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

摘要

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.