REDUCING UNCERTAINTY IN THE SUBSURFACE: APPLYING THE SUBSURFACE TREND ANALYSIS METHOD TO REFINE SUBSURFACE PROPERTY PREDICTIONS IN THE GULF OF MEXICO

Subsurface exploration and resource production efforts interact with diverse and often unpredictable environments with limited a priori information. Despite increasing availability of seismic and/or wellbore datasets, uncertainty about subsurface systems persists which can obstruct safe and efficient exploration for science and resources such as groundwater, oil, gas and geothermal energy. Deleterious oil spill events, such as the 2010 Deepwater Horizon blowout, exemplify the most extreme outcome when pre-drill predictions are erroneous about the geologic system. Beyond these extreme events, however, everyday uncertainty about subsurface properties such as pressure, temperature, porosity, and permeability impacts the cost of drilling and predictions of resources. Although it is an ongoing challenge to delineate geologic heterogeneity in subsurface systems, this uncertainty may be reduced by systematically utilizing existing knowledge and data about subsurface systems.

To improve our understanding of baseline in situ subsurface geologic systems, we present a hybrid spatio-temporal statistical-geologic approach known as the Subsurface Trend Analysis (STA). We will demonstrate application of this approach in an analysis of the northern Gulf of Mexico (GOM) and contrast the results of our approach versus previous studies. This evaluation was prepared using existing, publicly available datasets, aggregated reservoir data, and geologic literature. We identify broad spatial trends, patterns, and correlations of subsurface geological characteristics across the GOM, resulting in twenty-one individualized domain definitions that spatially constrain geologic and subsurface property information. The results are validated against new drilling information and well data. These analyses provide critical information to evaluate and reduce risks, identify and improve areas of scarce or discontinuous data, and provide inputs for multi-scale modeling efforts, from reservoir- to basin-scale. Ultimately, this systematic hybrid approach helps reduce uncertainty about subsurface properties and can be used to improve predictions, thereby enhancing subsurface exploration and drilling safety for a wide range of geologic environments.