The relationship between porosity and permeability is often only marginally useful in production performance prediction, and it is desirable to investigate alternative methodologies to improve permeability estimates. This paper presents a new workflow using logging-while-drilling (LWD) high-resolution microresistivity images for permeability estimation, without the use of porosity data.

High-resolution microresistivity images were used for picking symmetric and asymmetric features using conductive pixels. The conductive pixels were identified based on the assumption that in water-based mud (WBM) systems, the voids encountered during drilling are filled with conductive fluid; consequently, they display as darker pixels on the high-resolution microresistivity image. For the workflow, a frequency histogram of the button resistivity values was constructed corresponding to the full azimuthal coverage. A cutoff value was then derived using the combination of the mathematical mean of button resistivities together with laterolog ring resistivity measurements as an indicator of mud filtrate invasion. A conductive pixels ratio was then calculated by dividing the number of conductive pixels below the cutoff with the total number of pixels at each depth. This continuous ratio, which is assumed to be a qualitative permeability indicator, is finally normalized to formation tester mobility measurements. Other sources of log-derived permeability values using acoustic Stoneley waves and nuclear magnetic resonance (NMR) data were also evaluated for comparison and correlation purposes to help benchmark future data gathering requirements.

The workflow also includes partitioning of the horizontal section based on the derived permeability profiles and petrophysical attributes because the determination of lateral permeability variations is considered to be a crucial factor for optimizing the stimulation and completion design in this field development project.

Multiwell interpretation in the vicinity of the subject reservoir sector, together with further data integration, is desirable to fine-tune this methodology and workflow.

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