Engineering Visual Presentation E04: Predicting ground surface deformation induced from CO2 plume movement using machine learning
Ibrahim M. Ibrahim
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Ibrahim M. Ibrahim is a PhD student at the Queensland University of Technology and a postgraduate research student in the Energy Unit at CSIRO, where he actively contributes to cutting-edge research in the field. As a Sessional Academic, he imparts his knowledge to engineering to students at the university. Prior to his current academic pursuits, he had the privilege of being a research scholar at California Polytechnic State University in the field of Renewable Energy and Assistant Lecturer at AASTMT. Ibrahim has excelled in academia, holding an MSc degree in Mechanical Engineering, achieving a remarkable GPA of four and earning recognition through research publications in high-impact international journals. Ibrahim is an honoured recipient of one of the most prestigious awards in the field of Energy research, the Eni award for Young Talents from Africa under the distinguished presence of the President of Italy, who presented the award to him. |
Abstract
Engineering Visual Presentation E04
Carbon capture and storage (CCS), which involves injecting carbon dioxide (CO2) into subsurface, is an increasingly popular process for mitigating human caused greenhouse gas emissions. In order to ensure the safety and efficacy of CCS implementation, it is necessary to possess a comprehensive understanding of the complex behaviour of CO2 plumes within geological formations and their potential impact on ground surface deformation. Therefore, conducting research and analysis on these critical aspects is of vital importance. This research provides a methodology to anticipate ground surface deformations, which result from the motion of CO2 plumes utilising an advanced machine learning (ML) technique. The ML surrogate model has been developed using conditional Generative Adversarial Networks (cGAN). The dataset used for the model training and testing comprises ground surface measurements (tiltmeters), reservoir properties, as well as pressure/volume data. The model has been trained and tested using a set of samples created using a forward finite element model. Results show that the surrogate model is capable of predicting reasonably accurate results while running much faster than the forward model.
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Keywords: carbon capture and storage (CCS), CO2 plume prediction, energy storage, greenhouse gas emissions, ground surface deformation, inverse analysis, machine learning surrogate model, tiltmeters.
Ibrahim M. Ibrahim is a PhD student at the Queensland University of Technology and a postgraduate research student in the Energy Unit at CSIRO, where he actively contributes to cutting-edge research in the field. As a Sessional Academic, he imparts his knowledge to engineering to students at the university. Prior to his current academic pursuits, he had the privilege of being a research scholar at California Polytechnic State University in the field of Renewable Energy and Assistant Lecturer at AASTMT. Ibrahim has excelled in academia, holding an MSc degree in Mechanical Engineering, achieving a remarkable GPA of four and earning recognition through research publications in high-impact international journals. Ibrahim is an honoured recipient of one of the most prestigious awards in the field of Energy research, the Eni award for Young Talents from Africa under the distinguished presence of the President of Italy, who presented the award to him. |
