Applied 3D full-waveform inversion: increasing the resolution and depth penetration

High-resolution velocity models, at near surface and deeper reservoir depths are produced with three-dimensional, acoustic, anisotropic, full-waveform inversion. Industry experts show eager interest in the development of this technology with a drive to push its application to reflection-dominated streamer datasets as well as ocean-bottom-node datasets in geologically complex environments. Here, a robust methodology employing the use of key strategies to address the inversion of such datasets, attaining increased resolution and depth penetration, is explored. Synthetic tests were undertaken to exploit the use of reflected energy. Key strategies: muting of direct arrivals, time windowing, and layer-stripping, all produced highly resolved, full waveform inversion models. These strategies have been incorporated into inversion schemes focusing solely on reflection targets. Strategies to further improve model resolution for field datasets were then investigated. Close examination of a full-waveform inversion model for a shallow-water ocean-bottom-node dataset, revealed a systematic mismatch between the observed and predicted data. After conducting a series of tests, it was illustrated that systematic errors in the starting model, source wavelet, incomplete convergence, or an inadequate finite-difference mesh did not cause the mismatch. Instead, inadequacies in the physics used during inversion are believed to be the cause. The introduction of an offsetvariable density scheme during inversion, compensated efficiently and heuristically for these inaccuracies, removing the mismatch and increasing the model resolution. The sensitivity of full-waveform inversion to local minima, where the computed model is stuck away from the real global-minimum solution and further iterations of the optimisation bring no reward, was kept in mind during the inversion of two deep-water ocean-bottom node datasets. Thus, full-waveform inversion was undertaken using conditioned data obtained through adaptive matching, incorporating higher frequencies and a greater weight on reflected energy, valuably pushing the limits of resolution and depth penetration of the update. The use of all these robust methodologies improved the travel-time match; better flattened common-image gathers giving a closer fit to well logs and an improvement in the pre-stack depth-migrated image. Effectively, the reflectivity was non-linearly migrated into the velocity model via the inversion acting on raw unprocessed waveforms. Thus, full waveform inversion can eventually replace conventional processing and migration – all that is needed, is a full-bandwidth velocity model.Open Acces