Over the last few years, the analytics tools available to geoscientists and engineers have become more powerful — and, in many cases, more sophisticated. For smaller technical teams, that evolution has prompted a thoughtful reassessment of how we apply the tools available to us in the most effective way.
When I joined Canadian Discovery back in 2018, Spotfire quickly became my go-to tool for making sense of complex subsurface data. It felt like the perfect bridge between geoscience and analytics — powerful, flexible, with strong geospatial capabilities. We used Spotfire to dig into relationships between pressure, temperature, fluid chemistry, and well production – relationships which are discussed in this SPE paper I co-authored on the Montney Formation. It also became a critical tool during our work on NE BC Carbon Capture and Storage Atlas, where we needed to evaluate pressure distribution at depth and examine porosity–permeability trends to help identify which areas and zones were most suitable for long-term CO₂ storage. Over the last few years, we’ve also been using Blue River Analytics’ Analytics for Energy Suite, built in Spotfire, to run decline curves and forecasts for CDL’s Play Maps and Catalyst.
Over the last year, I’ve also been spending more time getting acquainted with Microsoft’s Power BI. While Power BI wasn’t originally known for its geospatial capabilities, those features have steadily improved, particularly for business-focused mapping. With options like Azure Maps and ArcGIS integration — along with external visuals such as ZoomCharts and Icon Map — Power BI can now support a wide range of geospatial use cases.
Across multiple teams, I’ve seen that the most effective approach is rarely about choosing a single platform. Different tools excel in different contexts. Spotfire’s depth, flexibility, and advanced analytics capabilities remain incredibly powerful for complex subsurface analysis, and I’ve been genuinely wowed by the dashboards showcased at their recent Energy Forum. Power BI’s integration with Excel, SharePoint, and the broader Microsoft stack makes it a natural fit for many collaborative workflows.
This evolving landscape is what led my husband and I to expand our conversations beyond specific tools and toward something broader. Matt specializes in database architecture, database management, and application development, and together we saw a growing need in the oil and gas data analytics space — particularly for smaller companies navigating increasingly complex data environments.
After discussions with management at CDL, we decided to forge a new path dedicated to advancing applied data and analytics in the energy sector, and started ClearCurrent Analytics. Our focus is straightforward: helping companies better understand, structure, and visualize their data — using the platforms that best fit their workflows and long-term goals.
This post is the first in a series we’ll be sharing throughout 2026. While we can’t publish client work, we’ll be working with publicly available datasets — including those from the British Columbia Energy Regulator Data Centre and the Alberta Energy Regulator Products and Service Catalogue — and bringing them together to showcase data structures, workflows, and visualization use cases. The goal is to share practical ideas that help teams get more value from the tools they already use.
If this is something you’re navigating as well, I hope you’ll check back regularly and follow along as the series unfolds.