RESERVOIR CHARACTERIZATION USING INTELLIGENT SEISMIC INVERSION F. Emre ArtunIntegrating different types of data having different scales is the major challenge in reservoir characterization studies. Seismic data is among those different types of data, which is usually used by geoscientists for structural mapping of the subsurface and making interpretations of the reservoir's facies distribution. Yet, it has been a common aim of geoscientists to incorporate seismic data in high-resolution reservoir description through a process called seismic inversion. Using geostatistical models in this kind of studies becomes insufficient in dealing with the uncertainty and the non-linearity, because of the stationarity assumption of variogram models. As an alternate, soft computing has been widely used in reservoir characterization, as a method which is tolerant of uncertainty, imprecision, and partial truth.In this study, a new intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. A synthetic seismic model is developed by using real data and seismic interpretation. This model represents the Atoka and Morrow formations, and the overlying Pennsylvanian sequence of the Buffalo Valley Field in NewMexico. Generalized regression neural network (GRNN) is used to build two independent correlation models between; 1) Surface seismic and VSP, 2) VSP and well logs. After generating virtual VSP's from the surface seismic, well logs are predicted by using the correlation between VSP and well logs. The values of the density log, which is a surrogate for reservoir porosity, are predicted for each seismic trace through the seismic line with a classification approach, having a correlation coefficient of 0.81. The same methodology is then applied to real data taken from the Buffalo Valley Field, to predict interwell gamma ray logs and neutron porosity logs through the seismic line of interest. The same procedure can be applied to a complete 3D seismic block to obtain 3D distributions of reservoir properties with less uncertainty than the geostatistical estimation methods, which would hopefully help to increase the success of drilling new wells during field development. To my mother, Nilgün; my father, Bilsel; and my sister, Selin.iii Acknowledgements First of all, I would like to express my sincere gratitude and appreciation to my advisor, Prof. Shahab Mohaghegh, for his endless support and guidance during my studies. He has not been only a great advisor, but also a mentor and a friend to me, and wherever I go through out my future career, I will carry on the fundamentals and professionalism that he taught me.

Absfracl-The galvanising process is usually complex and difficult to model. However, as a result of production requirements this process usually works on a reduced set of working points leading to process data with a cluster structure. An accurate description of process data can be given at a low computational cost by specifically assigning a local model to each cluster in process data space. This paper describes a virtual sensor design for coating thickness estimation in a hot dip galvanising line based on local models using SOM and G R " neural networks.

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