Sequential fully implicit newton method for flow and transport with natural black-oil formulation

Abstract

There is a rising interest in developing robust and efficient sequential methods for reservoir simulation due to potential benefits from specialized nonlinear and linear solvers, flexible discretizations, and adaptivity. The recently proposed sequential fully implicit Newton (SFIN) method addresses a major bottleneck of the sequential strategies: the slow sequential coupling convergence when flow and transport problems are strongly coupled. However, the original SFIN algorithm requires fixed primary variables during the simulation. For the natural formulation widely used in reservoir simulation, primary variables will switch when a phase change happens. In this work, we proposed strategies to address the issue of inconsistent primary variables and extended the SFIN method to the natural black-oil formulation. Several challenging numerical cases are presented to demonstrate that the extended SFIN method can achieve significant sequential convergence acceleration and improved overall performance for natural formulation when phase changes and primary variable switch happen frequently.