Extrapolation of log properties by integrating fuzzy-self organizing maps and local linear modeling
Mehdi Eftekharifar · 2009
The goal of different methods for clustering of seismic attributes has been to analyze the discrimination ability of the chosen set of attributes. When enough wells are drilled, 3D log properties can be modeled by calculating the acoustic impedance cube. Estimation of log properties from complex seismic attributes becomes very important when only one well is drilled. In this paper, using a subset of complex seismic attributes and the data from one well, sonic log values were estimated in the location of the second well and compared with the actual values of the sonic log at that location (cross-validation). Kohonen's self organizing maps (SOM or Kohonen's clustering network) are well known for cluster analysis (unsupervised learning). This class of algorithms is a set of heuristic procedures that suffers from several major problems. A new unsupervised method was used by integrating the fuzzy c-means clustering and Gustafson-Kessel algorithms into the learning and updating strategies of the Kohonen clustering network and attributes are classified using this technique. Embedding the centers of clusters in the Local Linear Modeling Neural Network's hidden layers leads to a powerful and very fast tool for extrapolation of log properties. Therefore using the minimum possible log data, a method is proposed which can estimate the log values in areas far from the drilled well with a reasonable accuracy. For clustering the attribute data, a few traces are used from different locations in the 3D seismic data; therefore the training time is much less than the conventional methods. The results of modeling, tested on a real data set, confirm the robustness and accuracy of this method compared toconventional tools.