机器学习在油气行业中的应用进展综述

近年来，随着深度学习的兴起，机器学习在油气领域得到了进一步深入发展。但是，由于油气行业的特殊性和复杂性，目前还没有建成适用于深度学习的训练样本库，也没有针对性的模型建立和选择方法体系。此外，深度学习方法的不可解释性，导致了学习的模型对环境的高度依赖，制约了机器学习在油气行业中的推广应用。从机器学习的发展阶段出发，介绍机器学习在油气行业各领域的应用中所涉及的重大突破及仍然存在的问题。针对油气行业中不同类型数据的处理方法、样本建立以及如何进行模型适应性分析等方面给出了建议，提出可解释机器学习在油气人工智能上的发展潜力以及研究方向。

A Review of the Application Progress of Machine Learning in Oil and Gas Industry

With the rise of deep learning in recent years, machine learning has been further developed in the oil and gas field. However, due to the particularity and complexity of the oil and gas industry, there is no training sample base suitable for deep learning, nor a targeted model establishment and selection method system. In addition, the uninterpretability of methods such as deep learning leads to the high dependence of learning models on the environment, which restricts the popularization and application of machine learning in the oil and gas industry. Starting from the development stage of machine learning, this paper introduces the major breakthroughs and problems in the application of machine learning in various fields of oil and gas industry. Then, suggestions are given on the processing methods and sample building of different types of data in the oil and gas industry, and how to carry out model adaptability analysis, etc. Finally, the development potential and research direction of machine learning in oil and gas artificial intelligence are proposed.