Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

Abstract Low-resistivity pay have been found throughout the world, the identification and characterization of low-resistivity pay is very challenging and important for the development of oil fields. The genesis of the low-resistivity oil pay is complex, and the logging response characteristics are variable. The weak difference in resistivity between the oil pay and the adjacent water pay makes it difficult to identify kinds of fluids by resistivity log analysis, which reduces the overall exploration benefit of the oilfield. Therefore, it is very important to study the genesis and identification technology of the low-resistivity oil pay. In this paper, we first analyzed the core experimental results such as X-ray diffraction scanning electron microscope, mercury intrusion, phase permeability, nuclear magnetic resonance, physical properties, electric petrophysical experiment, micro-CT technology and rock wettability, etc. Comprehensive analysis of the reservoir characteristics shows that the development of low-resistivity oil pays in the study area is controlled by irreducible water saturation and high gamma ray sandstone. The complicated pore structure and rock hydrophilicity are the factors that lead to the increase of irreducible water saturation. Then, the salinity of formation water and the invasion of drilling fluid also have a certain influence on the change of reservoir resistivity. According to the controlling factors of the low- resistivity oil pay, we extract the sensitive parameters to the logging response, amplify the difference between oil and water pay, and use the AC-RILD, SP-PSP, GR*GR*∆SP-RILD and(RILM-RILD)/RILD—RILD cross-plots, etc. Various methods such as cross-plots method, overlap method and movable water analysis are mutually constrained to identify low-resistivity oil pays. In the case study, the comprehensive application of the above identification flow path can effectively improve the accuracy of fluid recognition step by step. It provides reference for identifying more low-resistivity reservoirs with similar geological conditions.