Determining a Flow Zone Indicator in Conventional and Unconventional Reservoirs Using Resistivity Logs

The Flow Zone Indicator (FZI) depends on permeability and porosity. The permeability of a reservoir is one of the most difficult properties to determine, since the models to calculate permeability use input from several logs (gamma ray, density-neutron, sonic, etc.). Permeability modeling is done using complex methodologies such as: matching learning, fuzzy logic, cluster, multiple linear regression, etc., and at the end, permeability models end up being unique for each reservoir. The objective of this work is to determine a flow zone indicator (FZI) from the deep (Rt) and shallow resistivity (Rs) logs in conventional and unconventional reservoirs. Basically, the methodology consisted of studying the phenomenon of the invasion of mud filtration during the drilling of the wells, and the behavior of resistivity logs. Invasion affects some properties of porous and permeable formations in the vicinity of a well. In general, the invasion is small in very porous and permeable reservoirs, the opposite is in poorly permeable reservoirs, tight, vuggy carbonates or fractured formations. Based on the above, a mathematical model was proposed to determine the FZI, as a logarithmic function which is applicable in both water and oil-based muds. If the resistivities are equal, then there is no mud filtration invasion, and the permeability is zero. If the difference in resistivity is different from zero, there is invasion of the sludge filtrate and therefore the permeability is greater than zero, indicating flow zones in the reservoir. There are other borehole, formation and tool conditions that may generate difference between RT and RS which are not related to invasion. These will be explained briefly below. The FZI model identified the flow zones in the reservoir. FZI from core and Nuclear Magnetic Resonance (NMRI) log data correlated with the FZI calculated with the model. The mobility data of formation testers agreed with the calculated FZI. Also, spectral noise and PLT logs inflow zones showed excellent correlation with the FZI. This methodology can be applied in any type of reservoir. In exploration wells the methodology allows to define and propose the best zones to set the formation testers, thus optimizing operational time. Before performing a petrophysical assessment, FZI results can identify prospective zones of the reservoir based on rock quality. Also, the production and injection behavior of the wells can be monitored with the obtained results. And finally, the FZI as an independent variable decreases the uncertainty of a permeability model, because the FZI is intimately related to the texture of the rock and the facies. The simplest expression of this is the resistivity variation with changes of porosity. Therefore, if porosity is related to facies, so is the formation factor (F), and then the resistivity log becomes an excellent facies discriminator.