Improving the Drilling Parameter Optimization Method Based on the Fireworks Algorithm

Abstract

The rate of penetration (ROP) is a manifestation of drilling efficiency, and optimizing drilling parameters is an important way to improve it. To achieve a low ROP for a Permian formation in a certain oil and gas field, three single wells in this formation were selected for optimization. An improved fireworks optimization algorithm was proposed for drilling parameter optimization. We first established the objective function that predicted the ROPs for the three wells. The objective function employed a multilayer perceptron neural network as the optimization adaptation function. We then optimized four controllable parameters (weight on bit, rotary speed, pump discharge, and pump pressure) and improved the fireworks algorithm with an adaptive number of various factors. This improvement enhanced the debugging performance of the fireworks algorithm during optimization. The results indicated that the improved fireworks algorithm has significantly enhanced search performance, and the optimum ROPs for the three wells were increased by 38.55, 78.30, and 60.15%, which provides a reference for the controllable parameter setting in the area.