Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead
Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimension...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Verlag
2017
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/13615/ http://ir.unimas.my/id/eprint/13615/7/Hybrid%20intelligent%20-%20Copy.pdf |
| _version_ | 1848837443895164928 |
|---|---|
| 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 | Computational intelligence (CI) techniques have
positively impacted the petroleum reservoir characterization
and modeling landscape. However, studies have
showed that each CI technique has its strengths and
weaknesses. Some of the techniques have the ability to
handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ‘‘no free lunch’’ theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem
domain just as a technique that was written off on one
problem may be promising with another. There was the
need for robust techniques that will make the best use of
the strengths to overcome the weaknesses while producing
the best results. The machine learning concepts of hybrid
intelligent system (HIS) have been proposed to partly
overcome this problem. In this review paper, the impact of
HIS on the petroleum reservoir characterization process is
enumerated, analyzed, and extensively discussed. It was
concluded that HIS has huge potentials in the improvement
of petroleum reservoir property predictions resulting in
improved exploration, more efficient exploitation,
increased production, and more effective management of
energy resources. Lastly, a number of yet-to-be-explored
hybrid possibilities were recommended. |
| first_indexed | 2025-11-15T06:39:45Z |
| format | Article |
| id | unimas-13615 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:39:45Z |
| publishDate | 2017 |
| publisher | Springer Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-136152022-09-29T03:14:18Z http://ir.unimas.my/id/eprint/13615/ Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead Fatai Adesina, Anifowose Jane, Labadin Abdulazeez, Abdulraheem T Technology (General) TP Chemical technology Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ‘‘no free lunch’’ theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended. Springer Verlag 2017 Article PeerReviewed text en http://ir.unimas.my/id/eprint/13615/7/Hybrid%20intelligent%20-%20Copy.pdf Fatai Adesina, Anifowose and Jane, Labadin and Abdulazeez, Abdulraheem (2017) Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead. Journal of Petroleum Exploration and Production Technology, 7 (1). pp. 251-263. ISSN 2190-0558 http://link.springer.com/article/10.1007/s13202-016-0257-3 doi:10.1007/s13202-016-0257-3 |
| spellingShingle | T Technology (General) TP Chemical technology Fatai Adesina, Anifowose Jane, Labadin Abdulazeez, Abdulraheem Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead |
| title | Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead |
| title_full | Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead |
| title_fullStr | Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead |
| title_full_unstemmed | Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead |
| title_short | Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead |
| title_sort | hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead |
| topic | T Technology (General) TP Chemical technology |
| url | http://ir.unimas.my/id/eprint/13615/ http://ir.unimas.my/id/eprint/13615/ http://ir.unimas.my/id/eprint/13615/ http://ir.unimas.my/id/eprint/13615/7/Hybrid%20intelligent%20-%20Copy.pdf |