Research
Fracture characterization via AI-assisted analysis of temperature logs (2022-2024)
Fractures control mass and heat transport within a geothermal reservoir, so accurate characterization of fracture networks is a prerequisite for the optimal design and control of the reservoir’s exploitation. We developed a deep-learning procedure to identify fracture locations and estimate fracture flowrates via interpretation of temporally and spatially continuous temperature data from downhole measurements. A long short-term memory fully convolutional network (LSTM-FCN) was used to capture long-term dependencies in sequential temperature data and to distill local features around fractures at the same time.
Triggering mechanisms of induced seismicity and predictive modelling (2023-2025)
Understanding how induced seismicity is triggered is important for safety of Enhanced Geothermal Systems. During the development of Enhanced Geothermal Systems, high pressure water is injected into the target formation to stimulate fracture network and enhance permeability. However, this process carries the risk of activating nearby faults and triggering large earthquakes.
This study aims to understand triggering mechanisms of induced seismicity using physics-based models and to propose solutions to mitigate potential seismic hazards.
Inflow Measurement in Geothermal Wells Based on Chloride Concentration (2021-2024)
The ability to measure inflow from fractures formed after a stimulation is important to assess the success of geothermal wells. This measurement is also useful in understanding the fracture inflow evolution throughout the well lifetime. In this study, a downhole measurement technique was proposed to infer inflows from individual fractures by measuring chloride ion concentration of geothermal fluid along the wellbore. Expanding from a previous study based on a chloride measurement tool developed by Sandia National Laboratory, an analytical calculation was designed to suit fractured well configurations and multiple feed zones. The proposed technique is being developed in preparation for a field test at the Utah Frontier Observatory for Research in Energy (FORGE) site.
Stanford Thermal Model (STM) Developed Using Physics-Informed Graph Neural Networks (2023-2024)
This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks to develop temperature-at-depth maps for the conterminous United States. The model was trained to approximately satisfy the three-dimensional heat conduction law by simultaneously predicting subsurface temperature, thermal conductivity, and surface heat flow. In addition to bottomhole temperature measurements, we incorporated other physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity. Our model showed superior mean absolute error of nearly 5°C.
Techno-Economic Evaluation and Optimization of Flexible Geothermal Power (2021-2025)
Intermittent renewables (e.g., solar and wind) are characterized with diminishing effective load carrying capabilities, and cannot solely drive a reliable net-zero energy portfolio. Consequently, geothermal operators have been exploring flexible generation to supply dispatchable power. There is limited literature that investigated how dispatchable geothermal power can be achieved through steam vent-off , wellhead throttling, turbine bypass, storage, etc. However, these methods involve various technical and economic challenges. This research investigates the techno-economic viability of flexible geothermal power generation.
Deep Analysis of the Geothermal Literature Using Natural Language Processing Techniques (2020-2021)
With the globally growing volume of geothermal literature, data analysis has become useful to advance professional and academic research and development efforts. Furthermore, it is essential to leverage state-of-the-art algorithms to develop useful tools based on existing databases. This work utilized statistical and deep learning techniques to draw insights based on the geothermal literature. We retrieved papers from the International Geothermal Association (IGA) database using the Stanford University search engine.
Impact of Anisotropy on the Thermal Performance of Enhanced Geothermal Systems (2017-2021)
Accurate prediction of the thermal performance of Enhanced Geothermal Systems (EGS) depends on an understanding of how the heat transport is affected by the presence of the fracture(s) – the primary flow conduit of EGS. These fractures may have aperture variability that could create channels and alter flow paths, affecting the availability of surface area for heat transfer.
This study aimed to understand the fracture topology, investigate how it can impact flow and heat transport, and demonstrate ways Enhanced Geothermal Systems can be harnessed to optimize thermal performance.
Investigating Mechanical Interactions Between Fractures and Fracture Propagation Patterns in an EGS Reservoir (2016-2021)
Understanding how a reservoir is stimulated by hydraulic stimulation is necessary for characterizing a reservoir, deciding stimulation design, and optimizing production. In a reservoir where matrix permeability is very low, reservoir permeability enhancement by hydraulic stimulation occurs mainly by creating new fractures, shear dilation of preexisting natural fractures, and fracture connectivity enhancement.
