A New Method for Identifying Low-Resistivity Gas Zones, High-Resistivity Water Zones, and Water-Flooded Layers Based on SP and Array Induction Resistivity

Abstract The identification of oil, gas, water and water-flooded zones is one of the main tasks in well logging processing and interpretation, and its accuracy directly impacts hydrocarbon reservoir evaluation, reserve estimation, and development planning. Conventional fluid identification primarily relies on electrical property differences, where hydrocarbons (oil/gas) exhibit significantly higher resistivity than water zones. With the advancement of exploration and development, the study of unconventional reservoirs such as low-resistivity gas reservoirs has become increasingly important. Compared to conventional high-resistivity gas zones, low-resistivity gas reservoirs exhibit lower resistivity, often overlapping with water zones, making traditional resistivity-based identification challenging. To address this issue, this paper proposes a rapid fluid identification method based on spontaneous potential (SP) and array induction resistivity (AT). The workflow mainly includes the following 6 steps: 1）Investigate mud resistivity (Rmf) and formation water resistivity (Rw). 2）Compute array induction resistivity index (ATindex) to characterize resistivity differences between invaded zones and formation, enabling low-resistivity pay and high-resistivity water layer identification. 3）Derive SP index (SPindex) reflecting salinity differences to identify water-flooded zones. 4）Amplify fluid curve discrepancies and calculate fluid characterization index (Findex). 5）Establish fluid identification templates and cutoff values using 2D/3D crossplots. 6）Validate results with dynamic data for efficient fluid identification. In the actual application at Field R in South America, this method enables rapid identification of low-resistivity gas zones, high-resistivity water layers, and water-flooded zones, with results showing strong consistency with production data. This confirms the reliability and applicability of the approach, significantly enhancing reservoir characterization and providing critical guidance for the next phase of gas field development.