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

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.