Evolutionary optimization assisted delayed deep cycle reservoir modeling method with its application to ship heave motion prediction.

As one emerging reservoir modeling method, cycle reservoir with regular jumps (CRJ) provides one effective tool for many time series analysis tasks such as ship heave motion prediction. However, the shallow learning structure of single CRJ model limits its memory capacity and leads to unsatisfactory prediction performance. In order to pursue the stronger dynamic characteristic description of time series data, a delayed deep CRJ model is presented in this paper by integrating the deep learning framework with delay links and the evolutionary optimization for mixed-integer problem.

Conservative models: parametric entropy vs. temporal entropy in outcomes.

The geologic architecture in aquifer systems affects the behavior of fluid flow and the dispersion of mass. The spatial distribution and connectivity of higher-permeability facies play an important role. Models that represent this geologic structure have reduced entropy in the spatial distribution of permeability relative to models without structure.