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<!-- Why Petroleum Engineers Should Master Python coding language?


Data science has proven to be a very important technology in the upstream oil and gas industry. An overwhelming majority of petroleum engineering professionals and geoscientists are currently interested in learning technologies associated with data science, including artificial intelligence and machine learning. The academic programs must be including this aspect to provide at least the foundations of understanding and learning to use these technologies through free and open-source computer technology, as a good start.

Python for Petroleum Data Analytics. Actually combining petroleum engineering domain expertise with computer programming using "Python" as the most popular coding language for data science, artificial intelligence, and machine learning, 4.0 petroleum engineer should enable petroleum engineering to build predictive models to solve the most common petroleum engineering problems through data analytics.

One of the topics that interest me most these couple of years is the Application of data-driven analytics and predictive modeling in the oil and gas industry that is fairly new for me. A handful of domain experts have dedicated an extensive amount of time and effort to develop and present the next generation of tools that incorporates these technologies in the petroleum industry. Unfortunately, hypes, buzz words, and marketing schemes around data analytics have overwhelmed the petroleum industry in the same past couple of years. Many with little to no understanding and knowledge of physics and the geology of fluid flow through porous media have been marketing these hypes.

The objective is a modern Petroleum Engineer will demonstrate the power of Artificial Intelligence and Machine Learning and the difference they can make for informed decision making when it comes to objectives such as infill location optimization and reservoir production and recovery optimization once domain expertise becomes the foundation of their use and application in the hydrocarbon reservoirs.
I do not know if it is the case for you, BUT It is a crucial decision because once you start developing your project in a language, it is difficult to migrate in another language. Moreover, not all big data projects have the same goal. For example, in a big data project, the goal may be simply manipulating data or building analytics while in others it could be for the Internet of Things (IoT). 5 more reasons pushed me to stick to Python

Open-source
Library Support & Large Community support
Easy to learn
It’s a bag of powerful scientific packages (NumPy, ScPy, Matplotlib, Theano, TensorFlow… )
Schlumberger software compatible (Petel, Techlog, Blueback tool box..)
To conclude on Python and big data together provide a strong computational capability in big data analysis platform. If you are a first-time big data programmer, no doubt it is easy to learn for you than Java or other similar programming languages.

It is important to remember that the primary value from big data comes not from the data in its raw form but from the processing and analysis of it, and the insights, products, and services that emerge from the analysis. The sweeping changes in big data technologies and management approaches need to be accompanied by similarly dramatic shifts in how data supports decisions and product/service innovation (Big data, n.d.). Understanding the data can deliver tailored and sustainable outcomes that make quite a difference. With energy prices expected to remain to fluctuate, managing energy costs with an end-to-end solution, underpinned by data analytics, can be the most cost-effective way to get bills down

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For University Students PLEASE Master the basics of data analysis in Python. Expand your skillset. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, an Online free course focus on Python specifically for data science. You’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start Python now.
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