A Smart Classification Framework for Enhancing Reliability in Downhole Gas Bubble Sensing

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Virginie Schoepf ◽  
Alberto Marsala ◽  
Linda Abbassi
Keyword(s):  
Real Time ◽  
Classification Performance ◽  
Gas Bubble ◽  
Data Quality Control ◽  
Underbalanced Drilling ◽  
Bubble Detection ◽  
Classification Framework ◽  
Testing Dataset ◽  
Data Assessment ◽  
Productivity Potential

Abstract Production logging tools (PLTs) and formation testing, even in logging while drilling (LWD) conditions during underbalanced drilling, are key technologies for assessing the productivity potential of a gas well and therefore to maximize recovery. Gas bubble detection sensors are key components in determining the fluid phases in the reservoir and accurately quantify recoverable reserves, optimize well placement, geosteering and to qualify the production ability of the well. We present here a new nonlinear autoregressive - breakdown artificial intelligence (AI) detection framework for PLT gas bubble detection sensors that categorize in real-time whether and which sensors become unreliable or have broken down during the logging measurements. AI tools allow the automatization of this method that is critical during data quality control of post-drilling PLT, but it is essential when the measurements are performed in LWD as data assessment and processing need to occur in real time. This AI framework was validated on both a training and testing dataset, and exhibited strong classification performance. This method enables accurate real-time breakdown detection for gas bubble detection sensors.

scholarly journals A novel artificial intelligence automatic detection framework to increase reliability of PLT gas bubble sensing

2021 ◽  
Vol 11 (3) ◽  
pp. 1263-1273
Author(s):  
Klemens Katterbauer ◽  
Alberto F. Marsala ◽  
Virginie Schoepf ◽  
Eric Donzier
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Classification Performance ◽  
Gas Bubble ◽  
Data Quality Control ◽  
Underbalanced Drilling ◽  
Bubble Detection ◽  
Testing Dataset ◽  
Data Assessment ◽  
Productivity Potential

AbstractProduction logging tools (PLTs) and formation testing, even in logging while drilling (LWD) conditions during underbalanced drilling, are key technologies for assessing the productivity potential of a gas well and therefore to maximize recovery. Gas bubble detection sensors are key components in determining the fluid phases in the reservoir and accurately quantify recoverable reserves, optimize well placement, geosteering and to qualify the production ability of the well. We present here a new nonlinear autoregressive - breakdown artificial intelligence (AI) detection framework for PLT gas bubble detection sensors that categorize in real-time whether and which sensors become unreliable or have broken down during the logging measurements. AI tools allow the automatization of this method that is critical during data quality control of post-drilling PLT, but it is essential when the measurements are performed in LWD as data assessment and processing need to occur in real-time. This AI framework was validated on both a training and testing dataset, and exhibited strong classification performance. This method enables accurate real-time breakdown detection for gas bubble detection sensors.

scholarly journals Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features

2019 ◽  
Vol 9 (22) ◽  
pp. 4833 ◽  
Author(s):  
Ardo Allik ◽  
Kristjan Pilt ◽  
Deniss Karai ◽  
Ivo Fridolin ◽  
Mairo Leier ◽  
...  
Keyword(s):  
Physical Activity ◽  
Feature Selection ◽  
Real Time ◽  
Sampling Frequency ◽  
Classification Performance ◽  
Window Length ◽  
Decision Tree Classifier ◽  
Acceleration Signal ◽  
Wearable Systems ◽  
Tree Classifier

The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 ± 0.02) and 3 s (0.91 ± 0.02), while classification performance with 1 s were lower (0.87 ± 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers.

Ultrasound Gas Bubble Detection During Simulation of Space Suit Operations

1995 ◽  
Author(s):  
V. I. Chadov ◽  
S. N. Filipenkov ◽  
L. R. Iseev ◽  
V. N. Polyakov ◽  
G. F. Vorobiev
Keyword(s):  
Gas Bubble ◽  
Space Suit ◽  
Bubble Detection

scholarly journals A GPU-Based Architecture for Real-Time Data Assessment at Synchrotron Experiments

2011 ◽  
Vol 58 (4) ◽  
pp. 1447-1455 ◽  
Author(s):  
Suren Chilingaryan ◽  
Alessandro Mirone ◽  
Andrew Hammersley ◽  
Claudio Ferrero ◽  
Lukas Helfen ◽  
...  
Keyword(s):  
Real Time ◽  
Time Data ◽  
Data Assessment ◽  
Real Time Data

scholarly journals Facial Expression Analysis by Machine Learning

2010 ◽  
pp. 239-258
Author(s):  
Siu-Yeung Cho ◽  
Teik-Toe Teoh ◽  
Yok-Yen Nguwi
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Classification Performance ◽  
Difficult Problem ◽  
Facial Features ◽  
Expression Recognition ◽  
Real Time Processing ◽  
Good Classification Performance

Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

scholarly journals Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4138 ◽  
Author(s):  
Mikail Yayla ◽  
Anas Toma ◽  
Kuan-Hsun Chen ◽  
Jan Eric Lenssen ◽  
Victoria Shpacovitch ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Real Time ◽  
Domain Analysis ◽  
Haar Wavelet ◽  
Frequency Domain Analysis ◽  
Classification Performance ◽  
Mobile Systems ◽  
Resource Limited ◽  
Computationally Intensive ◽  
Classification Of Images

A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 μ s per image for the Fourier features and 17 μ s for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 μ s per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.

Intelligent Data Quality Control of Real-time Rig Data

2013 ◽  
Author(s):  
Arghad Arnaout ◽  
Philipp Zoellner ◽  
Gerhard Thonhauser ◽  
Neil Johnstone
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Real Time ◽  
Data Quality Control

Gas bubble detection in blood

Ultrasonics ◽  
1968 ◽  
Vol 6 (4) ◽  
pp. 267-269
Author(s):  
D.M.J.P. Manley
Keyword(s):  
Gas Bubble ◽  
Bubble Detection

Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet Processing

2005 ◽  
Vol 127 (3) ◽  
pp. 294-303 ◽  
Author(s):  
Piervincenzo Rizzo ◽  
Ivan Bartoli ◽  
Alessandro Marzani ◽  
Francesco Lanza di Scalea
Keyword(s):  
Guided Waves ◽  
Fourier Transforms ◽  
Damage Index ◽  
Classification Performance ◽  
Discrete Wavelet ◽  
Cross Sectional ◽  
Classification Framework ◽  
Discrete Wavelet Decomposition ◽  
Good Classification ◽  
Good Classification Performance

This paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall, the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters.

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