Addressing the Limitations of Oil and Gas 4.0 Surrounding Distributed Fiber Optic Data Streams

Abstract This paper outlines how the problem of previously unmanageable data volumes produced by distributed fiber optic well monitoring systems is solved through the use of the latest sensing and analytics platform. The platform significantly reduces fiber optic data volumes enabling data to be streamed, processed, stored and visualized; all in real-time. The platform was effectively utilized for real-time data processing and visualization of well injection profiles of fields in the Middle East. The platform addresses the big data challenge associated with streaming distributed fiber optic data in three key areas: Edge processing reduces Distributed Fiber Optic (DFO) data rates by orders of magnitude so it can be streamed from the edge to the end user in real-time.Tiled data storage utilizes innovative data storage strategy to enable fast query responses whether visualizing years or just seconds of DFO data.Elastic infrastructure of processing and storage enables the platform to seamlessly scale and handle variable data rates. Raw Distributed Acoustic Sensing (DAS) data can be generated at rates of 100 MBs per second and cannot feasibly be transferred over a standard internet connection. The sensing and analytics platform's algorithms extract features at the edge which reduce data rates by three orders of magnitude whilst still preserving all key information from the data. Processed DFO data is aggregated and tiled in real-time at tens of different resolutions with respect to both time and fiber length. This enables sub-second query response times even when requesting DFO data across years of historical data. All platform processing logic is designed to run asynchronously on serverless infrastructure. This enables the platform's infrastructure to rapidly scale up or down in response to variable data rates. The result is a cloud-based visualization dashboard capable of displaying DFO data in near real-time across any time range and fiber length. Use of this sensing and analytics platform allowed for seamless streaming of fiber optic data on the Middle East field for injection monitoring, allowing the operator to visualize injection profiles and optimize the injection program in real-time. This sensing and analytics fiber management platform enables the user to highly successfully stream and visualize DFO data in real-time. It enables visibility into the subsurface for production and injection wells, enabling field-wide efficiencies and optimization.

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Leveraging coherent wave field analysis and deep learning in fiber-optic seismology

10.5194/egusphere-egu21-7856 ◽

2021 ◽

Author(s):

Benjamin Schwarz ◽

Korbinian Sager ◽

Philippe Jousset ◽

Gilda Currenti ◽

Charlotte Krawczyk ◽

...

Keyword(s):

Deep Learning ◽

Wave Field ◽

Real Time ◽

Learning Strategies ◽

Data Storage ◽

Large Scale ◽

Fiber Optic ◽

Positive Impact ◽

Field Analysis ◽

Coherent Wave

Fiber-optic cables form an integral part of modern telecommunications infrastructure and are ubiquitous in particular in regions where dedicated seismic instrumentation is traditionally sparse or lacking entirely. Fiber-optic seismology promises to enable affordable and time-extended observations of earth and environmental processes at an unprecedented temporal and spatial resolution. The method&#8217;s unique potential for combined large-N and large-T observations implies intriguing opportunities but also significant challenges in terms of data storage, data handling and computation.Our goal is to enable real-time data enhancement, rapid signal detection and wave field characterization without the need for time-demanding user interaction. We therefore combine coherent wave field analysis, an optics-inspired processing framework developed in controlled-source seismology, with state-of-the-art deep convolutional neural network (CNN) architectures commonly used in visual perception. While conventional deep learning strategies have to rely on manually labeled or purely synthetic training datasets, coherent wave field analysis labels field data based on physical principles and enables large-scale and purely data-driven training of the CNN models. The shear amount of data already recorded in various settings makes artificial data generation by numerical modeling superfluous &#8211; a task that is often constrained by incomplete knowledge of the embedding medium and an insufficient description of processes at or close to the surface, which are challenging to capture in integrated simulations.Applications to extensive field datasets acquired with dark-fiber infrastructure at a geothermal field in SW Iceland and in a town at the flank of Mt Etna, Italy, reveal that the suggested framework generalizes well across different observational scales and environments, and sheds new light on the origin of a broad range of physically distinct wave fields that can be sensed with fiber-optic technology. Owing to the real-time applicability with affordable computing infrastructure, our analysis lends itself well to rapid on-the-fly data enhancement, wave field separation and compression strategies, thereby promising to have a positive impact on the full processing chain currently in use in fiber-optic seismology.

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Software Design of a Railway Signal Monitoring System based on Optical Fiber Sensing

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i08.9180 ◽

2018 ◽

Vol 14 (08) ◽

pp. 134

Author(s):

Ma Chun-ying ◽

Li Biqing

Keyword(s):

Real Time ◽

Monitoring System ◽

Data Storage ◽

Online Monitoring ◽

Railway Track ◽

Time Data ◽

Railway Systems ◽

Management Platform ◽

Railway Signal ◽

Optical Fiber Sensing

The current railway track circuit monitoring system is prone to disturbances that can result in accidents. Meanwhile, basic signaling equipment is slow and cannot achieve satisfactory real-time data acquisition speed. This study aims to solve the aforementioned problems by designing an online monitoring and management platform for railway signal infrastructure, which is based on the graphical programming language LabVIEW. Online monitoring and management of railways’ basic signaling equipment allow real-time collection and communication of various signal equipment data. These processes also enable signal processing, chart display, acousto-optic alarm, user authority management, data storage, data query analysis, and report printing. The test results show that the LabVIEW-based basic signaling equipment for monitoring and managing railway systems can transmit data correctly and steadily, thereby resulting in convenient and ideal operation.

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Real-time terahertz meta-cryptography using polarization-multiplexed graphene-based computer-generated holograms

Nanophotonics ◽

10.1515/nanoph-2020-0110 ◽

2020 ◽

Vol 9 (9) ◽

pp. 2861-2877 ◽

Cited By ~ 1

Author(s):

Hamid Rajabalipanah ◽

Kasra Rouhi ◽

Ali Abdolali ◽

Shahid Iqbal ◽

Lei Zhang ◽

...

Keyword(s):

Real Time ◽

Data Storage ◽

Secure Communication ◽

Information Science ◽

Near Field ◽

Polarization Rotation ◽

Data Rates ◽

Theoretical Predictions ◽

Perfect Secrecy ◽

Computer Generated Holograms

AbstractAs one of the cutting-edge technologies in advanced information science, wave-based cryptography is a prerequisite to enable a plethora of secure encrypting platforms which can be realized by smart multiplexing techniques together with suitable metasurface holograms (meta-holograms). Here, relying on the polarization multiplicity and re-writability of a computer-generated meta-hologram, a fully secure communication protocol is elaborately developed at the terahertz spectrum to host unique merits for exploring real-time metasurface-based cryptography (meta-cryptography) where highly restricted access of information is imposed. The proposed meta-cryptography exploits two dynamic near-field channels of a meta-hologram whose information can be instantaneously re-written without any polarization rotation and with high contrast and acceptable frequency bandwidth. The computer-generated meta-hologram is constructed based on the weighted Gerchberg–Saxton algorithm via a two-dimensional array of vertical graphene strips whose anisotropic reflection is merely determined by external biasing conditions. Several illustrative examples have been presented to demonstrate the perfect secrecy and polarization cross-talk of the proposed meta-cryptography. Numerical simulations corroborate well our theoretical predictions. As the first demonstration of dynamic THz meta-cryptography, the meta-hologram information channels can be deciphered into manifold customized messages which would be instrumental in data storage systems offering far higher data rates than electronic encryption can deliver.

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Design of Portable Electrocardiograph Monitoring System

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.3.030403 ◽

2020 ◽

Author(s):

Jia Hua-Ping ◽

Zhao Jun-Long ◽

Liu Jun

Keyword(s):

Real Time ◽

Monitoring System ◽

Data Storage ◽

P Wave ◽

Ecg Monitoring ◽

The Public ◽

Ecg Signals ◽

Early Warning Mechanism ◽

Timely Treatment ◽

Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

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Real-time measurement of optical fiber length with chaotic ring laser

Mechanical, Information and Industrial Engineering ◽

10.2495/icmiie140831 ◽

2015 ◽

Author(s):

Nian Fang ◽

Wei Ji ◽

Lutang Wang ◽

Zhaoming Huang

Keyword(s):

