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Importance of Machine Learning in Oil and Gas Industry

What is the use of Machine Learning in Oil and Gas industry? That’s a colloquial question asked to any proponent of digitisation in the Oil and Gas Industry.

Let’s learn as we break down this topic to help you understand what Machine Learning is and how we can make the most out of it in the Oil and Gas Industry.

What is Machine Learning?

Machine Learning can be, simply put, called as a process in which a computer learns in a human fashion, on its own, and can make a prediction based on the data inputs and several outputs fed.

Arthur Samuel coined the term Machine Learning while working at IBM, and describes it as “the field of study that gives computer the ability to learn without being explicitly programmed.”

For our understanding and in relevance with the Oil and Gas Industry, we shall define it as, “Empowering computer programs to learn on their own by parsing data and predicting any future trends.”

The process in Machine Learning is similar to that employed in data mining and predictive modelling. Data is constantly fed, and even output as well, so that our machines – Computers, Cell Phones, and other electronic devices – can make an informed decision and provide to us important and relevant suggestions.

Based on the data, which is fed to the system, we can categorise machine learning in two broad categories:

  1. Supervised – In this, a Data Scientist or Analyst is feeding the system with relevant insights and is modeling it to make an informed decision based on the permutations and combinations of the input data (and output data). In nutshell, the person (or operator) is using his machine learning skills to train a system. Example – Based on the previous data of a cricket match held between two teams, the pitch conditions, toss analysis, and player-to-player data; the machine can present the winning percentage of the teams.
  2. Unsupervised – However, in unsupervised machine learning process, the system need not be trained. It is fed huge chunks of data and with deep learning (an iterative approach which helps the system to analyse a huge volume data and predict the trend), which reviews a particular data and arrives with a result. Example – Based on the clusters of data of demographics of a region in any country, say the United States, a Digital Marketer can get a result if the advertisement he is going to run will attract some conversions.

A little tricky, is it? Let’s further simplify it for you.

Let’s try to decode it with a simple mathematical function and an insightful example.

Mathematical function: A linear equation where we have Y as a function of X with an error term.

Y = f(x) + E

In Supervised Learning, we know that X needs to be fed to get the desired output, which is Y.

But in Unsupervised Learning, we don’t know the Y! Yes, that’s the beauty of Deep Learning.

Example:

You go into a party of complete strangers and you haven’t met these people, right. How are you going to interact with them? You already can make some judgments: gender, age groups, certain eccentric behaviour and then you can choose to engage in a conversation with someone. Isn’t it? That’s Unsupervised Learning for a computer when there’s no Y. And, no one fed X as well.

Importance of Machine Learning in the Oil and Gas Industry

Now that we know what Machine Learning is, let us extend our discussion further the importance of this concept in our industry.

  1. Increasing Operational Efficiency – It’s nothing new, you might seem. But a close observation will help you understand how we can increase efficiency by use of Machine Learning. At the wake of the Oil Slump in 2014, we saw that a host of Oil and Gas Companies began to think of how to deal with the unplanned and unwelcomed downtime in the industry. One of the ever problematic and tricky situations for us is the volatility of the oil prices. Machine Learning has helped improve the decision-making capability by impacting the various important metrics in the Oil and Gas Supply Chain responsible for fluctuations in the oil prices: United States, OPEC, World Geopolitics, US Dollars vs. Foreign Currencies.  ML can intake a humungous amount of data and process it in real time. It’s a fact that for the last 10 years, many Oil and Gas Companies have incorporated sensors in the Oilrigs. But still, the companies use static models for maintenance. Predictive analysis by ML can help the industry to avoid downtime, can forecast weather hence provides us the time to be prepared, improve safety on offshore sites etc. Thus, it may reduce overall cost and improve efficiency.
  2. Process Optimisation – Another advanced form of Machine Learning (in the Unsupervised ML) is Deep Learning can really make a difference in the business processes in the Oil and Gas ML incorporated with Fuzzy Logic and Artificial Neural Networks (ANN) can help in reservoir simulation. It can help obtain a lot of real-time information and predict, say, how a tight rock formation can respond to hydraulic fracturing. ML can help optimise the processes without changing a lot of constraints in the process itself. Advanced ML can browse through a lot of pre-existing videos and help us improve performance across the supply chain of the industry.
Figure: Predictive Modeling in the Oil and Gas Industry, an architectural overview
                            Figure: Predictive Modeling in the Oil and Gas Industry, an architectural overview | Image Courtesy: Google Cloud
  1. Sales and Marketing Campaign Management – One of the most important advantages of ML is a predictive analysis, as we have learned in this article. And, it is an extremely important constraint while we are marketing an idea or a product in our industry. Who is going to buy? Where our buyer is? For how long can the product sustain itself? Or is this market suitable for this product? It is the conundrum of any marketer.

With the help of Supervised ML (SML), we can actually add demography details; basic information of our buyers or just a sample of information about the product can help us obtain information that can allow us to chalk out our campaign. For Digital Marketers in the Oil and Gas Industry, SML can play a significant role.

Multiple benefits of ML-based marketing are as follows:

  1. Real-time content and engagement with chatbots
  2. Improved marketing qualified leads (MQL)
  3. Increased precision of knowing your market
  4. Provide complete customer information: a 360-degree view
  5. Effective exploitation of opportunity

And, it can thus be said without any room for doubt that ML and AI are changing the Oil and Gas industry for good. According to a recent study, it was reported that AI in the Oil and Gas industry is a huge potential market, expected to reach US $2.85 billion by 2022.

Final Take

The digitisation of the Oil and Gas industry is happening but we need to closely monitor its progress. It is depressingly slow, given the advancement in technology it can be easily said that we need to incorporate the increased used of technological tools to improve profitability and efficiency in processes across the supply chain of the industry. The only disadvantage, or should it be said, concern, is that of cyber security. And, one can say, with certitude that we shall surely have more technological tools, which can avoid the risk of data theft and other things. Meanwhile, let us know your thoughts.

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Saurabh Sharma

Technical Content Writer

Trained in Electrical & Electronics Engineering, Saurabh decided to move into writing having realized his passion to write about the essence of technology in our dynamic world. His interest areas are impact of Social Media and Blockchain on the Supply Chain in Oil and Gas Industry.

1 comment

mm

Saurabh Sharma

Technical Content Writer

Trained in Electrical & Electronics Engineering, Saurabh decided to move into writing having realized his passion to write about the essence of technology in our dynamic world. His interest areas are impact of Social Media and Blockchain on the Supply Chain in Oil and Gas Industry.

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