Authors : A.B. Adeyemo , O.C. Akinyokun and A. Adesida
Abstract: Petrophysical log interpretation is one of the most useful and important tools available to a petroleum geologist. Well logs help to define physical rock characteristics such as lithology, porosity, permeability and to identify productive zones, to determine depth and thickness of zones, to distinguish between oil, gas, or water in a reservoir and to estimate hydrocarbon reserves. This study presents the results of a research that used unsupervised Self Organizing Map (SOM) artificial neural networks and fuzzy rules derived from log characteristics for the determination of oil well lithology from open-hole geophysical well logs. The methodology proposed for the identification of oil well lithology was tested with case data obtained from an oil well located in the Niger delta region of Nigeria. The result shows that the fuzzy logic based log interpretation model used for the analysis of the clusters (log-facies) generated from the well logs can be used to identify and classify the lithology of oil wells without the use of core sample data.
A.B. Adeyemo , O.C. Akinyokun and A. Adesida , 2007. Well Logs Analysis Using Neuro-Fuzzy Technology (Case Study of Niger-Delta Region of Nigeria). Asian Journal of Information Technology, 6: 1234-1242.