A novel, fast, and efficient machine learning approach for lithofacies prediction: A lithology identification case study in the Niobrara Formation

Abstract Identifying lithofacies is fundamental to petroleum geology and engineering, offering crucial insights into the distribution, quality, and behavior of hydrocarbon reservoirs. These insights are vital for efficient exploration, development, and production strategies while minimizing operational risks. However, utilizing seismic data to identify lithofacies presents challenges due to limitations in resolution, interpretation complexity, and the indirect nature of seismic measurements. Seismic data primarily capture the elastic properties of subsurface materials rather than direct lithologic characteristics. While advanced techniques like amplitude variation with offset analysis and inversion, supported by robust rock physics models, have mitigated some of these challenges, they are often constrained by their low dimensionality, low resolution, and reliance on fitting physical models. We propose a novel data-driven, machine-learning methodology for predicting lithofacies from seismic and well data. Unlike deterministic approaches, our method does not require seismic and well data to conform to physical models. It reduces ambiguity and nonuniqueness by incorporating multiple attributes into the analysis and enhances interpretability by categorizing each seismic sample with the most probable lithofacies and their associated probability. We create a litho-stratigraphic model of the subsurface by integrating two independent data sources: natural clusters of seismic attributes and lithofacies from well-log data. This model delineates lithofacies at a granular level, both vertically and horizontally, within each seismic sample. The methodology has been applied in several geologic settings and has proven efficient and effective.