Real-Time Detection of CO2 Leakage in Transparent Subsurface Formations
Climate change poses a critical global challenge, and CO2 geological sequestration is an effective strategy for reducing greenhouse gas emissions. While the successful injection of CO2 into subsurface formations is important, ensuring that it remains securely stored without leakage is crucial for the success of sequestration efforts. Immediate detection and remediation of any leakage are essential to prevent atmospheric release. Current methods, such as seismic monitoring, are often not real-time, have low accuracy, and are costly. Delayed detection means CO2 could be released for an extended period, undermining sequestration efforts. Therefore, real-time leakage detection is crucial. Experts from the Institute of Geology and Geophysics, Chinese Academy of Sciences, and Saudi Aramco have collaborated to develop a novel technology for this purpose. The major innovations include: 1. Automatic Interpretation Techniques: These techniques analyze large volumes of geological data, and stochastic discrete fracture network methods simulate various geological scenarios to enhance accuracy. 2. Deep-Learning Based Upscaling: This method generates computationally efficient geological models. 3. Bayesian Inversion Methods: Combined with deep learning algorithms, these methods identify potential leakage sites in real time. This innovative approach has been successfully adopted by Saudi Aramco and implemented in the largest carbon capture and storage (CCS) project in the MENA region.
Climate And Environmental Technologies
Climate change control technologies
30 days
Saudi arabia
1.Novelty and uniqueness: Our CO2 leakage detection solution excels at providing immediate leak detection compared to traditional methods like seismic monitoring, which offer delayed indications. By integrating Bayesian inversion with advanced deep learning techniques, it achieves highly accurate localization of leakage sites, surpassing conventional technologies that are influenced by external factors. 2.Cost-Effectiveness: This solution is cost-effective, utilizing existing observational wells and incurring only expenses related to time, labor, and maintenance for monitoring and detection. In contrast to the multi-million-dollar costs of detailed 4D seismic surveys, our solution offers negligible additional expenses. 3.High Replicability: The solution is highly replicable, with no significant external or internal barriers to implementation across various territories. The successful pilot trial in Saudi Arabia demonstrates excellent accuracy and efficiency, and the methodology can be applied to different geological settings, supporting its potential for widespread adoption. 4.Proven Effectiveness: The solution has proven effective in Saudi Aramco’s largest CCS project in the MENA region, showcasing practical reliability and real-world applicability. The methodology has been the subject of filed patents and published in prestigious journals with rigorous peer review.
1.The Chinese Academy of Sciences is the largest research institute in the world and is ranked No. 1 in the Nature Index. 2. Saudi Aramco is the second-largest company in the world by revenue. 3. Outstanding project in the area of 'Climate x Clean Power' from the GYS2023. 4. Outstanding Presentation Award. 5. First-Prize Paper Award. 6. Invited Speaker at the Annual Conference of IAEG. 7. Appointment as the Vice Secretary-General of CSRME. 8. Others, see attachment.
More than 15 high-quality journal papers related to this project have been published, and more than 10 Chinese and American patents have been granted or are under review. Here we list the two most relevant papers; details are in the attachments: 1. Zhu W*, Khirevich S, Patzek T W. HatchFrac: A fast open-source DFN modeling software. Computers and Geotechnics, 150: 104917, 2022. 2. He X, Zhu W*, Kwak H, Yousef A, Hoteit H*. Deep learning-assisted Bayesian framework for real-time CO2 leakage locating at geologic sequestration sites. Journal of Cleaner Production, 141484, 2024.
