Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline
For the stable supply of oil and gas resources, industry is pushing for various attempts and technology development to produce not only existing land fields but also deep-sea, where production is difficult. The development of flow assurance technology is necessary because hydrate is aggregated in th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/8/2313 |
| _version_ | 1797537064847147008 |
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| author | Youngjin Seo Byoungjun Kim Joonwhoan Lee Youngsoo Lee |
| author_facet | Youngjin Seo Byoungjun Kim Joonwhoan Lee Youngsoo Lee |
| author_sort | Youngjin Seo |
| collection | DOAJ |
| description | For the stable supply of oil and gas resources, industry is pushing for various attempts and technology development to produce not only existing land fields but also deep-sea, where production is difficult. The development of flow assurance technology is necessary because hydrate is aggregated in the pipeline and prevent stable production. This study established a system that enables hydrate diagnosis in the gas pipeline from a flow assurance perspective. Learning data were generated using an OLGA simulator, and temperature, pressure, and hydrate volume at each time step were generated. Stacked auto-encoder (SAE) was used as the AI model after analyzing training loss. Hyper-parameter matching and structure optimization were carried out using the greedy layer-wise technique. Through time-series forecast, we determined that AI diagnostic model enables depiction of the growth of hydrate volume. In addition, the average R-square for the maximum hydrate volume was 97%, and that for the formation location was calculated as 99%. This study confirmed that machine learning could be applied to the flow assurance area of gas pipelines and it can predict hydrate formation in real time. |
| first_indexed | 2024-03-10T12:10:46Z |
| format | Article |
| id | doaj.art-93c7a8c3be4a483ebeb0c71007d7d9e7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T12:10:46Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-93c7a8c3be4a483ebeb0c71007d7d9e72023-11-21T16:16:51ZengMDPI AGEnergies1996-10732021-04-01148231310.3390/en14082313Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas PipelineYoungjin Seo0Byoungjun Kim1Joonwhoan Lee2Youngsoo Lee3Department of Mineral Resource and Energy Engineering, Jeonbuk National University, Jeonju 54896, KoreaIT Application Research Center, Korea Electronics Technology Institute, Jeonju 54853, KoreaDivision of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Mineral Resource and Energy Engineering, Jeonbuk National University, Jeonju 54896, KoreaFor the stable supply of oil and gas resources, industry is pushing for various attempts and technology development to produce not only existing land fields but also deep-sea, where production is difficult. The development of flow assurance technology is necessary because hydrate is aggregated in the pipeline and prevent stable production. This study established a system that enables hydrate diagnosis in the gas pipeline from a flow assurance perspective. Learning data were generated using an OLGA simulator, and temperature, pressure, and hydrate volume at each time step were generated. Stacked auto-encoder (SAE) was used as the AI model after analyzing training loss. Hyper-parameter matching and structure optimization were carried out using the greedy layer-wise technique. Through time-series forecast, we determined that AI diagnostic model enables depiction of the growth of hydrate volume. In addition, the average R-square for the maximum hydrate volume was 97%, and that for the formation location was calculated as 99%. This study confirmed that machine learning could be applied to the flow assurance area of gas pipelines and it can predict hydrate formation in real time.https://www.mdpi.com/1996-1073/14/8/2313gas hydratediagnostic modelartificial intelligencestacked auto-encodergreedy layer-wise |
| spellingShingle | Youngjin Seo Byoungjun Kim Joonwhoan Lee Youngsoo Lee Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline Energies gas hydrate diagnostic model artificial intelligence stacked auto-encoder greedy layer-wise |
| title | Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline |
| title_full | Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline |
| title_fullStr | Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline |
| title_full_unstemmed | Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline |
| title_short | Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline |
| title_sort | development of ai based diagnostic model for the prediction of hydrate in gas pipeline |
| topic | gas hydrate diagnostic model artificial intelligence stacked auto-encoder greedy layer-wise |
| url | https://www.mdpi.com/1996-1073/14/8/2313 |
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