![]() AI can also be applied in areas like smart drilling, smart pipeline, smart refinery, enhanced oil recovery, and production forecast. ![]() AI-based techniques have successfully addressed various challenges in the industry, such as flow field visualization, identification of permeability configurations, and diagnosis tasks. AI technologies have the potential to revolutionize the industry by adopting the latest technologies to meet the increasing energy demand. What are the benefits of using artificial intelligence (AI) in the oil and gas industry? 5 answers The benefits of using artificial intelligence (AI) in the oil and gas industry include enhanced efficiency, cost reduction, improved safety, increased productivity, and economic benefits. However, there are still open research challenges that need to be addressed for the practical application of AI algorithms in critical domains of the energy sector. By utilizing AI algorithms, the energy industry can achieve an optimal solution for energy generation, distribution, storage, consumption, and trading, leading to increased operational performance and efficiency. AI techniques outperform traditional models in controllability, energy efficiency optimization, cyber-attack prevention, and predictive maintenance control. ![]() AI technologies, such as machine learning and deep learning, can be applied to manage big data, enhance judgment and management, and automate power grid management. AI algorithms can be used to optimize the entire value chain involved in generating, transporting, and storing energy, addressing multiple objectives simultaneously. How to utilize ai in energy industry? 5 answers Artificial intelligence (AI) can be utilized in the energy industry to improve efficiency, sustainability, and decision-making processes. These advancements in AI have the potential to greatly reduce the occurrence of lost circulation and improve drilling operations in the oil and gas industry. Additionally, AI has been used to analyze and cluster large amounts of lost circulation data, providing intelligent recommendations for plugging technology. Supervised machine learning models, including decision tree, random forest, and extra trees, have also been utilized to predict drilling fluid losses based on field data such as drilling fluid properties, drilling parameters, rock properties, and geomechanical parameters. AI techniques, such as support vector regression (SVR) models and artificial neural networks (ANN), have been employed to accurately predict lost circulation based on drilling features such as depth, torque, hanging weight, displacement, entrance density, and export density. Artificial intelligence (AI) has been successfully used to predict and prevent lost circulation in oil and gas wells.
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