Logging-while-drilling formation dip interpretation based on long short-term memory

Abstract

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.

Keywords

• logging while drilling
• azimuth gamma
• stratigraphic identification
• artificial intelligence
• long short-term memory
• wavelet transform