Oil and Gas Workshop | Applied Data Science and ML

Workshop Exercise

Key Objectives

Hands-on (Follow along with these videos/pause as needed)

Create a production dashboard from zero (~45 mins)

Import data 

Link all sheets based on UWI

Visualize key KPIs, production trends and geographical location of the Wells

Calculate monthly operating rates in Mscf/d for each product (production volume/production hours) *24

Record the UWI, the cumulative gas production, and the final operating gas rate for each Well

1.1 Import Data, KPI chart, Combination Chart (13:33)



1.2 Cross Table, Map Layer (TMS), Text area(26:55)







Note: The TMS Example URL is included as a .txt file on the Spotfire_workshop.zip available at the end of this page as an attachment.

1.3 Tips and tricks for good-looking dashboards

 

GeoAnalytics example: Map Contour (~15 mins)

In the Energy domain, different measurements might be gathered from a set of locations. The locations can be plotted on a map chart and in addition to visualizations such a bar graphs, heatmaps, etc. Contour lines on maps can be an excellent way of gaining visual insight into changes in measurements with respect to geography. We will cover:

How to consume R/Python scripts into your Spotfire app

How to enhance a map visualization with multiple layers

2.1 Map Contour (9:35)



Note: Exchange item link here

Applied Machine learning: K-means and regression Modeling (~20mins)

Machine Learning algorithms such as Classification, Similarity, Clustering, Regressions, etc. allow the user to get a better more detailed and accurate grasp on the patterns displayed by the data. Spotfire users can use out-of-the-box statistical capabilities from the tools menu. These methods when combined with visualizations and maps allow the user to benefit from statistical data analysis without needing specialized domain knowledge of the same.

3.1 ML Unsupervised Example K-means clustering (5:09)



3.2 Regression Modeling (9:54)

 

Applied Machine Learning: DCA Example (~20 mins)

Decline Curve Analysis (DCA) is a graphical procedure used for analyzing declining production rates and forecasting the future performance of oil and gas wells. Fitting a line through the performance history and assuming this same trend will continue in the future forms the basis of the DCA concept.

This example will show you how to consume a script (multiple programming languages are supported in Spotfire including R/Python) and use Spotfire as a visual interface to communicate the ML results.

Put an algorithm behind the button.

4.1 DCA Example in Spotfire (13:58)



Note: Exchange item link here

 

Appendix

Keep sharping your data science skills and scale data science across your organization to solve complex challenges faster and speed innovation with TIBCO® Data Science, a comprehensive platform for operationalizing data science



Ready for more exercises in Spotfire using O&G data? Click here to access the DUC Datathon 2020 Intermediate Level Bootcamp for TIBCO Spotfire

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Improve your Spotfire skills with resources from the Training Section. 

Join  Dr. Spotfire Office Hours and ask questions live. List of previous topics here