Abstract Petrofacies analysis is defined as the characterization and classification of pore types and fluid saturations as revealed by petrophysical measurements of a reservoir. The word “petrofacies” makes an explicit link between petroleum engineers' concerns with pore characteristics as arbiters of production performance and the facies paradigm of geologists as a methodology for genetic understanding and prediction. In petrofacies analysis, the porosity and resistivity axes of the classical Pickett plot are used to map water saturation, bulk volume water, and estimated permeability, as well as capillary pressure information where it is available. When data points are connected in order of depth within a reservoir, the characteristic patterns reflect reservoir rock character and its interplay with the hydrocarbon column. A third variable can be presented at each point on the crossplot by assigning a color scale that is based on other well logs, often gamma ray or photoelectric effect, or other derived variables. Contrasts between reservoir pore types and fluid saturations are reflected in changing patterns on the crossplot and can help discriminate and characterize reservoir heterogeneity. Many hundreds of analyses of well logs facilitated by spreadsheet and object-oriented programming have provided the means to distinguish patterns typical of certain complex pore types (size and connectedness) for sandstones and carbonate reservoirs, occurrences of irreducible water saturation, and presence of transition zones. The result has been an improved means to evaluate potential production, such as bypassed pay behind pipe and in old exploration wells, or to assess zonation and continuity of the reservoir. Petrofacies analysis in this study was applied to distinguishing flow units and including discriminating pore type as an assessment of reservoir conformance and continuity. The analysis is facilitated through the use of color image cross sections depicting depositional sequences, natural gamma ray, porosity, and permeability. Also, cluster analysis was applied to discriminate petrophysically similar reservoir rock.