摘要
现有油藏储量预测方法的精度远不能满足实际应用的需求。受循环神经网络和注意力机制的启发,提出一种专注智能油藏储量预测的深度时空注意力模型。该模型通过时间注意力模型来捕获输入数据之间的关键信息,空间注意力模型捕获隐藏状态之间的关系紧密程度,能够缓解数据波动对预测结果的不利影响,从而大幅减小预测误差。结果表明,相比传统方法和已有的深度学习方法,该模型预测精度有显著提高,为今后油藏储量预测提供一种更优的选择。
The accuracy of existing reservoir reserve prediction methods is far from satisfying the needs of practical application. Inspired by recurrent neural network(RNN) and attention mechanism, this paper proposes a deep spatio-temporal attention model focusing on intelligent forecasting of reservoir reserves, in which the time attention model can capture the key information within the input data, and the spatial attention model can capture the link between the hidden states. Therefore, the adverse effect of data fluctuation can be mitigated on the prediction results to greatly reduce the prediction error. Experimental results on the real data of a large oil field show that compared with traditional methods and existing deep learning methods, the prediction accuracy of the model by this method is significantly improved. Our approach therefore provides a better choice for reserve prediction in the future.
作者
李宗民
李亚传
赫俊民
张益政
姚纯纯
刘玉杰
LI Zongmin;LI Yachuan;HE Junmin;ZHANG Yizheng;YAO Chunchun;LIU Yujie(School of Computer Science and Technology in China University of Petroleum(East China),Qingdao 266580,China;Shengli College of China University of Petroleum,Dongying 257061,China;Geophysical Research Institute of Shengli Oilfield,SINOPEC,Dongying 257022,China)
出处
《中国石油大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2020年第4期77-82,共6页
Journal of China University of Petroleum(Edition of Natural Science)
基金
国家自然科学基金项目(61379106)
山东省自然科学基金项目(ZR2013FM036,ZR2015FM011)
国家重点研发计划(2019YFF0301800)。
关键词
油藏储量预测
循环神经网络
注意力机制
深度时空注意力模型
reservoir reserve prediction
recurrent neural network
attentional mechanism
deep spatio-temporal attention model