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,” found that 85% of corporate executives surveyed believed that AI would help their businesses gain or sustain competitive advantages. What does that look like for oil and
Six steps to automate your oil field is our discussion today. In yesterday’s blog, we discussed the tedious past and current methods for trying to optimize gas lift production. We found that it was a laborious process that could not physically allow operators enough time to adequately monitor each well
Mathematical Allocation verses Physics-Based Allocation? Today we are going to discuss estimating production accurately and levering your data. Estimating production rates may seem likely a trivial thing, but we have learned that many operators have put their wells on test separators only once in 15 days, or in some cases
Gas Lift Optimization Models is the topic of our discussion today. Recently, we did a series of ROI case studies to demonstrate the impact of early detection on your bottom line. In today’s blog, we are sharing a sample of 60 days of results of our gas injection recommended implementation.
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.
