Exploring Unconventional Resources: Contrasting Methods for Analyzing and Optimizing Assets

Register to watch this webinar on-demand here. 

Sponsored by S&P Global Commodity Insights, this session will cover three major techniques geologists and engineers use to understand their unconventional resources to optimize assets for the future. These include Decline Curve Analysis (DCA), Rate Transient Analysis (RTA)-based numerical modeling, and most recently, data-driven machine learning. Expert engineers and geologists from S&P Global will walk you through each technique and how they are used to evaluate unconventional resources.

Physics-based models that incorporate static and dynamic field measurements, like DCA and RTA, are very practical in characterizing reservoir performance and predicting production forecasts. These approaches have been shown to be powerful in addressing uncertainty but come with limitations and approximations. On the other hand, machine learning has shown promise as a complementary approach in its ability to learn complex data relationships from a variety of data sources. This technique can reveal key drivers of production, isolate each attribute from one another, and give subsurface teams the ability to analyze the impact of geology versus engineering on production.