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In the early stages of energy production, it is business-critical to combine and interpret upstream data sources that help quantify project risk and uncertainty. From geophysicists to reservoir engineers, teams must perform necessary quantitative analyses before oil production can begin in earnest.
Drilling and completions engineers are under rising pressure to mitigate costs while also maximizing well performance. Identifying opportunities for improvement – and analyzing the massive data repositories needed to do so – can be a daunting task.
In downstream processes, chemists routinely investigate manufacturing steps to understand the complex factors affecting quality, yield and production costs.
Use dynamic visualizations to bring insights to life while enabling engineers to view and drill down for further analysis.
Tap into advanced machine learning algorithms to understand well performance and behavior under varying conditions.
Improve forecast accuracy through easy-to-perform model comparisons and validations to prevent against overfitted results.
Predict downhole drill bit failures and root cause equipment issues to extend production lifetimes.
Connect to both local and cloud databases while leveraging built-in automated data prep tools to reduce time spent data wrangling and cleaning.
Maximize the use of streaming data sources by filtering and amplifying subsurface signals to reveal critical insights in fracturing and seismic data.
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