Revisiting Time-Rate-Pressure Production Analysis — Where Are We Almost 40 Years Later?

Abstract

Abstract In the mid-1980s "time-rate-pressure" production analysis was proposed by this author to estimate reservoir properties, as well as in-situ volumes and EUR. This approach was primarily applied to conventional and tight reservoirs using "material balance decline curves" in the early 1990s and became known as "Well Performance Analysis" (WPA) before being rebranded by a vendor as "Rate Transient Analysis" (RTA) in the early 2000's. This analysis has been extended beyond fixed models (e.g., type curves for an unfractured or fractured well in a simple closed reservoir (e.g., a circle)) into analytical models for almost any case, as well as using numerical models for virtually any well reservoir configuration and complex multiphase fluid flow. The "RTA" methodology has become a mainstay of modern reservoir engineering, but deserves a "look-back" and a "look-forward" in terms of where we can best utilize new modelling approaches (physical and data-defined models) to improve diagnostics and analyses. The primary goal of this work is to emphasize to both the casual and the experienced user that by whatever name we call it — Well Performance Analysis (WPA), Production Analysis (PA), or Rate Transient Analysis (RTA) — the purpose of these methodologies is to provide a diagnostically-driven analysis and interpretation of time-rate-pressure (TPR) data. WPA/PA/RTA are not just data fitting exercises, nor are they limited to simplified analyses (e.g., some sort of straight-line plots), these are fully integrated data diagnostic and analysis tools that warrant care and attention. The present push to provide automated/augmented analyses is actually a noble (and predictable) outcome of the methodologies developed in the 1980s to provide estimates of reservoir properties from data that were not being used for that purpose, but as we develop more comprehensive (and more complex) analyses for TPR data, we must acknowledge that these data are inherently affected by operational aspects as well as measurement capabilities. In short, these are just data, and we need to stay focused on the diagnostic value of the data before attempting any analyses and interpretations — no matter how simple or complex those analyses and interpretations may be.