Two‐step morphology‐based denoising and non‐local means smoothing improves micro‐computed tomography digital rock images

Abstract

AbstractDigital rock physics is a workflow that relies on imaging techniques to quickly and cost‐effectively estimate the petrophysical properties of small core samples taken from reservoirs. By using digital representations of rock samples as input, physics‐based simulators can estimate properties such as porosity and permeability. The accuracy of these estimates depends on the quality of the digital volumes generated from micro‐computed tomography scans. To enhance the accuracy, denoising is necessary to reduce image noise caused by various experimental factors like electronic noise and bad pixels. This study introduces a novel two‐step denoising pipeline that combines adaptive morphological filtering with non‐local means smoothing, ensuring both noise reduction and preservation of edges. The effectiveness of the proposed pipeline is assessed through qualitative evaluation using optimal segmentation results and quantitative evaluation using a non‐reference metric and equivalent number of looks. Comparing the results of the two‐step approach with traditional non‐local means and morphology‐based filtering using a multi‐resolution structurally varying bitonic filter, the non‐reference metric and equivalent number of looks values are higher, indicating improved denoising performance. Furthermore, the denoised rock volume is subjected to the next step in the digital rock workflow to compute important petrophysical properties like porosity and permeability. The findings indicate that our proposed pipeline significantly improves the accuracy of estimating physical parameters such as porosity and permeability.