DNA-Based Tracers for Fractured Reservoir Characterization (2014-2020)
The characterization of fluid flow pathways in subsurface reservoirs is crucial for building reliable reservoir models, designing stimulation strategies, predicting reservoir performance, and optimizing production. Tracer data (artificial or natural) provide direct information on reservoir flow properties.
Reservoir Characterization and Prediction Modeling Using Statistical Techniques (2017-2021)
Reservoir characterization and prediction modeling have long been among the more challenging tasks in geothermal reservoir engineering. The main reason is the presence of fractures and faults, which control the mass and heat transport in the subsurface. Nowadays, with a substantial increase in data due to advances in computer power and measuring equipment, the oil and gas as well as the geothermal industries are presented with some of today’s most complex data science problems.
Simulation of Steam-Water Phase Transitions (2013-2018)
Modeling of geothermal reservoirs poses significant difficulties for nonlinear solvers in reservoir simulation. This research focuses on designing novel numerical algorithms to overcome these nonlinear difficulties. These difficulties arise from the strong coupling between the mass and energy conservation equations. This strong coupling results in an apparent "negative compressibility" for blocks that have both liquid and steam phases.
Analytical and Experimental Study of Measuring Enthalpy in Geothermal Reservoirs with a Downhole Tool (2015-2017)
Wellhead measurements of enthalpy and mass flow rate are routine monitoring procedures in geothermal fields. Due to wellbore heat loss, measurement of surface enthalpy can only provide incomplete information about the wellbore and the reservoir, especially during the early testing phases of a development. Measurement of enthalpy downhole would allow for better understanding of the reservoir condition and so would be of great practical significance.
Towards a Better Understanding of the Impact of Fracture Roughness on Permeability-Stress Relationships using First Principles (2011-2017)
Accurate modeling of fracture flow behavior is important for tight reservoirs, such as shale gas and geothermal systems, where faults and fractures are the main conduits for flow. In enhanced geothermal systems (EGS), hydraulic fracturing is used to increase permeability by creating new fractures or inducing slip on preexisting fractures (McClure and Horne, 2011). If appropriate investments in research and development are made, EGS has a potential of having up to 100 GWe of generating capacity in the next 50 years (Tester et al., 2006).
Thermal Forecasting Ability of Temperature-Sensitive Tracers (2011-2016)
In order to assess whether particle tracers can provide more useful information about future thermal behavior of reservoirs than existing solute tracers, models were developed for both solute tracers and particle tracers. Three existing solute tracer types were modeled: conservative solute tracers (CSTs), reactive solute tracers with temperature dependent reaction kinetics (RSTs), and sorbing solute tracers that sorb reversibly to fracture walls (SSTs).
Fracture Connectivity of Fractal Fracture Networks Estimated Using Electrical Resistivity (2011-2013)
This study aimed to estimate the connectivity of fracture networks using direct current resistivity measurements. In these surveys, a direct current is sent into the ground through electrodes and the voltage differences between them are recorded. The input current and measured voltage difference give information about the subsurface resistivity, which can then be used to infer fracture locations. Other geophysical surveys used commonly to find hidden geothermal resources are self-potential and magnetotelluric surveys. Garg et al.
Fracture Characterization in Enhanced Geothermal Systems by Wellbore and Reservoir Analysis (2009-2012)
In summary, this project will develop systematic techniques and tools to characterize the entire fracture network in Enhanced Geothermal Systems, both in the wellbore and in the reservoir. A complete characterization of fractures in the region both near the wellbore and in the interwell reservoir regions is central to maximizing the efficiency of energy extraction from Enhanced Geothermal Systems. By allowing for the optimal design of the recovery process, the results of this research will permit the energy extraction from a given area of enhanced fractures to be maximized. Given the significant cost of producing an enhanced fracture system in the field, the improvement of energy recovery is a key to making this energy source viable economically.
Discrete Fracture Modeling of Hydraulic Stimulation in Enhanced Geothermal Systems (2010-2012)
In Enhanced Geothermal Systems (EGS), hydraulic stimulation is carried out by injecting water at high pressure into low permeability, typically crystalline rock. In most cases, the fluid injection causes slip on the preexisting fractures, enhancing their permeability and increasing well productivity. The simplest EGS arrangement is a two well doublet in which cool water is injected by an injector well, it heats up as it moves through the rock, and then is produced by a second well.