Research On Multistep Prediction And Optimization Of Drilling ROP Based On Cyclic Neural Network

Drilling is an important part of the exploration and development of the oil and gas industry,and the efficiency of drilling directly affects the entire drilling development cost.Improving drilling speed is one of the core issues that urgently need to be solved in drilling engineering.The rate of penetration(ROP)of drilling machinery can directly describe drilling efficiency,and its value is related to many complex and nonlinear factors such as drilling equipment and formation lithology.To improve ROP,it is necessary to first establish a highprecision ROP prediction model that quantitatively characterizes the relationship between complex influencing factors and ROP.Then,an optimization model needs to be built on the basis of the prediction model to achieve multi-objective parameter optimization during the drilling process,in order to obtain higher ROP and improve drilling efficiency.In response to the above requirements,this article adopts a recurrent neural network model to establish a prediction model,extract temporal features from sequence data,conduct in-depth research on multi-step prediction of oil drilling,and use intelligent optimization algorithms to optimize drilling speed for oil drilling.The research content and innovative points of this article are as follows:(1)In response to the diversity and complexity of factors that affect the ROP of drilling machinery,machine learning algorithms and correlation analysis techniques are used to analyze oil drilling data and identify the main controlling factors that affect the ROP of drilling machinery.(2)By utilizing the temporal characteristics of drilling data and considering the high complexity of the data,combined with the real-time parameter adjustment requirements of actual drilling engineering equipment,a multi-step prediction model for drilling machinery ROP is established using a recurrent neural network.This model can continuously predict the ROP values of multiple future time periods during the drilling process,which meets the actual drilling engineering requirements.(3)On the basis of establishing a multi-step prediction model and combining with actual drilling needs,the particle swarm optimization algorithm PSO is used to design an optimization strategy for the drilling speed ROP of petroleum drilling machinery in the entire drilling project,improving the efficiency of the entire drilling project and reducing construction costs.Further promoting the development of drilling engineering towards automation,digitization,and intelligence.