Auto Detect Flow Regimes in Spotfire: Supercharge your Rate Transient Analysis Workflow Part 1

We are starting a new blog post series, on Rate Transient Analysis (RTA) in Spotfire. In this first part we will discuss a mathematical framework to automate the flow regime detection in RTA. Unlike traditional reservoir engineering methods such as Decline Curve Analysis (DCA), RTA incorporates both fluid rates and flowing pressures, where the end goal is to understand the fluid flow in the reservoir. The industry has been doing this with Pressure Transient Analysis (PTA) for many years and RTA is built on the same theory, we are just using the data in a different way. I will refer you to the excellent presentation by Blasingame as a refresher on the fundamental theory for RTA. Following figure represents the important flow regimes in a Multi Fractured Horizontal Wells (MFHW) in conventional/unconventional reservoirs:

Blasingame (2015). Image Source: http://www.pe.tamu.edu/blasingame/data/z_Presentations/20151209_(Blasingame)_Pres_IPTC_Ask_the_Expert_(pdf).pdf

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Nitin is a Data Scientist at Ruths.ai working passionately towards helping companies realize maximum potential of their data. He has experience with machine learning problems in clustering, classification and regression applying ensemble and Bayesian approaches with toolsets from R, Python and Spotfire