Advanced analytics in Oil and Gas
Explore use cases and perspectives
Machine intelligence represents the next chapter in the oil and gas advanced analytics journey. Cognitive systems employ technology and algorithms to automatically extract concepts and relationships from oil and gas big data and "understand" their meaning, learn independently from data patterns and prior experience, and extend what either humans or machines could do on their own.
- Untapped sources of nontraditional data
- Strategic risk sensing
- Dynamic customer profiling
- Conditioned-based monitoring
- Geospatial analytics and visualization
Untapped sources of nontraditional data
Recent advances in computer vision, pattern recognition, and cognitive analytics are making it possible for companies to shine a light on untapped sources of nontraditional data—image, audio, and video files; machine and sensor information generated by the Internet of Things (IoT); and raw data found in the "deep web"—to derive insights that can lead to better experiences and decision-making across the oil and gas industry.
In addition, machine intelligence enhances oil and gas data analytics through algorithmic capabilities that can augment employee performance, automate increasingly complex workloads, and develop "cognitive agents" that simulate both human thinking and engagement.
Strategic risk sensing
Unstructured oil and gas data analytics identify strategic risks
Unstructured oil and gas data analytics use natural language processing of both internal and external sources (including news and social media) to identify geopolitical, regulatory, customer, competitive, or industry trends that might have a significant impact on a company's reputation, financial standing, shareholder value, capital planning decisions, social responsibility, sustainability, product safety, and other areas. Early warning of developing strategic risks can help an organization to alter course prior to encountering significant challenges to its brand value and reputation, operational effectiveness, or market position.
Dynamic customer profiling
Purchasing oil and gas behavior data enables customized product suggestions
A wide range of oil and gas data and analytical tools can inform decision-making by helping sales representatives assess customer profiles, drill down into historic order volume, and develop more efficient processes for capturing deals. Add a mobile-based interface, and sales representatives can interact with customers in any location at any time. For example, sales reps can give lubricants customers recommendations based on similar customers' past purchase behavior. Dynamic customer profiling transforms upselling from a static procedure to a dynamic process based on robust, pre-defined calculations.
Advanced oil and gas analytics can predict equipment maintenance issues
Predictive oil and gas analytics, such as condition-based monitoring and variable analysis, enables companies to create scenario-based simulations to predict potential future maintenance events and perform any required maintenance prior to equipment damage. Predictive maintenance reduces costly reactive maintenance events and downtime and, as part of a comprehensive maintenance
Geospatial analytics and visualization
Predictive modeling allows oil and gas companies to evaluate pipeline risk and recommend remediation activities
Geospatial analytics and visualization allow oil and gas companies to evaluate pipeline risk based on vulnerability (e.g., incident record, inspection frequency, and