From Ideas to Deployment - A Joint Industry-University Research Effort on Tackling Carbon Storage Challenges with AI
Junjie Xu (Tsinghua University), Jiesi Lei (Tsinghua University), Yang Li (Tsinghua University), Junfan Ren (College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing, China), Jian Qiu (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China), Biao Luo (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China), Lei Xiao (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China) and Wenwen Zhou (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China)
Abstract
Carbon capture and storage (CCS) offers a promising means for significant reductions in greenhouse gas emissions and climate change mitigation at a large scale. Modeling CO2 transport and pressure buildup is central to understanding the responses of geosystems after CO2 injection and assessing the suitability and safety of CO2 storage. However, numerical simulations of geological CO2 storage often suffer from its multi-physics nature and complex non-linear governing equations, and is further complicated by flexible injection designs including changes in injection rates, resulting in formidable computational costs. New ideas have emerged such as data-driven models to tackle such challenges but very few have been fully developed and deployed as reliable tools. With the joint efforts of industry and universities, we are currently working on a new mechanism of fostering cross-disciplinary collaboration, developing, deploying, and scaling data-driven tools for CCS. A deep learning suite that can act as an alternative to CCS variable rate injection simulation will be the first tool developed under this mechanism. Based on the surrogate model, optimal design of injection strategy under pressure buildup constraints will be enabled with machine learning.