Reservoir thermal energy storage (“RTES”) in high porosity and high permeability sedimentary settings offers the potential for large-scale and long-term heat energy storage for future any-time electricity production. Solar-generated thermal energy or excess solar or wind electricity are expended to heat water on the surface and is subsequently injected into adequately deep, porous formations. Three numerical models with single layer, two layers and four layers of high permeability (100mD) and high porosity (15%) separated by thinner layer (s) of low permeability (1mD) and low porosity (5%) are developed for potential multi-layer subsurface hot water storage reservoirs mimicking the Uinta Basin. Three operational scenarios are studied: in scenario1, hot water is injected for 8 hrs. per day (40kg/s) without production; in scenario2, the daily cycle is operated as 8 hrs. of injection (40kg/s), 10 hrs. of production (32kg/s) and 6 hrs. of shut-in; scenario3 is the combination of scenarios 1 and 2 where water is only injected (scenario1) for 30 days first and then daily cycle (scenario2) is continued. Issues considered include conformance (uniform injection and production over the multi-layers), thermal stratification, and heat loss; all requiring multi-layer calculations. Simulation results are discussed with conclusions presented for the optimization of multi-layer RTES applications in the Uinta Basin.


The importance of large-scale and long-term energy storage is clearly shown as the rapid adoption of solar and wind electrical generation proceeds (DOE 2020). Such storage is essential to effectively manage the intermittent nature of solar and wind (Green 2020, Andreas et al. 2021). Already, vast amounts of renewable solar and wind electricity are being curtailed. This is nearly free renewable electricity. Also, substantial solar and wind facilities are under construction and/or being planned. Energy storage has become the primary enabling technology to further implement solar and wind electric power generation worldwide (Agarwal and Giberti 2020, Morgan 2020).

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