Predictive Analysis

The Four C’s of Data Quality

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
Predictive Analysis, Unified Monitoring

Production UNIFIED MONITORING: Tools to Enable the Front Lines

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
Predictive Analysis

Predictive Analysis Requires Quality Data Across a Well Cohort

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
Predictive Analysis

Consistency and Connectedness of Source Data Quality

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
ospreydata predictive analysis coverage and continuity equate to source data quality
Predictive Analysis

Coverage and Continuity Equate to Source Data Quality

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.
Predictive Analysis

Prediction Quality: The 4C’s of Source Data Evaluation

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,
Predictive Analysis

Good Source Data Goes Beyond Sensors

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
Predictive Analysis

What Artificial Intelligence in Oil and Gas Brings to Production

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