22 NOVEMBER 2020 WorldWide Drilling Resource ® Using HDD Technology to Improve Mining Adapted from Information by Novamera, Inc. Based in Ontario, Canada, Novamera, with the help of Memorial University of Newfoundland in St. John’s, created a unique solution to mining steeply dipping narrow vein deposits using existing directional drilling equipment and ground-penetrating radar technology, called the Sustainable Mining by Drilling (SMD) process. This drilling system combines high-resolution subsurface imaging and directional drilling in a two-pass process which identifies the physical shape of a vein and is flexible to changes in vein geometry. The initial pass uses a diamond drill to create a pilot hole guided by the near borehole imaging tool (NBIT). The NBIT tool performs downhole surveys at regular intervals to measure hole trajectory and distance from the hanging wall and foot wall. The second pass utilizes large-diameter hole opening equipment to excavate the vein out to its full thickness. The cuttings are transported to surface using low-energy reverse circulation air lift assist methods. Typically, producers will leave hard-to-reach resources in the ground after developing a mine. This technology will allow them to recover those resources without having to build new mine infrastructure. Novamera says its new drilling technology consumes less energy and has a smaller footprint than conventional mining, and since it selectively targets the ore, it leaves more waste in the ground. Tailings can be put back into the holes after the ore is processed. SMD can be deployed from the surface in an open-pit or underground mine. The SMD system is a combination of patent-pending inventions and off-the-shelf equipment such as diamond drills, pile top drill rigs, ground-penetrating radar, and other tools. Some of the inventions include making downhole ground-penetrating radar directional and augmenting the hole opening equipment. Since narrow vein geology exists all over the world, the SMD process has the potential to increase the development of existing deposits which were previously thought to be uneconomic to mine. It could also extend the life of current mines by allowing safe excavation of ore beyond existing engineering limits. The company began within Anaconda Mining, and field testing will take place at Anaconda’s Romeo and Juliet deposit in Newfoundland in mid-2022. DIR Using Artificial Intelligence to Simulate Groundwater Systems Adapted from Information by Princeton University Researchers from Princeton are leading a $1 million project funded by the National Science Foundation (NSF) to use artificial intelligence to simulate the nation’s natural groundwater system in an effort to improve water management. One of 29 projects around the country to receive funding for the first phase of the NSF’s Convergence Accelerator program, this project will use the researchers’ strengths in data science, machine learning, and hydrology to improve hydrologic forecasting, the prediction of how much groundwater is available and how it can be sustainably managed. The research team will be led by Laura Condon, assistant professor of hydrology and atmospheric sciences at the University of Arizona (UA), and includes Princeton coprincipal investigators Reed Maxwell, professor of civil and environmental engineering and the Princeton Environmental Institute (PEI), and Peter Melchior, assistant professor of astrophysical sciences jointly appointed in Princeton’s Center for Statistics and Machine Learning. Project coleads also include Patrick O’Leary, assistant director of scientific computing at scientific software company Kitware, and Nirav Merchant, director of the UA Data Science Institute and cohead of CyVerse. According to Maxwell, it is difficult to provide effective, thorough hydrologic forecasting since there are several models and data sources maintained by numerous different scientists and institutions. “We don’t know how much groundwater we have, so we don’t know how much we can rely on it during normal years, let alone drought years, nor the extent to which it could exacerbate flooding, especially in mountain systems,” he explained. “This project brings many data sets and model results together in a seamless framework. This is a complicated problem that bridges disciplinary data, complex numerical simulations, substantial software development, data science and machine learning, user engagement, [as well as] education and outreach. We are bringing all of those elements together in a coherent way, which I believe is very unusual, if not unique, for an NSF-funded research project.” Groundwater models normally require more computer capacity than most researchers have access to. This project will build on a national hydrologic modeling platform Condon and Maxwell work on known as HydroFrame, which allows rapid and accessible modeling of any watershed in the country, by fine-tuning the framework to address specific water management needs. “Our team has developed some of the first national-scale groundwater simulations, but they require millions of core hours on super computers to generate, which can be a significant barrier for water managers and planners. Our project will leverage these computationally intensive and scientifically rigorous simulations to build machine learning models tailored to water management questions that can be easily built and run,” Condon said. “Simulating groundwater flow and groundwater surface water interactions is very computationally challenging,” Condon said. WTR
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