In our last 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
In using source data, understanding the behavior of a well can be tied to the details found in the signals provided by that well. However, what if those signals are somehow masking issues rather than illuminating them? To better explain, consider the figure above. This shows the gas injection rate
