Integrated data-driven 3D shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota, United States

Abstract

Integrated petrophysical analysis is used in conventional and unconventional reservoirs to visualize the distribution pattern of different lithofacies and interpret depositional environments. In this study, core data from 17 wells and well logs from 517 wells are used to construct 3D data-driven lithofacies models for the upper and lower shale members in the Bakken Formation of the Williston basin in North Dakota, United States. The principal objective of this multi-scale (core, well log, and regional) study is to use the petrophysical response of lithofacies defined in core from a very limited number of wells to identify different shale lithofacies in the Bakken Formation in the numerous and geographically extensive wells with wireline log suites. Shale lithofacies are defined in the core using quantitative mineralogy, Total Organic Carbon content, and petrophysical properties. These lithofacies are calibrated to advanced geochemical spectroscopy logs and conventional well logs at well scales. A machine learning algorithm, Support Vector Machine, is used to recognize the pattern of different shale lithofacies, associated with basic petrophysical parameters from ubiquitous conventional well log suites. Sequential Indicator Simulation is used to populate all lithofacies in a 3D grid, covering a large portion of the Williston basin in North Dakota. The results show that the Bakken shale members are vertically and laterally heterogeneous, but are successfully classified into five different lithofacies. Organic-rich shale lithofacies outweigh the proportion of organic-lean shale lithofacies. It appears several factors influenced the pattern of shale lithofacies distribution of the Bakken Formation.