Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

Jin, Wencheng; Atkinson, T.; Doughty, Christine; Neupane, Ghanashyam; Spycher, Nicolas; McLing, Travis; Dobson, Patrick; Smith, Robert E.; Podgorney, Robert

doi:10.1016/j.renene.2022.07.118

Cited by 17 publications

(5 citation statements)

References 46 publications

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“…With longterm operation, the cold front expands evenly, transitioning from round to conical. However, in fissure-structured reservoirs, partially connected fissures accelerate the tailwater flow and the local cold front migration, resulting in a jagge cold front shape during migration and expansion (Jin et al 2022).…”

Section: Thermal Reservoir Temperature Field Evolutionmentioning

confidence: 99%

Research progress on temperature field evolution of hot reservoirs under low-temperature tailwater reinjection

Wang,

Zhou,

Liu

et al. 2024

Journal of Groundwater Science and Engineering

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Section: Thermal Reservoir Temperature Field Evolutionmentioning

confidence: 99%

Research progress on temperature field evolution of hot reservoirs under low-temperature tailwater reinjection

Wang,

Zhou,

Liu

et al. 2024

Journal of Groundwater Science and Engineering

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“…As depicted in Fig. 2, artificial intelligence has the potential to enhance the efficiency and efficacy of these processes by identifying appropriate geological formations for carbon storage (Jin et al 2022), predicting the behavior of carbon dioxide once it is introduced into the storage sites (Chinh Nguyen et al 2022), optimizing the injection process (Elsheikh et al 2022), monitoring storage sites (Kishor and Chakraborty 2022), and devising new and innovative carbon sequestration methods (Gupta and Li 2022). Moreover, artificial intelligence can aid in accomplishing sustainability objectives and achieving carbon neutrality by reducing greenhouse gas emissions and mitigating climate change (Jahanger et al 2023;Sahil et al 2023).…”

Section: Carbon Sequestration and Storagementioning

confidence: 99%

Artificial intelligence-based solutions for climate change: a review

Chen

Zhang

et al. 2023

Environ Chem Lett

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Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.

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“…Simon Sandstone, Weber Sandstone, Saint Peter Sandstone, and the Lower Tuscaloosa Sandstone. The detailed modeling approach is described in our recent American Rock Mechanics Association (ARMA) paper and full journal paper under review (Jin et al, 2022a).…”

Section: Task 21 Stochastic Simulation-enabled Machine Learning (Ml) ...mentioning

confidence: 99%

“…Simon sandstone have a better performance than systems developed in the other two formations. A detailed explanation of the ANN model application is provided in our paper (Jin et al, 2022a).…”

Section: Task 21 Stochastic Simulation-enabled Machine Learning (Ml) ...mentioning

confidence: 99%

Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage Using Planet Earth as a Thermal Battery (GeoTES): Phase I Project (Final Report)

McLing

Dobson

Jin

et al. 2022

Self Cite

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Grid-scale energy storage has been identified by the U.S. Department of Energy's (DOE) Energy Storage Grand Challenge as a necessary technology to support the continued build-out of intermittent renewable energy resources required to attain a carbon-free energy future. To meet this goal, the 2018 Department of Energy Research and Innovation Act mandated the creation of a comprehensive program to accelerate the development and commercialization of next-generation energy storage technologies. One of numerous energy storage technology options is the storage of excess energy as heated geothermal brine in suitable geologic formations. This concept, known as reservoir thermal energy storage (RTES), geologic thermal energy storage (GeoTES), aquifer thermal energy storage (ATES), etc., relies on the storage of thermal energy in geologic formations for recovery and use in large-scale direct use geothermal (e.g., district heating, industrial processes, etc.) and electrical power generation applications. This thermal energy is derived from excess or waste heat from any high-temperature heat source, such as concentrated solar or from conventional thermal/nuclear generation. As such, RTES can potentially play a significant role in meeting the energy storage shortfall in the coming decades. RTES can provide energy arbitrage through both the storage and production of thermal energy stored in geologic formations for direct use applications and can serve as a source of hot fluids that can be used to generate electricity to support peak demand ramping, thus easing stress on transmission and distribution. This energy storage option has geographic benefits in that energy can be stored locally or regionally depending on the various needs/loads. RTES can also be located across an enormous geographic area, without the need for a traditional hydrothermal resource but where thermal gradients and hydrogeology allow economic exploitation of subsurface heat. The work conducted for this project includes (1) a review of lessons learned from past high-temperature RTES international projects; (2) geochemical experimental investigation and numerical simulations of potential domestic sedimentary reservoirs and (3) development of a thermo-hydrological-mechanical (THM) numerical simulation tool for optimizing formation properties and design parameters to maximize thermal energy storage performance. vi

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Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

Cited by 17 publications

References 46 publications

Research progress on temperature field evolution of hot reservoirs under low-temperature tailwater reinjection

Research progress on temperature field evolution of hot reservoirs under low-temperature tailwater reinjection

Artificial intelligence-based solutions for climate change: a review

Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage Using Planet Earth as a Thermal Battery (GeoTES): Phase I Project (Final Report)

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