
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