The prediction and optimization of ROP based on MLP-PSO

In an effort to reduce production costs in the oil and gas industry, this paper addresses the prediction and optimization of the Rate of Penetration (ROP), a critical factor in increasing drilling speed. The study first establishes an ROP prediction model using Multi-Layer Perceptron (MLP) to capture the relationships between real-time ROP and various influencing factors. The model considers adjustable drilling parameters such as weight on bit and drilling rate to establish an ROP maximization objective function, which is subsequently solved using the Particle Swarm Optimization (PSO) method. Results indicate that the MLP model effectively captures the relationship between drilling engineering parameters and ROP, achieving a relative error rate of 2.8%. Furthermore, by employing the optimization algorithm, the actual ROP value increases by 162%, significantly enhancing drilling efficiency. Both theoretical and practical case tests demonstrate that the MLP-PSO model proposed in this paper exhibits superior accuracy, reliability, and interpretability, providing a more dependable foundation for parameter optimization in production.