scholarly journals APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS FOR RESISTIVITY LOGS PROCESSING AND NON-ITERATIVE EXPRESS-INVERSION IN COMPLEX RESERVOIR ENVIRONMENTS

2021 ◽  
Vol 2 (2) ◽  
pp. 123-129
Author(s):  
Artem R. Leonenko ◽  
Kirill N. Danilovskiy ◽  
Aleksei M. Petrov
Keyword(s):  
High Performance ◽  
Data Interpretation ◽  
Thin Layers ◽  
Well Logs ◽  
Oil Well ◽  
Processing Stage ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Resistivity Logs ◽  
Resistivity Logging

The work is devoted to the development of techniques and software for the quantitative interpretation of resistivity oil well logs. The article considers the results of applying the neural network approach to the processing of resistivity logging data measured at intervals composed of thin layers with contrasting electrical properties. The proposed algorithms combine the advantages of data interpretation based on a two-dimensional axisymmetric medium model and high performance, which allows them to be used at the primary processing stage, increasing the reliability of express interpretation.

scholarly journals Pulsed Electromagnetic Cross-Well Exploration for Monitoring Permafrost and Examining the Processes of Its Geocryological Changes

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 60
Author(s):  
Viacheslav Glinskikh ◽  
Oleg Nechaev ◽  
Igor Mikhaylov ◽  
Kirill Danilovskiy ◽  
Vladimir Olenchenko
Keyword(s):  
High Performance ◽  
Geophysical Methods ◽  
Three Dimensional ◽  
High Sensitivity ◽  
Data Interpretation ◽  
Industrial Facilities ◽  
Wide Range ◽  
Vector Finite Element ◽  
A New Technique ◽  
Pulsed Electromagnetic

This paper is dedicated to the topical problem of examining permafrost’s state and the processes of its geocryological changes by means of geophysical methods. To monitor the cryolithozone, we proposed and scientifically substantiated a new technique of pulsed electromagnetic cross-well sounding. Based on the vector finite-element method, we created a mathematical model of the cross-well sounding process with a pulsed source in a three-dimensional spatially heterogeneous medium. A high-performance parallel computing algorithm was developed and verified. Through realistic geoelectric models of permafrost with a talik under a highway, constructed following the results of electrotomography field data interpretation, we numerically simulated the pulsed sounding on the computing resources of the Siberian Supercomputer Center of SB RAS. The simulation results suggest the proposed system of pulsed electromagnetic cross-well monitoring to be characterized by a high sensitivity to the presence and dimensions of the talik. The devised approach can be oriented to addressing a wide range of issues related to monitoring permafrost rocks under civil and industrial facilities, buildings, and constructions.

RADIOACTIVITY ANALYSES OF OIL WELL SAMPLES

Geophysics ◽  
1942 ◽  
Vol 7 (1) ◽  
pp. 90-94 ◽  
Author(s):  
Bruno Pontecorvo
Keyword(s):  
Laboratory Tests ◽  
Laboratory Method ◽  
Well Logs ◽  
Oil Well ◽  
Rock Samples

A laboratory method of analyzing the radioactivity of rock samples is described in which the laboratory tests are designed to simulate the conditions which prevail when radioactivity logs of wells are made. Thus the radioactivity of samples may be correlated with the results of such well logs and their interpretation improved thereby.

Investigation of the possibility of reconstructing strain diagrams using the neural network approach

2011 ◽  
Vol 47 (15) ◽  
pp. 1689-1695
Author(s):  
M. B. Bakirov ◽  
O. A. Mishulina ◽  
I. A. Kiselev ◽  
I. A. Kruglov
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network

An artificial neural network approach to discrete-event simulation

1995 ◽  
Vol 9 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ian Flood ◽  
Kenneth Worley
Keyword(s):  
Neural Network ◽  
Discrete Event Simulation ◽  
Network Architecture ◽  
Discrete Event ◽  
Network Approach ◽  
Neural Network Approach ◽  
Event Simulation ◽  
The Neural Network ◽  
Component Processes ◽  
And Performance

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks

2000 ◽  
Vol 1719 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Satish C. Sharma ◽  
Pawan Lingras ◽  
Guo X. Liu ◽  
Fei Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Monitoring ◽  
Network Approach ◽  
Annual Average ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Low Volume Roads ◽  
Annual Average Daily Traffic ◽  
Low Volume

Estimation of the annual average daily traffic (AADT) for low-volume roads is investigated. Artificial neural networks are compared with the traditional factor approach for estimating AADT from short-period traffic counts. Fifty-five automatic traffic recorder (ATR) sites located on low-volume rural roads in Alberta, Canada, are used as study samples. The results of this study indicate that, when a single 48-h count is used for AADT estimation, the factor approach can yield better results than the neural networks if the ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary to obtain reliable AADT estimates from a single 48-h count. The neural network approach can be particularly suitable for estimating AADT from two 48-h counts taken at different times during the counting season. In fact, the 95th percentile error values of about 25 percent as obtained in this study for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. The advantage of the neural network approach is that classification of ATR sites and sample site assignments to ATR groups are not required. The analysis of various groups of low-volume roads presented also leads to a conclusion that, when defining low-volume roads from a traffic monitoring point of view, it is not likely to matter much whether the AADT on the facility is less than 500 vehicles, less than 750 vehicles, or less than 1,000 vehicles.

