Intelligent Distributed Acoustic Sensing for In-well Monitoring

Abstract

Interval Control Valves ICVs and Inflow Control Devices ICDs can enable optimization of the production and injection profile along the wellbore. The ICV devices are operated to increase the hydrocarbon recovery and prevent unwanted fluid production. The ICDs are selected and positioned to optimize the water flood profile. Although the operations of devices are well understood, the optimization of these devices in-situ is complex. The real-time monitoring of fluid flow along the wellbore can provide valuable information to set the position of the inflow devices and optimize the fluid flow in multilateral zones. The intelligent Distributed Acoustic Sensor (iDAS) uniquely allows the simultaneous recording of acoustic energy along many kilometers of optical fiber deployed along the wellbore. The system uses a novel digital optoelectronics detection technique that captures the amplitude and phase of the acoustic waves generated and propagated along the wellbore over a wide frequency (1mHz – 100kHz) range with a high dynamic range (>120dB). A number of signal processing techniques have been developed for processing a large array of acoustic data that can be recorded along the wellbore for monitoring the inflow at different zones. The iDAS system can be used with single-mode as well as multimode optical fibers. The iDAS system was retrofitted to existing optical fibers that were already installed along several wells in Saudi Arabia. The acoustic noise energy generated across the inflow devices and propagating along the wellbore tubing was recorded. The acoustic noise spectrum can be used to monitor the fluid flow through the inflow devices. Using array processing, the speed of sound can be determined over several sections of the tubing to identify the fluid composition. This paper reports on the results of the field trial carried in 2012 in several wells equipped with inflow devices.

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

“…Several different approaches can be used to measure the multiphase flow, including but not limited to, conventional dedicated hardware-based flow meters [ 7 , 10 , 14 , 15 , 43 ], virtual flow meters [ 17 , 18 , 39 , 42 , 45 , 46 , 47 ], and distributed sensor flow estimations [ 8 , 37 , 40 , 48 ]. This paper focuses on providing a comprehensive review of the last approach, using distributed sensors with physical flow modelling and machine learning algorithms for multiphase flow estimation.…”

“…Therefore, the data should be divided into series of blocks for processing; the size of the blocks will represent the spatial and temporal resolution of the flow profile. It should be pointed out that spatial overlapping of consecutive blocks is often employed during processing in order to increase the spatial resolution and enhance the repeatability [ 48 ].…”

“…In fact, the SoS has been used as a strong feature to estimate the type of medium where the sound travels. The method is called phase estimation [ 9 , 48 ]. Figure 7 shows a possible range of mixture fluid percentages given the SoS within those fluids.…”

“…Even though quantitative analysis in [ 85 ] was limited, the work showed an interesting qualitative analysis that tied together well acoustic signatures and well behaviour. Xiao et al [ 48 ], also presented DAS data obtained using cables installed within producing wells. The work presented several data analysis technique for modelling and enhancing DAS performance, including RMS of acoustic energy, amplitude estimation, FFT transformation, and SoS with flow analysis.…”

Acoustic Sensors to Measure Speed of Oil Flow in Downhole Pipes

“…The nature of this scattered light is affected by tiny strain events within the optical fiber structure which themselves are determined by the localized acoustic signal. By recording the returning signal against time, a measurement of the acoustic field all along the fiber can be determined [13]. The iDAS has a frequency range from millihertz to hundreds of kilohertz.…”