Empirical Correlations for Predicting Flow Rates Using Distributed Acoustic Sensor Measurements, Validated with Wellbore and Flow Loop Data Sets

Distributed acoustic sensing (DAS) is an emerging surveillance technology that is becoming increasingly popular in the oil and gas industry for real-time flow monitoring. However, there are limited studies that rigorously quantify flow rates using DAS. This work expands the existing literature by presenting a detailed workflow for accurately estimating fluid flow rates from DAS data using time- and frequency-domain signal processing. Three simple empirical correlation functions (linear, exponential, and cubic) are developed and tested to predict flow rates from DAS. The proposed correlations are demonstrated for flow rates ranging from 50 to 300 gallons per minute (GPM) in a vertical 5,163-ft-deep wellbore and from 12 to 36 GPM in a horizontal surface flow loop. Tests were performed using a single-phase flow of water as well as using synthetic oil-based drilling mud. Time-domain DAS processing using root-mean-square (RMS) value and frequency-domain processing using frequency band energy (FBE) is evaluated, followed by a statistical approach to minimize the influence of outliers. The RMS and FBE approaches are individually compared for flow prediction, and the performance of the correlations is rigorously evaluated on a blind data set that was not originally used for developing the correlations. For both the wellbore and flow loop data sets, a coefficient of determination (or R2) greater than 0.95 with an average flow rate prediction error of less than 10% was achieved for the best-performing correlation for the blind test data. The analysis procedure and workflow presented in this study can be adopted and extended to different operating conditions for quantitative flow rate prediction using DAS.