Artificial intelligence and deep learning are becoming increasingly important in oil and gas field development, gradually becoming the development trend and research hotspot in the petroleum industry. Currently, over 70% of large oil and gas enterprises worldwide have listed the “physical-data dual-driven” model as their core strategy to make up for the shortcomings of pure data-driven models in generalization with small samples. This article focuses on the combination of the three major algorithms (CNN, RNN, and RL) with oil and gas development, reviews related research at home and abroad, and presents the main implementation methods of related core technologies as well as the problems they face. Focusing on the three mainstream machine learning algorithms, it explores the technical paths and key issues for the upgrade of the entire chain of perception, prediction, and decision-making in the oil and gas industry based on the data-driven paradigm, providing a reference for the intelligent transformation of the oil and gas industry.
Keywords: Artificial intelligence, Deep learning, Oil and gas development, Data-driven model
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