Quantitative Seismic Characterization in Deep-Water Channel System for Reservoir Modelling and Well De-Risking

The case study discussed in this paper was aimed at optimizing the quality of the reservoir characterization predictions and at reducing the time-to-market schedule of a deep-water project, by applying a methodology that integrates data coming from wells, sedimentological model and seismic inversion into a reservoir model. The studied field is composed by two Oligocene reservoirs in a water depth of more than 1000 meters. The two reservoirs belong to different turbiditic channel complexes, mainly composed by oil bearing sands. Within the identified seismic-stratigraphic layering, framed in a calibrated structural model derived from new PSDM data, the analysis of seismic attributes allowed to qualitatively recognize responses, interpreted as reference elements of deposition, characterized by morphologically consistent image and assigned depositional meaning. The simultaneous elastic inversion, in addition, delivered attributes used to classify litho-fluid facies by applying the Bayesian inference, generating probabilistic seismic facies volumes, in agreement to the reference petro-elastic framework. Both results were then integrated in the geomodelling workflow for driving facies and property distribution, using a geostatistical approach. The derived reservoir model was finally used to optimize well locations and, globally, to support the decision making process in the early stage of field production life.