A deep gated recurrent neural network for petroleum production forecasting

Journal article


Raghad Al-Shabandar, Ali Jaddoa, Panos Liatsis and Abir Jaafar Hussain 2020. A deep gated recurrent neural network for petroleum production forecasting. Machine Learning with Applications . 3, p. 100013. https://doi.org/10.1016/j.mlwa.2020.100013
AuthorsRaghad Al-Shabandar, Ali Jaddoa, Panos Liatsis and Abir Jaafar Hussain
Abstract

Forecasting of oil production plays a vital role in petroleum engineering and contributes to supporting engineers in the management of petroleum reservoirs. However, reliable production forecasting is difficult to achieve, particularly in view of the increase in digital oil big data. Although a significant amount of work has been reported in the literature in relation to the use of machine learning in the oil and gas domain, traditional forecasting approaches have limited potential in terms of representing the complex features of time series data. More specifically, in a high-dimensional nonlinear multivariate time series dataset, a shallow machine is incapable of inferring the dependencies between past and future values. In this context, a novel forecasting model for petroleum production is proposed in this work. The model is a deep-gated recurrent neural network consisting of multiple hidden layers, where each layer has a number of nodes. The proposed model has a low-complexity architecture and the capacity to track long-interval time-series datasets. To evaluate the robustness of our model, the proposed technique was benchmarked with various standard approaches. The extensive empirical results demonstrate that the proposed model outperforms existing approaches.

KeywordsDeep gated recurrent unit networks; Long short-term memory networks; Recurrent Neural Networks
Year2020
JournalMachine Learning with Applications
Journal citation3, p. 100013
PublisherElsevier
ISSN2666-8270
Digital Object Identifier (DOI)https://doi.org/10.1016/j.mlwa.2020.100013
Official URLhttps://www.sciencedirect.com/science/article/pii/S266682702030013X
Publication dates
Online23 Nov 2020
Print15 Mar 2021
Publication process dates
Accepted18 Nov 2020
Deposited10 Mar 2023
Publisher's version
License
Output statusPublished
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