Full Waveform Inversion for the Near Surface Characterization, Onshore UAE, Case Study

Abstract

Full-waveform inversion (FWI) has become a well-established method for obtaining a detailed earth model suitable for improved imaging, near-surface characterization and pore-pressure prediction. FWI for onshore data has always been challenging and has seen limited application (Vigh et al, 2018). It requires a dedicated data processing approach related to the lower signal-to-noise ratio, accounting for variable topography and complex near-surface related effects. During the past few years, ADNOC has been acquiring and processing one of the world's largest combined 3D onshore and offshore seismic surveys in the Emirate of Abu Dhabi. The modern acquisition parameters that were implemented enabled the acquisition of broadband onshore seismic data rich in low frequencies that could benefit the initial stages of the FWI workflow. Sand dunes and sabkha layers at the surface, and high-velocity carbonate and dolomite layers in the subsurface pose a significant challenge for near-surface modeling in the UAE. The purpose of this work is to evaluate FWI application onshore UAE for near-surface characterization. We will compare the FWI results with conventional approaches for the near-surface model building that has been used routinely on land datasets in UAE, such as data-driven image-based statics (DIBS, Zarubov et al, 2019). One of the main challenges is data preconditioning, as onshore seismic data typically exhibits high levels of noise. It is imperative to denoise gathers sufficiently prior to the FWI process. A well sonic velocity function with large smoothing was used to build the starting velocity model for FWI. The process aims to minimize the least-squared difference between predicted and observed seismic responses by means of updating the model on which the prediction is based. As the predicted and seismic responses are functions of model parameters as well as source signature, a good estimate of the source wavelet is important for update and convergence in FWI. During this FWI work, source wavelet inversion was done as a separate step and used in subsequent FWI passes. FWI inversion started with adjustive FWI (Kun et al, 2015) on lower frequencies, moving to higher frequencies where both adjustive and least square objective functions were used. We will further show assessment of the anisotropy, initial conditions, usage of geological constraints, and comparisons to the conventional solutions. A comparison of results shows that FWI has successfully added velocity details to the near-surface model that follow the geological trend and conforms to well information while producing a plausible static solution. We have demonstrated the application of FWI onshore UAE for near-surface modeling. Although turnaround time (TAT) has increased compared to the conventional approach, the learning that was gained during this trial will decrease TAT for the future FWI work.