The Physics-informed neural networks (PINNs) have received careful attention in various science and engineering disciplines. This technology has the capability to constraint the neural networks to honor the governing laws of physics, often described by partial differential equations (PDEs). Thanks to its inherently accurate and efficient automatic differentiation (AD), differential operators can be evaluated at random points in the computational domain without a need for temporal or spatial discretizations. This feature provides superiority to numerical methods that often exhibit gridding artifacts and discretization errors. The performance of PINNs has been demonstrated in various CFD applications, including inverse problems. A vital unsettled question is about the applicability and performance of PINNs in solving forward and inverse problems for multiphase fluid low in porous media, and whether this technology has the potential to replace the traditional computational methods such as finite different and finite element methods. In this talk, we address this hot question by reviewing the favorable features and capabilities of PINNs in solving general PDEs, and we highlight some of its major limitations that must be overcome to be applicable to model fluid flow problems in porous media.