A ROP Optimization Approach Based on Well Log Data Analysis using Deep Learning Network and PSO

One of the key aspects of a successful drilling is effective optimization of ROP (Rate of Penetration). Because of the complexity and heterogeneity of formation permeability, the traditional ROP analysis method are limited by drilling prediction. With the accumulation of geological data and drilling records, new methods such as artificial neural network and particle swarm optimization have become powerful tools for obtaining optimization parameters. A ROP optimization method based on deep learning neural network and particle swarm optimization is proposed. Firstly, the prediction model of target wells is established from well logging data by using deep learning neural network. Secondly, the optimized wellbore operation parameters are obtained by using PSO algorithm. At last, the RNN learning algorithm is updated by introducing recovery factor. And also, for the sake of the realization of constraints, a penalty function is introduced. After analyzed logging data of a group of wells in Shunbei area, the experimental results showed that this method can effectively use engineering data to predict drilling rate and optimize drilling parameters.