scholarly journals Sol–Gel Processing of Water-Soluble Carbon Nitride Enables High-Performance Photoanodes

2020 ◽  
Author(s):  
Christiane Adler ◽  
Igor Krivtsov ◽  
Dariusz Mitoraj ◽  
Lucía dos Santos-Gómez ◽  
Santiago García-Granda ◽  
...  
Keyword(s):  
Soft Matter ◽  
High Performance ◽  
Carbon Nitride ◽  
Sol Gel ◽  
Thin Layers ◽  
Water Soluble ◽  
New Paradigm ◽  
High Quality ◽  
Free Films ◽  
Gel Processing

In spite of the enormous promise that polymeric carbon nitride (PCN) materials hold for photoelectrochemical (PEC) applications, the fabrication of high-quality PCN photoelectrodes has been a largely elusive goal to date. Here we tackle this challenge by devising, for the first time, a sol–gel approach that enables facile preparation of photoanodes based on poly(heptazine imide) (PHI), a polymer belonging to the PCN family. The sol–gel process capitalizes on the use of a water-soluble PHI precursor composed of nanosized (~10 nm) particles that allows formation of a non-covalent hydrogel. The hydrogel can be deposited on a conductive substrate resulting in formation of mechanically stable porous polymeric thin layers (~400 nm), in contrast to the commonly obtained loosely attached thick particulate coatings. The resulting photoanodes exhibit unprecedented PEC performance in methanol reforming in neutral pH electrolytes with photocurrents of up to 177±27 mA cm<sup>-2</sup> (1 sun illumination) and 320±40 mA cm<sup>-2</sup> (2 sun illumination) at 1.23 V vs. RHE, maintaining such high photocurrents even down to ~0 V vs. RHE. These parameters permit effective operation even without any external electric bias, as demonstrated by bias-free photoreforming of methanol and glycerol, and highly selective (~100%) photooxidation of 4-methoxybenzyl alcohol (4-MBA). The robust binder-free films derived from sol–gel processing of water-soluble PCN thus represent a new paradigm for high-performance ‘soft-matter’ photoelectrocatalytic systems, and pave the way for further applications in which high-quality PCN films are required.

scholarly journals Petrophysical Properties of Nahr Umar Formation in Nasiriya Oil Field

2020 ◽  
Vol 21 (3) ◽  
pp. 9-18
Author(s):  
Ahmed Abdulwahhab Suhail ◽  
Mohammed H. Hafiz ◽  
Fadhil S. Kadhim
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Oil Field ◽  
Well Logs ◽  
High Porosity ◽  
Reservoir Properties ◽  
Petrophysical Parameters ◽  
Resistivity Logs ◽  
Lithology Prediction ◽  
Porosity And Permeability

   Petrophysical characterization is the most important stage in reservoir management. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umar Formation in Nasiriya oil field. The available well logs are (sonic, density, neutron, gamma-ray, SP, and resistivity logs). The petrophysical parameters such as the volume of clay, porosity, permeability, water saturation, were computed and interpreted using IP4.4 software. The lithology prediction of Nahr Umar formation was carried out by sonic -density cross plot technique. Nahr Umar Formation was divided into five units based on well logs interpretation and petrophysical Analysis: Nu-1 to Nu-5. The formation lithology is mainly composed of sandstone interlaminated with shale according to the interpretation of density, sonic, and gamma-ray logs. Interpretation of formation lithology and petrophysical parameters shows that Nu-1 is characterized by low shale content with high porosity and low water saturation whereas Nu-2 and Nu-4 consist mainly of high laminated shale with low porosity and permeability. Nu-3 is high porosity and water saturation and Nu-5 consists mainly of limestone layer that represents the water zone.

Prediction of Remaining Life Time Based on Measurements of Stress for Thermal Barrier Coatings

2001 ◽  
Author(s):  
E. H. Jordan ◽  
W. Xie ◽  
M. Gell ◽  
L. Xie ◽  
F. Tu ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Measurement ◽  
Life Time ◽  
Remaining Life ◽  
Neural Network Approach ◽  
Regression Methods ◽  
Signal Features ◽  
The Neural Network ◽  
Network Methods ◽  
Non Destructive

Abstract Non-destructive determination of the remaining life of coatings of gas turbine parts is highly desirable. The present paper describes early attempts to prove the feasibility of doing this based on the optical measurement of the stress in the oxide that attaches the coating to the metal component. Both regression methods and neural network methods are compared and it was found that the neural network approach was superior for the case where multiple signal features were present. All methods provide useful predictions for the idealized case considered. Challenges presented by more complicated thermal cycles are discussed briefly.

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