## 5 A Hybrid Computational Platform  
The forward and the associated two inverse forms of Equation (1) can be solved using conventional formulations and applicable solution protocols. However, these solutions typically prove to be prohibitively expensive and slow in terms of personnel requirements and computational overhead encountered. By coupling proxy-based algorithms with conventional numerical and/or analytical solution procedures on a common platform, it will be possible to generate rapid and much less expensive solutions with higher levels of accuracy towards identifying the uncertainty. Figure 3 shows a schematic representation of the computational structure of the platform that will be discussed here. The basic tenet of the computational platform shown in Figure 3 is to capitalize on the advantages of both high-fidelity models and artificial intelligence-based models by coupling them in computations in a synchronous manner. As a result of such an effective integration, it will be possible to achieve high accuracy, low-cost, low energy, and high-speed solutions that are tractable. The seamless integration of these two radically different modeling technologies with the help of well-established hand-shaking protocols adds a uniquely assembled powerful tool to the capabilities of reservoir engineers and geoscientists. On the left-hand-side of Figure 3, a suit of high-fidelity reservoir models is shown. These models are developed with the following functionalities: \begin{table}
\begin{tabular}{c c c} Data Categories & Reservoir Engineering Components \\ \hline \multirow{4}{*}{Reservoir characteristics (_Entronic_)} & \multirow{4}{*}{Peterophysical data} & Seismic surveys \\  & & Well Logs \\  & & Permeability distribution \\  & & Porosity distribution \\  & & Net pay thickness \\  & & Formation depth \\  & & Reservoir pressure \\  & & Reservoir temperature \\  & & Fluid contact \\  & & Fluid composition \\  & & FVI data \\  & & Relative permeability data \\  & & & Capillary pressure data \\  & & Well specifications, \\  & & Well architecture \\  & & Well pattern, \\  & & Well spacing \\  & & Process (EOR) project design \\  & & parameters \\  & & Rate (production/injection), \\  & & pressure data \\  & & Project economics \\ \end{tabular}
\end{table} 
Table 1: Data categories and their corresponding 1 st and 2 nd tier components, [6]. * Rectangular and radial-cylindrical grid systems: Both rectangular and radial-cylindrical grid systems are incorporated and supported in these models so that they become adaptable to problems for a set of varying physical boundaries and boundary conditions. * Black oil model: Black oil model with single, two, and three phase fluid flow conditions and variable bubble point formulation. * Compositional model: This model is used to represent the multi-phase compositional fluid flow with advanced flash calculation techniques (VLE and VLLE computations). * Shale gas/Coalbed Methane (CBM) model: A dual-porosity compositional shale gas/CBM model is developed as part of this module. * EOR process models: These models include thermal EOR models, chemical EOR models, and miscible gas injection models. On the right-hand-side of Figure 3, a library of artificial-neural-network based expert systems are shown. These modules can be considered three separate toolboxes. Figures 4-6 show the expert tools that are included in the enhanced oil recovery process, well test analysis, and general reservoir engineering toolboxes, respectively. It should be noted that tools highlighted in each of these figures should not be considered the final catalogue of each toolbox, as more tools can be added as they become available. Figure 3: The overall architecture of a computational platform on which the computational interactions of deterministic and proxy models are accomplished (Notes: VLE—vapor/liquid equilibria; VLLE—vapor/liquid/liquid equilibria; CBM—coalbed methane; EOR—enhanced oil recovery). Figure 4: Enhanced oil/gas recovery related project process screening and design toolbox.