This paper explores significant advancements in the numerical simulation of multiphase, multi-physics flows within underground reservoirs, driven by the necessity to understand and manage complex geological and engineered systems. It delves into the latest research in numerical simulation techniques at both the pore and Darcy scales, emphasizing the integration of traditional methods with emerging machine learning technologies. Key simulation methods reviewed at the pore scale include the lattice Boltzmann method, level set method, phase field method, and volume of fluid method, each offering unique advantages and facing limitations related to computational efficiency and stability. Special attention is given to spontaneous imbibition, where capillary action facilitates the movement of wetting fluids into porous media. Discussions at the Darcy scale focus on macroscopic simulation methods that simplify microscale interactions but face challenges in accurately modeling the multiscale and heterogeneous nature of fractured media. Furthermore, an overview of the basic principles, limitations, and potential of integrating machine learning algorithms with traditional numerical methods emphasizes their role in enhancing simulation efficiency and stability. Future research will aim to address existing challenges and maximize the use of advanced computational technologies to refine the accuracy, efficiency, and practical applicability of multiphase and multifield flow simulations in underground reservoirs.

Acidization is a widely used stimulation technique for carbonate reservoirs aimed at removing formation damage, and if successful, can result in the creation of wormholes of specific lengths and conductivities around the wellbore. The formation of wormholes depends on the injection rate for a particular acid-mineral system and can be predicted through numerical simulations of the reactive phenomenon during acidization. In this paper, the commonly used two-scale continuum model is enhanced to encompass fractured-vuggy porous media. The fractures are characterized by a pseudo-fracture model, while vugs are represented by a cluster of anomalous matrices with high porosity. Moreover, a method for generating random pore-fracture-vuggy models is proposed. The governing equations are discretized by the finite volume method and are solved under three-dimensional linear and radial conditions. Sensitivity analysis of dissolution dynamics with respect to fracture and vug parameters is performed. The simulation results indicate that both fractures and vugs significantly impact wormhole development. Except for fractures perpendicular to the acid flow direction, fractures in other directions play a crucial role in determining the direction of wormhole growth.

Reservoir history matching refers to the process of continuously adjusting the parameters of the reservoir model, so that its dynamic response will match the historical observation data, which is a prerequisite for making forecasts based on the reservoir model. With the development of optimization theory and machine learning algorithms, automatic history matching has made numerous breakthroughs for practical applications. In this perspective, the existing automatic history matching methods are summarized and divided into model-driven and surrogate-driven history matching methods according to whether the reservoir simulator needs to be run during the automatic history matching process. Then, the basic principles of these methods and their limitations in practical applications are outlined. Finally, the future trends of reservoir automatic history matching are discussed.