水文水井钻探泥浆特征参数在线监测技术OA北大核心CSTPCD

Hydrological well drilling is an important technical means for developing groundwater resources,and the discharge of mud generated during the drilling process can cause a certain degree of damage to the ecological environment.In order to purify the mud and recycle it effectively,the online monitoring scheme for the mud is studied.Firstly,the pipeline viscometer was used to measure the mud density,apparent viscosity,plastic vis-cosity,dynamic shear force in real-time.Then,through pre-trained neural network models based on mud densi-ty,rheology parameters and sand content,the sand content in the mud after solid phase control was predicted.The test results show that the average measurement error of mud density,apparent viscosity,plastic viscosity,and dynamic shear force through the monitoring of pipeline viscometer is 0.2%,1.7%,3.4%,and 3.7%re-spectively.The average prediction error of sand content through the neural network model is 15.9%.The pre-diction model does not rely on the mud formulations used for model training and can be applied to any other mud formulation with the same system,which shows that the model has a certain degree of generalization.This on-line mud monitoring system meets the requirements of green exploration and is suitable for on-site applications in hydrogeology and water well drilling.