Assessing Spatial Uncertainty on Seismic Data: Why It Matters & How to Do It

Spatial uncertainty is a primary source of economic loss in E&P asset management. Quantifying the uncertainty attached to seismic data before geomodeling is essential for improving geomodel reliability, well targeting accuracy, and overall asset performance. Within the Digital Intelligent Asset Management (DIAM) framework, Earth Intelligence (EI) algorithms compute the seismic “signal” standard deviation associated to its kriged estimation at each data location—that is the probabilistic definition of spatial uncertainty attached to data. This paper explains why and how spatial uncertainty should be assessed at the seismic input stage and demonstrates, through Middle East field and Nuclear storage site applications, that its quantification enables automation, optimization, and improved performance of geomodeling workflows. The significant improvement of P10–P50–P90 confidence intervals computed from EI driven E&P decision making workflows confirm that systematic assessment of seismic spatial uncertainty strengthens decision confidence on asset value and project RoI while reducing project turnaround time by more than 50%. This marks a key step toward fully intelligent E&P asset management.