Time series forecasting of petroleum production using deep LSTM recurrent networks

计算机科学 人工智能 机器学习 石油 时间序列 生产(经济) 系列(地层学) 石油生产 石油工程 地质学 宏观经济学 古生物学 经济
作者
Alaa Sagheer,Mostafa Kotb
出处
期刊:Neurocomputing [Elsevier BV]
卷期号:323: 203-213 被引量:693
标识
DOI:10.1016/j.neucom.2018.09.082
摘要

Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this paper, we propose a deep learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions. The proposed approach is a deep long-short term memory (DLSTM) architecture, as an extension of the traditional recurrent neural network. Genetic algorithm is applied in order to optimally configure DLSTM’s optimum architecture. For evaluation purpose, two case studies from the petroleum industry domain are carried out using the production data of two actual oilfields. Toward a fair evaluation, the performance of the proposed approach is compared with several standard methods, either statistical or soft computing. Using different measurement criteria, the empirical results show that the proposed DLSTM model outperforms other standard approaches.
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