RII Track-4: Inverse Methods of Hydraulic Fracturing for Enhanced Geothermal Systems in a Deep Mine

South Dakota School Of Mines And Technology, Rapid City SD

Investigators

Abstract

Nontechnical Description Geothermal energy is a renewable resource that potentially can be used to generate electricity. The extraction of geothermal energy from the deep rock requires a man-made geothermal reservoir created by injecting liquid into the rock at high pressure to cause fractures, a process known as hydraulic fracturing. However, our knowledge of fracture development is limited by internal processes within the rocks that have proven challenging to characterize. This fellowship will establish a long-term collaboration between the PI and researchers at Idaho National Laboratory to develop tools that will more effectively estimate the rock properties. This will be done by incorporating data collected from hydraulic fracturing experiments conducted in a deep mine into a state-of-the-art numerical model for fracture propagation. The proposed research will provide comprehensive training for the PI and a graduate student on hydraulic fracture modeling. The proposed project also has the potential to build a partnership between the PI's home institution and commercial entities that are interested in developing hydraulic fracturing techniques for enhanced oil recovery. Technical Description The goal of this research is to understand and quantify fracture growth and the exchange of the heat between rock and fluid in a geothermal reservoir. This will be achieved by leveraging the newly developed network flow-Discrete Element Model (DEM) by Idaho National Laboratory (INL) to model fracture propagation and building on hydraulic fracturing experiments conducted at the Sanford Underground Research Facility in South Dakota, a mined underground research laboratory. Research objectives and methods include: (1) modeling hydraulic fracture propagation under stimulation using network flow-DEM method; and (2) estimating geomechanical properties using monitoring data (e.g., pressure and temperature) via iterative normal-score ensemble smoothing, a novel inverse method developed by the PI for dealing with non-Gaussianity (i.e., connectivity). The proposed coupling of the network flow-DEM method and the iterative normal-score ensemble smoother is unique and will provide a framework for assessing the uncertainty of fracturing propagation due to the heterogeneity of lithology in geothermal engineering systems. The method will be designed to be scalable for other geothermal projects and provide important societal benefits of more efficient geothermal engineering design. This project will foster collaboration among the PI, INL, and industry in a range of potential projects such as carbon sequestration and deep borehole waste disposal, and will fill gaps in the field of geomechanical modeling at the PI's home institution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.