Predicting Oil Production in Single Well using Recurrent Neural Network

doi:10.1109/ICBAIE49996.2020.00095

	Predicting Oil Production in Single Well using Recurrent Neural Network
	Xia, Lin 1; Shun, Xu 2; Jiewen, Wu 3; Lan, Mi 1
	2020-06-01
会议名称	2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2020
会议录名称	Proceedings - 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2020
页码	423-430
会议日期	June 12, 2020 - June 14, 2020
会议地点	Virtual, Fuzhou, China
出版者	Institute of Electrical and Electronics Engineers Inc.
摘要	Single well production prediction is an essential task for oilfield development planning and analysis. Existing methods used for such prediction suffer from a few problems. In particular, current methods do not consider large-scale data labeling or production prediction in different water cut phases. To this end, we propose to holistically use the static, historical data of a single well, such as its geological and production data to enable data labeling in different phases via our labelling tool. In addition, we use Long Short-Term Memory (LSTM), a well-known Recurrent Neural Network, to build a predictive model for single-well production. The proposed model uses dominating features on well production and can train multiple wells together, which can generalize the application of the model. The model has also been fine-tuned to speed up training via the use of batch normalization. Compared with Random Forest (RF) and Support Vector Machine (SVM), our proposed LSTM model demonstrates better prediction accuracy and strong generalization capability and thus lends itself nicely to single well production prediction in various water saturation phases. © 2020 IEEE.
关键词	Big data Decision trees Forecasting Internet of things Oil field development Oil wells Petroleum industry Predictive analytics Support vector machines Generalization capability Large scale data Prediction accuracy Predictive modeling Production data Production prediction Single well production Water saturations
DOI	10.1109/ICBAIE49996.2020.00095
收录类别	EI
语种	英语
EI入藏号	20204309401755
EI主题词	Long short-term memory
来源库	Compendex
分类代码	512.1.1 Oil Fields - 512.1.2 Petroleum Deposits : Development Operations - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 961 Systems Science
引用统计	无
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/118197
专题	兰州理工大学
作者单位	1.Research Institute of Petroleum Exploration, Development PetroChina, Beijing, China; 2.Lanzhou University of Technology, Lanzhou, China; 3.Huawei Technologies Co. Ltd., Shenzhen, China
推荐引用方式 GB/T 7714	Xia, Lin,Shun, Xu,Jiewen, Wu,et al. Predicting Oil Production in Single Well using Recurrent Neural Network[C]:Institute of Electrical and Electronics Engineers Inc.,2020:423-430.