It seems somewhat simple, but the better the source data is, the better the resulting predictions or recommendations are going to be. Think of data as the rocket fuel for your AI journey. Here is a set of dimensions that can be used to evaluate source data: Coverage: Coverage is
We have created a tool that empowers both Engineers and Operators on the front lines. Operators are the “boots on the ground”, the front line, and empowering them is key to much of the success. The fewer programs that Operators have, the better things can be. Reducing the distance between
A well cohort describes the group of wells that OspreyData uses to create a modeled solution for your oilfield. Quality source data among well cohorts are essential to predictive analysis. In an artificial intelligence project, it is important to understand that all wells might not be candidates to participate in
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
Coverage and continuity of source data are important dimensions when evaluating source data. Coverage describes the number of data sources that are available for a well. Many of the models that are used in advanced systems, such as OspreyData, are combining sensor based machine learning models with physics based models.
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
Artificial Intelligence in Oil and Gas is becoming a very popular topic. MIT’s Sloan School of Management’s article, “Reshaping Business with Artificial Intelligence,” recently found that that 85% of corporate executives surveyed believe that AI will help their businesses gain or sustain competitive advantages. What might that look like for
