S.35.02 - Anxiolytic, hypnotic and analgesic properties of melatonin MT2 receptor partial agonists

Abstract

Most of the current subsurface simulation approaches consider a priority list for honoring the well and any other auxiliary data, and eventually adopt a middle ground between the quality of the model and conditioning it to hard data. However, as the number of datasets increases, such methods often produce undesirable features in the subsurface model. Due to their high flexibility, subsurface modeling based on training images (TIs) is becoming popular. Providing comprehensive TIs remains, however, an outstanding problem. In addition, identifying a pattern similar to those in the TI that honors the well and other conditioning data is often difficult. Moreover, the current subsurface modeling approaches do not account for small perturbations that may occur in a subsurface system. Such perturbations are active in most of the depositional systems. In this paper, a new methodology is presented that is based on an irregular gridding scheme that accounts for incomplete TIs and minor offsets. Use of the methodology enables one to use a small or incomplete TI and adaptively change the patterns in the simulation grid in order to simultaneously honor the well data and take into account the effect of the local offsets. Furthermore, the proposed method was used on various complex process-based models and their structures are deformed for matching with the conditioning point data. The accuracy and robustness of the proposed algorithm are successfully demonstrated by applying it to models of several complex examples.