Python script to calculate the fluid saturation of a 2-phase flow system within a rock matrix using 3D computed tomography (CT) scans. The script is designed to calculate the saturation over a series of scans. Outputs include a graphical representation of saturation as a function of scan series and a CSV with the same data. Saturation is calculated using the methodology described by Krevor et al. (2012). In this methodology, the CT number of the CO2SAT, BrineSAT, which are scans containing only CO2 and brine respectively in the pore spaces (furthermore referred to as end members) and the intermediate biphasic scans (CO2X) are used to calculate saturation in each voxel of the core during scCO2 injection (Equation 4) as follows:

The first step prior to running the python script is to create a mask, or region of interest, that removes extraneous image data such as the core sleeve, confining fluid, and core holder from any calculation. This is done by applying a zero value to all volume outside of the mask region and a value of 1 to all values inside of the mask volume. The mask creation was done in ImageJ and created to the same dimensions as the images of interest (Figure 6). The experimental images and mask image files are then imported into Python using Scikit-image(van der Walt et al. 2014) and subsequently converted into 64-bit arrays. The mask is then multiplied by the experimental image arrays to remove extraneous data by converting all outside volume to zero. These zero values are then converted to not-a-number (NaN) values; this removes these pixel values from inclusion in further calculations. The image arrays are then used in Equation 3 and the result exported as tabular saturation data.

Krevor, Samuel C.M., Ronny Pini, Lin Zuo, and Sally M. Benson. 2012. “Relative Permeability and Trapping of CO 2 and Water in Sandstone Rocks at Reservoir Conditions.” Water Resources Research 48 (2). https://doi.org/10.1029/2011WR010859

Van Der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., et al. (2014). Scikit-image: Image processing in python. PeerJ, 2014(1), e453. https://doi.org/10.7717/peerj.453