Azimuth gamma logging while drilling (LWD) is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is difficult. This study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data processing. By examining formation response characteristics of azimuth gamma ray (GR) curve, the preliminary formation change position is detected based on wavelet transform modulus maxima (WTMM) method, then the dynamic threshold is determined, and a set of contour points describing the formation boundary is obtained. The classification recognition model based on the long short-term memory (LSTM) is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation identification. Finally, relative dip angle is calculated by nonlinear least square method. Interpretation of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes, improve the accuracy of formation dip interpretation, and meet the needs of real-time LWD geosteering.

随钻方位伽马测井（LWD）是地质导向的重要技术之一，但实时数据传输信息有限，解释困难。本研究提出了一种在随钻测井数据解释中应用人工智能的方法，以提高实时数据处理的准确性和效率。通过检测方位伽马射线（GR）曲线的地层响应特性，基于小波变换模极大值（WTMM）方法检测出初步地层变化位置，确定动态阈值，描述地层边界的一组轮廓点为获得。基于长短期记忆（LSTM）的分类识别模型旨在判断轮廓点集描述的地层信息的真假，以提高地层识别的准确性。最后，通过非线性最小二乘法计算相对倾角。随钻方位角伽马数据解释和实时数据处理应用表明，该方法能够有效准确地判断地层变化，提高地层倾角解释精度，满足实时随钻地质导向的需要。