When you consider an Artificial Intelligence and Digital Oilfield project, it is important to understand that all of the wells might not be candidates to participate in the building and training of the solution. We call the set of wells that will be used for building our machine learning models a
In a previous webcast, OspreyData’s lead data scientist Dr. Venkat Putcha and Vice President of Operations Tim Burke spoke about the “Top 3 Problems AI Solves in Artificial Lift Production”. (Access a webcast replay of this here.) These problems are faced by most if not all organizations producing oil and gas
Learning how to optimize across a high volume of wells is one of the key features of our Production Intelligence Solutions. Not too long ago we had the WTI oil prices below $40/barrel. At this time, producing more oil was not economically viable, so reducing the production costs through optimization
For an operator, each day brings a different set of challenges as you look to improve your production and boost your profitability. Here at OspreyData, we are firm believers that you can use data in order to more efficiently tackle your daily workloads. That data helps you transform from being
OspreyData offers solutions that allow operators to tackle problems sooner and more efficiently. Solutions that minimize downtime, maximize recovery and increase the rate of return on your assets. These solutions work for a well, a field, or across an entire play. Often the process to build a Digital Oilfield is
Our OspreyData science and methodological approach is simple: Our expertise meets your expertise to empower your wells. Whether is it ESP, Rod Pump, Gas Lift, Plunger Lift, or Downhole Analysis, our approach is to help you optimize production. Below, we will discuss our our OspreyData science works: 1. We monitor
The impact of Artificial Intelligence on an organization is an important topic when considering machine learning. In a previous blog entitled, What Artificial Intelligence in Oil and Gas Brings to Production, we discussed a summary of key solutions that AI can target for problems facing most oil and gas organizations
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
