Figure 13. Marcellus Shale lithofacies 3D model by sequential indicator simulation algorithm: (a) Top of Otaka Creek Member in 3D view; (b) Top of Union Spring Member in 3D view; (c) cross sections; (d) fence diagram zoomed into Pennsylvania and north West Virginia. Tables Table 1. Comparison of four pattern recognition models (Kordon, 2010). Mathematic Model Strengths Weaknesses Simple Low complexity Discriminant Analysis Strong theory base No ability to learning Linear Non-linear Capture linguistic Low complexity ambiguity Difficult scale up Fuzzy Logic Computing with words Costly maintenance User friendly from data boxes Neural Network Universal approximating Poor extrapolation Fast development Maintenance nightmare from small data Black box models Support Vector records Difficult marketing Machine Model complexity control Limited infrastructure
Figure 13. Marcellus Shale lithofacies 3D model by sequential indicator simulation algorithm: (a) Top of Otaka Creek Member in 3D view; (b) Top of Union Spring Member in 3D view; (c) cross sections; (d) fence diagram zoomed into Pennsylvania and north West Virginia. Tables Table 1. Comparison of four pattern recognition models (Kordon, 2010). Mathematic Model Strengths Weaknesses Discriminant Analysis Simple Strong theory base Linear Low complexity No ability to learning Non-linear Fuzzy Logic Capture linguistic ambiguity Computing with words User friendly Low complexity Difficult scale up Costly maintenance Neural Network from data Universal approximating Fast development boxes Poor extrapolation Maintenance nightmare Support Vector Machine from small data records Model complexity control Black box models Difficult marketing Limited infrastructure