Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel associative model was developed to predict petroleum well performance after remedial treatments. This application is of interest, particularly for non-uniform oilfields such as naturally fractured ones, and can be used in decision support systems for water control or candidate well selection. The model is based on the Gamma classifier, a supervised pattern recognition model for mining patterns in data sets. The model works with multivariate inputs and outputs under the lack of available data and low-quality information sources. An experimental dataset was built based on historical data of a Mexican naturally fractured oilfield. As experimental results show, this classifier-based predictor shows competitive performance compared against other methods. © 2013 Springer-Verlag.
Original languageAmerican English
Title of host publicationMultivariate prediction based on the gamma classifier: A data mining application to petroleum engineering
Pages18-25
Number of pages15
ISBN (Electronic)9783642401725
DOIs
StatePublished - 25 Sep 2013
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2014 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8056 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/14 → …

Fingerprint

Petroleum engineering
Petroleum
Data mining
Data Mining
Classifiers
Classifier
Engineering
Prediction
Information Quality
Historical Data
Decision Support Systems
Decision support systems
Model
Pattern Recognition
Pattern recognition
Mining
Predictors
Crude oil
Water
Predict

Cite this

López-Yáñez, I., Sheremetov, L., & Camacho-Nieto, O. (2013). Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. In Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering (pp. 18-25). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8056 LNCS). https://doi.org/10.1007/978-3-642-40173-2_3
López-Yáñez, Itzamá ; Sheremetov, Leonid ; Camacho-Nieto, Oscar. / Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. 2013. pp. 18-25 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{1141bc714ddf49d383c77fbdfc613a56,
title = "Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering",
abstract = "A novel associative model was developed to predict petroleum well performance after remedial treatments. This application is of interest, particularly for non-uniform oilfields such as naturally fractured ones, and can be used in decision support systems for water control or candidate well selection. The model is based on the Gamma classifier, a supervised pattern recognition model for mining patterns in data sets. The model works with multivariate inputs and outputs under the lack of available data and low-quality information sources. An experimental dataset was built based on historical data of a Mexican naturally fractured oilfield. As experimental results show, this classifier-based predictor shows competitive performance compared against other methods. {\circledC} 2013 Springer-Verlag.",
author = "Itzam{\'a} L{\'o}pez-Y{\'a}{\~n}ez and Leonid Sheremetov and Oscar Camacho-Nieto",
year = "2013",
month = "9",
day = "25",
doi = "10.1007/978-3-642-40173-2_3",
language = "American English",
isbn = "9783642401725",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "18--25",
booktitle = "Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering",

}

López-Yáñez, I, Sheremetov, L & Camacho-Nieto, O 2013, Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. in Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8056 LNCS, pp. 18-25, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1/01/14. https://doi.org/10.1007/978-3-642-40173-2_3

Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. / López-Yáñez, Itzamá; Sheremetov, Leonid; Camacho-Nieto, Oscar.

Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. 2013. p. 18-25 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8056 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering

AU - López-Yáñez, Itzamá

AU - Sheremetov, Leonid

AU - Camacho-Nieto, Oscar

PY - 2013/9/25

Y1 - 2013/9/25

N2 - A novel associative model was developed to predict petroleum well performance after remedial treatments. This application is of interest, particularly for non-uniform oilfields such as naturally fractured ones, and can be used in decision support systems for water control or candidate well selection. The model is based on the Gamma classifier, a supervised pattern recognition model for mining patterns in data sets. The model works with multivariate inputs and outputs under the lack of available data and low-quality information sources. An experimental dataset was built based on historical data of a Mexican naturally fractured oilfield. As experimental results show, this classifier-based predictor shows competitive performance compared against other methods. © 2013 Springer-Verlag.

AB - A novel associative model was developed to predict petroleum well performance after remedial treatments. This application is of interest, particularly for non-uniform oilfields such as naturally fractured ones, and can be used in decision support systems for water control or candidate well selection. The model is based on the Gamma classifier, a supervised pattern recognition model for mining patterns in data sets. The model works with multivariate inputs and outputs under the lack of available data and low-quality information sources. An experimental dataset was built based on historical data of a Mexican naturally fractured oilfield. As experimental results show, this classifier-based predictor shows competitive performance compared against other methods. © 2013 Springer-Verlag.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84884398650&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84884398650&origin=inward

U2 - 10.1007/978-3-642-40173-2_3

DO - 10.1007/978-3-642-40173-2_3

M3 - Conference contribution

SN - 9783642401725

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 18

EP - 25

BT - Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering

ER -

López-Yáñez I, Sheremetov L, Camacho-Nieto O. Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. In Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering. 2013. p. 18-25. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-40173-2_3