Prediction of the Water Cut with the Hybrid Optimized SVR

Abstract

The moisture content identification of a water drive oilfield is related to how to formulate and adjust the development plan of the oil field. However, traditional prediction methods for the water cut usually have problems such as slow recognition speed and limited by the specific conditions of the oil well. Therefore, in order to avoid the influence of the above problems, a machine algorithm model of water cut based on hybrid optimization is proposed, which is based on the support vector regression (SVR) model. First, the data is constructed by time sliding window; secondly, on the basis of the fundamental SVR model, this paper combines the Particle Swarm Optimization (PSO) and the Artificial Fish Swarm Algorithm (AFSA) to optimize the hyperparameters of the SVR prediction model to achieve better experimental results; finally, if the hybrid model proposed in this article has some good experimental results, then it can be applied to the actual water cut prediction of the oilfield. After comparing four different models, the prediction model based on the hybrid optimization algorithm proposed in this paper has some good experimental results. The prediction curve and the real curve have the same trend as a whole, and the subtle errors are also the smallest. Thus, it performs better than the SVR prediction model optimized by the differential evolution algorithm, the SVR prediction model optimized by the genetic algorithm, and the SVR prediction model optimized by the PSO.