基于改进果蝇算法和长短期记忆神经网络的油田产量预测模型

产量预测是油田生产动态开发研究的重要内容之一。油田的长期生产积累了大量数据,但是波动幅度很大,直接应用长短期记忆神经网络预测油田的生产指标,会出现神经网络泛化性很差的问题。因此,首先利用双层长短期记忆神经网络(long-short term memory,LSTM)和随机式失活对神经网络架构进行调整,建立了深度学习神经网络模型;并提出了一种新的果蝇聚集方法,通过改进的果蝇优化算法对所建立的神经网络模型进行优化,避免其陷入局部最优解,搜寻解空间的最优解;最后,油田实例验证表明,优化后的深度学习网络的网络泛化能力和预测精度有了较大提高,对于油田波动性较大的数据也能较好地拟合。所建立油田产量预测模型可应用于矿场开发实际。

Oilfield Production Prediction Nodel Based on Improved Fruit Fly Algorithm and Long-Short Term Memory Neural Network

Production forecasting is one of the important contents of the research and development of oilfield production dynamics. The long-term production of oil fields has accumulated a large amount of data, but the fluctuation range is very large. The long-short term memory neural network is directly applied to predict the production indicators of oil fields, and the generalization of neural networks is very poor. Therefore, this paper firstly uses the two-layer LSTM and random inactivation layer to adjust the neural network architecture, establishes a deep learning neural network model; then proposes a new fruit fly aggregation method, through improved fruit fly optimization. The algorithm optimizes the established neural network model to avoid falling into the local optimal solution and searching for the optimal solution of the solution space. Finally, the field example verification shows that the network generalization ability and prediction accuracy of the optimized deep learning network have been greatly improved, and the data with large fluctuations in the oilfield can be fitted well. The oilfield production prediction model established in this paper can be applied to the actual development of the mine.