Well operations produce data from many sources. Combining datasets improves the insight for future well engineering. The benefits include identifying hazards, benchmarking performance, and reducing risk. However, the data analysis is often a manual process. Engineers spend non-productive time to find, import and transform the data before they can use it.

The shift to centralize data has increased its access to more offset wells. Yet it has not reduced the manual effort required to connect data and find value. Real advances in data analytics require the automation of the data processing tasks.

An automatic pipeline converts the incoming multi-source data to a common format. Two separate channels connect to the historical and to the real-time data sources. A parallel process then combines the data into organized structures. A self-service business intelligence (BI) tool provides access for multi-well integrated analysis.

The analysis is dynamic. First, new data arrives continuously from the real-time streams of concurrent wells. Second, each user can select from geographies, wells, rigs, crews, activities, or other attributes of interest. The BI tool displays the results instantly through user-defined visuals tailored for their specific needs.

The solution improved efficiency in performance-based contracts by helping:

• Eliminate the manual wrangling necessary to normalize and connect data for analytics. This often accounted for over 50% of the time spent during well engineering activities.

• Provide consistent measurement and reporting across all projects. It was easier to benchmark activities and highlight crews operating at higher efficiency.

• Asset teams identify risks and quantify potential improvement opportunities sooner. They were able to share experience and lessons learned during current well operations rather than waiting for the next well.

The innovativeness of this approach is dynamic inference. Continuous integration of real-time and historical data enables analysis on demand. It enables insights to be shared across a broader spectrum of geographic locations and a variety of operations. Most important it drives consistency and scales out expertise more efficiently.

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