Reservoir Facies Prediction from Geostatistical Inverted Seismic Data

Abstract The estimation of oil in place in a reservoir is a key element in reservoir management. Based on these estimations, economical decisions can be taken. Therefore, it is necessary to take into account all possible sources of uncertainty in reservoir characterization. To address the uncertainty from seismic inversion, we use pre-stack geostatistical inversion to arrive at a family of related IP and IS volumes. The geostatistical inversions explore the possible elastic subsurface models that are constrained by the seismic and well data together with geostatistical modeling parameters. These IP and IS volumes are transformed to a family of seismic facies volumes with the use of a well data derived cross-plot. On the cross-plot, seismic facies regions are interpreted while each seismic facies region contains points of the well identified geological facies. The seismic facies models obtained from the inversions are used to obtain geological facies models. To arrive at such a model, the uncertainty in the points sampled by the well is used to define thresholds for a truncated Gaussian simulation. In this manner, the uncertainties from the seismic data inversion are propagated to the geological facies model. With the availability of probability functions for the petrophysical parameters and the volumes of the reservoir grid cells, the oil in place can be computed. Repeating the procedure for each seismically inverted data volume, a histogram of the oil in place is obtained in which the uncertainty from the seismic inversion is propagated to a geological facies model which can be utilized in an integrated oil in place calculation.