深度学习在油气产量预测中的研究进展与技术展望

Research status and prospects of deep learning in oil and gas production prediction

随着大数据和人工智能的不断进步,数字和智能化的油气产量预测技术已经成为石油天然气行业发展的新趋势。深度学习与油气产量预测的有效结合为解决非常规油气和复杂场景下的产量预测等问题提供了新的方案与策略。为此,在系统回顾油气产量预测技术发展历程的基础上,重点阐述了基于深度学习方法的油气产量预测技术的应用现状及关键流程,归纳了油气产量预测领域的特征工程以及不同场景下的神经网络构建方法,最后深入探讨了智能油气产量预测技术的未来发展方向。研究结果表明:(1)油气产量预测技术发展历程主要划分为传统油气产量预测方法、机器学习方法和深度学习方法 3个阶段;(2)深度学习方法已大量应用于油气产量预测研究中,尤其在复杂地质条件下的非常规油气领域,该技术表现出了良好的应用前景;(3)多样化的神经网络构建方法能够解决不同场景下的精细化油气产量预测需求;(4)需进一步加强人工智能领域与油气领域跨学科理论技术研究,促进两者在理论技术和生产实践等方面的深入融合;(5)智能油气产量预测技术未来可在实时预测与优化、数据融合与增强、物理约束与解释和模型更新与适应等方面开展深度攻关研究。结论认为,深度学习模型可显著提高油气产量预测技术的准确性和可靠性,为复杂气藏及非常规油气开发提供参考和指导,建议继续深化人工智能与油气行业应用等方面的有机结合,以推动油气行业的技术创新和高质量发展。

With the advancement of big data and artificial intelligence(AI),digital and intelligent oil and gas production prediction technologies have become a new trend in the industry development.The effective combination of deep learning and oil and gas production prediction provides new solutions and strategies for predicting production of unconventional petroleum and in complex scenarios.After a systematic review of the development history of oil and gas production prediction techniques,this paper expounds the current application and key processes of oil and gas production prediction techniques based on deep learning methods,summarizes the feature engineering in the sector of oil and gas production prediction and the construction methods of neural networks in different scenarios,and finally discusses the prospects of intelligent oil and gas production prediction techniques.The following results are obtained.First,the development history of oil and gas production prediction techniques is mainly divided into three stages:traditional methods,machine learning methods,and deep learning methods.Second,deep learning methods have been widely used in oil and gas production prediction,and they are especially promising for unconventional petroleum in complex geological conditions.Third,diversified neural network construction methods allow for fine production prediction in different scenarios.Fourth,it is necessary to further enhance the interdisciplinary theoretical and technical research incorporating AI and the petroleum industry,and promote the deep integration of the two in theory and practices.Fifth,in view of intelligent oil and gas production prediction techniques,future efforts will focus on real-time prediction and optimization,data fusion and enhancement,physical constraints and interpretation,model update and adaptation.The conclusion suggests that deep learning models can enhance the accuracy and reliability of oil and gas production predictions,providing reference and guidance for complex gas reservoirs and unconventional petroleum development.It is recommended to continue deepening the integration of AI with the petroleum industry in the future to promote technological innovation and high-quality development in the sector.