Well-scale demonstration of distributed pressure sensing using fiber-optic DAS and DTS

Gerald K Ekechukwu,Jyotsna Sharma

doi:10.1038/s41598-021-91916-7

Abstract

In this study, we used data from optical fiber-based Distributed Acoustic Sensor (DAS) and Distributed Temperature Sensor (DTS) to estimate pressure along the fiber. A machine learning workflow was developed and demonstrated using experimental datasets from gas–water flow tests conducted in a 5163-ft deep well instrumented with DAS, DTS, and four downhole pressure gauges. The workflow is successfully demonstrated on two experimental datasets, corresponding to different gas injection volumes, backpressure, injection methods, and water circulation rates. The workflow utilizes the random forest algorithm and involves a two-step process for distributed pressure prediction. In the first step, single-depth predictive modeling is performed to explore the underlying relationship between the DAS (in seven different frequency bands), DTS, and the gauge pressures at the four downhole locations. The single-depth analysis showed that the low-frequency components (< 2 Hz) of the DAS data, when combined with DTS, consistently demonstrate a superior capability in predicting pressure as compared to the higher frequency bands for both the datasets achieving an average coefficient of determination (or R2) of 0.96. This can be explained by the unique characteristic of low-frequency DAS which is sensitive to both the strain and temperature perturbations. In the second step, the DTS and the low-frequency DAS data from two gauge locations were used to predict pressures at different depths. The distributed pressure modeling achieved an average R2 of 0.95 and an average root mean squared error (RMSE) of 24 psi for the two datasets across the depths analyzed, demonstrating the distributed pressure measurement capability using the proposed workflow. A majority of the current DAS applications rely on the higher frequency components. This study presents a novel application of the low-frequency DAS combined with DTS for distributed pressure measurement.

Highlights

In this study, we used data from optical fiber-based Distributed Acoustic Sensor (DAS) and Distributed Temperature Sensor (DTS) to estimate pressure along the fiber
The input features for the model are the DAS and DTS data, while the target output variable is the change in pressure relative to the initial pressure at the first time-step (∆P). 70% of the data were randomly selected for model training and the remaining 30% is used for blind testing
While data from two downhole gauge locations was used for training the model to demonstrate the workflow, even a single pressure gauge data may be used for model training if that is the only pressure measurement available with co-located DAS and DTS information

Summary

Introduction

We used data from optical fiber-based Distributed Acoustic Sensor (DAS) and Distributed Temperature Sensor (DTS) to estimate pressure along the fiber. The single-depth analysis showed that the low-frequency components (< 2 Hz) of the DAS data, when combined with DTS, consistently demonstrate a superior capability in predicting pressure as compared to the higher frequency bands for both the datasets achieving an average coefficient of determination (or R2) of 0.96. This can be explained by the unique characteristic of low-frequency DAS which is sensitive to both the strain and temperature perturbations. The DTS uses the backscattered Raman component to measure temperature, DAS uses the backscattered Raleigh component to measure vibrations or dynamic strain, while DSS uses the Brillouin component to measure strain[18,19,20]

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Discussion

Conclusion