Use Power BI to Monitor Your Oil Storage Capacity with DAX Moving Averages Excluding Zeros

Productioneer has an extensive paginated report library for our customers to utilize. We have everything from simple data exports to paginated reports that highlight variances and trends.

One of the reports we have had in our library for quite a while, is the Tank Stock report. The user selects a day and the report returns the tank stock and tank production for each tank in the organization for the selected day. It aggregates the stock and production at the tank, battery, field, and organization level and provides the tank strappings (feet and inches) for each tank.  It is a standard and widely used report and I know it has not had any significant changes requested to it for at least 3 years.

Suddenly, without any warning, drastic changes in the market caused everyone to take a closer look at their storage capacity. We were receiving requests from multiple clients to add more analytics to the Tank Stock report. Continue reading “Use Power BI to Monitor Your Oil Storage Capacity with DAX Moving Averages Excluding Zeros”

Power BI Line Chart with interactive log & linear scales

As an end-user of a Power BI report, a chart that looked great at first might look not so great once you start applying filters or using slicers. Very large values in the data might throw off the scales and now your line chart might be suddenly all squeezed at the top or the bottom. Does any of the above sound familiar?

Usually it’s the report designer who has all the power, this article is about giving more power to Power BI end users…

End users’ ability to change the scale, appearance or formatting of that chart is limited. That’s why I started creating the Mi4 Line Chart Power BI custom visual that lets you switch scales on the fly, and eventually have more overall control of the visual without having to edit the report.

Continue reading “Power BI Line Chart with interactive log & linear scales”

Processing the RRC Data in the Cloud with Azure Functions

As we wrote about earlier this month, the Texas Railroad Commission (RRC) released a treasure trove of data freely available to the public on their site. It was like Christmas in the Mi4 office. After we sang some carols and drank some hot chocolate, we realized that there was so much data. We didn’t know where to start.

Christmas in September

As my colleague @Talal wrote last week, we decided to get Lat/Long coordinates for every Texas well. In his post, he explained, there are many use cases for this data, so it seemed like an excellent place to start.

In this post I will go over my contribution to the exercise: creating a serverless function to process data in blob storage. Continue reading “Processing the RRC Data in the Cloud with Azure Functions”

TX RRC OData Feed – Intro

In this series of articles we will take the RRC data from the SQL database and serve it via an OData REST API and show how we did it.

The series will consist of 3 parts:

  • Part 1: Creating the OData REST API in Visual Studio using Entity Framework Core and C#
  • Part 2: Setup Continuous Integration and Delivery for automated build and release pipelines to publish to Azure using Azure DevOps
  • Part 3: Offering the API to the public via a secured Azure API Management gateway and developer portal

Sounds like a lot…I know, so if you want to fast forward to the end and start testing the API and the data just continue reading…you should be up and running in under 5 minutes.

Continue reading “TX RRC OData Feed – Intro”

Visualizing the RRC Data in the Cloud

Part 1: Well Locations

Last week I wrote a blog post talking about the TX RRC publishing data to the public that previously was only available via a paid subscription.

After downloading the different data sets and examining the various types of data and formats, I decided to take a closer look at the data that might prove to be useful to us and to our Productioneer clients.

It is not uncommon for location and depth data, as well as other well-header and meta data to be incomplete or non-existent during a software migration, after all this data might not be considered crucial for the daily gauge sheets. Especially in the case of Excel gauge sheets, where additional columns are a waste of “prime real estate” and might be considered as cluttering that particular production report.

It would be nice if we have a quick way to pull the Lat Long data in bulk to speed up the on-boarding process.

Continue reading “Visualizing the RRC Data in the Cloud”

Power BI Switching between Logarithmic and Linear Scales

Welcome to the second post in our miniseries: “Are You Developing Power BI Reports the Right Way?” In the two-part series we are designing a sample Power BI report visualizing the weather on Mars and using some real world techniques.

The highlights of our first post were:

    • Getting data from Mars
    • Using a JSON file as a data source
    • Performing operations in the “Get Data” phase using M
    • Implementing a Dynamic Slicer
    • Using Chiclet Slicer and Dummyimage to create a Legend Slicer
    • Making design decisions

Note: Due to the popularity of this article, we have developed a custom Power BI line chart visual that has built-in support for allowing the end users to switch the axis scales, read about it in @Talal’s post. and viewing them in Power BI Desktop.

This post will focus on:

    • Switching an Axis Between Logarithmic and Linear Scales via a Slicer
    • Adding a Date Slicer
    • Using a Dark Theme

Continue reading “Power BI Switching between Logarithmic and Linear Scales”

Power BI Legend Slicer from a JSON File with M and DAX

Data is king, or queen depending on your household dynamics. How you communicate that data and its impact to your clients can help or hurt your business. Both your short and long term relationships can hang in the balance, which is why the quality and delivery of your Power BI reports are everything.

Case Study: Power BI Report Development

In this blog post mini-series, we will be taking you through the process of creating a Power BI report. The demo report draws from one we created for a client as part of a larger dashboard project. It implements a line chart to visualize the weather on Mars.

The actual report we developed had nothing to do with Mars, space, or weather, but you should find it useful to understand how real-life issues can be resolved and optimizations can be employed. The first post in the series focuses on data-prep and implementation of a legend slicer.

Continue reading “Power BI Legend Slicer from a JSON File with M and DAX”