Search

Now showing items 1-10 of 13

Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization

Type: Journal Paper

Author : Fatai Anifowose; Abdulazeez Abdulraheem

Year: 2011

Request PDF

Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems

Type: Journal Paper

Author : Sunday Olusanya Olatunji; Ali Selamat; Abdulazeez Abdulraheem

Publisher: Elsevier Science

Year: 2011

Request PDF

Non-linear feature selection-based hybrid computational intelligence models for improved natural gas reservoir characterization

Type: Journal Paper

Author : Fatai Adesina Anifowose; Jane Labadin; Abdulazeez Abdulraheem

Year: 2014

Request PDF

Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density

Type: Journal Paper

Author : Hasan A. Nooruddin; Fatai Anifowose; Abdulazeez Abdulraheem

Year: 2014

Request PDF

Recent advances in the application of computational intelligence techniques in oil and gas reservoir characterisation: a comparative study

Type: Journal Paper

Author : Fatai Anifowose; Suli Adeniye; Abdulazeez Abdulraheem

Year: 2014

Request PDF

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

Type: Journal Paper

Author : Fatai Anifowose; Jane Labadin; Abdulazeez Abdulraheem

Year: 2015

Request PDF

Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization

Type: Journal Paper

Author : Fatai Adesina Anifowose; Jane Labadin; Abdulazeez Abdulraheem

Year: 2015

Request PDF

A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction

Type: Journal Paper

Author : S.O. Olatunji; Ali Selamat; Abdulazeez Abdulraheem

Year: 2012

Request PDF

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Type: Journal Paper

Author : Fatai Anifowose; Suli Adeniye; Abdulazeez Abdulraheem; Abdullatif Al-Shuhail

Year: 2016

Request PDF

Development of a new correlation to determine the static Young’s modulus

Type: Journal Paper

Author : Salaheldin Elkatatny; Mohamed Mahmoud; Ibrahim Mohamed; Abdulazeez Abdulraheem

Year: 2017

Request PDF

Search

Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization

Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems

Non-linear feature selection-based hybrid computational intelligence models for improved natural gas reservoir characterization

Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density

Recent advances in the application of computational intelligence techniques in oil and gas reservoir characterisation: a comparative study

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization

A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Development of a new correlation to determine the static Young’s modulus

Author

Publisher

Year

Type

Language (ISO)

Content Type

Publication Title

Search

Filters

Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization

Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems

Non-linear feature selection-based hybrid computational intelligence models for improved natural gas reservoir characterization

Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density

Recent advances in the application of computational intelligence techniques in oil and gas reservoir characterisation: a comparative study

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization

A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Development of a new correlation to determine the static Young’s modulus