The Oil & Gas industry heavily relies on new technologies to increase and optimize its capabilities and help it discover more energy. Here are some key Big Data Analytics use cases in the oil and gas industry:-
Big Data Analytics in Oil & Gas Industry
Upstream & Midstream Optimization:–
Challenges in the Upstream process of the oil and gas industry are to improve the performance of existing resources and search for new resources to maintain continuity in the supply of crude oil.
By leveraging Big Data and advanced analytics, the exploration efforts can be enhanced, productive seismic traces can be identified, and drill accuracy can be improved. By predicting future performance from the available historical data, Big Data facilitates optimized oil recovery rates, and optimized production, determines the optimum cost, and assesses new prospects.
ExxonMobil, the world’s 9th largest company, in partnership with the Massachusetts Institute of Technology, is working towards fulfilling the growing demand for energy and is going to design AI robots for Hydrocarbon Seep Detection.
We can optimize midstream operations by applying Predictive Analytics in the transportation and storage planning of oil and Gas.
Oil refineries use water in huge volumes in the processing of crude oil and gas. An American digital oilfield solutions company, Digital H2O has developed a predictive algorithm-based software system that is providing solutions for water management in the oil and gas industry.
Cloud-based services also help distributors by analyzing data effectively and increasing modeling speed for forecasting revenues. California-based Oracle Cloud is using Oracle EPM Cloud for this purpose.
Human safety and Environmental Conditions:-
Another major challenge of the oil and gas industry is a threat to human life, as well as to the environment, during the drilling process. Harmful emissions while extracting natural resources lead to severe health issues for the workers.
With Big Data and Predictive Analytics, new resources and harsh, remote locations can be identified. AI robots can detect any faults in the equipment and send alerts in case of any gas leaks. Safety can be ensured by replacing the personnel with robots, which can work in a dangerous and tedious environment, thereby optimizing the performance and cost incurred.
Unstructured data like maintenance reports, weather reports, media reports, etc can be analyzed with Natural Learning Processing and facilitate with business solutions for an effective decision.
Emma and Ethan are AI assistants for customer support launched by the leading global energy company, Shell. According to Shell, they can answer any technical queries related to the lubricant.
Another approach of AI assistants is in transforming and summarizing safety meetings that can help workers in the decision-making of solving a critical problem.
Upstream exploration and production data are very complex in nature with large amounts of data. We, hence, need a proper data management solution to aggregate and cleanse data collected and stored from different sources.
Modern Analytics tools such as seismic software, data visualization, etc are very useful in maintaining data. They facilitate effective time management, identification, and mapping of new oil reservoirs, and optimize operational costs.
Big Data has a number of applications in the oil and gas industry. Sectors of the oil and gas industry generate lots of structured and unstructured sensor data from upstream, downstream, and midstream. With Big Data Analytics we can explore these data and find the best location for drilling, improve recovery rates, avoid accidents and reduce environmental impact. Big Data Analytics helps in avoiding damage and reducing the downtime of the high maintenance machinery. It provides actionable insights that will be helpful in the critical decision-making process saving time.
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