Particle Swamp Optimized -Physics-Informed Deep Learning for Heterogeneous Oil and Gas Underground Reservoir Pressure Management.

Abstract

In recent years, Particle Swarm Optimization (PSO) has been integrated with machine learning algorithms, such as deep learning, to create powerful hybrid methods that can tackle complex optimization problems more effectively. In the domain of oil and gas reservoir management, underground pressure management is crucial to maximize the yield and efficiency of the reservoir. However, the heterogeneity of the reservoir, along with uncertainties in its properties, makes pressure management a complex and challenging task. To address this issue, researchers have proposed Physics-Informed Deep Learning (PIDL) techniques that incorporate domain-specific knowledge, such as the governing physical equations, into the deep learning framework. Particle Swarm Optimized-Physics-Informed Deep Learning (PSO-PIDL) is a novel hybrid approach that combines PSO with PIDL to optimize the pressure management of heterogeneous oil and gas underground reservoirs. In this approach, the PSO algorithm is used to find the optimal solution for the PIDL-based model that incorporates the governing physical equations of the reservoir. PSO-PIDL can effectively handle the uncertainties and heterogeneity of the reservoir, while also incorporating the physical constraints of the problem. Overall, PSO-PIDL is a promising approach for optimizing the pressure management of oil and gas reservoirs. It can help reduce the operational costs and improve the efficiency of the reservoir, while also ensuring the sustainable use of natural resources. Keywords: Physics-Informed Deep Learning, Particle Swarm Optimization, Bidirectional Long-Short-Term-Memory, Heterogeneous Reservoir, DuPont Finite Element Heat and Mass Transfer Code