Time series forecasting of petroleum production using deep LSTM recurrent networks
By Alaa El. Sagheer, Mostafa Kotb. Neurocomputing, 2019
863 citations recorded by the source corpus.
Abstract 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.
Subfields: Mechanics of Materials, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/1025b16f5fe8c0cc44d6775b6caf0bf7ceba3118
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In Mechanics of Materials
In Ocean Engineering
Citations in the corpus
- A review on the long short-term memory model (2020, background): “Sagheer and Kotb (2019) confirmed this finding that LSTM outperforms standard approaches when predicting petroleum production.”
- Probabilistic Forecasting with Temporal Convolutional Neural Network (2019, background): “…in many different sequential tasks such as machine translation (Sutskever et al., 2014), language modeling (Mikolov et al., 2010) and recently time series forecasting (Laptev et al., 2017; Wen et al., 2017; Salinas et al., 2019; Rangapuram et al., 2018; Sagheer and Kotb, 2019; Shen et al., 2019).”
- Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, methodology): “In [131] LSTM was used to forecast petroleum production.”