Well log calibration of Kohonen-classified seismic attributes using Bayesian logic

We present a new method for calibrating a classified 3D seismic volume. The classification process employs a Kohonen self-organizing map, a type of un supervised artificial neural network; the subsequent calibration is perform ed using one or more suites of well logs. Kohonen self-organizing maps and other unsupervised clustering methods generate classes of data based on the identification of various discriminating features. These methods seek an o rganization in a dataset and form relational organized clusters. However, t hese clusters may or may not have any physical analogues in the real world. In order to relate them to the real world, we must develop a calibration m ethod that not only defines the relationship between the clusters and real physical properties, but also provides an estimate of the validity of these relationships. With the development of this relationship, the whole datase t can then be calibrated. The clustering step reduces the multi-dimensional data into logically small er groups. Each original data point defined by multiple attributes is reduc ed to a one- or two-dimensional relational group. This establishes some log ical clustering and reduces the complexity of the classification problem. F urthermore, calibration should be more successful since it will have to con sider less variability in the data. In this paper, we present a simple calibration method that employs Bayesian logic to provide the relationship between cluster centres and the real wor ld. The output will give the most probable calibration between each self-or ganized map node and wellbore-measured parameters such as lithology, porosi ty and fluid saturation. The second part of the output comprises the calibr ation probability. The method is described in detail, and a case study is b riefly presented using data acquired in the Orange River Basin, South Afric a. The method shows promise as an alternative to current techniques for integr ating seismic and log data during reservoir characterization.