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A new method was developed to use core, well log, and post-stack seismic data to identify reservoir lithologies between wells. The method was based on a combination of rock physics modeling, seismic attribute generation and pattern recognition via neural network analysis. The result was a new lithologically calibrated attribute that showed the producing wells to be inside the indicated oil sand area and the non-producing wells to be outside this area.

Kohonen's Self Organizing Feature Maps (SOFM) and other unsupervised clustering methods generate groups based on the identification of various discriminating features. These methods seek an organization in the dataset and form relational organized clusters. However, these clusters may or may not have any physical analogues. A calibration method that relates SOM clusters to physical reality is desirable. This calibration method must define the relationship between the clusters and the observed physical properties; it should also provide an estimate of the validity of the relationships. With the development of a calibrated relationship, the whole dataset can be classified. The principal steps, therefore, are the Three-C's "Clustering, Calibration and Classification".

We present a new method for calibrating a classified 3 0 seismic volume. The classijication process employs a Kohonen self-organizing map, a type of unsupervised artificial neural network; the subsequent calibration is performed 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 organization in a dataset and form relational organized clusters. However, these 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 method that not only defines the relationship between the clusters and realphysicalproperties, but also provides an estimate of the validity of these relationships. With the development of this relationship, the whole dataset can then be calibrated.The clustering step reduces the multi-dimensional data into logically smaller groups.Each original data point defined by multiple attributes is reduced to a one-or twodimensional relational group. This establishes some logical clustering and reduces the complexity of the classijiication problem. Furthermore, calibration should be more successful since it will have to consider 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 world. The output will give the most probable calibration between each self-organized map node and wellboremeasured parameters such as lithology, porosity and fluid saturation. The second part of the output comprises the calibration probability.The method is described in detail, and a case study is briejly presented using data acquired in the Orange River Basin, South Africa. The method shows promise as an alternative to current techniques for integrating seismic and log data during reservoir characterization.