A deep gated recurrent neural network for petroleum production forecasting

Machine Learning with Applications

Ali Jaddoa¹  Raghad Al-Shabandar²  Panos Liatsis³  Abir Jaafar Hussain⁴ 

[1] Corresponding author.;Applied Computing Research Group, School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK;Computing and Mathematical Sciences, University of Greenwich, London, SE10 9LS, UK;Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates;

关键词: Deep gated recurrent unit networks;    Long short-term memory networks;    Recurrent Neural Networks;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown