Cuttings Analysis Using Machine Learning Reduces Reservoir Uncertainty

摘要

Cuttings are a valuable and cost-effective source of subsurface data from both vertical and lateral wells that is commonly overlooked in oil and gas exploration and development drilling. Advancements in analytical technologies have enhanced the data extraction from cuttings to provide key subsurface insights, in lieu of core or sidewall core data. However, there have always been challenges and uncertainties with sample depth allocations and upscaling/downscaling the data to log resolution for petrophysical analysis. This causes uncertainties throughout the subsurface characterization process, affecting results at all scales. A new methodology integrates high-resolution scanning electron microscopy (SEM) analysis with machine learning techniques to allow for semi-autonomous lithotyping of individual cuttings-particles within each cuttings bag (typically representing 10-30 foot intervals) and their direct correlation with measured well logs.