Research project

Project fact sheet

The role of mineralogy and rock texture in controlling the wetting state of oil-brine systems in permeable rocks

Project researcher(s): 
Dr Sam Krevor
Project supervisor(s): 
Gaetano Garfi
Project supervisor(s): 
Dr Cédric M. John
July, 2017 - June, 2021

The recent availability of laboratory mXCT has allowed for unprecedented detail in imaging the 3D structure of porous rocks at the scale of individual rock pores (microns to millimetres). This imagery is now used as a quantitative tool in 3D modeling of fluid flow through rocks, chemical reactions between fluids and minerals at the pore-fluid interface, and rock compaction and fracturing. This is leading a revolution in our understanding of important fluid-rock processes with wide ranging applications including petroleum production, and CO2 storage underground.

In this project, the focus will be on understanding the controls that rock properties – mineralogy, rock textures – have on the local contact angle of fluid-fluid-solid interfaces during multiphase flow through rocks. The contact angles, and what is described more generally as the wetting state of a rock fluid system, is a key property controlling fluid flow through rocks (see key petroleum engineering texts, and Anderson (1986). Only recently, however, has it become possible to observe contact angles directly in the pore space of rocks while fluid displacement is occurring (Andrew et al., 2014, Al-Menhali et al., 2016). For the first time, it is thus possible to understand the pore-scale controls on the wetting states important for oil production, e.g., the mixed-wet state characteristic of many carbonate reservoirs.

The techniques for quantifying the distribution of contact angle throughout the pore structure are under development (Scanziani et al., 2017). Thus far, they have not been used to evaluate the controls that mineralogy and rock texture may play in a specific location in determining the local contact angle and overall wetting state within a rock. It has also been demonstrated that X-ray CT imagery can be used to identify local mineral groups throughout the pore space (Figure 1, Lai et al., 2015). An advanced “dual-energy” approach may further aid in this segmentation process (Lai et al., 2017). Machine learning algorithms have shown significant promise in both mineral phase identification and textural classification of rock components (Chauhan et al., 2016). The aim of this project is to evaluate and implement existing tools to classify mineral groups and rock textures in 3D X-ray imagery of permeable rock. This should implement and build on the work of Lai et al., 2015, 2017, as well as potential machine guided pattern recognition algorithms, e.g.,, Chauhan et al., 2016. This classification may then be used to evaluate the results of fluid flow experiments in which local contact angles are mapped throughout the pore space of the rock.