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In engineering, gelled acids play a crucial role in facilitating reactive flow phenomena across diverse mediums. This study undertakes a comprehensive numerical investigation into the reactive flow dynamics within fracture-vuggy carbonates, employing gelled acid as the agent. The mathematical model intricately couples thermal, hydrological, and chemical aspects to provide a holistic understanding of the process. Fractures are meticulously modeled using a pseudo-fracture approach, while vugs are delineated as highly porous matrix clusters. The simulation meticulously examines the influence of vugs and fractures, in conjunction with the rheological behavior of gelled acid, on the dissolution process. Our findings reveal that compared to hydrochloric acid, gelled acid is more effective in treating high-temperature carbonate rocks. A lower power law index induces a more pronounced response in shear stress, resulting in a more uniform dissolution pattern. Moreover, the presence of vugs and fractures exerts a significant impact on both the trajectory of wormhole growth and its penetration depth. As the length of fractures increases and their number multiplies, their dominant influence on the growth of wormholes becomes more pronounced. Furthermore, an abundance of fractures may attenuate the influence of vugs. This study highlights the importance of controlling the power-law index and understanding the complex interactions between fractures and vugs for reactive flow.

The rapid and accurate forecasting of two-phase flow in porous media is a critical challenge in oil field development, exerting a substantial impact on optimization and decision-making processes. Although the Convolutional Long Short-Term Memory (ConvLSTM) network effectively captures spatiotemporal dynamics, its generalization in predicting complex engineering problems remains limited. Similarly, although the Fourier Neural Operator (FNO) demonstrates adeptness at learning operators for solving partial differential equations (PDEs), it struggles with three-dimensional, long-term prediction. In response to these limitations, we introduce an innovative hybrid model, the Convolutional Long Short-Term Memory-Fourier Neural Operator (CL-FNO), specifically designed for the long-term prediction of three-dimensional two-phase flows. This model integrates a 3D convolutional encoder–decoder structure to extract and generate hierarchical spatial features of the flow fields. It incorporates physical constraints to enhance the model's forecasts with robustness through the infusion of prior knowledge. Additionally, a temporal function, constructed using gated memory-forgetting mechanisms, augments the model's capacity to analyze time series data. The efficacy and practicality of the CL-FNO model are validated using a synthetic three-dimensional case study and application to an actual reservoir model. [ABSTRACT FROM AUTHOR]

Machine learning techniques have garnered significant attention in various engineering disciplines due to their potential and benefits. Specifically, in reservoir numerical simulations, the core process revolves around solving the partial differential equations delineating oil, gas, and water flow dynamics in porous media. Discretizing these partial differential equations via numerical methods is one cornerstone of this simulation process. The synergy between traditional numerical methods and machine learning can enhance the precision of partial differential equation discretization. Moreover, machine learning algorithms can be employed to solve partial differential equations directly, yielding rapid convergence, heightened computational efficiency, and accuracies surpassing 95%. This manuscript offers an overview of the predominant numerical methods in reservoir simulations, focusing on integrating machine learning methodologies. The innovations in fusing deep learning techniques to solve reservoir partial differential equations are illuminated, coupled with a concise discussion of their inherent advantages and constraints. As machine learning continues to evolve, its conjunction with numerical methods is poised to be pivotal in addressing complex reservoir engineering challenges. [ABSTRACT FROM AUTHOR]

Acidizing is an economical and effective practice to remove the near wellbore damage, which is performed by injecting acid into the formation through the wellbore. The injected acid dissolves the rock, by which the permeability nearby the wellbore can be improved. For a carbonate reservoir, the injected acid dissolves some of the minerals and some narrow and long channels, named wormholes, are formed then. These wormholes can bypass the damaged zone and hence improve the productivity of the well. The process for acid dissolving rocks involves complex physicochemical change, including the chemical reactions at the pore scale and the fluid flow at Darcy scale. In this paper, a 3-D reactive flow model with non-Darcy framework is developed based on the two-scale continuum model, and is solved by using the finite volume method. Five types of dissolution patterns, named face dissolution, conical wormhole, wormhole, ramified wormhole, and uniform dissolution, are obtained as the injection velocity increases. The effect of non-Darcy flow on dissolution pattern and breakthrough volume is analyzed. It is found that there is no effect of non-Darcy on dissolution structure and breakthrough volume when the injection velocity is very low. However, when the injection velocity is very high, the generated wormhole has more branches when using the Forchheimer equation than using the Darcy equation. Moreover, the optimal injection velocity is found to be the same whether considering the non-Darcy flow or not.