Four Answers to the Question: What Can I Learn From Analytics?

Abstract

The future of data-driven analysis in exploration and production (E&P) will depend on whether it can add value in the field. Four examples of what is possible were presented recently at the 2016 Unconventional Resources Technology Conference (URTEC) in San Antonio with the authors of papers posing questions such as: Does it matter if a lateral is drilled toe-up or toe-down? What are the changes in a fracturing design that will offer the biggest production payoff? Why has the drilling slowdown not depressed production from unconventional gas plays? What is the half-life of my field, and why should I care? The stories below bring together advanced statistical analysis methods with multiple names: analytics, big data, machine learning, and even a “physio statistical engine for automatic stochastic production forecasting.” All this new E&P math is aimed at identifying patterns and relationships that otherwise would be missed. But automatic and self-learning does not mean all-knowing. For example, one of the programs used to analyze fracturing used facial recognition to classify pressure changes during each stage to sort them into different classes. Production data revealed that one of the classes of fractures, characterized by a pressure bump near the end of the stage that could mean trouble, were often more productive than those that went exactly according to plan. But it was up to the completion team to figure out how to apply that observation. The process is “brutally empirical,” said Roger Anderson, president of AKW Analytics, who led the study for Range Resources. He said the method is good at identifying what is happening, but it is unable to determine why or how it happened.