In our continuing discussion around Energy 4.0 we are looking at the role data plays in today’s energy sector, primarily oil and gas.

As we all know, in today’s connected world data is king: the more you have, the more you know, the more you are prepared.  Energy companies have the challenge of developing insight from their enormous amounts of data collected to help them make better, more informed decisions.  

By applying advanced data analytics and artificial intelligence (a future article), energy companies can use predictive analytics to identify trends and predict events throughout processes to quickly respond to disruptions and improve efficiencies.  This can increase capabilities further and enable the type of decision making that keeps operations running at full speed.  This shift to digitization and use of big data positions a company to be innovators in their field and prepare future generations of oil and gas innovations.

Before we get into oil and gas we need to understand what exactly is “big data”.  Well, big data really started as a buzzword around large amounts of data, but over the past few years information has grown more complex and vast in quantity.  Organizations continue to struggle to how best gather, curate, understand, and use all of this data effectively.

The Four “V”s of Big Data

Data professionals describe big data by the four “Vs.” These characteristics are what make big data a big deal. The four Vs distinguish and define big data and describe its challenges.

Volume

The most well-known characteristic of big data is the volume generated. Businesses have grappled with the ever-increasing amounts of data for years. However, now it’s possible to store data for pennies on the dollar.  With increased volume comes increased management of the data.  Without strategies to use and access huge volumes of data, that data will sit stagnant, lose value, and fail to surface insights.

Velocity

Not only are businesses producing a lot of data, but they are also doing it at an ever-increasing rate. Technologies have to be ready for the speed and volume of that data to keep up with the pace of business. As the volume increases, velocity becomes more and more difficult to manage as it becomes more important.  

Value

Velocity, volume, and variety were the three original characteristics associated with big data. Having all of this data is one thing but what value does it bring to the business?  It needs to be able to fulfil goals and metrics, uncover risks and opportunities, and much more.  Analytics technologies have evolved and can now augment analysts’ abilities to find correlations, identify outliers, and predict outcomes with data.

Variety

As digital transformation occurs across different industries and all sizes of organizations, that means there are more types of data to store and analyze, plus more sources to pull from. The variety of structured has exploded and it has become more important to leverage metadata as well as ways to wrangle unstructured data. 

Big data is crucial because of its untapped potential, but recent technology such as visual analytics finally allows businesses to discover critical, even surprising insights that give us a clearer view into processes and human behaviors. On some occasions, these processes and behaviors must be refined for the sake of the business and its future success. There are three types of data:

Structured Data

Structured data is the neatly organized data you keep in databases, datasets, and spreadsheets. It’s easy for traditional analytics tools to read this data. Organizing unstructured data into structured data is time-consuming, but possible with the right solution. It involves data cataloging, data mapping, and data transformation.

Unstructured Data

Unstructured data, or raw data, is increasing at a higher rate compared to structured data. Platforms like Facebook generate hundreds of terabytes of information per day. Unstructured data can also include survey data from customers, notes, and emails. Because unstructured data is growing, big data technologies that can seamlessly analyze this data will be crucial to businesses.

Semi-structured Data

Semi-structured data has some organizational structure, but isn’t easy to analyze as-is. With some organizing or cleaning, semi-structured data could be imported into a relational database just like structured data. Semi-structured data and structured data can be analyzed and visualized.
Now that we have a better understanding of what Big Data is, let’s look at how it is used as it relates to the oil and gas industry.

Big Data analytics for the oil and gas business is essential for the entire industry and provides benefits at all three levels of operation:

• Upstream – creating detailed analytics that facilitate the exploration, development, and production.
• Midstream – determined better and more effective ways to facilitate sales and transportation, and analysis for better supervision of the refining process.
• Downstream – develop ways to optimize its distribution, as well as its retailing.

Let’s look at three key areas for big data to be used:

Increase Operational Safety and Security

It is common knowledge that oil and gas business poses many safety risks to professionals of particular concern to those furthest upstream in the industry such as exploration or production.  Big data can help companies to provide insights that improves the safety of all.

Improve and Optimize Maintenance Procedures

The oil and gas industry involves many complex facilities that often operate 24/7. This level of utilization is difficult to manage while maintaining 100% efficiency. To get as close as possible, data analytics can be used to predict both the quantities required for production and the possible interruptions that could cut into your revenue.

Reduce Production Costs

While difficult to reduce the costs of a highly specialized workforce or of established and profitable installations, production costs must be optimized. Using big data and AI, will provide companies insight to streamline processes and improve efficiencies to reduce costs.

For the oil and gas industry, data analysis helps to develop the strong potential that can only be revealed by fine and relevant analysis.

Big data analysis and solutions can help the oil and gas industry in five particular areas:

Logistics can be significantly improved.
By using analysis of transportation and production costs. The economic factors that will increase demand are more easily determined, as well it can factor other things in certain weather patterns. Understanding this can improve the scheduling the transportation of refined products and reduce potential costs.

Simplified production forecasting.
These sophisticated data models can not only be used for oil gas exploration at more profitable locations, but can also be used to scrutinize reservoir capacities. Management of crude and refined product inventories can be done finely to find optimal solutions to industry challenges.

Refine exploration and drilling data.
On drilling sites, the more accurate the data, the higher the return on investment for the exploitation of potential wells. Moreover, the mapping of prospective areas can also be used to predict the complexity of a drilling site. From there it is possible to anticipate the potential site drilling incidents.

Extend the life of equipment.
Oil and gas uses expensive equipment and they have many sensors collecting massive amounts of data. This data can allow companies to be more proactive in maintenance service. This is based on predictive analytics and maintenance based on machine learning. Extend uninterrupted drilling periods and reduce downtime to save money by avoiding frequent material breakdowns.

The industry carbon footprint.
We know it is important to reduce emissions as much as possible. Using big data to help improve the above four areas is a good way to make a positive commitment to the preservation of the environment. This is another area where analytics can provide valuable insights.

The oil and gas industry, and energy sector in general, continues to face many challenges. Its business processes are complex, and it is difficult to take a long-term view of all operational activities. There is a continuous search for better performance using more and more sophisticated equipment. Supply chains and logistics are much more complex than in most other industries. In addition to these problems, environmental concerns have become a major preoccupation of the industry. All three operational levels of the industry can benefit from the advantages of Big Data analytics to give the industry greater insight into improving efficiency, safety and reducing costs.