Abstract:An unexpected and unwanted influx of gas or "kick" into the wellbore during hydrocarbon drilling can cause catastrophic blowout incidents, resulting in human casualties, ecological damage, and asset losses. The ability of the oil and gas industry to control gas kick depends on our ability to accurately detect and monitor gas migration in a borehole in real-time. This study demonstrates the application of optical fiber-based Distributed Acoustic Sensors (DAS) for early detection and monitoring of gas in wellbor… Show more
“…DAS systems, began to emerge in the late 2000s, have been used for early-warning system for earthquake and seismic activity monitoring [ 30 ], hydraulic fracture detection [ 29 ], traffic pattern analysis and monitoring [ 31 ], gas leak detection [ 77 ], pipeline surveillance [ 78 ], Vertical Seismic Profiling (VSP) [ 28 ], Steam Assisted Gravity Drainage (SADG) monitoring [ 79 ], and in-well flow profiling that not only used in the laboratory or field trials but also in the real time operations [ 6 , 8 , 32 , 33 ]. Combining DTS and DAS data has also been explored, for example, to address the three-phase flow estimation of oil, water, and gas for the downhole well simulations [ 8 ], which was less accurate and seems unsolvable when only using DAS or DTS alone.…”
Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.
“…DAS systems, began to emerge in the late 2000s, have been used for early-warning system for earthquake and seismic activity monitoring [ 30 ], hydraulic fracture detection [ 29 ], traffic pattern analysis and monitoring [ 31 ], gas leak detection [ 77 ], pipeline surveillance [ 78 ], Vertical Seismic Profiling (VSP) [ 28 ], Steam Assisted Gravity Drainage (SADG) monitoring [ 79 ], and in-well flow profiling that not only used in the laboratory or field trials but also in the real time operations [ 6 , 8 , 32 , 33 ]. Combining DTS and DAS data has also been explored, for example, to address the three-phase flow estimation of oil, water, and gas for the downhole well simulations [ 8 ], which was less accurate and seems unsolvable when only using DAS or DTS alone.…”
Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.
“… Figure 2 ( a ) DAS and DTS installation on the test-well at PERTT. ( b ) Test-well schematic showing the location of the four downhole gauges and fiber-optic DAS and DTS 32 . …”
Section: Data Acquisitionmentioning
confidence: 99%
“…( b ) Test-well schematic showing the location of the four downhole gauges and fiber-optic DAS and DTS 32 . …”
Section: Data Acquisitionmentioning
confidence: 99%
“…2 b). The objective of the experiments was to observe and characterize the gas rise in water using the fiber-optic sensors and downhole gauges as described in detail in the references 32 , 33 .…”
Section: Data Acquisitionmentioning
confidence: 99%
“…A detailed interpretation of the gas signature can be found in the references 33 . Since the LFDAS data are sensitive to both dynamic strain and temperature changes, Band-LF (0 to 2 Hz) has both positive and negative numbers depending on whether the fiber section is experiencing compressive or tensile strain or heating or cooling phenomenon 32 . Figure 3 Waterfall plots showing the DAS values in the different frequency bands for Dataset-1.…”
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.
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