Comparison of geostatistics and artificial neural networks in reservoir property estimation

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Jeoloji Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2009

Öğrenci: SADUN ARZUMAN

Danışman: NURKAN KARAHANOĞLU

Özet:

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.