Big Data analytics helps to improve operations in 3 areas in the oil and gas industry:

• Upstream– refers to the exploration and production of crude oil and natural gas
• Midstream- refer to the transportation and storage of crude oil and natural gas
• Downstream– refers to the processes applied after extraction through to it being delivered to the customer in whatever format required.

Here are some use cases of Data analytics in the oil and gas industry.

 Upstream

Optimizing drilling operations

Some of the challenges in the upstream processes of the oil and gas industry involve improving the performance of existing resources and finding new resources (exploration) to maintain a continuous supply of crude oil. The exploration process primarily involves interpreting seismic data which demands sophisticated computing equipment with powerful visualization capabilities. The seismic data is used to create a subsurface picture. This remains one of the most important applications of Big Data and advanced analytics in the oil and gas industry.

Improving Offshore Operations 

According to a report by McKinsey, a typical offshore platform runs only at 77 percent of its maximum production capacity. On an average, this shortfall represents $200 billion in annual revenue. A primary reason for this performance gap is the operational complexities in production and processing facilities. Control room operations of an offshore platform must analyze humongous amounts of data generated by as many as 30,000 sensors and also consider external factors that influence the production including temperature, humidity and wave heights.

Advanced analytics powered by machine learning leverage statistical methods to analyze highly complex and copious amounts of data and find patterns. These patterns can then be used to build algorithms which analyze the factors critical to production, efficiency and quality, alert operators about potential scenarios in the future that can impact rig operations, and enable them to respond in time. Advanced analytics can be used to identify bottlenecks and suggest prescriptive actions essential for smooth operating conditions.

Midstream

A logistical challenge affecting the oil and gas sector is the safe transportation of petroleum. To minimize potential risks, companies employ sensors and prognostic maintenance. This helps to identify different malformations in tankers and pipelines, including:

• Stress corrosion
• Fatigue cracks
• Seismic ground displacement

Downstream

Predictive maintenance

Oil and Gas companies can use predictive analysis to develop simulations that predict maintenance incidences. Ideally, predictive maintenance brings down the cost associated with untimely downtime maintenance.

Advanced analytics is developing at a rapid rate. It is expected that soon there will be fully autonomous control systems for multifaceted processing facilities in oil and gas companies. This is based on the fact that more industry executives believe that Big Data is the solution to boost their business operations. Oil and gas industries that have adopted advanced data analytics are more likely to become the future industry leaders.