On the Use of Distributed Temperature Sensing and Distributed Acoustic Sensing for the Application of Gas Lift Surveillance

Hemink, Gijs; Horst, Juun van der

doi:10.2118/191130-pa

Cited by 9 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even though, the DTS was derived from commercial thermal simulator [ 34 ], the work shows a promising result on combining SoS from DAS and

from simulated DTS to provide an accurate multiphase estimation. Hemink et al [ 88 ], on the other hand, showed that

from actual DTS does not always provide reliable results as a straightforward thermal model to identify gas-injection. Instead, the work proposed an improvement by considering the temperature response measured by DTS where the fibre is clamped and bending away from the tubing.…”

Section: Physical Flow Modellingmentioning

confidence: 99%

See 1 more Smart Citation

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

Arief

Wiktorski

Thomas

2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…Even though, the DTS was derived from commercial thermal simulator [ 34 ], the work shows a promising result on combining SoS from DAS and

from simulated DTS to provide an accurate multiphase estimation. Hemink et al [ 88 ], on the other hand, showed that

Section: Physical Flow Modellingmentioning

confidence: 99%

“…Thermal-and-hydraullic modelling [88] 2018 DAS and DTS Thorough analysis on combining DAS and DTS data for identifying gas flow.…”

Section: Physical Flow Modellingmentioning

confidence: 99%

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

Arief

Wiktorski

Thomas

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The DTS data has the potential to detect multiple points of injection and determine gas rate through each injection point. All these are possible as the event is happening and require no shut-in when measuring DTS temperature data [8]. Therefore, DTS helps capture the temperature response throughout the entire length of the well simultaneously as the event is happening.…”

Section: Dts Monitoring Of Well Completion Processesmentioning

confidence: 99%

Distributed Temperature Sensing Monitoring of Well Completion Processes in a CO2 Geological Storage Demonstration Site

Lee

Park

Lee

et al. 2018

Sensors

View full text Add to dashboard Cite

The Distributed Temperature Sensing (DTS) profiles obtained during well completion of a CO2 monitoring well were analyzed to characterize each well completion process in terms of temperature anomalies. Before analysis, we corrected the depth by redistributing the discrepancy, and then explored three temperature calibration methods. Consequently, we confirmed the depth discrepancy could be well corrected with conventional error redistribution techniques. Among three temperature calibration methods, the conventional method shows the best results. However, pointwise methods using heat coil or in-well divers also showed reliable accuracy, which allows them to be alternatives when the conventional method is not affordable. The DTS data revealed that each well completion processes can be characterized by their own distinctive temperature anomaly patterns. During gravel packing, the sand progression was monitorable with clear step-like temperature change due to the thermal bridge effect of sand. The DTS data during the cementing operation, also, clearly showed the progression up of the cement slurry and the exothermic reaction associated with curing of cement. During gas lift operations, we could observe the effect of casing transition as well as typical highly oscillating thermal response to gas lifting.

show abstract

“…Hermank et al proposed the use of distributed thermal sensing (DTS) and distributed acoustic sensing (DAS) for the gas lift surveillance [12]. The method is less invasive and can provide a precise injection point based on changing temperature and sound signals.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model for Determining Dual String Gas Lift Split Factor in Oil Producers

et al. 2019

View full text Add to dashboard Cite

Upstream oil production using dual string completion, i.e., two tubing inside a well casing, is common due to its cost advantage. High pressure gas is employed to lift the oil to the surface when there is insufficient reservoir energy to overcome the liquids static head in the tubing. However, gas lifting for this type of completion can be complicated. This is due to the operating condition where total gas is injected into the common annulus and then allowed to be distributed among the two strings without any surface control. High uncertainties often result from the methods used to determine the split factor—the ratio between the gas lift rate to one string over the total gas injected. A hybrid model which combined three platforms: the Visual Basics for Application programme, PROSPER (a nodal analysis tool) and Excel spreadsheet, is proposed for the estimation of the split factor. The model takes into consideration two important parameters, i.e., the lift gas pressure gradient along the annulus and the multiphase pressure drop inside the tubing to estimate the gas lift rate to the individual string and subsequently the split factor. The proposed model is able to predict the split factor to within 2% to 7% accuracy from the field measured data. Accurate knowledge of the amount of gas injected into each string leads to a more efficient use of lift gas, improving the energy efficiency of the oil productions facilities and contributing toward the sustainability of fossil fuel.

show abstract

On the Use of Distributed Temperature Sensing and Distributed Acoustic Sensing for the Application of Gas Lift Surveillance

Cited by 9 publications

References 7 publications

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

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

Distributed Temperature Sensing Monitoring of Well Completion Processes in a CO2 Geological Storage Demonstration Site

Hybrid Model for Determining Dual String Gas Lift Split Factor in Oil Producers

Contact Info

Product

Resources

About