Intelligent Petroleum Engineering

Mirza, Mohammad Ali; Ghoroori, Mahtab; Chen, Zhangxin

doi:10.1016/j.eng.2022.06.009

Cited by 9 publications

(2 citation statements)

References 51 publications

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“…The advent of this simulation approach has facilitated a transition in reservoir research, shifting from a qualitative paradigm to a quantitative one [8]. Given the intricate internal architecture of reservoirs and the inherent unrepeatability in reservoir development, numerical simulation techniques in reservoir studies have witnessed an expansive surge [9,15].…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence promises to deliver nuanced descriptions and precision-driven development of oil reservoirs, paving the way for an intelligent oilfield equipped for real-time optimization and adaptability. Such advancements aim to revitalize the core technologies underpinning oil exploration and development, further instigating a transformative shift in the oil exploration and development industry at a technological echelon [3,15].…”

Section: Introductionmentioning

confidence: 99%

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Progress and Challenges of Integrated Machine Learning and Traditional Numerical Algorithms: Taking Reservoir Numerical Simulation as an Example

Chen,

Zhang,

et al. 2023

Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%