A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction
Various computational intelligence techniques have been used in the prediction of petroleum reservoir properties. However, each of them has its limitations depending on different conditions such as data size and dimensionality. Hybrid computational intelligence has been introduced as a new para...
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| Format: | Article |
| Language: | English |
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Springer London
2013
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| Online Access: | http://ir.unimas.my/id/eprint/8469/ http://ir.unimas.my/id/eprint/8469/1/A%20least-square-driven%20functional%20networks%20type-2%20fuzzy%20logic%20hybrid%20model%20for%20efficient%20petroleum%20reservoir%20properties%20prediction%20%28abstract%29.pdf |
| _version_ | 1848836384026001408 |
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| author | Fatai Adesina, Anifowose Jane, Labadin Abdulazeez, Abdulraheem |
| author_facet | Fatai Adesina, Anifowose Jane, Labadin Abdulazeez, Abdulraheem |
| author_sort | Fatai Adesina, Anifowose |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Various computational intelligence techniques
have been used in the prediction of petroleum reservoir
properties. However, each of them has its limitations
depending on different conditions such as data size and
dimensionality. Hybrid computational intelligence has
been introduced as a new paradigm to complement the
weaknesses of one technique with the strengths of another
or others. This paper presents a computational intelligence
hybrid model to overcome some of the limitations of the
standalone type-2 fuzzy logic system (T2FLS) model by
using a least-square-fitting-based model selection algorithm
to reduce the dimensionality of the input data while
selecting the best variables. This novel feature selection
procedure resulted in the improvement of the performance
of T2FLS whose complexity is usually increased and performance
degraded with increased dimensionality of input
data. The iterative least-square-fitting algorithm part of
functional networks (FN) and T2FLS techniques were
combined in a hybrid manner to predict the porosity and
permeability of North American and Middle Eastern oil
and gas reservoirs. Training and testing the T2FLS block of
the hybrid model with the best and dimensionally reduced
input variables caused the hybrid model to perform better
with higher correlation coefficients, lower root mean
square errors, and less execution times than the standalone
T2FLS model. This work has demonstrated the promising capability of hybrid modelling and has given more insight
into the possibility of more robust hybrid models with
better functionality and capability indices. |
| first_indexed | 2025-11-15T06:22:54Z |
| format | Article |
| id | unimas-8469 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:22:54Z |
| publishDate | 2013 |
| publisher | Springer London |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-84692015-08-04T02:42:38Z http://ir.unimas.my/id/eprint/8469/ A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction Fatai Adesina, Anifowose Jane, Labadin Abdulazeez, Abdulraheem T Technology (General) Various computational intelligence techniques have been used in the prediction of petroleum reservoir properties. However, each of them has its limitations depending on different conditions such as data size and dimensionality. Hybrid computational intelligence has been introduced as a new paradigm to complement the weaknesses of one technique with the strengths of another or others. This paper presents a computational intelligence hybrid model to overcome some of the limitations of the standalone type-2 fuzzy logic system (T2FLS) model by using a least-square-fitting-based model selection algorithm to reduce the dimensionality of the input data while selecting the best variables. This novel feature selection procedure resulted in the improvement of the performance of T2FLS whose complexity is usually increased and performance degraded with increased dimensionality of input data. The iterative least-square-fitting algorithm part of functional networks (FN) and T2FLS techniques were combined in a hybrid manner to predict the porosity and permeability of North American and Middle Eastern oil and gas reservoirs. Training and testing the T2FLS block of the hybrid model with the best and dimensionally reduced input variables caused the hybrid model to perform better with higher correlation coefficients, lower root mean square errors, and less execution times than the standalone T2FLS model. This work has demonstrated the promising capability of hybrid modelling and has given more insight into the possibility of more robust hybrid models with better functionality and capability indices. Springer London 2013 Article NonPeerReviewed text en http://ir.unimas.my/id/eprint/8469/1/A%20least-square-driven%20functional%20networks%20type-2%20fuzzy%20logic%20hybrid%20model%20for%20efficient%20petroleum%20reservoir%20properties%20prediction%20%28abstract%29.pdf Fatai Adesina, Anifowose and Jane, Labadin and Abdulazeez, Abdulraheem (2013) A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction. Neural Computing and Applications, 23 (1). pp. 179-190. ISSN 1433-3058 http://link.springer.com/article/10.1007%2Fs00521-012-1298-2 10.1007/s00521-012-1298-2 |
| spellingShingle | T Technology (General) Fatai Adesina, Anifowose Jane, Labadin Abdulazeez, Abdulraheem A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction |
| title | A least-square-driven functional networks type-2 fuzzy logic
hybrid model for efficient petroleum reservoir properties
prediction |
| title_full | A least-square-driven functional networks type-2 fuzzy logic
hybrid model for efficient petroleum reservoir properties
prediction |
| title_fullStr | A least-square-driven functional networks type-2 fuzzy logic
hybrid model for efficient petroleum reservoir properties
prediction |
| title_full_unstemmed | A least-square-driven functional networks type-2 fuzzy logic
hybrid model for efficient petroleum reservoir properties
prediction |
| title_short | A least-square-driven functional networks type-2 fuzzy logic
hybrid model for efficient petroleum reservoir properties
prediction |
| title_sort | least-square-driven functional networks type-2 fuzzy logic
hybrid model for efficient petroleum reservoir properties
prediction |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/8469/ http://ir.unimas.my/id/eprint/8469/ http://ir.unimas.my/id/eprint/8469/ http://ir.unimas.my/id/eprint/8469/1/A%20least-square-driven%20functional%20networks%20type-2%20fuzzy%20logic%20hybrid%20model%20for%20efficient%20petroleum%20reservoir%20properties%20prediction%20%28abstract%29.pdf |