Oil & Gas

Elastic full-waveform inversion (FWI) is increasingly recognized as a powerful tool for building compressional velocity (Vp) models, primarily utilizing diving and reflected P-waves. However, updating shear velocity (Vs) models through S-wave inversion in elastic FWI has traditionally been more challenging due to data acquisition limitations and the reduced sensitivity of surface seismic to Vs. This paper introduces a practical methodology for low-wavenumber Vs updates using elastic FWI, driven by converted waves in multi-component ocean-bottom seismic data. The approach involves two key steps: first, building a high-quality Vp model using hydrophone and vertical geophone data, and second, using horizontal geophones to reconstruct the low-wavenumber components of the Vs model from converted wave kinematics. We demonstrate the effectiveness of this methodology in a field application, showcasing improved PS reverse time migration (RTM) imaging and better alignment with PP RTM. Additionally, we compare the elastic FWI approach to a Born-based PS-reflection FWI method, with the elastic FWI model yielding superior PS RTM imaging.

The Santos Basin’s pre-salt oil fields in Brazil have moved from exploration to development, requiring deeper reservoir insight. This study showcases how five years of ocean-bottom node (OBN) processing advancements enhance understanding of the Tupi field’s complex pre-salt reservoirs. Techniques like elastic Time-Lag FWI, RTM angle gathers with spherical binning, and internal multiple attenuation improve velocity models, fault definition, and AVA response. These advancements reduce uncertainty in Vp/Vs-based reservoir inversion, enabling more precise subsurface characterization.

This paper presents an AI-driven workflow designed for the automatic interpretation of large seismic volumes, applied to a 15,000 km² offshore area in Abu Dhabi. The workflow begins with fault detection for low-magnitude, strike-slip faults, achieved through fine-tuning deep neural networks (DNNs). This fault detection process is enhanced by structure-preserving denoising to maintain critical features. Next, automated horizon interpretation and flattening reveal a variety of geological features that are otherwise difficult to identify. Finally, structure-enhancing denoising improves geological feature detection, using channel detection as an example. This workflow is set to be expanded to cover the entire offshore Abu Dhabi region, offering significant potential for large-scale geological analysis.