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

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Virginie Schoepf ◽  
Alberto Marsala ◽  
Linda Abbassi

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.


2021 ◽  
Vol 11 (3) ◽  
pp. 1263-1273
Author(s):  
Klemens Katterbauer ◽  
Alberto F. Marsala ◽  
Virginie Schoepf ◽  
Eric Donzier

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.


2013 ◽  
Vol 220 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Artur Andruszkiewicz ◽  
Kerstin Eckert ◽  
Sven Eckert ◽  
Stefan Odenbach

2019 ◽  
Vol 46 (3) ◽  
pp. 261-275
Author(s):  
César Yepes ◽  
Jorge Naude ◽  
Federico Mendez ◽  
Margarita Navarrete ◽  
Fátima Moumtadi

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