Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition
Developing a forecasting model for oilfield well production plays a significant role in managing mature oilfields as it can help to identify production loss earlier. It is very common that mature fields need more frequent production measurements to detect declining production. This study proposes a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI
2022
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/282820/1/Prasetyo_TK.pdf |
| _version_ | 1826050556136783872 |
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| author | Prasetyo, Joko Nugroho Setiawan, Noor Akhmad Adji, Teguh Bharata |
| author_facet | Prasetyo, Joko Nugroho Setiawan, Noor Akhmad Adji, Teguh Bharata |
| author_sort | Prasetyo, Joko Nugroho |
| collection | UGM |
| description | Developing a forecasting model for oilfield well production plays a significant role in managing mature oilfields as it can help to identify production loss earlier. It is very common that mature fields need more frequent production measurements to detect declining production. This study proposes a machine learning system based on a hybrid empirical mode decomposition backpropagation higher-order neural network (EMD-BP-HONN) for oilfields with less frequent measurement. With the individual well characteristic of stationary and non-stationary data, it creates a unique challenge. By utilizing historical well production measurement as a time series feature and then decomposing it using empirical mode decomposition, it generates a simpler pattern to be learned by the model. In this paper, various algorithms were deployed as a benchmark, and the proposed method was eventually completed to forecast well production. With proper feature engineering, it shows that the proposed method can be a potentially effective method to improve forecasting obtained by the traditional method. |
| first_indexed | 2024-03-14T00:05:51Z |
| format | Article |
| id | oai:generic.eprints.org:282820 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:05:51Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2828202023-11-16T08:50:14Z https://repository.ugm.ac.id/282820/ Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition Prasetyo, Joko Nugroho Setiawan, Noor Akhmad Adji, Teguh Bharata Electrical and Electronic Engineering Engineering Developing a forecasting model for oilfield well production plays a significant role in managing mature oilfields as it can help to identify production loss earlier. It is very common that mature fields need more frequent production measurements to detect declining production. This study proposes a machine learning system based on a hybrid empirical mode decomposition backpropagation higher-order neural network (EMD-BP-HONN) for oilfields with less frequent measurement. With the individual well characteristic of stationary and non-stationary data, it creates a unique challenge. By utilizing historical well production measurement as a time series feature and then decomposing it using empirical mode decomposition, it generates a simpler pattern to be learned by the model. In this paper, various algorithms were deployed as a benchmark, and the proposed method was eventually completed to forecast well production. With proper feature engineering, it shows that the proposed method can be a potentially effective method to improve forecasting obtained by the traditional method. MDPI 2022-06-06 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282820/1/Prasetyo_TK.pdf Prasetyo, Joko Nugroho and Setiawan, Noor Akhmad and Adji, Teguh Bharata (2022) Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition. Processes, 10 (1137). pp. 1-15. ISSN 22279717 https://www.mdpi.com/2227-9717/10/6/1137 https:// doi.org/10.3390/pr10061137 |
| spellingShingle | Electrical and Electronic Engineering Engineering Prasetyo, Joko Nugroho Setiawan, Noor Akhmad Adji, Teguh Bharata Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition |
| title | Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition |
| title_full | Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition |
| title_fullStr | Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition |
| title_full_unstemmed | Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition |
| title_short | Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition |
| title_sort | forecasting oil production flowrate based on an improved backpropagation high order neural network with empirical mode decomposition |
| topic | Electrical and Electronic Engineering Engineering |
| url | https://repository.ugm.ac.id/282820/1/Prasetyo_TK.pdf |
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