Tools capable of producing rich analytic reports and data visualizations are a valuable resource for decision makers in any field. Dash is an open-sourced based framework for building dynamic web applications that seamlessly integrates interactive graphics and tabular data summaries, serving as a catalyst for data-driven decision-making.

Several features of Dash contribute to its demonstrated success as an analysis and data presentation tool within the oil and gas industry. These are briefly discussed here, but for an even sharper picture of what the framework can do, visit our Dash gallery (which also links to sample code in both R and Python).

Operationalizing traditional techniques and modern, interactive visualizations
Analysts working in the energy sector frequently leverage industry-specific, proprietary algorithms and computation packages. While these tools are often tailor-made for the application at hand, post-processing and plotting are often performed using other software, which may prove problematic for real-time data analysis needs.

Dash permits analysts to operationalize these proprietary tools in a user-friendly web application, facilitating both data exploration and collaboration, allowing you to truly leverage the power of R.

To demonstrate how a simple application interface can help make complex comparisons quick and easy, check out the Oil and Gas Ternary Map demo in Plotly’s Dash Gallery:

This application combines a map-based selector with tools to drill-down by operator, or compare shale mineralogy, well counts, and annual production, all in a single browser window.

Support for highly customized data visualizations
In many cases, oil and gas applications require specialized figures, such as vertical well log charts.

These figures are straightforward to produce with Plotly and are effortlessly combined with tabular summaries using Dash Table to rapidly produce flexible, highly customized reports, as seen in the LAS Report feature in the Gallery:

Beyond ternary, well log, or even polar coordinate charts, there is a wealth of figure types available in Plotly.js, and the list of supported chart types continues to grow.

In addition, embedding Plotly figures or graphs produced with ggplot2 in Dash apps is straightforward thanks to the plotly package. The Graph component in Dash Core Components accepts objects produced using either the plot_ly or ggplotly functions via its figure property.

Bringing it all together
Aside from interactivity and leveraging industry-specific tools and modern visualizations, it’s hard to discern a Dash app from a full stack web application assembled by a team of professional developers. For enterprise customers, Plotly offers Dash Design Kit, which makes it even easier to arrange, style, and customize Dash apps.

Big Data
Data analysts in the energy sector frequently explore massive volumes of data, whether from equipment sensors or other sources. Support for caching, task queueing, and databases such as Redis and PostgreSQL enables Dash apps to easily scale according to the resource needs of virtually any application.

The New York Oil and Gas Production Overview app demonstrates how Dash can combine clean styling with intuitive interfaces, and provide a pleasant and productive experience for users and developers alike — and offers a glimpse of what a parsimonious design can do for analyses at any scale:

Harnessing the power of Dash to produce insightful, interactive web applications with real time data is not only easy but bridges the gap between data scientists and the realm of full stack application development. For more information about Dash and our enterprise product offerings, click here.