Seismic Inversion Methods and some of their constraints

The interest in seismic inversion techniques has been growing steadily over the last couple of years. Integrated studies are essential to hydrocarbon development projects (e.g. Vazquez et al. 1997, Cosentino 2001) and inversion is one of the means to extract additional information from seismic data. Various seismic inversion techniques are briefly presented. Inversion replaces the seismic signature by a blocky response, corresponding to acoustic and/or elastic impedance layering. It facilitates the interpretation of meaningful geological and petrophysical boundaries in the subsurface. Inversion increases the resolution of conventional seismics in many cases and puts the study of reservoir parameters at a different level. It results in optimised volumetrics, improved ranking of leads/ prospects, better delineation of drainage areas and identification of ‘sweet spots’ in field development studies (e.g. Veeken et al. 2002). The main steps in an inversion study are: ■ Quality control and pre-conditioning of the input data. ■ Well–to-seismic match, zero-phasing of data in zone of interest and extraction of the wavelet. ■ Running of the inversion algorithm with generation of acoustic or elastic impedance cubes and extraction of attributes. ■ Visualisation and interpretation of the results in terms of reservoir development. The inversion methods are either deterministic or probabilistic and the approach can be post- and/or pre-stack. Inversion schemes generally use migrated time data as basic input. The pre-stack method exploits AVO effects on migrated CDP gathers. There is a trade-off between method/cost/time and quality of inversion results. Feasibility studies with synthetic modelling are recommended before embarking on an inversion or AVO project (Da Silva et al., in prep.). The past track record has demonstrated the benefits of the seismic inversion method. However, it should be realised that the inversion procedure is not a unique process, i.e. there is no single solution to the given problem. Care should be taken when interpreting the inversion results. Adequate data preconditioning is a prerequisite for quantitative interpretation of the end results.