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.
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
Artificial Intelligence in Oil and Gas has become 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
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,” 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
