Petrophysical analyses are undertaken within a framework of ordered geological systems. The recognition of the natural geological ordering through the grouping and partitioning of petrophysical data is known to be a fundamental requirement of successful petrophysical interpretations. The mathematical basis and examples of the use of a variety of techniques of value in partitioning data are reviewed. Beginning with the simple use of curve discrimination on conventional crossplots, the discussion proceeds by covering increasingly complex but relatively well-known methods including discriminant function analysis, cluster analysis and principal component analysis, and goes on to include multi-dimensional scaling, ‘pigeon-holing’, fuzzy clustering, characteristic analysis, projection pursuit and neural networks. The discussion highlights some of the benefits, and some of the pitfalls, of each technique through the included examples. The discussion encourages the use of exploratory data analysis techniques in the processing and interpretation of petrophysical data and provides a reference framework from which can follow further evaluation of the value of the techniques in circumstances particular to the interested reader

The commonly occurring ranges and the principal controlling factors of the porosity, permeability, capillary characteristics and fluid saturations of the Brent sandstones are presented. Whilst there is insufficient space to discuss these parameters in specific detail, their variability and the degree to which they are influenced by the geological characteristics of the Brent sandstones is reviewed. The paper shows, through the use of examples, that a thorough understanding of the petrophysical characteristics of a reservoir rock requires the integration of several types of data into a single model. The wireline log and laboratory core analyses provide a quantitative input, whilst geological factors such as mineralogy, diagenetic history, and sedimentary fabric provide a most necessary framework within which the pore geometry variability, detected by log and core data, can be examined. An appendix summarizes the principal quantitative techniques involved.