Semi-Automated Rock Classification for Permeability Estimation Using High-Resolution Computed Tomography Scan Images, Core Photos, and Well Logs

Abstract

Summary Reliable estimation of petrophysical properties can be challenging, especially in geological formations with rapid variation in the spatial distribution of rock components. The spatial distribution of rock components, also known as rock fabric, is often not captured by conventional well logs that are typically used for the estimation of petrophysical properties. The aforementioned challenge is rooted in the limited vertical resolution of conventional well logging tools. Alternatively, specialized tools and techniques, such as nuclear magnetic resonance (NMR) and computed tomography (CT) scan images, can provide information on the variation in the rock fabric of geological formations. The objectives of this paper are (a) to use 2D CT scan images and core photos, conventional well logs, NMR logs, and core-measured properties for semi-automated rock classification, (b) to develop class-based rock physics models for enhanced petrophysical properties estimation, and (c) to provide a method to expedite the detection of quantitative image-based rock classes in cored wells. First, we conducted conventional formation evaluation (CFE) for the initial assessment of petrophysical properties. Then, we implemented three different rock classification techniques for class-based estimation of petrophysical properties. The first of the rock classification techniques uses routine core analysis (RCA) data to define hydraulic units. The second rock classification technique uses NMR data to characterize the changes in pore-size distribution of the evaluated formation. The last rock classification technique integrates quantitative image-based features from CT scan images and core photos with NMR data. Finally, the obtained rock classes from the abovementioned rock classification techniques are used to derive class-based permeability models. We applied the proposed workflow to a pilot well drilled in a saline water aquifer formation that will be used for CO2 injection and storage in the Northern Lights carbon capture and storage (CCS) project. The extracted image-based rock fabric features were in agreement with the visual aspect of the evaluated depth intervals. The detected rock classes captured the fluid-flow behavior using a permeability-based cost function in two of the implemented rock classification techniques, the variation in petrophysical and compositional properties through well logs, and quantitative rock fabric of the evaluated depth interval through the core image data. Finally, the use of class-based rock physics models improved permeability estimates, decreasing the mean relative error by up to 37% compared with formation-based permeability estimates from a conventional method (formation-based porosity-permeability correlations). One of the key contributions of the proposed workflow is that it integrates conventional well logs, core-measured properties, NMR logs, and high-resolution image data. As a result, the obtained integrated rock classes capture key petrophysical and geological parameters of the evaluated depth intervals that are typically not included in rock classification efforts. The obtained integrated rock classes can potentially improve the development of accurate geological models, which are used in simulation efforts as a screening tool for the selection of geological formations for CO2 storage as well as for storage capacity, selection of CO2 injection intervals, and containment forecasting.