Structure Optimization of Slip by the Combination of Artificial Neural Network and Genetic Algorithm

The bridge plug is a staple tool used in downhole operation and the performance of the slips has a directly influence on the oil well productivity and production safety. We raised an optimize method based on BP network and genetic algorithm to make sure the slips satisfy the high temperature and high pressure demands. Establishing the slips system and making finite element analysis by ANSYS, abtaining sixteen group datas to constitute the BP network training samples, establishing the BP simulation model reflecting curvature radius of the slip fluke, dip angle of the fluke, angle of the fluke and distance between flukes using nonlinearity mapping ability of the neural network, applying optimize design for the simulation model using global optimization ability of the genetic algorithm and abtaining the optimum structure parameters of the slip. The optimized results indicate the whole performance of the slips system has increased notably.