Efficient back analysis of multiphysics processes of gas hydrate production through artificial intelligence. (1st September 2022)

• Record Type: Journal Article
• Authors: Zhou, Mingliang; Shadabfar, Mahdi; Huang, Hongwei; Leung, Yat Fai; Uchida, Shun
• Is Part Of: Fuel. Volume 323(2022)
• Publication Date: 2022-09-01
• Subjects: Efficient back analysis -- Gas hydrate production -- Multiphysics processes -- Meta-modeling -- AI-based approach; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.124162
• Languages: English
• ISSNs: 0016-2361

Abstract

Abstract: Natural gas hydrate, a crystalline solid existing under high-pressure and low-temperature conditions, has been regarded as a potential alternative energy resource. It is globally widespread and occurs mainly inside the pores of deepwater sediments and sediments under permafrost area. Hydrate production via well depressurization is deemed well-suited to existing technology, in which the pore pressure is lowered, the natural gas hydrate is dissociated into water and gas, and the water and gas are produced from well. This method triggers multiphysics processes such as fluid flow, heat transfer, energy adsorption, chemical reaction and sediment deformation, all of which are dependent on the amount of gas hydrates remaining in the pores. Therefore, modeling of hydrate production is computationally intensive and expensive. While back-analysis through observed production history is essential for better understanding of the reservoir characteristics and reliable prediction for future gas hydrate production, a large number of required simulations makes it impractical. This study employs Artificial Intelligence (AI) to achieve an efficient back-analysis of the gas hydrate production conducted at the offshore Nankai site, Japan, in 2013. The results show that the AI-based metamodel is capable of reproducing outputs of heavy computation of the multiphysics processes and thus performs back-analysis greatly efficiently. The efficient AI-based metamodel also makes it possible to carry out sensitivity analysis and it is found that the permeability and the preyield plasticity parameter are most influential to reservoir responses. The approach of this study can be applicable to other reservoirs and will reveal the ground truth in-situ properties and the most influential properties, contributing to better understanding of reservoir behavior for future gas hydrate production. Highlights: A coupled THM simulator is adopted to generate a database of reservoir responses. An ANN-based meta-model is developed using the established dataset. An efficient back analysis is introduced to conduct the history matching. Sensitivity analyzes are performed to identify the most influential parameters. The proposed approach is validated via the 2013 Nankai hydrate production test data.