Predicting ground surface deformation induced from CO2 plume movement using machine learning
Ibrahim M. Ibrahim
A B * , Saeed Salimzadeh A , Dane Kasperczyk A and Teeratorn Kadeethum C
A
B
C
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. |
Dr Saeed Salimzadeh is a Senior Research Scientist at Subsurface Engineering and Technology team at CSIRO Energy, Clayton, Australia. He obtained his PhD in Geomechanics at University of New South Wales (UNSW Sydney) in 2014, and since has been working in international research institutes at Imperial College London; DTU, Denmark; and CSIRO, Australia. Saeed is an expert in reservoir geomechanics and hydraulic fracturing, through numerical modelling, machine learning, and inversion. He has developed the hydraulic fracturing simulator CSMP-HF and has supervised many master’s and PhD students. |
Dane Kasperczyk is a senior engineer working in the Energy Resources group at CSIRO, he holds degrees from University of Melbourne in Civil Engineering and Science (Earth Sciences). For the past decade, he has worked on research and projects related to fracture mechanics modelling, hydraulic fracturing environmental risk probabilities and preconditioning for block caves, sublevel caves, and underground mines. Through this he has developed capability in subsurface monitoring using tiltmeters that has seen applicable use for mining, CO2 sequestration and subsurface energy storage. |
Teeratorn Kadeethum has a bachelor’s degree in mechanical engineering from Chulalongkorn University in Thailand (2007), a master’s degree in chemical engineering from the University of Calgary in Alberta, Canada (2016), and a PhD in Applied Mathematics and Computer Science from the Technical University of Denmark (2020). Following his PhD, he was a postdoctoral associate in mechanical and aerospace engineering at Cornell University in New York, USA (2020–2021). He is currently a senior technical staff member at the Climate Change Security Center at Sandia National Laboratories in New Mexico, USA. Prior to this, he accumulated 4 years of industrial experience as a reservoir engineer at PTT Exploration and Production, an international oil company. Teeratorn has authored over 20 research articles, and his research interests include scientific machine learning, advanced finite element approximations, and model order reduction, particularly in the context of nonlinear partial differential equations. |
Abstract
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.
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. |
Dr Saeed Salimzadeh is a Senior Research Scientist at Subsurface Engineering and Technology team at CSIRO Energy, Clayton, Australia. He obtained his PhD in Geomechanics at University of New South Wales (UNSW Sydney) in 2014, and since has been working in international research institutes at Imperial College London; DTU, Denmark; and CSIRO, Australia. Saeed is an expert in reservoir geomechanics and hydraulic fracturing, through numerical modelling, machine learning, and inversion. He has developed the hydraulic fracturing simulator CSMP-HF and has supervised many master’s and PhD students. |
Dane Kasperczyk is a senior engineer working in the Energy Resources group at CSIRO, he holds degrees from University of Melbourne in Civil Engineering and Science (Earth Sciences). For the past decade, he has worked on research and projects related to fracture mechanics modelling, hydraulic fracturing environmental risk probabilities and preconditioning for block caves, sublevel caves, and underground mines. Through this he has developed capability in subsurface monitoring using tiltmeters that has seen applicable use for mining, CO2 sequestration and subsurface energy storage. |
Teeratorn Kadeethum has a bachelor’s degree in mechanical engineering from Chulalongkorn University in Thailand (2007), a master’s degree in chemical engineering from the University of Calgary in Alberta, Canada (2016), and a PhD in Applied Mathematics and Computer Science from the Technical University of Denmark (2020). Following his PhD, he was a postdoctoral associate in mechanical and aerospace engineering at Cornell University in New York, USA (2020–2021). He is currently a senior technical staff member at the Climate Change Security Center at Sandia National Laboratories in New Mexico, USA. Prior to this, he accumulated 4 years of industrial experience as a reservoir engineer at PTT Exploration and Production, an international oil company. Teeratorn has authored over 20 research articles, and his research interests include scientific machine learning, advanced finite element approximations, and model order reduction, particularly in the context of nonlinear partial differential equations. |
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