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

Event Detection: What Kind of Events Can AI Detect?

When it comes to event detection, a common question we receive is, what kind of events can AI detect?  We sub-categorize these events of interest into four different types: 1) High-Risk Events, 2) Sub-Optimal States, 3) Events Needing More Lead Time, and 4) Silent Killers. We will explain these event
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

Collaboration of Operators and Data Scientists Is Key

A major challenge has been to foster cooperation between two groups with such different skills and backgrounds, O&G operation and data science. Failure of many projects is due to failure of collaboration of these groups to understand one another to achieve shared objectives. Data science often thinks models can be
Predictive Analysis

Why Data Quality Matters

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,
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,