Lithofacies Control on Reservoir Quality of the Viola Limestone in Southwest Kansas and Unsupervised Machine Learning Approach of Seismic Attributes Facies-Classification

Abstract

The hydrocarbon development of the Viola Limestone in southern Kansas, USA, has encountered challenges, regarding the development of a robust data-based model of the reservoir-quality controls. The legacy understanding that hydrocarbon entrapment and reservoir-quality are controlled by structure, has resulted in less than optimal drilling results. In this study, an integration of petrographic and geophysical well-logs analyses established the main reservoir quality control as dolomitization-induced porosity. The dolomitization control is supported by comparing best-fit trends on density-porosity well log values with typical model-trends of limestone and dolomite density-porosity. Furthermore, this study presents unsupervised artificial neural network (ANN) classification, based on five seismic attributes (instantaneous frequency, energy, band width, absorption quality factor, seismic amplitude), that comes in agreement with Ca–Mg ratio and the observed sonic transit time (DT log) variation with dolomitization/porosity increase. The hydrocarbon reservoir facies identified by the attributes classification explains the drilling results, with high accuracy/match to facies class centers, and can be used effectively in other settings. The integration, of multi-scale multi-data analysis and modeling, has provided a solid understanding of the reservoir-quality control and distribution. This study can be considered as a reliable platform for placing future infill wells in the study area, to lower the risk of drilling dry holes.