Logging identification of complex lithology of the Lower Jurassic Da'anzhai Member in the eastern slope of the western Sichuan Depression

Abstract

In recent years, with the in-depth development of oil and gas exploration in the eastern slope of the western Sichuan Depression, breakthroughs have been made in the research on high-quality gas reservoirs in the Da'anzhai Member of the Ziliujing Formation in this area. Fine logging identification of lithology in the Da'anzhai Member in the eastern slope of the western Sichuan Depression is the core subject of reservoir sweet spot prediction. In this paper, taking the Da'anzhai Member in the western Sichuan Basin as an example, the work flow and comparisons of multi-lithology logging models have been systematically conducted, using a large number of cores, geological data and logging interpretation models. The research shows that there are four main lithologies developed in the Da'anzhai Member, namely shale, marl-bearing limestone, shell limestone and sandstone. Intersection and spider diagram methods can effectively screen out the logging parameters that are sensitive to lithology, including natural gamma (GR), neutron (CNL), deep lateral resistivity (RD), and acoustic wave time difference (AC). The lithology of the Da'anzhai Member in the study area can be identified with high precision via the "lithology probability factor", "BP neural network" and cluster analysis methods. Among them, the "lithologic probability factor" and "BP neural network" methods have a prediction accuracy of lithology exceeds 80%. Therefore, these two methods are optimized as the most effective methods for logging identification of lithology in the Da'anzhai Member. This study has certain reference value for the lithology identification of similar gas reservoirs worldwide. • The calculation and comparison of multi-lithology logging models have been systematically conducted, using a large number of cores, geological data and logging interpretation models. • The lithology of the Da'anzhai Member in the study area can be identified with high precision by the "lithology probability factor" method, the "BP neural network" method and the cluster analysis method. • A complex lithology identification model is established.