Reducing uncertainties in hydrocarbon prediction through seismic inversion

Abstract

Petroleum systems in offshore are often very complex and subtle because of variety of depositional environments. Delineating a reservoir based on conventional seismic and well-log stratigraphic analysis in such complex settings often leads to uncertainties. A reliable model of reservoir can forecast the production and performance of a reservoir which can reduce the drilling risks and associated uncertainties significantly. This work is aimed to develop a new concept in reservoir modeling by integrating seismic inversion and rock physics tools. First, in order to define litho facies, rock physics modeling was carried out through well log analysis separately for each facie. Next, the available subsurface information is incorporated in a Bayesian engine which outputs the several realizations of elastic reservoir properties and their respective probabilities, which then are used for post-inversion analysis. Seismic inversion fully exploited the vast areal coverage of the seismic data and integrated it with the well logs resulted in high-resolution acoustic impedance realizations. 3D impedance models coupled with the petrophysical analysis were then used to delineate the reservoir bodies.