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Reinventing the Oil and Gas Industry with Cognitive Computing

Oct 3, 2017

The future has arrived, people’s prediction that one-day computers will be smarter than humans exceeding human cognition is no more science fiction. The power of cognitive computing coupled with human intuition and insights is capable of changing the landscape for oil and gas industries. Cognitive computing will allow oil and gas companies to make better, faster, and more informed business decisions by leveraging historical as well as real-time data. Such computing power will give humans the freedom to use the saved time in high-value activities; thereby, driving profits, quality, and ensuring safety.

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Improved Decision-Making Through Cognitive Computing

Cognitive computing involves self-learning algorithms that mimic human thinking by using data to recognize patterns to automate the problem-solving process without human assistance. Oil and gas companies must address issues such as energy-price collapse, rising resource development cost, new safety, and environmental responsibilities, and reserves replenishment and extraction to improve productivity and agility. For instance, companies can save millions of dollars by accurately estimating the potential for a stuck drill-bit by monitoring all the drilling sensor data and comparing it with drilling reports and geological data. Additionally, it can greatly improve the way oil companies visualize and map oil plays by helping them make strategic decisions on exploration and production activities.


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Benefits of Cognitive IoT

1. Companies can save millions of dollars in downtime maintenance by using cognitive IoT to predict incidents like stuck drill-bit accurately

2. Analyzing machinery data such as spec sheets, work order history, next scheduled maintenance, and current condition with cognitive computing can inform system engineers to take the best actions to enhance maintenance operations

3. Companies can use satellite, aircraft, drone, and historical data to predict the optimum time to drill for oil in order to mitigate weather risks

4. Instead of waiting for sensors to trigger an alarm when reaching threshold levels, companies can use predictive analytics to identify anomalies in the data and optimize operations

5. Companies can draw vast engineering information and enhance their problem-solving abilities by learning from past behaviors, experiences, data, and resolved incidences

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