Sandia Labs FY21 LDRD Annual Report

FY21 ANNUAL REPORT

Supporting aging wellbore infrastructure through precise micro drilling. By developing a revolutionary, remote, precision measurement capability, this team is enabling direct wellbore integrity assessment and supporting the aging wellbore infrastructure both in the United States and abroad. A microhole drilling system is being pursued to allow access to the cemented annular region between the casing and the rock mass enabling direct measurements of critical factors addressing wellbore integrity. The wellbore integrity platform consists of a microhole (< 0.100”) drilling system capable of reaching arbitrarily deep into the casing/ formation. The emplacement sub-system

This drilling testbed collected force and torque data, which helped to develop the material transition detection algorithm for the microhole drilling system.

is coupled with a machine-learning based leak detection algorithm that can localize a leak based on measured downhole data. A material transition detection algorithm will support the precision emplacement of sensors within the wellbore stack. This work, with a submitted patent application, will significantly impact the prevention of catastrophic oil and gas release events and is relevant to drilling automation and to other national security applications. (PI: Jiann Su) Forecasting marine sediment properties. The Arctic Ocean is one of Earth’s last frontiers. As climate change continues to expose this once frozen marine expanse, it is becoming the newest and most challenging theater of maritime operations for the military. The Navy relies on acoustic waves (sound) in the ocean to perform a variety of tasks critical to its national security mission. Sound can be altered significantly as it penetrates the seafloor and interacts with geologic structures or trapped gases before returning to the water column to propagate further. This project demonstrated the integration of geospatial machine learning prediction and sediment thermodynamic/physical modeling to create continuous, high-resolution, probabilistic maps of geoacoustic and geomechanical seafloor sediment properties even when existing observations of the seafloor are sparse. This new technique incorporated software developed in partnership with the University of Texas at Austin (UT Austin) and will be able to produce more reliable estimates of Arctic seafloor properties to support Arctic Naval operations relying on sonar performance and seabed

strength, improve understanding of permafrost-associated natural gas hydrate resources, and constrain models of shallow tomographic structure important for nuclear treaty compliance monitoring/detection. (PI: Jennifer Frederick) Output maps from the U.S. Naval Research Laboratory’s Global Predictive Seabed Model over the area 29°N–45°N and 82°W–66°W. Locations with known total organic carbon (TOC) values are marked by small white dots. Methane seeps (large yellow dots) identified by Skarke et al. (2014) are also plotted and support the increased TOC prediction between (35.4°N, 75.0°W) and (39.0°N, 72.0°W). Modeled gas hydrate locations were identified through models developed by UT Austin and are marked with a magenta X.

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LABORATORY DIRECTED RESEARCH & DEVELOPMENT