Uncertainty in Chemical Flooding: A new data optimization algorithm for modelling of surfactant flood

A data optimization algorithm has been created to solve the inversion modelling by fractional-flow theory for chemical enhanced oil recovery processes. The routine is scalable, flexible, and easy to deploy in other programming and numerical computing environments for uncertainty analysis especially when upscaling methods is not available. Fractional-flow theory provides valuable insights for 1-D calculations, and reservoir model validation. We used the MATLAB programming environment to develop an adaptable framework, deployed the code to manipulate the plotting of functions, and data needed to calibrate and derive relative permeability functions for upscaling. The code is published in open format and is accessible online for research purposes. The approach integrates three computational processes to attain the effective properties, total mobility, oil bank saturation, mass balance, and flow velocity. We used predicted and regularization terms to define the multi-objective function, then minimized using an active-region quasi-newton optimization algorithm. The graphical user interface is embedded with an adjustable response cost function manifold used to ensure the solution converges to the observed flow parameters. The method was applied to design relative permeability curves for miscible and immiscible surfactant displacement systems, which may be encountered in experimental or simulation studies.