loading
Documents

Research.Publish.Connect.

Paper

Authors: Flavia Bernardini 1 ; Rodrigo Monteiro 1 ; Inhauma Ferraz 2 ; Jose Viterbo 1 and Adriel Araujo 1

Affiliations: 1 Institute of Computing, Fluminense Federal University, Niteroi, RJ, Brazil, ADDLabs — Active Documentation Design Laboratory, Fluminense Federal University, Niteroi, RJ, Brazil ; 2 ADDLabs — Active Documentation Design Laboratory, Fluminense Federal University, Niteroi, RJ, Brazil

ISBN: 978-989-758-423-7

Keyword(s): Interactive Labeling, Supervised Machine Learning, Artificial Neural Networks, Cementation Quality.

Abstract: Oil and Gas area presents many problems in which the experts need to analyze different data sources and they must be very specialized in the domain to correctly analyze the case. So, approaches that uses artificial intelligence techniques to help the experts to help them turning explicit their expert knowledge and analysing the cases is very important. Analysing cementation quality in oil wells is one of these cases. Primary cementation operation of an oil well is creating a hydraulic seal in the annular space formed between the coating pipe and the open well wall, preventing the flow between different geological zones bearing water or hydrocarbons. To evaluate the quality of this seal at determined depths, acoustic tools are used, aiming to collect sonic and ultrasonic signals. Verifying the quality of the available data for cementation quality evaluation is a task that consumes time and effort of the domain experts, mainly due to data dispersion in different data sources and missing labels in data. This work presents an approach for helping acquiring knowledge from domains where these problems are presented using machine learning. Interactive labeling and multiple data sources for acquiring knowledge from experts can help to construct better systems in complex scenarios, such as cementation quality. We obtained promising results in our case study scenario. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.234.143.26

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bernardini, F.; Monteiro, R.; Ferraz, I.; Viterbo, J. and Araujo, A. (2020). An Approach for Acquiring Knowledge in Complex Domains Involving Different Data Sources and Uncertinty in Label Information: A Case Study on Cementation Quality Evaluation.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 563-570. DOI: 10.5220/0009418905630570

@conference{iceis20,
author={Flavia Bernardini. and Monteiro, R. and Inhauma Ferraz. and Jose Viterbo. and Adriel Araujo.},
title={An Approach for Acquiring Knowledge in Complex Domains Involving Different Data Sources and Uncertinty in Label Information: A Case Study on Cementation Quality Evaluation},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={563-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009418905630570},
isbn={978-989-758-423-7},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Approach for Acquiring Knowledge in Complex Domains Involving Different Data Sources and Uncertinty in Label Information: A Case Study on Cementation Quality Evaluation
SN - 978-989-758-423-7
AU - Bernardini, F.
AU - Monteiro, R.
AU - Ferraz, I.
AU - Viterbo, J.
AU - Araujo, A.
PY - 2020
SP - 563
EP - 570
DO - 10.5220/0009418905630570

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.