人工智能在测井地层评价中的应用现状及前景

测井是求取储层物性参数、发现与评价油气藏、预测油气储量的重要手段。测井技术更新换代、测井技术种类发展多样化、测井数据管理方式多元化等多重因素导致测井信息具有测量种类多、数据量大和多源异构等大数据特征。人工智能技术的快速发展为解决测井地层评价中的多解性、不确定性等难题提供了新的思路和方法，"测井＋人工智能"也是一个亟待探索的新领域。在系统回顾人工智能在测井领域的研究现状和进展基础上，重点讨论了有监督机器学习和半监督机器学习、神经网络和深度学习等人工智能技术在测井曲线重构、岩相预测和物性参数计算等测井地层评价中的应用。受样本数量有限、处理流程不完善和自我调节能力较差等因素制约，人工智能在流体研究、储层综合评价等测井领域具有较大的发展空间和广阔的应用前景。

Application status and prospects of artificial intelligence in well logging and formation evaluation

Well logging is an important method for obtaining physical parameters of reservoirs, discovering and evaluating oil and gas reservoirs, and predicting oil-gas reserves. Multiple factors such as the upgrading of logging technology, development of diversified technology types, and diverse management methods of logging data have resulted in the logging information with big data characteristics such as multiple measurement types, large volume of data, and multi-source heterogeneity. The rapid development of artificial intelligence technology has provided new ideas and methods for solving the problems such as multiplicity of solutions, uncertainty in logging formation evaluation by well logging. The integration of well logging and artificial intelligence is also a new field that needs to be explored urgently. Based on systematically reviewing the research status and progress of artificial intelligence in the field of well logging, this paper focuses on the application of artificial intelligence technologies such as supervised machine learning and semi-supervised machine learning, neural network and deep learning in logging curve reconstruction, lithofacies prediction and the calculation of physical property parameters during the well logging and formation evaluation. Constrained by factors such as limited sample size, imperfect process flow, and poor self-regulation capabilities, artificial intelligence has a large development space and broad application prospects in the logging fields of fluid research and comprehensive reservoir evaluation.