Abstract — We introduce a new digital rock physics (DRP) workflow to estimate petrophysical properties of rocks. We focus on predictively estimating density and porosity of rocks, and we show the reliability of the method by testing four Berea Sandstone plugs. Measuring density, porosity, and elastic properties from physical rock samples is necessary in geosciences to calibrate models from geophysical surveys. Digital rock physics is one way to estimate these properties. X-ray computed tomography (CT) images can be used to create 3D numerical models of rocks. Numerical simulations on such models are proxies for tests performed in the lab. Commonly, an essential digital rock physics processing step is called segmentation, where each voxel in a 3D model is assigned a property of a mineral phase or pore fluid. Any errors in this process are carried forward and affect all estimations that follow (including density, porosity, and elastic properties). The density and porosity analyses are not predictive, as they are typically calibrated to lab tests. We explore a method that does not use segmentation and instead preserves the scaling relationship between the voxel values that are originally recorded in units of CT attenuation. We name the method “targeted”, or “segmentation-less”. Targets or phantoms of known density were scanned alongside the rock and used as calibration points in calibration curve converting CT attenuation to density. A porosity model can be created as it this property is negatively related to density. We predict sample densities within 0.054 g/cm3 of laboratory measurements, and porosities are estimated within 1.5 porosity percentage/units of the laboratory measurement. Targeted digital rock physics allows rock properties to be estimated quickly and accurately, on large samples, rare samples, and uniquely shaped samples that may not fit into laboratory equipment. The technique is also less arbitrary than segmentation, and is not as invasive or cumbersome as some laboratory techniques. Keywords: Permeability and porosity; Image processing; Numerical modelling; Tomography