Publications

AVO reconnaissance type approaches have been for a long time adopted for 2D and more efficiently nowadays for 3D seismic data. More detailed semiquantitative or quantitative techniques, both for 2D and 3D data, can also be applied. These are based on an analysis of seismic data which is more fine-tuned, as well as on the petrophysical and acoustic modeling of the pre-stack seismic response for calibrating AVO interpretation.This case history comes from the Kutei basin of Indonesia, where the need of reliably predicting the distribution and characteristics of gas and liquid hydrocarbons, proven by exploration wells, represents a key factor for the success of the development project.This has been targeted through a probabilistic inversion of AVO data, based upon a stochastic AVO modeling, that allow an educated extrapolation of known AVO information to predict reservoir fluids ahead of the future drilling. This ENI proprietary “Fluid Inversion methodology is focused at estimating the probability that an assigned AVO response, measured on real pre-stack seismic data, can be ascribed to the presence of either brine, gas, oil in a sand reservoir (Cardamone et. al., 1999)The developed software compares the real AVO response at each single bin of the several target levels with a generalized probabilistic AVO model. This takes into account the expected variability of all the involved petrophysical parameters. This model is developed through a statistic al analysis of all the available borehole data and information in the study area.The methodology allows an effective and powerful extrapolation of the AVO information modeled at the well to any new target belonging to a homogeneous geological scenario, even at significantly different burial depth. The resulting fluid probability maps represent indeed a new way to use pre-stack seismic information to benefit the reservoir assessment process.