Estimation of Permeability Combining NMR Derived Viscosity and Downhole Fluid Mobility: A Case Study From Offshore Mexico

Formation permeability is a key petrophysical property that defines the producibility of subsurface formations. Nuclear magnetic resonance (NMR) logs are commonly used to estimate near-wellbore permeability; however, NMR permeability models are empirical in nature and often require core data for calibration. Collecting and testing downhole cores is a costly, time-intensive process. Formation pressure testing and sampling (FT) data provide a measure of fluid mobility, which requires viscosity to translate to permeability. A case study from offshore Mexico is presented to demonstrate that the integration of NMR and FT can provide a meaningful estimate of permeability in near-real time. NMR data are inverted in two-dimensional D-T2int space to identify and quantify fluid types. Different NMR viscosity models are tested to estimate the viscosity of formation oil and that of any mud filtrate detected. Drawdown rates and pressure drop data from downhole formation tests provide mobility estimates at tool stations. NMR and FT data are combined to first estimate permeability at FT station depths and then use these points to calibrate NMR log permeability from the Coates-Timur model. Coates-Timur permeability, determined from a default parameter setting, initially did not agree with FT permeability estimates; however, a good match was achieved between the two by a small adjustment to Coates parameters. The study includes a comparative analysis of permeability measured by NMR vs. FT and the advantages and limitations of the proposed methodology. We assess the effect of cleanup during pumpout and of near-wellbore damage on pre- and post-mobilities and how the effects can impact reconciliation with NMR permeabilities. A relatively simple exercise of combining FT mobility and NMR viscosity benefits customers with an improved and consistent measure of reservoir permeability. In a high-cost deepwater drilling environment, this enables quicker decisions.