- author = "Deyi Xie and David Alan Wilkinson and Tina Yu",
- title = "Permeability Estimation Using a Hybrid Genetic Programming and Fuzzy/Neural Inference Approach",
- booktitle = "SPE Annual Technical Conference and Exhibition (ATCE 2005)",
- year = "2005",
- volume = "1",
- pages = "176--182?",
- address = "Dallas, Texas, USA",
- month = "9-12 " # oct,
- publisher = "Society of Petroleum Engineers",
- keywords = "genetic algorithms, genetic programming, 4.1.2 Separation and Treating, 5.8.7 Carbonate Reservoir, 5.1 Reservoir Characterisation, 5.1.5 Geologic Modelling, 4.2 Pipelines, Flowlines and Risers, 6.1.5 Human Resources, Competence and Training, 1.6.9 Coring, Fishing, 1.2.3 Rock properties, 2.4.3 Sand/Solids Control, 4.1.5 Processing Equipment, 5.6.1 Open hole/cased hole log analysis",
- isbn13 = "978-1-55563-150-5",
-
URL = "
https://www.onepetro.org/conference-paper/SPE-95167-MS?sort=&start=0&q=Permeability+Estimation+Using+a+Hybrid+Genetic+Programming+and+Fuzzy%2FNeural+Inference+Approach&from_year=&peer_reviewed=&published_between=&fromSearchResults=true&to_year=&rows=100#",
-
URL = "
http://www.cs.mun.ca/~tinayu/Publications_files/SPE2005_Final.pdf",
-
URL = "http://www.proceedings.com/00158.html",
-
DOI = "
doi:10.2118/95167-MS",
-
abstract = "We have developed a methodology that provides
permeability estimates for all rock-types or
lithologies, for a wide range of permeability. This is
a hybrid Genetic Programming and Fuzzy/Neural Net
inference system and which uses lithologic and
permeability facies as indicators. This work was
motivated by a need to have a volumetric estimate of
permeability for reservoir modelling purposes. To this
end, for our purposes,the inputs to this process are
limited to properties that can be estimated from
seismic data. The permeability transform is first
estimated at the well locations using core
permeability, elastic parameter logs and porosity. The
output from the process can then be used, in
conjunction with estimates of these properties from 3D
seismic data, to provide an estimate of permeability on
a volume basis. The inputs are then, the volume of
shale (Vsh) or any other log type used to determine
lithology, the sonic and density logs, the porosity log
and core permeability measurements. The transform
system is composed of three distinct modules. The first
module serves to classify lithology and separates the
reservoir interval into user-defined lithology types.
The second module, based on Genetic Programming, is
designed to predict permeability facies within
lithology type. A permeability facies is defined as as
a low, medium or high permeability set associated with
each lithology type. A Fuzzy/Neural Net inference
algorithm makes up the third module of the system, in
which a TSK fuzzy logic relationship is formed, for
each permeability facies and lithology.
The system has been applied in two oil fields, both offshore West Africa. In comparison with current estimation approaches, this system yields more consistent estimated permeability. The results from conducting cross-validation suggest this methodology is robust in estimating permeability in complex heterogeneous reservoirs.This system is designed to use elastic log properties inverted from seismic data, such as acoustic velocity and density as input so permeability volume can be obtained.",
-
notes = "p3 'In comparison with current estimation approaches,
this system yields the estimated permeability that
matches core permeability more consistently.'
SPE 95167. ChevronTexaco",
