Improving Velocity Model Using Double Parabolic RMO Picking (ModelC) and Providing High-End RTM (RTang) Imaging for OML 79 Shallow Water, Nigeria
J. C Osimobi, Ekemezie Ifeanyi, Obobi Onwuka, Uraechu Deborah, Magnus Kanu
Abstract
Abstract In this paper, we will be discussing how we solved a legacy seismic issue using pragmatic geophysics techniques- from seismic acquisition, through processing and interpretation. The legacy seismic had an inherent seismic acquisition issue as it was a streamer acquisition acquired along the geologic structural strike direction. With the legacy seismic it was difficult to mature and explore the especially the deeper opportunities in the field and not allowing for full realization of the hydrocarbon potentials in the area. To solve these pending issues, we had a campaign to acquire one of the biggest Ocean Bottom Node (OBN) seismic dataset in the entire Shell group as at the time of the acquisition, we deployed the best of seismic pre-processing (denoise, deconvolution, de-multiple, amplitude balancing etc) tools, built a robust seismic velocity model using the Double Parabolic RMO picking methodology which gave rise a detailed and geologic velocity mode. On top of this we deployed an advanced imaging technology of Revers Time Migration (RTM) and took a longer route and more expensive flavour of RTANG option to generate at optimum stack at RFLANG 0-42 degree. Furthermore, understanding and observing residual noise in the form of conflicting dips to the geologic dips of the survey, we deployed the DIP_FLT tool which took out the conflicting dips and left us with a cleaner dataset. Higher-order double parabolic moveouts were picked in azimuthal direction to account for the velocity variations in the Residual Moveout (RMO). The picks were then inverted using conventional Travel Time Tomography (TTI) workflow. This resulted in robust velocity model, shows more realistic geological details. Sonic log (DT) also indicated that Model C was an improved Model compared to Model B. The seismic imaging approach was using the Reverse Time Migration (RTM) algorithm for BASE RTMIG image and then used the RTANG (entailed Compute angle and azimuth from local subsurface-offset analysis and reflector dips, write image, angle, azimuth volumes to disk) option to select optimum REFLECTION angle for optimum stacking, and the DSSRT post migration provided a noise filtered version of the BASE RTMIG image.