Machine Learning (ML), an application of Artificial Intelligence (AI), has proved beneficial in a variety of industries by enabling systems to learn and improve automatically without explicit programming. As data is accessed, this technology learns for itself, and it’s becoming increasingly popular within the oil & gas industry.
What is Machine Learning?
Traditionally, computers require a preset program to perform any task. Regardless of how complex the task might be, it could only perform that task. As opposed to this, machine learning (ML) is a system that will be able to perform automated tasks and think for itself through an algorithm that will integrate and absorb new information and experiences.
ML works by analyzing a large dataset and then making predictions based on its findings. Based on that knowledge, the algorithm will be able to analyze and properly react to future information. Automation based on this technology is the ideal solution for parsing large data sets and making predictions that are both fast and accurate.
The Relationship Between Artificial Intelligence and Machine Learning
Both terms are often used interchangeably by experts. Artificial intelligence (AI) research today is primarily focused on machine learning. AI, however, came first, making ML a branch of AI, albeit one of great significance.
The goal of artificial intelligence is to create computer systems with human-like intelligence, and machine learning is a major method for accomplishing that goal. In other words, AI and ML are linked, but ML is simply a way to bring AI to life.
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.
Key Applications for Machine Learning in Oil and Gas Industry
- Automation
- Data collection and assessment
- Algorithms
- Analytics in a consumable format
- Recommendations
- Maximized efficiencies
- Automated adjustments
Upstream Services
- Predictive analysis
- Accurate modeling
- Exploration
- Dig sites
- Well logging
- Oilfield operations
- Drilling efficiencies
- Rig optimization
- Risk detection
- Remote operations
- Completion
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 extremely fast, 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
Investing in machine learning can become a crucial factor in the health of your organization. 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 benefit 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.
Please contact us for more information regarding the implementation and integration machine learning services in your organization.