Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition

Abstract

Fluid imaging is one of the key geophysical technologies for the energy industry during energy transition to zero footprint. We propose better Cloud-based fluid distribution imaging to allow better, more optimized production, thus reducing carbon dioxide (CO2) footprint per barrel produced. For CO2 storage, the location knowledge of the stored fluids is mandatory. Electromagnetics is the preferred way to image reservoir fluids due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to cost optimization of field operations. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and even, for the interpretation, to close to 24 h. This then opens new doors for the breakthrough of this technology from exploration to production and monitoring. It allows the application envelope to be enlarged to much noisier environments where real time acquisition can be optimized based on the acquired data. Once all components are commercialized, the full implementation could become a real game changer by providing near real time 3-dimensional subsurface images in support of the energy transition.

Keywords

• controlled source electromagnetics
• CSEM
• artificial intelligence
• energy transition using electromagnetics
• reservoir monitoring
• CCUS