基于深度长短期记忆神经网络的油气井产量预测优化方法

Optimization method for oil and gas well production prediction based on deep long short-term memory neural network

在油气田开发领域,油气井产量的准确预测对于优化油藏开发策略、简化生产管理和促进决策制定具有重要意义。然而,目前传统产量预测方法遇到了许多挑战,包括影响开发因素多、建模复杂、计算量大,难以捕捉生产数据中的时间特征、开发过程中的油气井产量关系等问题。介绍了一种基于深度长短期记忆神经网络(DLSTM)的油气井产量预测优化方法,以提高油气井产量预测的准确性。以华北油田北部某区块的生产数据为例,运用DLSTM架构对该区块油气井进行产量预测,并将测试集的均方根误差进行对比。通过实验结果表明,新模型产量预测效果显著。该研究对应用深度神经网络模型进行油气井产量预测具有重要意义。

In oil and gas field development,accurate prediction of oil and gas well production is significant for optimizing reservoir development strategies,simplifying production management and promoting decision-making.However,traditional production prediction methods currently encounter many challenges,including many factors affecting development,complex modeling,extensive calculations,difficulty capturing the time characteristics in production data,and the relationship between oil and gas well production during development.An optimization method for oil and gas well production prediction based on deep long short-term memory(DLSTM)neural networks is introduced to improve the accuracy of oil and gas well production prediction.Taking the production data of a block in the northern part of the Huabei oilfield as an example,the DLSTM architecture was used to predict the production of oil and gas wells in the block,and the root mean square errors of the test set were compared.The experimental results show that the new model has a significant yield prediction effect.This research is significant for applying deep neural network models to predict oil and gas well production.