Estimating reservoir permeability with borehole radar

Feng Zhou* (Corresponding Author), Iraklis Giannakis, Antonios Giannopoulos, Klaus Hollinger, Evert Slob

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
5 Downloads (Pure)


In oil drilling, mud filtrate penetrates into porous formations and alters the compositions and properties of the pore fluids. This disturbs the logging signals and brings errors to reservoir evaluation. Drilling and logging engineers therefore deem mud invasion as undesired, and attempt to eliminate its impacts. However, the mud-contaminated formation carries valuable information, notably with regard to its key hydraulic properties. Typically, the invasion depth critically depends on the formation porosity and permeability. Therefore, if adequately characterized, mud invasion effects could be utilized for reservoir evaluation. To pursue this objective, we apply borehole radar to measure mud invasion depth considering its high radial spatial resolution compared with conventional logging tools, which then allows us to estimate the reservoir permeability based on the acquired invasion depth. We investigate the feasibility of this strategy numerically through coupled electromagnetic and fluid modeling in an oil-bearing layer drilled using freshwater based mud. Time-lapse logging is simulated to extract the signals reflected from the invasion front, and a dual-offset downhole antenna mode enables time-to-depth conversion to determine the invasion depth. Based on drilling, coring, and logging data, a quantitative interpretation chart is established, mapping porosity, permeability, and initial water saturation into invasion depth. The estimated permeability is in a good agreement with the actual formation permeability. Results therefore suggest that borehole radar has significant potential to estimate permeability through mud invasion effects.
Original languageEnglish
Pages (from-to)H51-H60
Number of pages10
Issue number4
Early online date10 Jun 2020
Publication statusPublished - 1 Jul 2020


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