Subject ▸ Data Science

Transforming Petroleum Engineers in Data Science Wizards

Once in a while I get messages from colleagues asking for tips on Data Science applied to Petroleum Engineering. This is stuff I have collected over time (responses), advice to follow to become a Petroleum Engineer and Data Science wizard: Complete any of the Python or R online courses on Data Science. My favorites are the ones from Johns Hopkins in Coursera, complementing with DataCamp short workshops. Just two that come quick to my mind.

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Data Science for Petroleum Engineering - Part 5.3 Finding and filling missing well data in alphanumerics

This is what we will reviewing in this lecture. NOTE. You can find the PDF version of the R markdown notebook in GitHub at this link. The reproducible R markdown notebook itself is here. Both are full versions of this LinkedIn article. For the time being, LinkedIn publishing does not support markdown which would make sharing scientific and engineering documents much easier. Load the raw data file # code We will see that some well names can be fixed manually and others should be done automatically with a script.

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Data Science for Petroleum Engineering - Part 5.2: Finding and filling missing data

NOTE. You can find the PDF version of the R markdown notebook in GitHub at this link. The reproducible R markdown notebook (.Rmd) itself is here. Both are full versions of this LinkedIn article. For the time being, LinkedIn publishing does not support markdown which would make sharing scientific and engineering documents much easier. Mistyped data One of the challenges in cleaning up well data is having uniform and standard well names.

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Data Science for Petroleum Engineering - Part 5: "Transforming Excel well raw data into datasets.​"

One of the big challenges of this new era of data science. machine learning and artificial intelligence is getting unhooked from the habit of working with spreadsheets. They have been around for 30+ years and were awesome. But spreadsheets - or worksheets - do not scale well with massive amounts of data; or continuous streams of data; or other characteristics that are key for taking good and sound decisions such as reproducibility.

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Data Science for Petroleum Engineering - Part 5.1: Data Introspection with R

NOTE. You can find the PDF version of the R markdown notebook in GitHub at this link. The reproducible R markdown notebook (.Rmd) itself is here. Both are full versions of this LinkedIn article. For the time being, LinkedIn publishing does not support markdown which would make sharing scientific and engineering documents much easier. Transforming Excel well raw data into datasets This section is about getting familiar with our data.

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Data Science for Petroleum Production Engineering. Part 2: Acquiring the data

In the past session we got an introduction to the multiwell statistics package showing a few of the things that we can do with the Petronas PTech Engineering Library. Now, we will explore some more functionality. It is incredible the huge amount of information when we get from all the wells in one scan pass. The data starts to have sense. A well in isolation or standalone doesn’t tell used much about the field or differences between well parameters from well to well.

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Data Science for Petroleum Production Engineering

In the last century, the production engineer built the well models one by one and analyzed the results also one by one. With the ubiquity of the personal computer, desktops and laptops, an unimaginable computational power has been put in our hands. But we need the right tools! The spreadsheet was invented in the 80’s and was a great invention. The beauty of it is that you can produce results right away.

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