Sat. Sep 26th, 2020

Today’s oil and gas operations need more than real-time monitoring; organization in this industry need to know what’s coming. Real-time predictive analytics has the flexibility to adjust to today’s priorities—and the agility to be prepared for tomorrow’s demands.

Operational insights make staying on time, on budget and in bounds possible and help remove operational complexity. IBM offers analytics solutions that are designed to do just that, while helping reduce key asset maintenance costs and preventing equipment failures. The goal is to enhance exploration and production, improve global operations both upstream and downstream, and improve refining and processing efficiency.

But saying this on (digital) paper isn’t enough. Sometimes, sharing stories to make a point has greater impact. Here are three cases in which IBM helped clients achieve differentiation:

  • LNGTOP Tech Co. Ltd.  This partially-owned subsidiary of Zhangjiagang Furui Special Equipment Co. Ltd.—a leading provider of liquefied natural gas (LNG) supply chain equipment—wanted a better way to incorporate big data and Internet of Things data into its equipment. By working with IBM to integrate sensors and sensor data, the organization was able to achieve a high-level view into customer demand, supply chain needs and operational optimization.
  • Repsol SA – What can predictive analytics do for an international global energy company with revenues of more than €44.67 billion? In this highly competitive industry, adapting and staying ahead of the game is vital. And there’s nothing more innovative than applying a symbiotic, cognitive approach to operations. In 2014, Repsol did just that when it partnered with IBM Research to put cognitive technologies to work. The organization mapped out two new upstream solutions: one to improve the exploration block bids, and the other to optimize around well drilling strategies.

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