Cluster analysis of core measurements using heterogeneous data sources: An application to complex Miocene reservoirs

An integrated clustering approach is suggested for the interpretation of petrophysical properties measured on core samples. Porosity, carbonate content, bulk and grain density, permeability, irreducible water saturation and capillary pressure curves measured from different sources are processed simultaneously for a more reliable evaluation of hydrocarbon reservoirs. The specialty of the problem is that the input dataset is composed of observations at different boreholes and depth intervals, where the number of rock specimens and measurement types strongly vary. Several statistical methods such as cluster and factor analyses are traditionally used for rock typing, which are either highly limited or not feasible at all for the processing of such incomplete datasets. To overcome this difficulty, the sparse matrix of multivariate observations is fully filled with reliable estimates of petrophysical parameters before cluster analysis. In the first phase, a correlation-based interpolation method is proposed for replacing the missing data with synthetic ones estimated from the available petrophysical information. Then, principal component analysis of the filled data matrix is performed to investigate the relative contribution of different variables to the solution and reduce the large number of measured parameters into fewer variables. In the last step, non-hierarchical cluster analysis of the principal components is made to separate the lithological units and reservoir zones. The suggested statistical workflow is tested in a Hungarian oilfield, where Miocene reservoirs of different lithologies are evaluated using a large amount of laboratory data collected for three decades. When clustering all petrophysical variables in a joint procedure, not just the lithological properties but also the fluid and other reservoir characteristics are taken into account to differentiate the pay zones from unproductive intervals. In addition to the current application, the statistical method may serve as useful tool for improved well log analysis, well-to-well correlation and reservoir modeling on larger scales.