Quantitative Evaluation of Water-Flooded Zone in a Sandstone Reservoir with Complex Porosity–Permeability Relationship Based on J-Function Classification: A Case Study of Kalamkas Oilfield

Abstract

The water-flooded zone in a sandstone reservoir with a complex porosity–permeability relationship is difficult to interpret quantitatively. Taking the P Formation of Kalamkas Oilfield in Kazakhstan as an example, this paper proposed a reservoir classification method that introduces the J-function into the crossplot of resistivity and oil column height to realize the classification of sandstone reservoirs with a complex porosity–permeability relationship. Based on the classification results, the initial resistivity calculation models of classified reservoirs were established. The oil–water seepage experiment was performed for classified reservoirs to measure the lithoelectric parameters and establish the relationship between water production rate and resistivity for these reservoirs, and then water production was quantitatively calculated according to the difference between the inverted initial resistivity and the measured resistivity. The results show that the reservoirs with an unclear porosity–permeability relationship can be classified by applying the J-function corresponding to grouped capillary pressure curves to the crossplot of oil column height and resistivity, according to the group average principle of capillary pressure curves. This method can solve the problem that difficult reservoir classification caused by a weak porosity–permeability correlation. Moreover, based on the results of reservoir classification, the water production rate and resistivity model of classified reservoirs is established. In this way, the accuracy of quantitative interpretation of the water-flooded zone in the reservoir can be greatly improved.

Characterization of Tight Gas Sandstone Properties Based on Rock Physical Modeling and Seismic Inversion Methods

“…It is advantageous to directly estimate the reservoir parameters (e.g., pore parameters) using seismic data [23][24][25]. Furthermore, petrophysical reservoir properties have also been evaluated by using seismic methods based on extended elastic impedance inversion [26], pre-stack elastic inversion [27], and quantitative interpretations [28,29]. By using seismic methods, despite the successful use of direct estimation of petrophysical properties, more rock physical constraints should be incorporated in the inversion based on well-log analyses and rock physical modeling.…”