A Deep Learning Technology Suite for Cost-Effective Sequestered CO2 Monitoring (Papers Track)

Wenyi Hu (SLB); Son Phan (SLB); Cen Li (SLB); Aria Abubakar (SLB)

Poster File Cite
Carbon Capture & Sequestration Earth Observation & Monitoring Climate Science & Modeling Hybrid Physical Models Unsupervised & Semi-Supervised Learning


Carbon capture and storage (CCS) is a way of reducing carbon emissions to help tackle global warming. Injecting CO2 into rock formations and preventing it from escaping to the surface is a main step in a CCS project. Therefore, monitoring of geologically sequestered CO2 is important for CCS security assessment. Time-lapse seismic (4D seismic) is one of the most effective tools for CO2 monitoring. Unfortunately, the main challenge of 4D seismic is the high cost due to repeated monitoring seismic data acquisition surveys and the subsequent time-consuming data processing that involves imaging and inversion. To address this, we developed a technology suite powered by deep learning engines that significantly reduces the cost by (1) acquiring very sparse monitoring data; (2) firing multiple seismic sources simultaneously; (3) converting 2D images to 3D volume; (4) enforcing repeatability between baseline data and monitoring data; and (5) nonlinearly mapping seismic data to subsurface property model to bypass complex wave-equation-based seismic data processing procedures.