Identification of low resistivity oil and gas reservoirs with multiple linear regression model

Abstract

In petroleum exploration and development, one of the most fundamental tasks of geophysical well log interpretation is to accurately identify oil (gas) lays, transitional oil water layers, water layers and dry layers on the borehole sections. The resistivity of the specific Tertiary oil and gas layers in Tabei area, Xinjiang, are below or near that of the water layers. It is unrealistic to directly distinguish the specific oil and gas layers from water layers according to the electrical properties. In addition, the characteristics and influential factors of the oil and gas layers in the study area are different with that in the other oilfields. Therefore, different methods and ideas should be used to solve the specific issues in the study area. A discrimination model for identifying the properties of reservoir fluids is built by using the borehole log and reservoir parameters of oil (gas) layers, transitional oil water layers, water layers, and dry layers. Pattern recognition methods, multiple linear regression model is used to interpret the real well logging data. The results, achieved with multiple linear regression model is basically in accord with the formation testing results, and no oil and gas layers are missed.