Comparison of geostatistics and artificial neural networks in reservoir property estimation

Arzuman, Sadun
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.


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Eker, Arif Mert; Akgün, Haluk; Department of Geological Engineering (2009)
The purpose of this study is to assess the engineering geological and geotechnical characteristics and to perform seismic hazard studies of the Upper Pliocene to Quaternary (Plio-Quaternary) deposits located towards the north of Ankara through surface wave testing methods. Based on a general engineering geological and seismic site characterization studies, site classification systems are assigned in seismic hazard assessments. The objective of the research is to determine the regional and local seismic soil...
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Citation Formats
S. Arzuman, “Comparison of geostatistics and artificial neural networks in reservoir property estimation,” Ph.D. - Doctoral Program, Middle East Technical University, 2009.