基于IPSO-GNN的油田指标预测模型研究

针对油田开发指标预测问题,提出将灰色神经网络(GNN)与改进粒子群算法(IPSO)相结合的组合预测模型(IPSOGNN),通过IPSO对GNN的a、u参数进行优化,改善了GNN的不足,有效地保证了预测精度。以油田开发指标中的含水率作预测算例,仿真结果表明:此模型的预测精度高于灰色预测模型、灰色神经网络以及BP神经网络模型,同时也表明了此方法的可行性与有效性。

Forecast Model of Oilfield Indexes Based on IPSO-GNN

Aiming at he prediction of oilfield development indexes, this paper puts forward a prediction model(IPSO-GNN) combining Gray Neural Network(GNN) and Improved Particle Swarm Optimization algorithm(IPSO),to make up for the shortage of the GNN and guarantee the forecast accuracy, it optimized the GNN parameters through the IPSO. With the prediction of moisture content in oilfield development indexes as an example, the simulation results show that the prediction accuracy of the combination prediction model it higher than gray prediction model, the gray neural network and the BP neural network model. It also shows the feasibility and effectiveness of the method.