The Groundwater Program at the Alberta Geological Survey is focused on identifying, characterizing and quantifying Alberta's groundwater resources. Characterization of deep groundwater resources is becoming increasingly important as the Government of Alberta implements its Water Conservation Policy seeking to minimize freshwater use. Conducting an inventory of saline water (non-traditional) resources for source water in various energy development scenarios, at basin scales, pose significant challenges given the potential competing uses and demand for groundwater resources. Current research activities are seeking to improve our methods to characterize deep aquifers in data-rich sedimentary basins. Two methods are discussed here: 1) identifying production/injection influenced Drill Stem Test (DST) measurements for mapping distributions of hydraulic heads (both present-day and prior to development) in deep units; and 2) analyzing variable density flow effects. DSTs are transient pressure tests usually performed for assessing potential oil and gas productivity. These tests measure pressures using gauges at the surface and down-hole. The measured pressures can be strongly influenced in cases where the test interval is located in the vicinity of a production or injection well, which generally happens in mature sedimentary basins such as the Alberta basin. To identify production influences this study utilized a cumulative inference index (CII) based approach. A new application was developed in C-code to implement the CII and will be demonstrated on a sample DST dataset from the Western Canada Sedimentary Basin. Fluid flow in sedimentary basins is often inferred using freshwater hydraulic heads, reference formation water densities and pressure-depth plots. Previous studies in the Alberta basin have often neglected density variations. Effects of density driven flow needs to be taken into account in cases where dense brines are present, a large aquifer dip or small hydraulic gradients exist. This study implemented a vectoral analysis to identify flow directions in regions where density driven flow is important and can change the inferred magnitude and direction of flow. A python based script has been developed and will be demonstrated on a sample dataset from the Williston basin.