A Guide to Machine Learning in the Oil & Gas Industry

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From checking your email and scrolling through Netflix to self-driving cars and healthcare decisions, machine learning is a large part of our daily world. However, most of us assume it’s used only by people building artificially intelligent robots. In reality, it’s all around us, but most people don’t understand the technology or its larger applications in big industries like oil and gas. 

In this article, we will explain the following aspects of machine learning:

  • A working definition in everyday terms
  • How it works
  • The applications to the oil and gas industry

We want to help you realize exactly how machine learning in oil and gas can benefit your company.

What is Machine Learning?

Traditional computer systems require a preset program to do anything. No matter how complex a task might be, it could only do that task and nothing else. Machine learning (ML), on the other hand, is a computer system that can learn to perform automated tasks and think for itself through an algorithm that absorbs new data and experiences.

ML starts working when a basic algorithm analyzes a large data set and then makes predictions based upon what it finds in the data. The algorithm will apply that knowledge to learn new ways of analyzing and acting upon future data sets.

It’s the perfect technology for automating tasks that require parsing large collections of data and making predictions with both speed and accuracy.

What is the relationship between artificial intelligence and machine learning?

For many experts, the terms are synonymous. Machine learning is currently the most common application for contemporary research into artificial intelligence (AI). However, AI came first, which makes ML a branch of AI, albeit a very important one.

Think of it this way: If artificial intelligence is the study of creating computer systems that operate with human-like intelligence, then machine learning is a prominent way of achieving that goal.

Hence, AI and ML are definitely linked together, but ML is simply a way of making AI come to life.

What are the Different Types of Machine Learning?

There are two leading ways that ML happens, but there are also two other, newer schools vying for inclusion and acceptance into the mainstream ML community. 

Supervised learning

Contrary to the term’s connotation, supervised learning doesn’t involve a teacher or proctor monitoring the algorithm’s happenings. Instead, supervised learning means that the people in charge of the data place various tags, markers, and descriptors on the data. This identifying information gives the algorithm the merest bit of structure for a large data dump.

Unsupervised learning

As you might imagine, unsupervised learning occurs when the data added to the system doesn’t have any preset information attached. Instead, the algorithm simply receives a bunch of data without any context in hopes it can learn something from the experience.

Semi-supervised learning

A growing school of thought in the ML community, this approach actually entails specific human oversight. With traditional supervised learning, everything is labeled, so the data is on the same footing. But with semi-supervised, only some data is labeled, which means there’s the chance of bias entering the algorithm. The idea is to determine how the algorithm learns and makes value judgments based upon what humans decided to label and not label in the data.

Reinforcement learning

The newest entrant into the ML world, this school of thought involves consequences — much like you are training a rat in a maze. If the system makes the right choice, it receives positive reinforcement, but if it makes a wrong choice or learns the wrong thing, it receives negative reinforcement. The problem with this approach is that it needs a specific endpoint or result for it to be effective. If you simply want to give your system some data for some open-ended learning, this format won’t be effective.

Ultimately, machine learning doesn’t occur in a vacuum. Even without a specific program telling the algorithm how to learn and adapt, ML still needs a few guidelines in place —- and they all focus on the data sets.

What are the Key Applications for Machine Learning in Oil and Gas?

While your average oil and gas business doesn’t design robots that use artificial intelligence, plenty of them are using equipment that can be programmed to do routine tasks. Moreover, those routine tasks often require analyzing complex data sets so that the work is with maximum efficiency and return on investment.

Thus, machine learning has amazing potential for changing the game in the oil and gas industry, including the following:

  • Automation
  • Data collection and assessment
  • Algorithms
  • Analytics in a consumable format 
  • Recommendations
  • Maximized efficiencies
  • Automated adjustments

These applications come to life in different ways across the oil and gas value chain:

Upstream services

Wildcatting simply isn’t profitable anymore, and it really hasn’t been for decades. Machine learning provides assistance with both locating the most efficient place to start a well and improving how a company extracts oil and gas. Such improvements include:

  • Predictive analysis
    • Accurate modeling
    • Exploration
    • Dig sites
    • Well logging
  • Oilfield operations
  • Drilling efficiencies
  • Rig optimization
  • Risk detection
  • Remote operations
  • Completion

Machine learning streamlines these replicable processes because the computer system can analyze large collections of data points faster and more efficiently than a human employee.

Midstream services

One of the more unheralded aspects of machine learning in the oil and gas involves simply getting the product from the field to the refinery.

  • Gathering
  • Transportation
  • Logistics
  • Pipelines

Again, because your algorithm can crunch numbers so quickly, it can provide specific recommendations for improving the efficiency of your delivery systems.

Downstream services

Many of the same applications of machine learning in upstream and midstream processes are relevant for downstream production services.

  • Processing
  • Refining
  • Remote systems operation
  • Risk analysis

Since you literally can’t have enough human employees to observe, analyze, and report each moving part of the refinery, machine learning can absorb that information to make informed decisions to help your people.

Back-office management

Machine learning improves the office environment, too. Because your systems are observing so many working elements of your operations, they can use the data being collected to make specific recommendations that impact your business.

  • Maintenance
  • Performance
  • Services and equipment
  • Market analysis
  • Forecasting
  • Retail sales
  • Marketing the product

Every business should want to save money with proactive decision-making, and machine learning helps with that.

The Future of Machine Learning in Oil and Gas

Let’s face it: it will be a long time before oil and gas companies see $80+ barrels of oil. The current product glut in the market results in a lower lid on costs, which means the already tight margins will stay that way.

Investing in machine learning can become a crucial factor in the health of your business. Increasing the accuracy of your drill modeling can result in both increased production and decreased labor costs. In turn, this can lead to increased revenues that come from slightly larger and more stable profit margins.

Additionally, machine learning can lead to great increases in employee and business efficiencies. Since younger workers both in the field and in the office are quicker to notice and adopt the benefits provided by technology, they are more prone to use machine learning and see how far they can push it to help the company. This level of “self-service” allows average employees to do more work on their terms, instead of badgering IT to run models. 

Machine learning can be a substantial boon to the long-term health of the oil and gas industry. By focusing on automation, data crunching, and process automation, a business can concentrate its resources with greater effectiveness and attention to detail.

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