Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs
Permeability is one of the critical properties of reservoir rocks used to describe the ability in conducting fluids through pore spaces. This parameter cannot be simply predicted since there are nonlinear and unknown relationships between permeability and other reservoir properties. To obtain inform...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/29402 |
| _version_ | 1848752793534332928 |
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| author | Gholami, Raoof Moradzadeh, A. Maleki, S. Amiri, S. Hanachi, J. |
| author_facet | Gholami, Raoof Moradzadeh, A. Maleki, S. Amiri, S. Hanachi, J. |
| author_sort | Gholami, Raoof |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Permeability is one of the critical properties of reservoir rocks used to describe the ability in conducting fluids through pore spaces. This parameter cannot be simply predicted since there are nonlinear and unknown relationships between permeability and other reservoir properties. To obtain information about permeability, core samples are analyzed or well tests are performed conventionally. These are, however, very expensive and time-consuming to perform. Well log data is another source of information which is always available and much cheaper than core sample and well testing analysis. Thus establishing a relationship between reservoir permeability and well log data can be very helpful in estimation of this vital parameter. However, establishing relationship between well logs and permeability is not a simple task and cannot be done using a simple linear or nonlinear method. Relevance Vector Regression (RVR) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of RVR in prediction of permeability in three wells located in a carbonate reservoir in south part of Iran. To do this, Genetic Algorithm (GA) was used as an optimizer to find the best logs for prediction of permeability. Comparing the results of RVR with that of a Support Vector Regression (SVR) indicated more accuracy of RVR in prediction of permeability. However, SVR can still be considered as a second option for prediction of petrophysical properties due to its reliable efficiency. However, it should be noticed that all of the predictions using well logs data are limited to the intervals where logs are available. Thus more studies are still required to propose alternative methods whose results can be used for the entire reservoir. |
| first_indexed | 2025-11-14T08:14:16Z |
| format | Journal Article |
| id | curtin-20.500.11937-29402 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:14:16Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-294022017-09-13T15:53:34Z Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs Gholami, Raoof Moradzadeh, A. Maleki, S. Amiri, S. Hanachi, J. Permeability is one of the critical properties of reservoir rocks used to describe the ability in conducting fluids through pore spaces. This parameter cannot be simply predicted since there are nonlinear and unknown relationships between permeability and other reservoir properties. To obtain information about permeability, core samples are analyzed or well tests are performed conventionally. These are, however, very expensive and time-consuming to perform. Well log data is another source of information which is always available and much cheaper than core sample and well testing analysis. Thus establishing a relationship between reservoir permeability and well log data can be very helpful in estimation of this vital parameter. However, establishing relationship between well logs and permeability is not a simple task and cannot be done using a simple linear or nonlinear method. Relevance Vector Regression (RVR) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of RVR in prediction of permeability in three wells located in a carbonate reservoir in south part of Iran. To do this, Genetic Algorithm (GA) was used as an optimizer to find the best logs for prediction of permeability. Comparing the results of RVR with that of a Support Vector Regression (SVR) indicated more accuracy of RVR in prediction of permeability. However, SVR can still be considered as a second option for prediction of petrophysical properties due to its reliable efficiency. However, it should be noticed that all of the predictions using well logs data are limited to the intervals where logs are available. Thus more studies are still required to propose alternative methods whose results can be used for the entire reservoir. 2014 Journal Article http://hdl.handle.net/20.500.11937/29402 10.1016/j.petrol.2014.09.007 Elsevier restricted |
| spellingShingle | Gholami, Raoof Moradzadeh, A. Maleki, S. Amiri, S. Hanachi, J. Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs |
| title | Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs |
| title_full | Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs |
| title_fullStr | Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs |
| title_full_unstemmed | Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs |
| title_short | Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs |
| title_sort | applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs |
| url | http://hdl.handle.net/20.500.11937/29402 |