Predicting Oil Production in Single Well using Recurrent Neural Network

doi:10.1109/ICBAIE49996.2020.00095

CORC > 兰州理工大学 > 兰州理工大学

	Predicting Oil Production in Single Well using Recurrent Neural Network
	Xia, Lin 3; Shun, Xu 1; Jiewen, Wu 2; Lan, Mi 3
	2020-06-01
会议日期	June 12, 2020 - June 14, 2020
会议地点	Virtual, Fuzhou, China
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
页码	423-430
英文摘要	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.
会议录	Proceedings - 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2020
会议录出版者	Institute of Electrical and Electronics Engineers Inc.
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/118197]
专题	兰州理工大学
作者单位	1.Lanzhou University of Technology, Lanzhou, China; 2.Huawei Technologies Co. Ltd., Shenzhen, China 3.Research Institute of Petroleum Exploration, Development PetroChina, Beijing, 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]. 见:. Virtual, Fuzhou, China. June 12, 2020 - June 14, 2020.