Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques

Computational Intelligence (CI) techniques have been applied in the prediction of various petroleum reservoir properties but with ample room for improvement. The major objective of the reservoir characterization process is to provide accurate estimates of the reservoir properties to populate full...

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Main Author:	Anifowose, Fatai Adesina
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/9327/1/Fatai%20Adesina%20Anifowose%20ft.pdf
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spelling	my-unimas-ir.93272023-03-24T00:21:17Z Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques 2015 Anifowose, Fatai Adesina QA75 Electronic computers. Computer science Computational Intelligence (CI) techniques have been applied in the prediction of various petroleum reservoir properties but with ample room for improvement. The major objective of the reservoir characterization process is to provide accurate estimates of the reservoir properties to populate full-field simulation models. The recent use of advanced and sophisticated data acquisition tools has led to a data explosion accompanied by very high dimensional data and increased uncertainties. There is the need for robust techniques that will utilize the strengths of some to overcome the weaknesses of others to produce the best results. Despite the persistent quest for better prediction accuracies in the prediction of petroleum reservoir properties, the application of advanced CI methodologies of hybrids and ensembles has either been slowly embraced or not adequately applied. In this thesis, new non-linear feature-selection assisted methods and ensemble learning models are proposed. The algorithms were implemented with optimized tuning parameters and validated with real-life porosity and permeability datasets obtained from diverse and heterogeneous petroleum reservoirs after they have passed on testing them with a benchmark dataset from the UCI Machine Learning Repository. Common metrics were used to evaluate the performance of the proposed models. The standard machine learning paradigm of dividing datasets into training and testing subsets was employed. When implemented on real petroleum engineering datasets, the proposed Functional Networks-Support Vector Machine assisted model attained the highest R-Square performance of 0.96 and 0.87 on the porosity and permeability datasets respectively (compared to the benchmark of 0.90 and 0.82 respectively) and a total execution time of less than 5 seconds. The ensemble models of Artificial Neural Networks with sequentially searched number of hidden neurons, Support Vector Regression with diverse number of the regularization parameter and Extreme Learning Machine assisted with feature selection and randomized assignment of the number of hidden attained the highest R-Square of 0.99 on the porosity and permeability datasets respectively (compared to the benchmark of 0.89 and 0.90 respectively). A thorough analysis of the comparative results showed that our proposed methods and algorithms outperformed the benchmarks. It was concluded that the proposed assisted and ensemble models will significantly increase petroleum exploration efficiency. A number of hybrid and ensemble possibilities have been recommended for future study. University Malaysia Sarawak, UNIMAS 2015 Thesis http://ir.unimas.my/id/eprint/9327/ http://ir.unimas.my/id/eprint/9327/1/Fatai%20Adesina%20Anifowose%20ft.pdf text en validuser phd doctoral University Malaysia Sarawak, UNIMAS Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Anifowose, Fatai Adesina Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques
description	Computational Intelligence (CI) techniques have been applied in the prediction of various petroleum reservoir properties but with ample room for improvement. The major objective of the reservoir characterization process is to provide accurate estimates of the reservoir properties to populate full-field simulation models. The recent use of advanced and sophisticated data acquisition tools has led to a data explosion accompanied by very high dimensional data and increased uncertainties. There is the need for robust techniques that will utilize the strengths of some to overcome the weaknesses of others to produce the best results. Despite the persistent quest for better prediction accuracies in the prediction of petroleum reservoir properties, the application of advanced CI methodologies of hybrids and ensembles has either been slowly embraced or not adequately applied. In this thesis, new non-linear feature-selection assisted methods and ensemble learning models are proposed. The algorithms were implemented with optimized tuning parameters and validated with real-life porosity and permeability datasets obtained from diverse and heterogeneous petroleum reservoirs after they have passed on testing them with a benchmark dataset from the UCI Machine Learning Repository. Common metrics were used to evaluate the performance of the proposed models. The standard machine learning paradigm of dividing datasets into training and testing subsets was employed. When implemented on real petroleum engineering datasets, the proposed Functional Networks-Support Vector Machine assisted model attained the highest R-Square performance of 0.96 and 0.87 on the porosity and permeability datasets respectively (compared to the benchmark of 0.90 and 0.82 respectively) and a total execution time of less than 5 seconds. The ensemble models of Artificial Neural Networks with sequentially searched number of hidden neurons, Support Vector Regression with diverse number of the regularization parameter and Extreme Learning Machine assisted with feature selection and randomized assignment of the number of hidden attained the highest R-Square of 0.99 on the porosity and permeability datasets respectively (compared to the benchmark of 0.89 and 0.90 respectively). A thorough analysis of the comparative results showed that our proposed methods and algorithms outperformed the benchmarks. It was concluded that the proposed assisted and ensemble models will significantly increase petroleum exploration efficiency. A number of hybrid and ensemble possibilities have been recommended for future study.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Anifowose, Fatai Adesina
author_facet	Anifowose, Fatai Adesina
author_sort	Anifowose, Fatai Adesina
title	Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques
title_short	Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques
title_full	Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques
title_fullStr	Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques
title_full_unstemmed	Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques
title_sort	prediction of petroleum reservoir properties using nonlinear feature selection and ensembles of computational intelligence techniques
granting_institution	University Malaysia Sarawak, UNIMAS
granting_department	Faculty of Computer Science and Information Technology
publishDate	2015
url	http://ir.unimas.my/id/eprint/9327/1/Fatai%20Adesina%20Anifowose%20ft.pdf
_version_	1783728048834609152

Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques

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