In this dissertation, 3D surface seismic data was integrated with the well logs
to be able to define the properties in every location for the reservoir under
investigation. To accomplish this task, geostatistical and artificial neural networks
(ANN) techniques were employed.
First, missing log sets in the study area were estimated using common
empirical relationships and ANN. Empirical estimations showed linear dependent
results that cannot be generalized. On the other hand, ANNs predicted missing logs
with an very high accuracy. Sonic logs were predicted using resistivity logs with 90%
correlation coefficient. Second, acoustic impedance property was predicted in the
study area. AI estimation first performed using sonic log with GRNN and 88% CC
was obtained. AI estimation was repeated using sonic and resistivity logs and the
result were improved to 94% CC.
In the final part of the study, SGS technique was used with collocated
cokriging techniques to estimate NPHI property. Results were varying due to nature
of the algorithm. Then, GRNN and RNN algorithms were applied to predict NPHI
property. Using optimized GRNN network parameters, NPHI was estimated with
high accuracy.
Results of the study were showed that ANN provides a powerful solution for
reservoir parameter prediction in the study area with its flexibility to find out nonlinear
relationships from the existing available data.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/12611192/index.pdf |
| Date | 01 September 2009 |
| Creators | Arzuman, Sadun |
| Contributors | Karahanoglu, Nurkan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | Access forbidden for 1 year |
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