Oil and gas operators must process, analyze, and react in real time to increasing volumes and rates of streaming data in order to improve safety, compliance, and profit. For example, real-time analysis of streaming data from drilling rig sensors, intelligent wells, and digital oilfield installations enables early detection of drilling hazards and pending equipment failures, thereby reducing rig time, intervention, and shut-ins.

Data problems include the need for real-time integration of operational systems and an inability to maintain accurate and current information across all systems and data warehouses. Poor integration leads to duplication of data and systems, and a lack of visibility across all monitored assets, resulting in the delayed identification of the root cause of problems.

This paper presents a streaming data architecture that is capable of processing and analyzing rig data and smart oilfield data in real time. Further, the architecture supports the continuous, streaming integration of any and all of an organization's sensor data, operational platforms, and data warehouses. The approach aggregates and analyzes live, streaming data on the fly, without the need to store the data first. Large volumes of high velocity streaming data in any format and from all sources can be processed continuously and delivered to existing operational systems and data warehouses.

The architecture increases efficiency by removing data silos and by ensuring that all systems and data warehouses are kept accurate and up to date in real time. This results in faster decisions that are better informed and based on consistent, accurate, and timely information.

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