Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline

Youngjin Seo, Byoungjun Kim, Joonwhoan Lee and Youngsoo Lee
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Youngjin Seo: Department of Mineral Resource and Energy Engineering, Jeonbuk National University, Jeonju 54896, Korea
Byoungjun Kim: IT Application Research Center, Korea Electronics Technology Institute, Jeonju 54853, Korea
Joonwhoan Lee: Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea
Youngsoo Lee: Department of Mineral Resource and Energy Engineering, Jeonbuk National University, Jeonju 54896, Korea

Energies, 2021, vol. 14, issue 8, 1-22

Abstract: 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.

Keywords: gas hydrate; diagnostic model; artificial intelligence; stacked auto-encoder; greedy layer-wise (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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