Optical Fiber ◽

Real Time ◽

Ring Laser ◽

Fiber Length ◽

Time Measurement ◽

Real Time Measurement

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Real-time interrogation of fiber optic biosensor using TiO2 coated etched long-period grating

Review of Scientific Instruments ◽

10.1063/5.0020571 ◽

2020 ◽

Vol 91 (12) ◽

pp. 125001

Author(s):

Mandeep Singh ◽

Sanjeev Kumar Raghuwanshi

Keyword(s):

Real Time ◽

Fiber Optic ◽

Long Period Grating ◽

Long Period

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Optimization of time and frequency fiber-optic links exploiting bi-directional amplifiers, based on real-time performance measurement

Optical Fiber Technology ◽

10.1016/j.yofte.2021.102465 ◽

2021 ◽

Vol 62 ◽

pp. 102465

Author(s):

Karol Salwik ◽

Łukasz Śliwczyński ◽

Przemysław Krehlik ◽

Jacek Kołodziej

Keyword(s):

Performance Measurement ◽

Real Time ◽

Fiber Optic ◽

Time Performance

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Dryer Effluent Monitoring in a Chemical Pilot Plant via Fiber-Optic Near-Infrared Spectroscopy

Applied Spectroscopy ◽

10.1366/0003702981944139 ◽

1998 ◽

Vol 52 (5) ◽

pp. 717-724 ◽

Cited By ~ 10

Author(s):

Charity Coffey ◽

Alex Predoehl ◽

Dwight S. Walker

Keyword(s):

Real Time ◽

Pilot Plant ◽

Near Infrared ◽

Fiber Optic ◽

Tunable Filter ◽

Calibration Model ◽

Gas Cell ◽

Rotary Dryer ◽

Vapor Stream ◽

Effluent Stream

The monitoring of the effluent of a rotary dryer has been developed and implemented. The vapor stream between the dryer and the vacuum is monitored in real time by a process fiber-optic coupled near-infrared (NIR) spectrometer. A partial least-squares (PLS) calibration model was developed on the basis of solvents typically used in a chemical pilot plant and uploaded to an acousto-optic tunable filter NIR (AOTF-NIR). The AOTF-NIR is well suited to process monitoring as it electrically scans a crystal and hence has no moving parts. The AOTF-NIR continuously fits the PLS model to the currently collected spectrum. The returned values can be used to follow the drying process and determine when the material can be unloaded from the dryer. The effluent stream was monitored by placing a gas cell in-line with the vapor stream. The gas cell is fiber-optic coupled to a NIR instrument located 20 m away. The results indicate that the percent vapor in the effluent stream can be monitored in real time and thus be used to determine when the product is free of solvent.

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Processing Panorama Video in Real-time

International Journal of Semantic Computing ◽

10.1142/s1793351x14400054 ◽

2014 ◽

Vol 08 (02) ◽

pp. 209-227 ◽

Cited By ~ 1

Author(s):

Håkon Kvale Stensland ◽

Vamsidhar Reddy Gaddam ◽

Marius Tennøe ◽

Espen Helgedagsrud ◽

Mikkel Næss ◽

...

Keyword(s):

Real Time ◽

Graphics Processing Units ◽

Low Cost ◽

Video Camera ◽

Wide Field ◽

High Data ◽

Data Rates ◽

Camera Arrays ◽

Analysis System ◽

Performance Results

There are many scenarios where high resolution, wide field of view video is useful. Such panorama video may be generated using camera arrays where the feeds from multiple cameras pointing at different parts of the captured area are stitched together. However, processing the different steps of a panorama video pipeline in real-time is challenging due to the high data rates and the stringent timeliness requirements. In our research, we use panorama video in a sport analysis system called Bagadus. This system is deployed at Alfheim stadium in Tromsø, and due to live usage, the video events must be generated in real-time. In this paper, we describe our real-time panorama system built using a low-cost CCD HD video camera array. We describe how we have implemented different components and evaluated alternatives. The performance results from experiments ran on commodity hardware with and without co-processors like graphics processing units (GPUs) show that the entire pipeline is able to run in real-time.

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