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Oil-Water Flow Monitoring in Wellbores with Inflow Control Valves Using Distributed Acoustic Sensing.

Chuang Xiao1, Ge Jin1, Yilin Fan2

  • 1Geophysics Department, Colorado School of Mines, Golden, CO 80401, USA.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Distributed Acoustic Sensing (DAS) monitors oil-water two-phase flow in intelligent completions. This method uses acoustic data to predict flow rates and pressure drops, offering potential for real-time well management.

Keywords:
ICVdistributed fiber-optic sensingmultiphase flowproduction monitoring

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Published on: October 31, 2011

Area of Science:

  • Petroleum Engineering
  • Sensor Technology
  • Acoustic Monitoring

Background:

  • Intelligent completion technologies, like Inflow Control Valves (ICVs), are crucial for managing complex well production.
  • Conventional sensors face limitations in space-constrained and harsh downhole environments, hindering flow rate and phase fraction quantification.
  • Distributed Acoustic Sensing (DAS) offers a novel solution by transforming fiber-optic cables into dense acoustic sensor arrays.

Purpose of the Study:

  • To develop and validate a DAS-based methodology for characterizing oil-water two-phase flow in intelligent completions.
  • To explore the potential of DAS for real-time flow monitoring and control in challenging downhole conditions.
  • To correlate acoustic spectral features with flow parameters like pressure drop and flow rate.

Main Methods:

  • Collected acoustic data using DAS in horizontal experiments simulating field conditions.
  • Applied Short-Time Fourier Transform (STFT) to generate time-frequency spectrograms from DAS data.
  • Extracted dynamic spectral features and correlated them with pressure drop and flow rate.
  • Trained a power-law model using spectral features to predict flow rate and phase fractions.

Main Results:

  • Demonstrated strong predictive capability for pressure drop and flow rate under laboratory conditions.
  • Identified distinct frequency band behaviors in acoustic data corresponding to fluid changes.
  • Highlighted the potential of DAS for multiphase flow diagnostics in intelligent completions.
  • Water cut prediction proved challenging due to complex flow condition-DAS response relationships, identified for future work.

Conclusions:

  • DAS presents a promising technology for real-time multiphase flow monitoring in intelligent completions.
  • The developed data-driven framework offers a new approach for leveraging acoustic sensing in oil and gas production.
  • Further research is needed to refine water cut prediction accuracy for field applications.