When addressing wells, or cohorts of wells, eventually the question comes to mind. Why does the quality of the data matter? For production, if an organization is consistently using allocated production, actual production may vary by roughly 10%. At OspreyData, we have seen much higher rates when the allocated wells

When we consider the good source data required to power machine learning,
we must consider the full life-cycle of the well. Most use-cases that OspreyData is building have moved beyond utilizing just a set of time-series sensor values. Well designs and completion reports provide insight to the physical aspects of the

In Production Operations, more and more devices have data collection and the type and frequency of the data collected is growing exponentially. We often hear from organizations about their struggles with their available data. It may be collection, or retention, or the quality. This is even regardless of the size of

In case you missed the live webcast on Wednesday, you can still watch our webcast replay! In the asset-heavy industry of Oil & Gas, leveraging data and undergoing a digital transformation is top-of-mind for many producers. As companies seek to keep valuations high by maximizing production and operating as efficiently

We present our Webcast Replay entitled “Don’t Lose the AI Race: Why You Need a Data Quality Strategy in Oil & Gas.”  In this webcast, key members of OspreyData’’s Executive Team discuss one of the most crucial issues being faced in the Oil & Gas AI Revolution: developing a robust

In a previous blog, we covered the importance of quality data across well cohorts.  In this last blog of our series on data quality, we will address why data quality matters. For production, if an organization is consistently using allocated production, actual production may vary by roughly 10%. At OspreyData,

The consistency and connectedness of source data are also important dimensions when evaluating source data.  Consistency refers to the frequency of updates or new values in a time series data stream, while connectedness indicates the ability to trace a thread of connections for a well across all of the source

Prediction quality is not the same thing as data quality.  Last week, we suggested that the concept of “more data is always better” must be tempered with a thoughtful assessment of how that source data provides additional information from your well.  This speaks to a position of not more data,

When we consider the good source data required to power machine learning,
we must consider the full life-cycle of the well. Most use-cases that OspreyData is building have moved beyond utilizing just a set of time-series sensor values. Well designs and completion reports provide insight to the physical aspects of the