A deep gated recurrent neural network for petroleum production forecasting
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 sign...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-03-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266682702030013X |
| _version_ | 1819092253237313536 |
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| author | Raghad Al-Shabandar Ali Jaddoa Panos Liatsis Abir Jaafar Hussain |
| author_facet | Raghad Al-Shabandar Ali Jaddoa Panos Liatsis Abir Jaafar Hussain |
| author_sort | Raghad Al-Shabandar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T22:52:41Z |
| format | Article |
| id | doaj.art-ae37c232e8cb40aaba0503a98090a3ec |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-12-21T22:52:41Z |
| publishDate | 2021-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-ae37c232e8cb40aaba0503a98090a3ec2022-12-21T18:47:32ZengElsevierMachine Learning with Applications2666-82702021-03-013100013A deep gated recurrent neural network for petroleum production forecastingRaghad Al-Shabandar0Ali Jaddoa1Panos Liatsis2Abir Jaafar Hussain3Applied Computing Research Group, School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK; Corresponding author.Computing and Mathematical Sciences, University of Greenwich, London, SE10 9LS, UKDepartment of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesApplied Computing Research Group, School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UKForecasting 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.http://www.sciencedirect.com/science/article/pii/S266682702030013XDeep gated recurrent unit networksLong short-term memory networksRecurrent Neural Networks |
| spellingShingle | Raghad Al-Shabandar Ali Jaddoa Panos Liatsis Abir Jaafar Hussain A deep gated recurrent neural network for petroleum production forecasting Machine Learning with Applications Deep gated recurrent unit networks Long short-term memory networks Recurrent Neural Networks |
| title | A deep gated recurrent neural network for petroleum production forecasting |
| title_full | A deep gated recurrent neural network for petroleum production forecasting |
| title_fullStr | A deep gated recurrent neural network for petroleum production forecasting |
| title_full_unstemmed | A deep gated recurrent neural network for petroleum production forecasting |
| title_short | A deep gated recurrent neural network for petroleum production forecasting |
| title_sort | deep gated recurrent neural network for petroleum production forecasting |
| topic | Deep gated recurrent unit networks Long short-term memory networks Recurrent Neural Networks |
| url | http://www.sciencedirect.com/science/article/pii/S266682702030013X |
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