A systematic, science-driven approach for predicting subsurface properties

Abstract

As human exploration of the subsurface increases, there is a need for better data- and knowledge-driven methods to improve prediction of subsurface properties. Present subsurface predictions often rely upon disparate and limited a priori information. Even regions with concentrated subsurface exploration still face uncertainties that can obstruct safe and efficient exploration of the subsurface. Uncertainty may be reduced, even for areas with little or no subsurface measurements, using methodical, science-driven geologic knowledge and data. We have developed a hybrid spatiotemporal statistical-geologic approach, subsurface trend analysis (STA), that provides improved understanding of subsurface systems. The STA method assumes that the present-day subsurface is not random, but is a product of its history, which is a sum of its systematic processes. With even limited data and geologic knowledge, the STA method can be used to methodically improve prediction of subsurface properties. To demonstrate and validate the improved prediction potential of the STA method, it was applied in an analysis of the northern Gulf of Mexico. This evaluation was prepared using only existing, publicly available well data and geologic literature. Using the STA method, this information was used to predict subsurface trends for in situ pressure, in situ temperature, porosity, and permeability. The results of this STA-based analysis were validated against new reservoir data. STA-driven results were also contrasted with previous studies. Both indicated that STA predictions were an improvement over other methods. Overall, STA results can provide critical information to evaluate and reduce risks, identify and improve areas of scarce or discontinuous data, and provide inputs for multiscale modeling efforts, from reservoir scale to basin scale. Thereby, the STA method offers an ideal framework for guiding future science-based machine learning and natural language processing to optimize subsurface analyses and predictions.