Complexities associated with heterogeneities of the porous media at macro and mega scales are investigated. Model recognition processes with neural network is augmented with quantitative estimates. Methods are being developed to handle multiple parameter estimators for the conceptual models. Geostatistical Methods Techniques are being developed for short to long range characterization of flow units using stochastic modeling of reservoir response. Geostatistical models are developed for a variety of sedimentation processes.
Studies are in progress to model discontinuities commonly observed in turbidite series.
A combination of sequence stratigraphy, geological modeling, and geostatistical technique are necessary to examine discontinuity of flow units and crossflow across such units. A major concern in stochastic modeling of subterranean reservoirs is history matching methods that can capture the essence of various geological realization models. Without an effective dynamic programming approach, the process can become computationally cumbersome. Complexity of the problem becomes more intense when finely girded structures are selected to represent detail flow unit heterogeneities. Studies are underway to develop methodologies for addressing the history matching for stochastic models.