density testing of drilling fluids,and the data viewing and operation at the mobile terminal.This study enhanced the accuracy and stability of the testing instrument through sensor data fusion and filtering algorithms.The testing instrument was em-ployed to test the performance of various drilling fluid systems,yielding funnel viscosity and density errors controlled at±1 s and±0.01 g/cm3,respectively.Furthermore,it allowed for continuous automatic performance testing for drilling flu-ids.The laboratory and field testing results suggest that the testing instrument boasts convenient operations,accurate testing results,high repeatability,minimal manual measurement errors,and labor saving.Therefore,it meets the demand for performance testing of drilling fluids in practical drilling construction,demonstrating high universality and great po-tential for application.
基金项目
国家自然科学基金青年科学基金项目(42002311)
省级大学生创新创业训练计划重点支持领域项目(S202310491186)
维普
万方数据