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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | E-Article |
| Language: | English |
| Published: |
Springer London
2013
|
| Subjects: | |
| Online Access: | 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 http://ir.unimas.my/id/eprint/8469/ http://link.springer.com/article/10.1007%2Fs00521-012-1298-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|