Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
The use of fractal geostatistics and artificial neural networks for carbonate reservoir characterization
Date
2000-11-01
Author
Yeten, B
Gumrah, F
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
182
views
0
downloads
Cite This
In this study, a carbonate oil reservoir located in the southeast part of Turkey was characterized by the use of kriging and the fractal geometry. The three-dimensional porosity and permeability distributions were generated by both aforementioned methods by using the wireline porosity logs and core plug permeability measurements taken from six wells of the field. Since classical regression (lognormal or polynomial) and geostatistical techniques (cross variograms) fail to estimate permeability from wireline log-porosity data, the use of artificial neural networks (ANNs) is proposed in this study to generate permeability data at uncored intervals of porosity logs. For both of the methods, kriging and fractal techniques, the validation of the estimated/simulated data with known wellbore data resulted with acceptable agreements, especially for porosity. Also the comparison of both methods at unsampled locations show better agreements for porosity than permeability.
Subject Keywords
Artificial neural networks
,
Carbonate reservoir characterization
,
Geostatistics
,
Fractals
URI
https://hdl.handle.net/11511/65695
Journal
TRANSPORT IN POROUS MEDIA
DOI
https://doi.org/10.1023/a:1006725709303
Collections
Department of Petroleum and Natural Gas Engineering, Article
Suggestions
OpenMETU
Core
The spatial and temporal variability of limnological properties of a very large and deep reservoir
Soyupak, S; Yemisen, D; Mukhallalati, L; Erdem, S; Akbay, N; Yerli, S (1998-01-01)
The reservoir of Keban Dam is a very deep and large water body of temperate zone exhibiting special characteristics with respect to its limnological properties. One of the major characteristics is the development and seasonal persistence of metalimnetic oxygen minima. This characteristic has been observed for three consecutive years. Spatial heterogeneity with respect to several parameters was another distinct property that has been clearly identified. The long term study has identified the existence of an ...
The application of artificial neural networks for the prediction of water quality of polluted aquifer
Gumrah, F; Oz, B; Guler, B; Evin, S (2000-04-01)
From hydrocarbon reservoirs, beside of oil and natural gas, the brine is also produced as a waste material, which may be discharged at the surface or re-injected into the ground. When the wastewater is injected into the ground, it may be mixed with fresh water source due to to several reasons. Forecasting the pollutant concentrations by knowing the historical data at several locations on a field has a great importance to take the necessary precautions before the undesired situations are happened.
The Use of multimetric framework in calibrating the HBV model
Sürer, Serdar; Akyürek, Sevda Zuhal; Yılmaz, Koray Kamil; Department of Geodetic and Geographical Information Technologies (2015)
In this study, the HBV model is applied on the upper Euphrates basin in Turkey. Individual sensitivity of the parameters is analyzed by calibrating the model using the Multi-Objective Shuffled Complex Evolution (MOSCEM) algorithm. The calibration is performed against snow cover area (SCA) in addition to runoff data for the water years 2009, 2010, 2011 and 2012. Detailed validation studies are also performed for the snow products namely snow recognition (H10) and snow water equivalent (H13) over Turkey and A...
A Comparison of Local Site Conditions with Passive and Active Surface Wave Methods
Akgün, Haluk; Koçkar, Mustafa Kerem (null; 2010-05-29)
This study encompasses dynamic soil characterization and seismic hazard mapping of the Plio-Quaternary and especially Quaternary alluvial sediments of the Çubuk district and its close vicinity that is situated towards the north of Ankara. The project site is located at a region which has a potential of being seriously affected by a possible earthquake occurring along the Çubuk Fault Zone that is thought to be a continuation of the Dodurga Fault Zone and a sub-fault belt of the North Anatolian Fault System t...
The structure of the Palaeozoic schists in the southern Menderes Massif, western Turkey: a new approach to the origin of the main Menderes Metamorphism and its relation to the Lycian Nappes
Bozkurt, Erdin (1999-01-01)
The Early Eocene to Early Oligocene tectonic history of the Menderes Massif involves a major regional Barrovian-type metamorphism (M-1, Main Menderes Metamorphism, MMM), present only in the Palaeozoic-Cenozoic metasediments (the so-called "cover" of the massif), which reached upper amphibolite facies with local anatectic melting at structurally lower levels of the cover rocks and gradually decreased southwards to greenschist facies at structurally higher levels. it is not present in the augen gneisses (the ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Yeten and F. Gumrah, “The use of fractal geostatistics and artificial neural networks for carbonate reservoir characterization,”
TRANSPORT IN POROUS MEDIA
, pp. 173–195, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65695.