Diagnosis and operation strategy of submerged oil electric pump based on intelligent optimization algorithm

This paper proposes an intelligent optimization algorithm-based working condition diagnosis and operation strategy research method for the submerged oil electric pump in the complex and changing working condition environment in the oilfield extraction process. Combined with the actual working condition data of the submersible oil electric pump, an efficient and accurate fault diagnosis model is constructed by using intelligent optimization techniques such as genetic algorithm and particle swarm optimization to effectively extract and select key features. Through the analysis of the diagnosis results, the operation strategy based on the optimization algorithm is designed to realize the dynamic adjustment and optimal control of the operating state of the submersible oil electric pump. Through the field data verification, it is proved that the proposed method has significant effects in improving the diagnosis accuracy, reducing the equipment failure rate and improving the operation efficiency. This study provides theoretical basis and technical support for intelligent operation and maintenance of submersible oil electric pumps, and has good application prospects and promotion value.